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stevenhao/lasso_and_svm.ipynb

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6.867 final project code
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neural_net.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import linear_model\n",
"import pandas as pd\n",
"import math\n",
"import numpy as np\n",
"import pickle\n",
"import random\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" A B C D\n",
"a 0.309820 0.391489 -1.614929 0.280465\n",
"b -0.380030 -0.818184 -1.437799 -0.352417\n",
"c -0.073331 1.109104 0.120078 -0.513755\n",
"d 0.706321 0.585333 -2.545554 1.151304\n",
"e -0.249365 -1.129583 -0.003820 0.043180\n",
"f 0.957231 -1.436758 -0.397996 0.863859\n",
"g 0.099758 0.831572 1.113102 2.777581\n",
"h 0.112530 -0.139209 -0.001489 -0.844989\n"
]
},
{
"data": {
"text/plain": [
"0.30981991066322229"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates = ['a','b','c','d','e','f','g','h']\n",
"df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])\n",
"print(df)\n",
"df['A']['a']"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2]\n"
]
}
],
"source": [
"x = np.array([1,2,3,4])\n",
"\n",
"print(x[1:2])"
]
},
{
"cell_type": "code",
"execution_count": 346,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('axy', 'bc', 1), ('abdc', 'bcbca')]\n",
"True\n"
]
}
],
"source": [
"x = [(\"abc\",\"bc\",1),(\"abdc\",\"bcbca\")]\n",
"print([(a[0].replace(\"bc\",\"xy\"),)+a[1:] for a in x])\n",
"\n",
"y = \"cbc\" in \"bcbca\"\n",
"print(y)"
]
},
{
"cell_type": "code",
"execution_count": 359,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 2 3 4\n"
]
}
],
"source": [
"a = \"1 2 3 4 5\"\n",
"print(a.rsplit(\" \",1)[0])\n",
"\n",
"x = [\"\"]"
]
},
{
"cell_type": "code",
"execution_count": 368,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 2006, u'Pittsburgh', u'Miami', 1.5)\n",
"['New Orleans Saints', 'Pittsburgh Steelers', 'New England Patriots', 'Tampa Bay Buccaneers', 'Philadelphia Eagles', 'St. Louis Rams', 'Atlanta Falcons', 'Cleveland Browns', 'Cincinnati Bengals', 'Oakland Raiders', 'Buffalo Bills', 'New York Giants', 'Detroit Lions', 'Carolina Panthers', 'San Francisco 49ers', 'Indianapolis Colts', 'Seattle Seahawks', 'Arizona Cardinals', 'Houston Texans', 'Tennessee Titans', 'Jacksonville Jaguars', 'Chicago Bears', 'San Diego Chargers', 'Miami Dolphins', 'New York Jets', 'Baltimore Ravens', 'Kansas City Chiefs', 'Denver Broncos', 'Washington Redskins', 'Green Bay Packers', 'Minnesota Vikings', 'Dallas Cowboys']\n",
"{'Chicago': 'Bears', 'Philadelphia': 'Eagles', 'Denver': 'Broncos', 'Dallas': 'Cowboys', 'San Francisco': '49ers', 'New Orleans': 'Saints', 'Carolina': 'Panthers', 'Indianapolis': 'Colts', 'Tampa Bay': 'Buccaneers', 'Pittsburgh': 'Steelers', 'New England': 'Patriots', 'San Diego': 'Chargers', 'Tennessee': 'Titans', 'Atlanta': 'Falcons', 'Jacksonville': 'Jaguars', 'Green Bay': 'Packers', 'Miami': 'Dolphins', 'Kansas City': 'Chiefs', 'Detroit': 'Lions', 'Minnesota': 'Vikings', 'Arizona': 'Cardinals', 'Buffalo': 'Bills', 'Baltimore': 'Ravens', 'St. Louis': 'Rams', 'Houston': 'Texans', 'Oakland': 'Raiders', 'Cincinnati': 'Bengals', 'Washington': 'Redskins', 'New York': 'Jets', 'Cleveland': 'Browns', 'Seattle': 'Seahawks'}\n"
]
},
{
"data": {
"text/plain": [
"'New Orleans Saints'"
]
},
"execution_count": 368,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xx = pickle.load(open(\"betting_lines.pickle\"))\n",
"sorted_vegas_lines = sorted(xx,key = lambda x: x[1])\n",
"print(sorted_vegas_lines[0])\n",
"print(get_teams(2006))\n",
"team_in_question = \"New Orleans\"\n",
"\n",
"team_name_dict = {}\n",
"team_list =get_teams(2006)\n",
"for team in team_list:\n",
" team_data = team.rsplit(\" \",1)\n",
" team_name_dict[team_data[0]] = team_data[1]\n",
"print(team_name_dict)\n",
"team_in_question +\" \"+ team_name_dict[\"New Orleans\"] "
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(1, 2006, u'Pittsburgh', u'Miami', 1.5), (1, 2006, u'St. Louis', u'Denver', -3.5), (1, 2006, u'Tennessee', u'NY Jets', 3.0), (1, 2006, u'New England', u'Buffalo', 9.5), (1, 2006, u'Tampa Bay', u'Baltimore', 3.0), (1, 2006, u'Kansas City', u'Cincinnati', 1.0), (1, 2006, u'Detroit', u'Seattle', -6.0), (1, 2006, u'Carolina', u'Atlanta', 4.5), (1, 2006, u'Houston', u'Philadelphia', -6.0), (1, 2006, u'Cleveland', u'New Orleans', 3.0), (1, 2006, u'Jacksonville', u'Dallas', -2.0), (1, 2006, u'Green Bay', u'Chicago', -3.5), (1, 2006, u'Arizona', u'San Francisco', 9.5), (1, 2006, u'NY Giants', u'Indianapolis', -3.0), (1, 2006, u'Washington', u'Minnesota', 4.0), (1, 2006, u'Oakland', u'San Diego', -3.0), (2, 2006, u'Baltimore', u'Oakland', 13.0), (2, 2006, u'Indianapolis', u'Houston', 12.5), (2, 2006, u'Cincinnati', u'Cleveland', 10.0), (2, 2006, u'Miami', u'Buffalo', 6.5), (2, 2006, u'Chicago', u'Detroit', 7.5), (2, 2006, u'Minnesota', u'Carolina', 2.0), (2, 2006, u'Philadelphia', u'NY Giants', 2.5), (2, 2006, u'Atlanta', u'Tampa Bay', 4.5), (2, 2006, u'Green Bay', u'New Orleans', -1.5), (2, 2006, u'San Francisco', u'St. Louis', -3.0), (2, 2006, u'Seattle', u'Arizona', 7.0), (2, 2006, u'NY Jets', u'New England', -6.0), (2, 2006, u'San Diego', u'Tennessee', 11.5), (2, 2006, u'Denver', u'Kansas City', 10.5), (2, 2006, u'Dallas', u'Washington', 7.0), (2, 2006, u'Jacksonville', u'Pittsburgh', -2.5), (3, 2006, u'Buffalo', u'NY Jets', 6.0), (3, 2006, u'Pittsburgh', u'Cincinnati', 2.0), (3, 2006, u'Indianapolis', u'Jacksonville', 6.5), (3, 2006, u'Miami', u'Tennessee', 10.5), (3, 2006, u'Houston', u'Washington', -4.0), (3, 2006, u'Minnesota', u'Chicago', -4.0), (3, 2006, u'Tampa Bay', u'Carolina', -3.5), (3, 2006, u'Detroit', u'Green Bay', 6.5), (3, 2006, u'Cleveland', u'Baltimore', -7.0), (3, 2006, u'Arizona', u'St. Louis', 4.5), (3, 2006, u'Seattle', u'NY Giants', 3.5), (3, 2006, u'San Francisco', u'Philadelphia', -6.5), (3, 2006, u'New England', u'Denver', 6.5), (3, 2006, u'New Orleans', u'Atlanta', -4.0), (4, 2006, u'NY Jets', u'Indianapolis', -7.5), (4, 2006, u'Baltimore', u'San Diego', 1.0), (4, 2006, u'Buffalo', u'Minnesota', 2.0), (4, 2006, u'Tennessee', u'Dallas', -9.5), (4, 2006, u'Kansas City', u'San Francisco', 7.0), (4, 2006, u'Carolina', u'New Orleans', 7.0), (4, 2006, u'Atlanta', u'Arizona', 7.5), (4, 2006, u'Houston', u'Miami', -3.5), (4, 2006, u'St. Louis', u'Detroit', 5.0), (4, 2006, u'Cincinnati', u'New England', 5.5), (4, 2006, u'Washington', u'Jacksonville', -2.0), (4, 2006, u'Oakland', u'Cleveland', -1.0), (4, 2006, u'Chicago', u'Seattle', 3.5), (4, 2006, u'Philadelphia', u'Green Bay', 11.5), (5, 2006, u'Indianapolis', u'Tennessee', 17.5), (5, 2006, u'NY Giants', u'Washington', 5.5), (5, 2006, u'Minnesota', u'Detroit', 6.5), (5, 2006, u'New Orleans', u'Tampa Bay', 7.0), (5, 2006, u'Green Bay', u'St. Louis', -2.5), (5, 2006, u'New England', u'Miami', 9.5), (5, 2006, u'Chicago', u'Buffalo', 9.5), (5, 2006, u'Carolina', u'Cleveland', 8.0), (5, 2006, u'Jacksonville', u'NY Jets', 7.0), (5, 2006, u'Arizona', u'Kansas City', -3.5), (5, 2006, u'San Francisco', u'Oakland', 3.5), (5, 2006, u'Philadelphia', u'Dallas', 1.0), (5, 2006, u'San Diego', u'Pittsburgh', 3.5), (5, 2006, u'Denver', u'Baltimore', 5.0), (6, 2006, u'Tampa Bay', u'Cincinnati', -4.5), (6, 2006, u'Washington', u'Tennessee', 12.0), (6, 2006, u'Dallas', u'Houston', 13.0), (6, 2006, u'Detroit', u'Buffalo', -1.5), (6, 2006, u'St. Louis', u'Seattle', -3.0), (6, 2006, u'Atlanta', u'NY Giants', 3.0), (6, 2006, u'New Orleans', u'Philadelphia', -3.0), (6, 2006, u'Baltimore', u'Carolina', 3.0), (6, 2006, u'NY Jets', u'Miami', 2.5), (6, 2006, u'San Francisco', u'San Diego', -10.0), (6, 2006, u'Pittsburgh', u'Kansas City', 6.5), (6, 2006, u'Denver', u'Oakland', 15.5), (6, 2006, u'Arizona', u'Chicago', -12.5), (7, 2006, u'Kansas City', u'San Diego', -6.0), (7, 2006, u'Houston', u'Jacksonville', -7.5), (7, 2006, u'Buffalo', u'New England', -5.5), (7, 2006, u'Atlanta', u'Pittsburgh', -3.0), (7, 2006, u'Miami', u'Green Bay', 6.0), (7, 2006, u'Tampa Bay', u'Philadelphia', -6.5), (7, 2006, u'NY Jets', u'Detroit', 4.0), (7, 2006, u'Cincinnati', u'Carolina', 3.0), (7, 2006, u'Cleveland', u'Denver', -4.5), (7, 2006, u'Indianapolis', u'Washington', 8.0), (7, 2006, u'Seattle', u'Minnesota', 6.5), (7, 2006, u'Oakland', u'Arizona', -3.0), (7, 2006, u'Dallas', u'NY Giants', 3.0), (8, 2006, u'Tennessee', u'Houston', 3.0), (8, 2006, u'Philadelphia', u'Jacksonville', 8.0), (8, 2006, u'Cincinnati', u'Atlanta', 4.0), (8, 2006, u'NY Giants', u'Tampa Bay', 9.0), (8, 2006, u'Chicago', u'San Francisco', 16.5), (8, 2006, u'Green Bay', u'Arizona', 4.0), (8, 2006, u'Kansas City', u'Seattle', 5.5), (8, 2006, u'New Orleans', u'Baltimore', 2.0), (8, 2006, u'San Diego', u'St. Louis', 9.5), (8, 2006, u'Oakland', u'Pittsburgh', -9.0), (8, 2006, u'Cleveland', u'NY Jets', 2.0), (8, 2006, u'Denver', u'Indianapolis', 3.0), (8, 2006, u'Carolina', u'Dallas', 4.5), (8, 2006, u'Minnesota', u'New England', -2.0), (9, 2006, u'St. Louis', u'Kansas City', 3.0), (9, 2006, u'Baltimore', u'Cincinnati', 3.0), (9, 2006, u'NY Giants', u'Houston', 13.0), (9, 2006, u'Jacksonville', u'Tennessee', 9.5), (9, 2006, u'Washington', u'Dallas', -3.0), (9, 2006, u'Buffalo', u'Green Bay', 3.5), (9, 2006, u'Tampa Bay', u'New Orleans', 1.0), (9, 2006, u'Detroit', u'Atlanta', -5.5), (9, 2006, u'Chicago', u'Miami', 13.0), (9, 2006, u'San Francisco', u'Minnesota', -4.0), (9, 2006, u'San Diego', u'Cleveland', 13.0), (9, 2006, u'Pittsburgh', u'Denver', 3.0), (9, 2006, u'New England', u'Indianapolis', 3.0), (9, 2006, u'Seattle', u'Oakland', 7.0), (10, 2006, u'Miami', u'Kansas City', 1.5), (10, 2006, u'Jacksonville', u'Houston', 10.0), (10, 2006, u'Cincinnati', u'San Diego', -1.0), (10, 2006, u'Atlanta', u'Cleveland', 8.0), (10, 2006, u'Tennessee', u'Baltimore', -6.5), (10, 2006, u'Indianapolis', u'Buffalo', 12.5), (10, 2006, u'Pittsburgh', u'New Orleans', 6.5), (10, 2006, u'Philadelphia', u'Washington', 8.0), (10, 2006, u'NY Giants', u'Chicago', 1.0), (10, 2006, u'Minnesota', u'Green Bay', 5.0), (10, 2006, u'New England', u'NY Jets', 10.0), (10, 2006, u'Detroit', u'San Francisco', 6.5), (10, 2006, u'Oakland', u'Denver', -9.0), (10, 2006, u'Seattle', u'St. Louis', 3.0), (10, 2006, u'Arizona', u'Dallas', -6.5), (10, 2006, u'Carolina', u'Tampa Bay', 10.0), (11, 2006, u'Kansas City', u'Oakland', 8.0), (11, 2006, u'Dallas', u'Indianapolis', -1.5), (11, 2006, u'New Orleans', u'Cincinnati', 3.5), (11, 2006, u'Cleveland', u'Pittsburgh', -4.5), (11, 2006, u'Philadelphia', u'Tennessee', 13.0), (11, 2006, u'Baltimore', u'Atlanta', 3.5), (11, 2006, u'Carolina', u'St. Louis', 6.5), (11, 2006, u'Houston', u'Buffalo', 3.0), (11, 2006, u'Green Bay', u'New England', -6.0), (11, 2006, u'Tampa Bay', u'Washington', 3.0), (11, 2006, u'NY Jets', u'Chicago', -6.0), (11, 2006, u'Miami', u'Minnesota', 3.5), (11, 2006, u'Arizona', u'Detroit', 2.0), (11, 2006, u'San Francisco', u'Seattle', -3.0), (11, 2006, u'Denver', u'San Diego', 3.0), (11, 2006, u'Jacksonville', u'NY Giants', 3.5), (12, 2006, u'Detroit', u'Miami', -2.5), (12, 2006, u'Dallas', u'Tampa Bay', 12.5), (12, 2006, u'Kansas City', u'Denver', 2.0), (12, 2006, u'Buffalo', u'Jacksonville', -3.0), (12, 2006, u'NY Jets', u'Houston', 5.0), (12, 2006, u'Baltimore', u'Pittsburgh', 3.0), (12, 2006, u'Cleveland', u'Cincinnati', -3.5), (12, 2006, u'Minnesota', u'Arizona', 7.0), (12, 2006, u'St. Louis', u'San Francisco', 4.5), (12, 2006, u'Atlanta', u'New Orleans', 3.0), (12, 2006, u'Washington', u'Carolina', -5.5), (12, 2006, u'New England', u'Chicago', 4.5), (12, 2006, u'Indianapolis', u'Philadelphia', 10.5), (12, 2006, u'Tennessee', u'NY Giants', -3.5), (12, 2006, u'San Diego', u'Oakland', 13.5), (12, 2006, u'Seattle', u'Green Bay', 10.0), (13, 2006, u'Cincinnati', u'Baltimore', 3.0), (13, 2006, u'Chicago', u'Minnesota', 9.0), (13, 2006, u'Pittsburgh', u'Tampa Bay', 8.0), (13, 2006, u'St. Louis', u'Arizona', 6.0), (13, 2006, u'Tennessee', u'Indianapolis', -7.5), (13, 2006, u'Miami', u'Jacksonville', 1.5), (13, 2006, u'New Orleans', u'San Francisco', 7.0), (13, 2006, u'Washington', u'Atlanta', -1.0), (13, 2006, u'Cleveland', u'Kansas City', -4.0), (13, 2006, u'New England', u'Detroit', 13.5), (13, 2006, u'Buffalo', u'San Diego', -5.5), (13, 2006, u'Green Bay', u'NY Jets', -0.0), (13, 2006, u'NY Giants', u'Dallas', -3.5), (13, 2006, u'Oakland', u'Houston', 3.0), (13, 2006, u'Denver', u'Seattle', 4.5), (13, 2006, u'Philadelphia', u'Carolina', -3.0), (14, 2006, u'Pittsburgh', u'Cleveland', 7.0), (14, 2006, u'Kansas City', u'Baltimore', 3.0), (14, 2006, u'Tampa Bay', u'Atlanta', -3.5), (14, 2006, u'Detroit', u'Minnesota', 1.5), (14, 2006, u'Houston', u'Tennessee', 1.0), (14, 2006, u'Carolina', u'NY Giants', -3.0), (14, 2006, u'Dallas', u'New Orleans', 7.5), (14, 2006, u'NY Jets', u'Buffalo', 4.5), (14, 2006, u'Jacksonville', u'Indianapolis', -1.0), (14, 2006, u'Washington', u'Philadelphia', -2.0), (14, 2006, u'Cincinnati', u'Oakland', 11.0), (14, 2006, u'Miami', u'New England', -3.5), (14, 2006, u'San Francisco', u'Green Bay', 4.0), (14, 2006, u'Arizona', u'Seattle', -3.0), (14, 2006, u'San Diego', u'Denver', 7.5), (14, 2006, u'St. Louis', u'Chicago', -6.0), (15, 2006, u'Seattle', u'San Francisco', 10.0), (15, 2006, u'Atlanta', u'Dallas', -4.0), (15, 2006, u'Minnesota', u'NY Jets', 3.5), (15, 2006, u'Baltimore', u'Cleveland', 12.5), (15, 2006, u'New England', u'Houston', 12.5), (15, 2006, u'Buffalo', u'Miami', 1.0), (15, 2006, u'Carolina', u'Pittsburgh', -3.0), (15, 2006, u'New Orleans', u'Washington', 10.0), (15, 2006, u'Tennessee', u'Jacksonville', -3.5), (15, 2006, u'Chicago', u'Tampa Bay', 13.5), (15, 2006, u'NY Giants', u'Philadelphia', 5.5), (15, 2006, u'Green Bay', u'Detroit', 6.0), (15, 2006, u'Arizona', u'Denver', -3.0), (15, 2006, u'San Diego', u'Kansas City', 9.0), (15, 2006, u'Oakland', u'St. Louis', 3.0), (15, 2006, u'Indianapolis', u'Cincinnati', 3.5), (16, 2006, u'Green Bay', u'Minnesota', 4.0), (16, 2006, u'Oakland', u'Kansas City', -7.0), (16, 2006, u'Buffalo', u'Tennessee', 5.0), (16, 2006, u'NY Giants', u'New Orleans', 3.0), (16, 2006, u'Atlanta', u'Carolina', 6.5), (16, 2006, u'St. Louis', u'Washington', 1.5), (16, 2006, u'Houston', u'Indianapolis', -9.0), (16, 2006, u'Pittsburgh', u'Baltimore', 4.0), (16, 2006, u'Cleveland', u'Tampa Bay', 3.0), (16, 2006, u'Detroit', u'Chicago', -6.0), (16, 2006, u'Jacksonville', u'New England', 3.0), (16, 2006, u'San Francisco', u'Arizona', 3.5), (16, 2006, u'Denver', u'Cincinnati', 3.0), (16, 2006, u'Seattle', u'San Diego', -4.0), (16, 2006, u'Dallas', u'Philadelphia', 7.0), (16, 2006, u'Miami', u'NY Jets', 2.0), (17, 2006, u'Washington', u'NY Giants', -2.0), (17, 2006, u'Baltimore', u'Buffalo', 9.5), (17, 2006, u'Chicago', u'Green Bay', 3.5), (17, 2006, u'Cincinnati', u'Pittsburgh', 6.0), (17, 2006, u'Dallas', u'Detroit', 13.5), (17, 2006, u'Houston', u'Cleveland', 6.0), (17, 2006, u'Indianapolis', u'Miami', 9.0), (17, 2006, u'Kansas City', u'Jacksonville', 2.5), (17, 2006, u'Minnesota', u'St. Louis', -1.5), (17, 2006, u'New Orleans', u'Carolina', -2.5), (17, 2006, u'NY Jets', u'Oakland', 10.5), (17, 2006, u'Philadelphia', u'Atlanta', 6.5), (17, 2006, u'Tampa Bay', u'Seattle', 3.0), (17, 2006, u'Tennessee', u'New England', 3.5), (17, 2006, u'Denver', u'San Francisco', 11.0), (17, 2006, u'San Diego', u'Arizona', 13.5), (1, 2007, u'Indianapolis', u'New Orleans', 5.5), (1, 2007, u'Houston', u'Kansas City', 3.0), (1, 2007, u'Buffalo', u'Denver', -3.0), (1, 2007, u'Cleveland', u'Pittsburgh', -5.5), (1, 2007, u'Jacksonville', u'Tennessee', 7.5), (1, 2007, u'St. Louis', u'Carolina', 1.5), (1, 2007, u'Green Bay', u'Philadelphia', -3.5), (1, 2007, u'Minnesota', u'Atlanta', 3.0), (1, 2007, u'Washington', u'Miami', 3.0), (1, 2007, u'NY Jets', u'New England', -6.5), (1, 2007, u'Seattle', u'Tampa Bay', 6.0), (1, 2007, u'San Diego', u'Chicago', 7.0), (1, 2007, u'Oakland', u'Detroit', 3.0), (1, 2007, u'Dallas', u'NY Giants', 6.5), (1, 2007, u'Cincinnati', u'Baltimore', 3.0), (1, 2007, u'San Francisco', u'Arizona', 3.5), (2, 2007, u'Pittsburgh', u'Buffalo', 10.0), (2, 2007, u'Cleveland', u'Cincinnati', -7.5), (2, 2007, u'Tennessee', u'Indianapolis', -7.0), (2, 2007, u'Carolina', u'Houston', 6.5), (2, 2007, u'St. Louis', u'San Francisco', 3.0), (2, 2007, u'NY Giants', u'Green Bay', 3.0), (2, 2007, u'Jacksonville', u'Atlanta', 10.5), (2, 2007, u'Tampa Bay', u'New Orleans', -5.0), (2, 2007, u'Detroit', u'Minnesota', 3.0), (2, 2007, u'Miami', u'Dallas', -3.5), (2, 2007, u'Arizona', u'Seattle', -3.0), (2, 2007, u'Baltimore', u'NY Jets', 9.5), (2, 2007, u'Denver', u'Oakland', 9.5), (2, 2007, u'Chicago', u'Kansas City', 12.0), (2, 2007, u'New England', u'San Diego', 3.5), (2, 2007, u'Philadelphia', u'Washington', 7.0), (3, 2007, u'Houston', u'Indianapolis', -6.5), (3, 2007, u'Green Bay', u'San Diego', -6.0), (3, 2007, u'Kansas City', u'Minnesota', 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u'Green Bay', -4.5), (15, 2016, u'Minnesota', u'Indianapolis', 5.0), (15, 2016, u'Buffalo', u'Cleveland', 10.5), (15, 2016, u'Kansas City', u'Tennessee', 6.0), (15, 2016, u'Houston', u'Jacksonville', 3.5), (15, 2016, u'Arizona', u'New Orleans', 3.0), (15, 2016, u'Atlanta', u'San Francisco', 13.5), (15, 2016, u'Denver', u'New England', -3.0), (15, 2016, u'San Diego', u'Oakland', -2.5), (15, 2016, u'Cincinnati', u'Pittsburgh', -3.0), (15, 2016, u'Washington', u'Carolina', 7.0), (16, 2016, u'Philadelphia', u'NY Giants', 1.0), (16, 2016, u'Chicago', u'Washington', -3.0), (16, 2016, u'Buffalo', u'Miami', 4.5), (16, 2016, u'New Orleans', u'Tampa Bay', 3.0), (16, 2016, u'Carolina', u'Atlanta', -3.0), (16, 2016, u'Green Bay', u'Minnesota', 6.5), (16, 2016, u'New England', u'NY Jets', 17.0), (16, 2016, u'Jacksonville', u'Tennessee', -4.5), (16, 2016, u'Cleveland', u'San Diego', -4.5), (16, 2016, u'Oakland', u'Indianapolis', 3.5), (16, 2016, u'Los Angeles', u'San Francisco', 6.0), (16, 2016, u'Seattle', u'Arizona', 9.0), (16, 2016, u'Houston', u'Cincinnati', 3.0), (16, 2016, u'Pittsburgh', u'Baltimore', 5.5), (16, 2016, u'Kansas City', u'Denver', 3.5), (16, 2016, u'Dallas', u'Detroit', 6.5), (17, 2016, u'Buffalo', u'NY Jets', -3.0), (17, 2016, u'Carolina', u'Tampa Bay', 10.0), (17, 2016, u'Miami', u'New England', -10.0), (17, 2016, u'Cincinnati', u'Baltimore', 9.5), (17, 2016, u'Atlanta', u'New Orleans', 6.0), (17, 2016, u'Houston', u'Jacksonville', 6.0), (17, 2016, u'Cleveland', u'Pittsburgh', -13.0), (17, 2016, u'Kansas City', u'Oakland', 6.5), (17, 2016, u'Indianapolis', u'Tennessee', 4.0), (17, 2016, u'Dallas', u'Washington', 3.0), (17, 2016, u'Chicago', u'Detroit', -2.5), (17, 2016, u'NY Giants', u'Philadelphia', 3.5), (17, 2016, u'Green Bay', u'Minnesota', 3.0), (17, 2016, u'Denver', u'San Diego', 10.0), (17, 2016, u'San Francisco', u'St. Louis', -3.0), (17, 2016, u'Arizona', u'Seattle', 6.0), (1, 2017, u'New England', u'Kansas City', 9.0), (1, 2017, u'Buffalo', u'NY Jets', 7.5), (1, 2017, u'Chicago', u'Atlanta', -7.0), (1, 2017, u'Houston', u'Jacksonville', 5.5), (1, 2017, u'Washington', u'Philadelphia', -1.0), (1, 2017, u'Detroit', u'Arizona', -2.0), (1, 2017, u'Tennessee', u'Oakland', 2.5), (1, 2017, u'Cincinnati', u'Baltimore', 3.0), (1, 2017, u'Cleveland', u'Pittsburgh', -10.0), (1, 2017, u'LA Rams', u'Indianapolis', 3.5), (1, 2017, u'Green Bay', u'Seattle', 2.5), (1, 2017, u'San Francisco', u'Carolina', -4.5), (1, 2017, u'Dallas', u'NY Giants', 6.0), (1, 2017, u'Minnesota', u'New Orleans', 3.0), (1, 2017, u'Denver', u'LA Chargers', 3.0), (2, 2017, u'Cincinnati', u'Houston', 5.5), (2, 2017, u'Jacksonville', u'Tennessee', -1.5), (2, 2017, u'Baltimore', u'Cleveland', 7.5), (2, 2017, u'Carolina', u'Buffalo', 6.5), (2, 2017, u'New Orleans', u'New England', -6.0), (2, 2017, u'Indianapolis', u'Arizona', -7.0), (2, 2017, u'Kansas City', u'Philadelphia', 4.5), (2, 2017, u'Pittsburgh', u'Minnesota', 8.5), (2, 2017, u'Tampa Bay', u'Chicago', 7.0), (2, 2017, u'LA Chargers', u'Miami', 3.5), (2, 2017, u'Oakland', u'NY Jets', 14.0), (2, 2017, u'LA Rams', u'Washington', 3.0), (2, 2017, u'Denver', u'Dallas', -2.5), (2, 2017, u'Seattle', u'San Francisco', 13.5), (2, 2017, u'Atlanta', u'Green Bay', 3.0), (2, 2017, u'NY Giants', u'Detroit', 3.0), (3, 2017, u'San Francisco', u'LA Rams', -3.0), (3, 2017, u'Jacksonville\\n\\n(London)\\n', u'Baltimore', -3.0), (3, 2017, u'Indianapolis', u'Cleveland', -1.0), (3, 2017, u'Chicago', u'Pittsburgh', -7.0), (3, 2017, u'NY Jets', u'Miami', -5.5), (3, 2017, u'Buffalo', u'Denver', -3.5), (3, 2017, u'New England', u'Houston', 13.5), (3, 2017, u'Carolina', u'New Orleans', 5.0), (3, 2017, u'Minnesota', u'Tampa Bay', -1.0), (3, 2017, u'Detroit', u'Atlanta', -3.0), (3, 2017, u'Philadelphia', u'NY Giants', 5.5), (3, 2017, u'Tennessee', u'Seattle', 2.0), (3, 2017, u'LA Chargers', u'Kansas City', -3.0), (3, 2017, u'Green Bay', u'Cincinnati', 7.0), (3, 2017, u'Washington', u'Oakland', -3.5), (3, 2017, u'Arizona', u'Dallas', -3.0), (4, 2017, u'Green Bay', u'Chicago', 7.5), (4, 2017, u'New England', u'Carolina', 9.5), (4, 2017, u'Dallas', u'LA Rams', 5.5), (4, 2017, u'Minnesota', u'Detroit', 2.5), (4, 2017, u'Houston', u'Tennessee', -2.5), (4, 2017, u'NY Jets', u'Jacksonville', -4.0), (4, 2017, u'Cleveland', u'Cincinnati', -3.0), (4, 2017, u'Baltimore', u'Pittsburgh', -3.5), (4, 2017, u'Atlanta', u'Buffalo', 8.5), (4, 2017, u'Tampa Bay', u'NY Giants', 2.5), (4, 2017, u'LA Chargers', u'Philadelphia', 2.0), (4, 2017, u'Arizona', u'San Francisco', 6.5), (4, 2017, u'Denver', u'Oakland', 3.5), (4, 2017, u'Seattle', u'Indianapolis', 12.5), (4, 2017, u'Kansas City', u'Washington', 7.0), (5, 2017, u'Tampa Bay', u'New England', -4.0), (5, 2017, u'NY Giants', u'LA Chargers', 3.0), (5, 2017, u'Cincinnati', u'Buffalo', 3.0), (5, 2017, u'Cleveland', u'NY Jets', -1.5), (5, 2017, u'Pittsburgh', u'Jacksonville', 7.5), (5, 2017, u'Miami', u'Tennessee', 1.0), (5, 2017, u'Indianapolis', u'San Francisco', 1.5), (5, 2017, u'Philadelphia', u'Arizona', 6.5), (5, 2017, u'Detroit', u'Carolina', 2.0), (5, 2017, u'LA Rams', u'Seattle', 2.0), (5, 2017, u'Oakland', u'Baltimore', 3.0), (5, 2017, u'Dallas', u'Green Bay', 2.5), (5, 2017, u'Houston', u'Kansas City', -2.0), (5, 2017, u'Chicago', u'Minnesota', -3.5), (6, 2017, u'Carolina', u'Philadelphia', 3.0), (6, 2017, u'Houston', u'Cleveland', 7.5), (6, 2017, u'NY Jets', u'New England', -9.5), (6, 2017, u'Atlanta', u'Miami', 13.5), (6, 2017, u'New Orleans', u'Detroit', 5.5), (6, 2017, u'Minnesota', u'Green Bay', -3.0), (6, 2017, u'Baltimore', u'Chicago', 5.5), (6, 2017, u'Washington', u'San Francisco', 11.0), (6, 2017, u'Jacksonville', u'LA Rams', 1.0), (6, 2017, u'Arizona', u'Tampa Bay', -2.5), (6, 2017, u'Kansas City', u'Pittsburgh', 3.5), (6, 2017, u'Oakland', u'LA Chargers', 3.0), (6, 2017, u'Denver', u'NY Giants', 13.5), (6, 2017, u'Tennessee', u'Indianapolis', 7.0), (7, 2017, u'Oakland', u'Kansas City', -3.0), (7, 2017, u'Cleveland', u'Tennessee', -6.0), (7, 2017, u'Indianapolis', u'Jacksonville', -3.0), (7, 2017, u'Pittsburgh', u'Cincinnati', 4.0), (7, 2017, u'Minnesota', u'Baltimore', 5.0), (7, 2017, u'Miami', u'NY Jets', 3.0), (7, 2017, u'Buffalo', u'Tampa Bay', 3.0), (7, 2017, u'Chicago', u'Carolina', -3.0), (7, 2017, u'Green Bay', u'New Orleans', -4.0), (7, 2017, u'zona', u'LA Rams(At London)', -3.0), (7, 2017, u'San Francisco', u'Dallas', -6.5), (7, 2017, u'NY Giants', u'Seattle', -4.0), (7, 2017, u'LA Chargers', u'Denver', -1.0), (7, 2017, u'New England', u'Atlanta', 3.0), (7, 2017, u'Philadelphia', u'Washington', 5.5), (8, 2017, u'Baltimore', u'Miami', 3.0), (8, 2017, u'New Orleans', u'Chicago', 8.5), (8, 2017, u'NY Jets', u'Atlanta', -6.5), (8, 2017, u'Tampa Bay', u'Carolina', 1.0), (8, 2017, u'Philadelphia', u'San Francisco', 13.0), (8, 2017, u'Buffalo', u'Oakland', 2.0), (8, 2017, u'Cincinnati', u'Indianapolis', 10.5), (8, 2017, u'New England', u'LA Chargers', 7.0), (8, 2017, u'Seattle', u'Houston', 6.5), (8, 2017, u'Washington', u'Dallas', -3.0), (8, 2017, u'Detroit', u'Pittsburgh', -3.0), (8, 2017, u'Kansas City', u'Denver', 7.0), (17, 2017, u'Tennessee', u'Houston', 3.0), (17, 2017, u'NY Jets', u'Buffalo', -4.0), (17, 2017, u'Cincinnati', u'Baltimore', -2.5), (17, 2017, u'Washington', u'NY Giants', 9.0), (17, 2017, u'Detroit', u'Green Bay', -3.5), (17, 2017, u'Indianapolis', u'Jacksonville', 5.5), (17, 2017, u'Philadelphia', u'Dallas', 6.5), (17, 2017, u'Minnesota', u'Chicago', 6.5), (17, 2017, u'Tampa Bay', u'Carolina', 3.5), (17, 2017, u'Pittsburgh', u'Cleveland', 3.5), (17, 2017, u'Atlanta', u'New Orleans', 7.5), (17, 2017, u'Miami', u'New England', -8.0), (17, 2017, u'Los Angeles', u'Arizona', -7.0), (17, 2017, u'San Diego', u'Kansas City', -6.0), (17, 2017, u'San Francisco', u'Seattle', -11.5), (17, 2017, u'Denver', u'Oakland', 1.0)]\n"
]
}
],
"source": [
"xx = pickle.load(open(\"betting_lines.pickle\"))\n",
"sorted_vegas_lines = sorted(xx,key = lambda x: x[1])\n",
"print(sorted_vegas_lines)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"#GET VEGAS LINES IN USABLE FORM\n",
"\n",
"def sort_vegas_lines():\n",
" team_name_dict = {}\n",
" team_list =get_teams(2006)\n",
" for team in team_list:\n",
" team_data = team.rsplit(\" \",1)\n",
" team_name_dict[team_data[0]] = team_data[1]\n",
" \n",
" xx = pickle.load(open(\"betting_lines.pickle\"))\n",
" sorted_vegas_lines = sorted(xx,key = lambda x: x[1])\n",
" vegas_line_dict = {}\n",
" vegas_years = range(2006,2018)\n",
" for year in vegas_years:\n",
" year_dict = {}\n",
" for week in range(1,18):\n",
" year_dict[week] = {}\n",
" vegas_line_dict[year] = year_dict\n",
"\n",
"\n",
" for game in sorted_vegas_lines:\n",
" week = game[0]\n",
" year = game[1]\n",
" home_team = str(game[2])\n",
" try:\n",
" if \"London\" in home_team or \"Toronto\" in home_team:\n",
" pass\n",
" elif home_team =='NY Giants':\n",
" home_team = \"New York Giants\"\n",
" elif home_team =='NY Jets':\n",
" home_team = \"New York Jets\"\n",
" else:\n",
" home_team = home_team + \" \" + team_name_dict[home_team]\n",
" points_favored = game[4]\n",
" vegas_line_dict[year][week][home_team] = points_favored\n",
" except:\n",
" pass\n",
" return vegas_line_dict\n",
"\n",
"vegas_line_dict = sort_vegas_lines()\n",
"#print(vegas_line_dict[2014][1])\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Week Day Date Time Winner/tie \\\n",
"0 1 Thu September 5 9:13PM Denver Broncos \n",
"1 1 Sun September 8 1:02PM Detroit Lions \n",
"2 1 Sun September 8 1:02PM Miami Dolphins \n",
"3 1 Sun September 8 1:02PM New England Patriots \n",
"4 1 Sun September 8 1:02PM Chicago Bears \n",
"5 1 Sun September 8 1:02PM Indianapolis Colts \n",
"6 1 Sun September 8 1:02PM Tennessee Titans \n",
"7 1 Sun September 8 1:03PM New York Jets \n",
"8 1 Sun September 8 1:03PM Kansas City Chiefs \n",
"9 1 Sun September 8 1:03PM New Orleans Saints \n",
"10 1 Sun September 8 1:04PM Seattle Seahawks \n",
"11 1 Sun September 8 4:23PM St. Louis Rams \n",
"12 1 Sun September 8 4:26PM San Francisco 49ers \n",
"13 1 Sun September 8 8:30PM Dallas Cowboys \n",
"14 1 Mon September 9 7:10PM Philadelphia Eagles \n",
"15 1 Mon September 9 10:25PM Houston Texans \n",
"16 2 Thu September 12 8:29PM New England Patriots \n",
"17 2 Sun September 15 1:02PM Green Bay Packers \n",
"18 2 Sun September 15 1:02PM Kansas City Chiefs \n",
"19 2 Sun September 15 1:02PM Baltimore Ravens \n",
"20 2 Sun September 15 1:02PM Buffalo Bills \n",
"21 2 Sun September 15 1:02PM San Diego Chargers \n",
"22 2 Sun September 15 1:02PM Miami Dolphins \n",
"23 2 Sun September 15 1:03PM Atlanta Falcons \n",
"24 2 Sun September 15 1:03PM Houston Texans \n",
"25 2 Sun September 15 1:03PM Chicago Bears \n",
"26 2 Sun September 15 4:05PM New Orleans Saints \n",
"27 2 Sun September 15 4:05PM Arizona Cardinals \n",
"28 2 Sun September 15 4:25PM Oakland Raiders \n",
"29 2 Sun September 15 4:25PM Denver Broncos \n",
".. ... ... ... ... ... \n",
"226 16 Sun December 22 1:02PM New York Jets \n",
"227 16 Sun December 22 1:02PM Indianapolis Colts \n",
"228 16 Sun December 22 1:02PM Cincinnati Bengals \n",
"229 16 Sun December 22 1:02PM Denver Broncos \n",
"230 16 Sun December 22 1:02PM Tennessee Titans \n",
"231 16 Sun December 22 1:03PM Dallas Cowboys \n",
"232 16 Sun December 22 1:04PM Carolina Panthers \n",
"233 16 Sun December 22 4:05PM New York Giants \n",
"234 16 Sun December 22 4:06PM Arizona Cardinals \n",
"235 16 Sun December 22 4:25PM New England Patriots \n",
"236 16 Sun December 22 4:25PM San Diego Chargers \n",
"237 16 Sun December 22 4:25PM Pittsburgh Steelers \n",
"238 16 Sun December 22 8:30PM Philadelphia Eagles \n",
"239 16 Mon December 23 8:40PM San Francisco 49ers \n",
"240 17 Sun December 29 1:02PM Pittsburgh Steelers \n",
"241 17 Sun December 29 1:02PM Tennessee Titans \n",
"242 17 Sun December 29 1:02PM New York Jets \n",
"243 17 Sun December 29 1:02PM Indianapolis Colts \n",
"244 17 Sun December 29 1:02PM Cincinnati Bengals \n",
"245 17 Sun December 29 1:04PM New York Giants \n",
"246 17 Sun December 29 1:05PM Carolina Panthers \n",
"247 17 Sun December 29 1:05PM Minnesota Vikings \n",
"248 17 Sun December 29 4:25PM New Orleans Saints \n",
"249 17 Sun December 29 4:25PM New England Patriots \n",
"250 17 Sun December 29 4:25PM Green Bay Packers \n",
"251 17 Sun December 29 4:25PM San Francisco 49ers \n",
"252 17 Sun December 29 4:25PM Denver Broncos \n",
"253 17 Sun December 29 4:26PM San Diego Chargers \n",
"254 17 Sun December 29 4:26PM Seattle Seahawks \n",
"255 17 Sun December 29 8:30PM Philadelphia Eagles \n",
"\n",
" Loser/tie Home Away Vegas \\\n",
"0 Baltimore Ravens Denver Broncos Baltimore Ravens 0 \n",
"1 Minnesota Vikings Detroit Lions Minnesota Vikings 0 \n",
"2 Cleveland Browns Cleveland Browns Miami Dolphins 0 \n",
"3 Buffalo Bills Buffalo Bills New England Patriots 0 \n",
"4 Cincinnati Bengals Chicago Bears Cincinnati Bengals 0 \n",
"5 Oakland Raiders Indianapolis Colts Oakland Raiders 0 \n",
"6 Pittsburgh Steelers Pittsburgh Steelers Tennessee Titans 0 \n",
"7 Tampa Bay Buccaneers New York Jets Tampa Bay Buccaneers 0 \n",
"8 Jacksonville Jaguars Jacksonville Jaguars Kansas City Chiefs 0 \n",
"9 Atlanta Falcons New Orleans Saints Atlanta Falcons 0 \n",
"10 Carolina Panthers Carolina Panthers Seattle Seahawks 0 \n",
"11 Arizona Cardinals St. Louis Rams Arizona Cardinals 0 \n",
"12 Green Bay Packers San Francisco 49ers Green Bay Packers 0 \n",
"13 New York Giants Dallas Cowboys New York Giants 0 \n",
"14 Washington Redskins Washington Redskins Philadelphia Eagles 0 \n",
"15 San Diego Chargers San Diego Chargers Houston Texans 0 \n",
"16 New York Jets New England Patriots New York Jets 0 \n",
"17 Washington Redskins Green Bay Packers Washington Redskins 0 \n",
"18 Dallas Cowboys Kansas City Chiefs Dallas Cowboys 0 \n",
"19 Cleveland Browns Baltimore Ravens Cleveland Browns 0 \n",
"20 Carolina Panthers Buffalo Bills Carolina Panthers 0 \n",
"21 Philadelphia Eagles Philadelphia Eagles San Diego Chargers 0 \n",
"22 Indianapolis Colts Indianapolis Colts Miami Dolphins 0 \n",
"23 St. Louis Rams Atlanta Falcons St. Louis Rams 0 \n",
"24 Tennessee Titans Houston Texans Tennessee Titans 0 \n",
"25 Minnesota Vikings Chicago Bears Minnesota Vikings 0 \n",
"26 Tampa Bay Buccaneers Tampa Bay Buccaneers New Orleans Saints 0 \n",
"27 Detroit Lions Arizona Cardinals Detroit Lions 0 \n",
"28 Jacksonville Jaguars Oakland Raiders Jacksonville Jaguars 0 \n",
"29 New York Giants New York Giants Denver Broncos 0 \n",
".. ... ... ... ... \n",
"226 Cleveland Browns New York Jets Cleveland Browns 0 \n",
"227 Kansas City Chiefs Kansas City Chiefs Indianapolis Colts 0 \n",
"228 Minnesota Vikings Cincinnati Bengals Minnesota Vikings 0 \n",
"229 Houston Texans Houston Texans Denver Broncos 0 \n",
"230 Jacksonville Jaguars Jacksonville Jaguars Tennessee Titans 0 \n",
"231 Washington Redskins Washington Redskins Dallas Cowboys 0 \n",
"232 New Orleans Saints Carolina Panthers New Orleans Saints 0 \n",
"233 Detroit Lions Detroit Lions New York Giants 0 \n",
"234 Seattle Seahawks Seattle Seahawks Arizona Cardinals 0 \n",
"235 Baltimore Ravens Baltimore Ravens New England Patriots 0 \n",
"236 Oakland Raiders San Diego Chargers Oakland Raiders 0 \n",
"237 Green Bay Packers Green Bay Packers Pittsburgh Steelers 0 \n",
"238 Chicago Bears Philadelphia Eagles Chicago Bears 0 \n",
"239 Atlanta Falcons San Francisco 49ers Atlanta Falcons 0 \n",
"240 Cleveland Browns Pittsburgh Steelers Cleveland Browns 0 \n",
"241 Houston Texans Tennessee Titans Houston Texans 0 \n",
"242 Miami Dolphins Miami Dolphins New York Jets 0 \n",
"243 Jacksonville Jaguars Indianapolis Colts Jacksonville Jaguars 0 \n",
"244 Baltimore Ravens Cincinnati Bengals Baltimore Ravens 0 \n",
"245 Washington Redskins New York Giants Washington Redskins 0 \n",
"246 Atlanta Falcons Atlanta Falcons Carolina Panthers 0 \n",
"247 Detroit Lions Minnesota Vikings Detroit Lions 0 \n",
"248 Tampa Bay Buccaneers New Orleans Saints Tampa Bay Buccaneers 0 \n",
"249 Buffalo Bills New England Patriots Buffalo Bills 0 \n",
"250 Chicago Bears Chicago Bears Green Bay Packers 0 \n",
"251 Arizona Cardinals Arizona Cardinals San Francisco 49ers 0 \n",
"252 Oakland Raiders Oakland Raiders Denver Broncos 0 \n",
"253 Kansas City Chiefs San Diego Chargers Kansas City Chiefs 0 \n",
"254 St. Louis Rams Seattle Seahawks St. Louis Rams 0 \n",
"255 Dallas Cowboys Dallas Cowboys Philadelphia Eagles 0 \n",
"\n",
" PtsW PtsL YdsW TOW YdsL TOL Vegas Line \n",
"0 49.0 27.0 510.0 2.0 393.0 2.0 0.0 \n",
"1 34.0 24.0 469.0 2.0 330.0 4.0 0.0 \n",
"2 23.0 10.0 275.0 1.0 291.0 3.0 0.0 \n",
"3 23.0 21.0 431.0 3.0 286.0 2.0 0.0 \n",
"4 24.0 21.0 323.0 1.0 340.0 3.0 0.0 \n",
"5 21.0 17.0 274.0 0.0 372.0 2.0 0.0 \n",
"6 16.0 9.0 229.0 0.0 194.0 2.0 0.0 \n",
"7 18.0 17.0 304.0 2.0 250.0 2.0 0.0 \n",
"8 28.0 2.0 292.0 0.0 178.0 2.0 0.0 \n",
"9 23.0 17.0 419.0 1.0 367.0 2.0 0.0 \n",
"10 12.0 7.0 370.0 1.0 253.0 2.0 0.0 \n",
"11 27.0 24.0 366.0 2.0 390.0 2.0 0.0 \n",
"12 34.0 28.0 494.0 0.0 385.0 2.0 0.0 \n",
"13 36.0 31.0 331.0 1.0 478.0 6.0 0.0 \n",
"14 33.0 27.0 443.0 2.0 382.0 3.0 0.0 \n",
"15 31.0 28.0 449.0 1.0 263.0 1.0 0.0 \n",
"16 13.0 10.0 232.0 0.0 318.0 4.0 0.0 \n",
"17 38.0 20.0 580.0 1.0 422.0 1.0 0.0 \n",
"18 17.0 16.0 313.0 0.0 318.0 2.0 0.0 \n",
"19 14.0 6.0 296.0 1.0 259.0 0.0 0.0 \n",
"20 24.0 23.0 436.0 2.0 308.0 1.0 0.0 \n",
"21 33.0 30.0 539.0 2.0 511.0 0.0 0.0 \n",
"22 24.0 20.0 398.0 1.0 448.0 1.0 0.0 \n",
"23 31.0 24.0 393.0 0.0 421.0 1.0 0.0 \n",
"24 30.0 24.0 452.0 2.0 248.0 0.0 0.0 \n",
"25 31.0 30.0 411.0 4.0 350.0 3.0 0.0 \n",
"26 16.0 14.0 371.0 2.0 273.0 2.0 0.0 \n",
"27 25.0 21.0 348.0 1.0 322.0 1.0 0.0 \n",
"28 19.0 9.0 340.0 1.0 248.0 0.0 0.0 \n",
"29 41.0 23.0 414.0 1.0 376.0 4.0 0.0 \n",
".. ... ... ... ... ... ... ... \n",
"226 24.0 13.0 422.0 0.0 283.0 2.0 0.0 \n",
"227 23.0 7.0 367.0 0.0 287.0 3.0 0.0 \n",
"228 42.0 14.0 426.0 1.0 209.0 4.0 0.0 \n",
"229 37.0 13.0 511.0 0.0 240.0 2.0 0.0 \n",
"230 20.0 16.0 346.0 1.0 289.0 1.0 0.0 \n",
"231 24.0 23.0 309.0 2.0 297.0 1.0 0.0 \n",
"232 17.0 13.0 222.0 1.0 365.0 2.0 0.0 \n",
"233 23.0 20.0 279.0 2.0 355.0 3.0 0.0 \n",
"234 17.0 10.0 307.0 4.0 192.0 2.0 0.0 \n",
"235 41.0 7.0 300.0 0.0 358.0 4.0 0.0 \n",
"236 26.0 13.0 344.0 3.0 265.0 2.0 0.0 \n",
"237 38.0 31.0 343.0 2.0 370.0 2.0 0.0 \n",
"238 54.0 11.0 514.0 1.0 257.0 2.0 0.0 \n",
"239 34.0 24.0 379.0 0.0 402.0 2.0 0.0 \n",
"240 20.0 7.0 292.0 2.0 293.0 2.0 0.0 \n",
"241 16.0 10.0 311.0 1.0 288.0 4.0 0.0 \n",
"242 20.0 7.0 374.0 0.0 296.0 3.0 0.0 \n",
"243 30.0 10.0 379.0 0.0 350.0 2.0 0.0 \n",
"244 34.0 17.0 392.0 4.0 222.0 3.0 0.0 \n",
"245 20.0 6.0 278.0 3.0 251.0 4.0 0.0 \n",
"246 21.0 20.0 283.0 2.0 307.0 1.0 0.0 \n",
"247 14.0 13.0 345.0 2.0 245.0 0.0 0.0 \n",
"248 42.0 17.0 468.0 0.0 290.0 1.0 0.0 \n",
"249 34.0 20.0 382.0 1.0 393.0 0.0 0.0 \n",
"250 33.0 28.0 473.0 2.0 345.0 2.0 0.0 \n",
"251 23.0 20.0 375.0 0.0 482.0 2.0 0.0 \n",
"252 34.0 14.0 458.0 0.0 255.0 1.0 0.0 \n",
"253 27.0 24.0 405.0 1.0 332.0 0.0 0.0 \n",
"254 27.0 9.0 269.0 0.0 158.0 2.0 0.0 \n",
"255 24.0 22.0 366.0 1.0 414.0 3.0 0.0 \n",
"\n",
"[256 rows x 16 columns]\n"
]
}
],
"source": [
"vegas_line_dict = sort_vegas_lines()\n",
"year = 2013\n",
"reg_games, po_games = get_game_data(year)\n",
"for index, row in reg_games.iterrows():\n",
" vegas_line = 0\n",
" try:\n",
" vegas_line = vegas_line_dict[year][row[\"Week\"]][row[\"Home\"]]\n",
" except:\n",
" pass\n",
" reg_games.loc[index, \"Vegas Line\"] = vegas_line\n",
"print(reg_games)"
]
},
{
"cell_type": "code",
"execution_count": 328,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 0 0 0 \n",
"Pittsburgh Steelers 0 0 0 \n",
"New England Patriots 0 0 0 \n",
"Tampa Bay Buccaneers 0 0 0 \n",
"Philadelphia Eagles 0 0 0 \n",
"St. Louis Rams 0 0 0 \n",
"Atlanta Falcons 0 0 0 \n",
"Cleveland Browns 0 0 0 \n",
"Cincinnati Bengals 0 0 0 \n",
"Oakland Raiders 0 0 0 \n",
"Buffalo Bills 0 0 0 \n",
"New York Giants 0 0 0 \n",
"Detroit Lions 0 0 0 \n",
"Carolina Panthers 0 0 0 \n",
"San Francisco 49ers 0 0 0 \n",
"Indianapolis Colts 0 0 0 \n",
"Seattle Seahawks 0 0 0 \n",
"Arizona Cardinals 0 0 0 \n",
"Houston Texans 0 0 0 \n",
"Tennessee Titans 0 0 0 \n",
"Jacksonville Jaguars 0 0 0 \n",
"Chicago Bears 0 0 0 \n",
"San Diego Chargers 0 0 0 \n",
"Miami Dolphins 0 0 0 \n",
"New York Jets 0 0 0 \n",
"Baltimore Ravens 0 0 0 \n",
"Kansas City Chiefs 0 0 0 \n",
"Denver Broncos 0 0 0 \n",
"Washington Redskins 0 0 0 \n",
"Green Bay Packers 0 0 0 \n",
"Minnesota Vikings 0 0 0 \n",
"Dallas Cowboys 0 0 0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 0 0 0 \n",
"Pittsburgh Steelers 0 0 0 \n",
"New England Patriots 0 0 0 \n",
"Tampa Bay Buccaneers 0 0 0 \n",
"Philadelphia Eagles 0 0 0 \n",
"St. Louis Rams 0 0 0 \n",
"Atlanta Falcons 0 0 0 \n",
"Cleveland Browns 0 0 0 \n",
"Cincinnati Bengals 0 0 0 \n",
"Oakland Raiders 0 0 0 \n",
"Buffalo Bills 0 0 0 \n",
"New York Giants 0 0 0 \n",
"Detroit Lions 0 0 0 \n",
"Carolina Panthers 0 0 0 \n",
"San Francisco 49ers 0 0 0 \n",
"Indianapolis Colts 0 0 0 \n",
"Seattle Seahawks 0 0 0 \n",
"Arizona Cardinals 0 0 0 \n",
"Houston Texans 0 0 0 \n",
"Tennessee Titans 0 0 0 \n",
"Jacksonville Jaguars 0 0 0 \n",
"Chicago Bears 0 0 0 \n",
"San Diego Chargers 0 0 0 \n",
"Miami Dolphins 0 0 0 \n",
"New York Jets 0 0 0 \n",
"Baltimore Ravens 0 0 0 \n",
"Kansas City Chiefs 0 0 0 \n",
"Denver Broncos 0 0 0 \n",
"Washington Redskins 0 0 0 \n",
"Green Bay Packers 0 0 0 \n",
"Minnesota Vikings 0 0 0 \n",
"Dallas Cowboys 0 0 0 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 19.0 14.0 19.0 \n",
"Pittsburgh Steelers 28.0 17.0 28.0 \n",
"New England Patriots 19.0 17.0 19.0 \n",
"Tampa Bay Buccaneers 0.0 27.0 0.0 \n",
"Philadelphia Eagles 24.0 10.0 24.0 \n",
"St. Louis Rams 18.0 10.0 18.0 \n",
"Atlanta Falcons 20.0 6.0 20.0 \n",
"Cleveland Browns 14.0 19.0 14.0 \n",
"Cincinnati Bengals 23.0 10.0 23.0 \n",
"Oakland Raiders 0.0 27.0 0.0 \n",
"Buffalo Bills 17.0 19.0 17.0 \n",
"New York Giants 21.0 26.0 21.0 \n",
"Detroit Lions 6.0 9.0 6.0 \n",
"Carolina Panthers 6.0 20.0 6.0 \n",
"San Francisco 49ers 27.0 34.0 27.0 \n",
"Indianapolis Colts 26.0 21.0 26.0 \n",
"Seattle Seahawks 9.0 6.0 9.0 \n",
"Arizona Cardinals 34.0 27.0 34.0 \n",
"Houston Texans 10.0 24.0 10.0 \n",
"Tennessee Titans 16.0 23.0 16.0 \n",
"Jacksonville Jaguars 24.0 17.0 24.0 \n",
"Chicago Bears 26.0 0.0 26.0 \n",
"San Diego Chargers 27.0 0.0 27.0 \n",
"Miami Dolphins 17.0 28.0 17.0 \n",
"New York Jets 23.0 16.0 23.0 \n",
"Baltimore Ravens 27.0 0.0 27.0 \n",
"Kansas City Chiefs 10.0 23.0 10.0 \n",
"Denver Broncos 10.0 18.0 10.0 \n",
"Washington Redskins 16.0 19.0 16.0 \n",
"Green Bay Packers 0.0 26.0 0.0 \n",
"Minnesota Vikings 19.0 16.0 19.0 \n",
"Dallas Cowboys 17.0 24.0 17.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 14.0 1 0 \n",
"Pittsburgh Steelers 17.0 1 0 \n",
"New England Patriots 17.0 1 0 \n",
"Tampa Bay Buccaneers 27.0 0 1 \n",
"Philadelphia Eagles 10.0 1 0 \n",
"St. Louis Rams 10.0 1 0 \n",
"Atlanta Falcons 6.0 1 0 \n",
"Cleveland Browns 19.0 0 1 \n",
"Cincinnati Bengals 10.0 1 0 \n",
"Oakland Raiders 27.0 0 1 \n",
"Buffalo Bills 19.0 0 1 \n",
"New York Giants 26.0 0 1 \n",
"Detroit Lions 9.0 0 1 \n",
"Carolina Panthers 20.0 0 1 \n",
"San Francisco 49ers 34.0 0 1 \n",
"Indianapolis Colts 21.0 1 0 \n",
"Seattle Seahawks 6.0 1 0 \n",
"Arizona Cardinals 27.0 1 0 \n",
"Houston Texans 24.0 0 1 \n",
"Tennessee Titans 23.0 0 1 \n",
"Jacksonville Jaguars 17.0 1 0 \n",
"Chicago Bears 0.0 1 0 \n",
"San Diego Chargers 0.0 1 0 \n",
"Miami Dolphins 28.0 0 1 \n",
"New York Jets 16.0 1 0 \n",
"Baltimore Ravens 0.0 1 0 \n",
"Kansas City Chiefs 23.0 0 1 \n",
"Denver Broncos 18.0 0 1 \n",
"Washington Redskins 19.0 0 1 \n",
"Green Bay Packers 26.0 0 1 \n",
"Minnesota Vikings 16.0 1 0 \n",
"Dallas Cowboys 24.0 0 1 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 53.0 41.0 34.0 \n",
"Pittsburgh Steelers 28.0 26.0 0.0 \n",
"New England Patriots 43.0 34.0 24.0 \n",
"Tampa Bay Buccaneers 3.0 41.0 3.0 \n",
"Philadelphia Eagles 48.0 40.0 24.0 \n",
"St. Louis Rams 31.0 30.0 13.0 \n",
"Atlanta Falcons 34.0 9.0 14.0 \n",
"Cleveland Browns 31.0 53.0 17.0 \n",
"Cincinnati Bengals 57.0 27.0 34.0 \n",
"Oakland Raiders 6.0 55.0 6.0 \n",
"Buffalo Bills 33.0 25.0 16.0 \n",
"New York Giants 51.0 50.0 30.0 \n",
"Detroit Lions 13.0 43.0 7.0 \n",
"Carolina Panthers 19.0 36.0 13.0 \n",
"San Francisco 49ers 47.0 47.0 20.0 \n",
"Indianapolis Colts 69.0 45.0 43.0 \n",
"Seattle Seahawks 30.0 16.0 21.0 \n",
"Arizona Cardinals 44.0 48.0 10.0 \n",
"Houston Texans 34.0 67.0 24.0 \n",
"Tennessee Titans 23.0 63.0 7.0 \n",
"Jacksonville Jaguars 33.0 17.0 9.0 \n",
"Chicago Bears 60.0 7.0 34.0 \n",
"San Diego Chargers 67.0 7.0 40.0 \n",
"Miami Dolphins 23.0 44.0 6.0 \n",
"New York Jets 40.0 40.0 17.0 \n",
"Baltimore Ravens 55.0 6.0 28.0 \n",
"Kansas City Chiefs 16.0 32.0 6.0 \n",
"Denver Broncos 19.0 24.0 9.0 \n",
"Washington Redskins 26.0 46.0 10.0 \n",
"Green Bay Packers 27.0 60.0 27.0 \n",
"Minnesota Vikings 35.0 29.0 16.0 \n",
"Dallas Cowboys 44.0 34.0 27.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 27.0 2 0 \n",
"Pittsburgh Steelers 9.0 1 1 \n",
"New England Patriots 17.0 2 0 \n",
"Tampa Bay Buccaneers 14.0 0 2 \n",
"Philadelphia Eagles 30.0 1 1 \n",
"St. Louis Rams 20.0 1 1 \n",
"Atlanta Falcons 3.0 2 0 \n",
"Cleveland Browns 34.0 0 2 \n",
"Cincinnati Bengals 17.0 2 0 \n",
"Oakland Raiders 28.0 0 2 \n",
"Buffalo Bills 6.0 1 1 \n",
"New York Giants 24.0 1 1 \n",
"Detroit Lions 34.0 0 2 \n",
"Carolina Panthers 16.0 0 2 \n",
"San Francisco 49ers 13.0 1 1 \n",
"Indianapolis Colts 24.0 2 0 \n",
"Seattle Seahawks 10.0 2 0 \n",
"Arizona Cardinals 21.0 1 1 \n",
"Houston Texans 43.0 0 2 \n",
"Tennessee Titans 40.0 0 2 \n",
"Jacksonville Jaguars 0.0 2 0 \n",
"Chicago Bears 7.0 2 0 \n",
"San Diego Chargers 7.0 2 0 \n",
"Miami Dolphins 16.0 0 2 \n",
"New York Jets 24.0 1 1 \n",
"Baltimore Ravens 6.0 2 0 \n",
"Kansas City Chiefs 9.0 0 2 \n",
"Denver Broncos 6.0 1 1 \n",
"Washington Redskins 27.0 0 2 \n",
"Green Bay Packers 34.0 0 2 \n",
"Minnesota Vikings 13.0 2 0 \n",
"Dallas Cowboys 10.0 1 1 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 76.0 44.0 23.0 \n",
"Pittsburgh Steelers 48.0 54.0 20.0 \n",
"New England Patriots 50.0 51.0 7.0 \n",
"Tampa Bay Buccaneers 27.0 67.0 24.0 \n",
"Philadelphia Eagles 86.0 64.0 38.0 \n",
"St. Louis Rams 47.0 44.0 16.0 \n",
"Atlanta Falcons 37.0 32.0 3.0 \n",
"Cleveland Browns 45.0 68.0 14.0 \n",
"Cincinnati Bengals 85.0 47.0 28.0 \n",
"Oakland Raiders 6.0 55.0 6.0 \n",
"Buffalo Bills 53.0 53.0 20.0 \n",
"New York Giants 81.0 92.0 30.0 \n",
"Detroit Lions 37.0 74.0 24.0 \n",
"Carolina Panthers 45.0 60.0 26.0 \n",
"San Francisco 49ers 71.0 85.0 24.0 \n",
"Indianapolis Colts 90.0 59.0 21.0 \n",
"Seattle Seahawks 72.0 46.0 42.0 \n",
"Arizona Cardinals 58.0 64.0 14.0 \n",
"Houston Texans 49.0 98.0 15.0 \n",
"Tennessee Titans 33.0 76.0 10.0 \n",
"Jacksonville Jaguars 47.0 38.0 14.0 \n",
"Chicago Bears 79.0 23.0 19.0 \n",
"San Diego Chargers 67.0 7.0 40.0 \n",
"Miami Dolphins 36.0 54.0 13.0 \n",
"New York Jets 68.0 60.0 28.0 \n",
"Baltimore Ravens 70.0 20.0 15.0 \n",
"Kansas City Chiefs 16.0 32.0 6.0 \n",
"Denver Broncos 36.0 31.0 17.0 \n",
"Washington Redskins 57.0 61.0 31.0 \n",
"Green Bay Packers 58.0 84.0 31.0 \n",
"Minnesota Vikings 51.0 48.0 16.0 \n",
"Dallas Cowboys 44.0 34.0 27.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 3.0 3 0 \n",
"Pittsburgh Steelers 28.0 1 2 \n",
"New England Patriots 17.0 2 1 \n",
"Tampa Bay Buccaneers 26.0 0 3 \n",
"Philadelphia Eagles 24.0 2 1 \n",
"St. Louis Rams 14.0 2 1 \n",
"Atlanta Falcons 23.0 2 1 \n",
"Cleveland Browns 15.0 0 3 \n",
"Cincinnati Bengals 20.0 3 0 \n",
"Oakland Raiders 28.0 0 2 \n",
"Buffalo Bills 28.0 1 2 \n",
"New York Giants 42.0 1 2 \n",
"Detroit Lions 31.0 0 3 \n",
"Carolina Panthers 24.0 1 2 \n",
"San Francisco 49ers 38.0 1 2 \n",
"Indianapolis Colts 14.0 3 0 \n",
"Seattle Seahawks 30.0 3 0 \n",
"Arizona Cardinals 16.0 1 2 \n",
"Houston Texans 31.0 0 3 \n",
"Tennessee Titans 13.0 0 3 \n",
"Jacksonville Jaguars 21.0 2 1 \n",
"Chicago Bears 16.0 3 0 \n",
"San Diego Chargers 7.0 2 0 \n",
"Miami Dolphins 10.0 1 2 \n",
"New York Jets 20.0 2 1 \n",
"Baltimore Ravens 14.0 3 0 \n",
"Kansas City Chiefs 9.0 0 2 \n",
"Denver Broncos 7.0 2 1 \n",
"Washington Redskins 15.0 1 2 \n",
"Green Bay Packers 24.0 1 2 \n",
"Minnesota Vikings 19.0 2 1 \n",
"Dallas Cowboys 10.0 1 1 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 94.0 65.0 18.0 \n",
"Pittsburgh Steelers 48.0 54.0 20.0 \n",
"New England Patriots 88.0 64.0 38.0 \n",
"Tampa Bay Buccaneers 27.0 67.0 24.0 \n",
"Philadelphia Eagles 117.0 73.0 31.0 \n",
"St. Louis Rams 88.0 78.0 41.0 \n",
"Atlanta Falcons 69.0 42.0 32.0 \n",
"Cleveland Browns 69.0 89.0 24.0 \n",
"Cincinnati Bengals 98.0 85.0 13.0 \n",
"Oakland Raiders 27.0 79.0 21.0 \n",
"Buffalo Bills 70.0 65.0 17.0 \n",
"New York Giants 81.0 92.0 30.0 \n",
"Detroit Lions 71.0 115.0 34.0 \n",
"Carolina Panthers 66.0 78.0 21.0 \n",
"San Francisco 49ers 71.0 126.0 0.0 \n",
"Indianapolis Colts 121.0 87.0 31.0 \n",
"Seattle Seahawks 78.0 83.0 6.0 \n",
"Arizona Cardinals 68.0 96.0 10.0 \n",
"Houston Texans 66.0 113.0 17.0 \n",
"Tennessee Titans 47.0 121.0 14.0 \n",
"Jacksonville Jaguars 77.0 74.0 30.0 \n",
"Chicago Bears 116.0 29.0 37.0 \n",
"San Diego Chargers 80.0 23.0 13.0 \n",
"Miami Dolphins 51.0 71.0 15.0 \n",
"New York Jets 96.0 91.0 28.0 \n",
"Baltimore Ravens 86.0 33.0 16.0 \n",
"Kansas City Chiefs 57.0 32.0 41.0 \n",
"Denver Broncos 36.0 31.0 17.0 \n",
"Washington Redskins 93.0 91.0 36.0 \n",
"Green Bay Packers 67.0 115.0 9.0 \n",
"Minnesota Vikings 63.0 65.0 12.0 \n",
"Dallas Cowboys 89.0 48.0 45.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 21.0 3 1 \n",
"Pittsburgh Steelers 28.0 1 2 \n",
"New England Patriots 13.0 3 1 \n",
"Tampa Bay Buccaneers 26.0 0 3 \n",
"Philadelphia Eagles 9.0 3 1 \n",
"St. Louis Rams 34.0 3 1 \n",
"Atlanta Falcons 10.0 3 1 \n",
"Cleveland Browns 21.0 1 3 \n",
"Cincinnati Bengals 38.0 3 1 \n",
"Oakland Raiders 24.0 0 3 \n",
"Buffalo Bills 12.0 2 2 \n",
"New York Giants 42.0 1 2 \n",
"Detroit Lions 41.0 0 4 \n",
"Carolina Panthers 18.0 2 2 \n",
"San Francisco 49ers 41.0 1 3 \n",
"Indianapolis Colts 28.0 4 0 \n",
"Seattle Seahawks 37.0 3 1 \n",
"Arizona Cardinals 32.0 1 3 \n",
"Houston Texans 15.0 1 3 \n",
"Tennessee Titans 45.0 0 4 \n",
"Jacksonville Jaguars 36.0 2 2 \n",
"Chicago Bears 6.0 4 0 \n",
"San Diego Chargers 16.0 2 1 \n",
"Miami Dolphins 17.0 1 3 \n",
"New York Jets 31.0 2 2 \n",
"Baltimore Ravens 13.0 4 0 \n",
"Kansas City Chiefs 0.0 1 2 \n",
"Denver Broncos 7.0 2 1 \n",
"Washington Redskins 30.0 2 2 \n",
"Green Bay Packers 31.0 1 3 \n",
"Minnesota Vikings 17.0 2 2 \n",
"Dallas Cowboys 14.0 2 1 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 118.0 86.0 24.0 \n",
"Pittsburgh Steelers 61.0 77.0 13.0 \n",
"New England Patriots 108.0 74.0 20.0 \n",
"Tampa Bay Buccaneers 48.0 91.0 21.0 \n",
"Philadelphia Eagles 155.0 97.0 38.0 \n",
"St. Louis Rams 111.0 98.0 23.0 \n",
"Atlanta Falcons 69.0 42.0 32.0 \n",
"Cleveland Browns 81.0 109.0 12.0 \n",
"Cincinnati Bengals 98.0 85.0 13.0 \n",
"Oakland Raiders 47.0 113.0 20.0 \n",
"Buffalo Bills 77.0 105.0 7.0 \n",
"New York Giants 100.0 95.0 19.0 \n",
"Detroit Lions 88.0 141.0 17.0 \n",
"Carolina Panthers 86.0 90.0 20.0 \n",
"San Francisco 49ers 105.0 146.0 34.0 \n",
"Indianapolis Colts 135.0 100.0 14.0 \n",
"Seattle Seahawks 78.0 83.0 6.0 \n",
"Arizona Cardinals 88.0 119.0 20.0 \n",
"Houston Texans 66.0 113.0 17.0 \n",
"Tennessee Titans 60.0 135.0 13.0 \n",
"Jacksonville Jaguars 118.0 74.0 41.0 \n",
"Chicago Bears 156.0 36.0 40.0 \n",
"San Diego Chargers 103.0 36.0 23.0 \n",
"Miami Dolphins 61.0 91.0 10.0 \n",
"New York Jets 96.0 132.0 0.0 \n",
"Baltimore Ravens 89.0 46.0 3.0 \n",
"Kansas City Chiefs 80.0 52.0 23.0 \n",
"Denver Broncos 49.0 34.0 13.0 \n",
"Washington Redskins 96.0 110.0 3.0 \n",
"Green Bay Packers 87.0 138.0 20.0 \n",
"Minnesota Vikings 89.0 82.0 26.0 \n",
"Dallas Cowboys 113.0 86.0 24.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 21.0 4 1 \n",
"Pittsburgh Steelers 23.0 1 3 \n",
"New England Patriots 10.0 4 1 \n",
"Tampa Bay Buccaneers 24.0 0 4 \n",
"Philadelphia Eagles 24.0 4 1 \n",
"St. Louis Rams 20.0 4 1 \n",
"Atlanta Falcons 10.0 3 1 \n",
"Cleveland Browns 20.0 1 4 \n",
"Cincinnati Bengals 38.0 3 1 \n",
"Oakland Raiders 34.0 0 4 \n",
"Buffalo Bills 40.0 2 3 \n",
"New York Giants 3.0 2 2 \n",
"Detroit Lions 26.0 0 5 \n",
"Carolina Panthers 12.0 3 2 \n",
"San Francisco 49ers 20.0 2 3 \n",
"Indianapolis Colts 13.0 5 0 \n",
"Seattle Seahawks 37.0 3 1 \n",
"Arizona Cardinals 23.0 1 4 \n",
"Houston Texans 15.0 1 3 \n",
"Tennessee Titans 14.0 0 5 \n",
"Jacksonville Jaguars 0.0 3 2 \n",
"Chicago Bears 7.0 5 0 \n",
"San Diego Chargers 13.0 3 1 \n",
"Miami Dolphins 20.0 1 4 \n",
"New York Jets 41.0 2 3 \n",
"Baltimore Ravens 13.0 4 1 \n",
"Kansas City Chiefs 20.0 2 2 \n",
"Denver Broncos 3.0 3 1 \n",
"Washington Redskins 19.0 2 3 \n",
"Green Bay Packers 23.0 1 4 \n",
"Minnesota Vikings 17.0 3 2 \n",
"Dallas Cowboys 38.0 2 2 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 145.0 110.0 27.0 \n",
"Pittsburgh Steelers 106.0 84.0 45.0 \n",
"New England Patriots 108.0 74.0 20.0 \n",
"Tampa Bay Buccaneers 62.0 104.0 14.0 \n",
"Philadelphia Eagles 179.0 124.0 24.0 \n",
"St. Louis Rams 139.0 128.0 28.0 \n",
"Atlanta Falcons 83.0 69.0 14.0 \n",
"Cleveland Browns 81.0 109.0 12.0 \n",
"Cincinnati Bengals 111.0 99.0 13.0 \n",
"Oakland Raiders 50.0 126.0 3.0 \n",
"Buffalo Bills 94.0 125.0 17.0 \n",
"New York Giants 127.0 109.0 27.0 \n",
"Detroit Lions 108.0 158.0 20.0 \n",
"Carolina Panthers 109.0 111.0 23.0 \n",
"San Francisco 49ers 124.0 194.0 19.0 \n",
"Indianapolis Colts 135.0 100.0 14.0 \n",
"Seattle Seahawks 108.0 111.0 30.0 \n",
"Arizona Cardinals 111.0 143.0 23.0 \n",
"Houston Texans 72.0 147.0 6.0 \n",
"Tennessee Titans 85.0 157.0 25.0 \n",
"Jacksonville Jaguars 118.0 74.0 41.0 \n",
"Chicago Bears 180.0 59.0 24.0 \n",
"San Diego Chargers 151.0 55.0 48.0 \n",
"Miami Dolphins 78.0 111.0 17.0 \n",
"New York Jets 116.0 149.0 20.0 \n",
"Baltimore Ravens 110.0 69.0 21.0 \n",
"Kansas City Chiefs 87.0 97.0 7.0 \n",
"Denver Broncos 62.0 37.0 13.0 \n",
"Washington Redskins 118.0 135.0 22.0 \n",
"Green Bay Packers 87.0 138.0 20.0 \n",
"Minnesota Vikings 89.0 82.0 26.0 \n",
"Dallas Cowboys 147.0 92.0 34.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 24.0 5 1 \n",
"Pittsburgh Steelers 7.0 2 3 \n",
"New England Patriots 10.0 4 1 \n",
"Tampa Bay Buccaneers 13.0 1 4 \n",
"Philadelphia Eagles 27.0 4 2 \n",
"St. Louis Rams 30.0 4 2 \n",
"Atlanta Falcons 27.0 3 2 \n",
"Cleveland Browns 20.0 1 4 \n",
"Cincinnati Bengals 14.0 3 2 \n",
"Oakland Raiders 13.0 0 5 \n",
"Buffalo Bills 20.0 2 4 \n",
"New York Giants 14.0 3 2 \n",
"Detroit Lions 17.0 1 5 \n",
"Carolina Panthers 21.0 4 2 \n",
"San Francisco 49ers 48.0 2 4 \n",
"Indianapolis Colts 13.0 5 0 \n",
"Seattle Seahawks 28.0 4 1 \n",
"Arizona Cardinals 24.0 1 5 \n",
"Houston Texans 34.0 1 4 \n",
"Tennessee Titans 22.0 1 5 \n",
"Jacksonville Jaguars 0.0 3 2 \n",
"Chicago Bears 23.0 6 0 \n",
"San Diego Chargers 19.0 4 1 \n",
"Miami Dolphins 20.0 1 5 \n",
"New York Jets 17.0 3 3 \n",
"Baltimore Ravens 23.0 4 2 \n",
"Kansas City Chiefs 45.0 2 3 \n",
"Denver Broncos 3.0 4 1 \n",
"Washington Redskins 25.0 2 4 \n",
"Green Bay Packers 23.0 1 4 \n",
"Minnesota Vikings 17.0 3 2 \n",
"Dallas Cowboys 6.0 3 2 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 145.0 110.0 27.0 \n",
"Pittsburgh Steelers 144.0 125.0 38.0 \n",
"New England Patriots 136.0 80.0 28.0 \n",
"Tampa Bay Buccaneers 85.0 125.0 23.0 \n",
"Philadelphia Eagles 200.0 147.0 21.0 \n",
"St. Louis Rams 139.0 128.0 28.0 \n",
"Atlanta Falcons 124.0 107.0 41.0 \n",
"Cleveland Browns 88.0 126.0 7.0 \n",
"Cincinnati Bengals 128.0 113.0 17.0 \n",
"Oakland Raiders 72.0 135.0 22.0 \n",
"Buffalo Bills 100.0 153.0 6.0 \n",
"New York Giants 163.0 131.0 36.0 \n",
"Detroit Lions 132.0 189.0 24.0 \n",
"Carolina Panthers 123.0 128.0 14.0 \n",
"San Francisco 49ers 124.0 194.0 19.0 \n",
"Indianapolis Colts 171.0 122.0 36.0 \n",
"Seattle Seahawks 121.0 142.0 13.0 \n",
"Arizona Cardinals 120.0 165.0 9.0 \n",
"Houston Texans 99.0 154.0 27.0 \n",
"Tennessee Titans 85.0 157.0 25.0 \n",
"Jacksonville Jaguars 125.0 101.0 7.0 \n",
"Chicago Bears 180.0 59.0 24.0 \n",
"San Diego Chargers 178.0 85.0 27.0 \n",
"Miami Dolphins 102.0 145.0 24.0 \n",
"New York Jets 147.0 173.0 31.0 \n",
"Baltimore Ravens 110.0 69.0 21.0 \n",
"Kansas City Chiefs 117.0 124.0 30.0 \n",
"Denver Broncos 79.0 44.0 17.0 \n",
"Washington Redskins 140.0 171.0 22.0 \n",
"Green Bay Packers 121.0 162.0 34.0 \n",
"Minnesota Vikings 120.0 95.0 31.0 \n",
"Dallas Cowboys 169.0 128.0 22.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 24.0 5 1 \n",
"Pittsburgh Steelers 41.0 2 4 \n",
"New England Patriots 6.0 5 1 \n",
"Tampa Bay Buccaneers 21.0 2 4 \n",
"Philadelphia Eagles 23.0 4 3 \n",
"St. Louis Rams 30.0 4 2 \n",
"Atlanta Falcons 38.0 4 2 \n",
"Cleveland Browns 17.0 1 5 \n",
"Cincinnati Bengals 14.0 4 2 \n",
"Oakland Raiders 9.0 1 5 \n",
"Buffalo Bills 28.0 2 5 \n",
"New York Giants 22.0 4 2 \n",
"Detroit Lions 31.0 1 6 \n",
"Carolina Panthers 17.0 4 3 \n",
"San Francisco 49ers 48.0 2 4 \n",
"Indianapolis Colts 22.0 6 0 \n",
"Seattle Seahawks 31.0 4 2 \n",
"Arizona Cardinals 22.0 1 6 \n",
"Houston Texans 7.0 2 4 \n",
"Tennessee Titans 22.0 1 5 \n",
"Jacksonville Jaguars 27.0 3 3 \n",
"Chicago Bears 23.0 6 0 \n",
"San Diego Chargers 30.0 4 2 \n",
"Miami Dolphins 34.0 1 6 \n",
"New York Jets 24.0 4 3 \n",
"Baltimore Ravens 23.0 4 2 \n",
"Kansas City Chiefs 27.0 3 3 \n",
"Denver Broncos 7.0 5 1 \n",
"Washington Redskins 36.0 2 5 \n",
"Green Bay Packers 24.0 2 4 \n",
"Minnesota Vikings 13.0 4 2 \n",
"Dallas Cowboys 36.0 3 3 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 167.0 145.0 22.0 \n",
"Pittsburgh Steelers 157.0 145.0 13.0 \n",
"New England Patriots 167.0 87.0 31.0 \n",
"Tampa Bay Buccaneers 88.0 142.0 3.0 \n",
"Philadelphia Eagles 206.0 160.0 6.0 \n",
"St. Louis Rams 163.0 166.0 24.0 \n",
"Atlanta Falcons 153.0 134.0 29.0 \n",
"Cleveland Browns 108.0 139.0 20.0 \n",
"Cincinnati Bengals 155.0 142.0 27.0 \n",
"Oakland Raiders 92.0 148.0 20.0 \n",
"Buffalo Bills 100.0 153.0 6.0 \n",
"New York Giants 180.0 134.0 17.0 \n",
"Detroit Lions 132.0 189.0 24.0 \n",
"Carolina Panthers 137.0 163.0 14.0 \n",
"San Francisco 49ers 134.0 235.0 10.0 \n",
"Indianapolis Colts 205.0 153.0 34.0 \n",
"Seattle Seahawks 149.0 177.0 28.0 \n",
"Arizona Cardinals 134.0 196.0 14.0 \n",
"Houston Texans 121.0 182.0 22.0 \n",
"Tennessee Titans 113.0 179.0 28.0 \n",
"Jacksonville Jaguars 138.0 107.0 13.0 \n",
"Chicago Bears 221.0 69.0 41.0 \n",
"San Diego Chargers 216.0 109.0 38.0 \n",
"Miami Dolphins 102.0 145.0 24.0 \n",
"New York Jets 160.0 193.0 13.0 \n",
"Baltimore Ravens 145.0 91.0 35.0 \n",
"Kansas City Chiefs 152.0 152.0 35.0 \n",
"Denver Broncos 110.0 78.0 31.0 \n",
"Washington Redskins 140.0 171.0 22.0 \n",
"Green Bay Packers 152.0 176.0 31.0 \n",
"Minnesota Vikings 127.0 126.0 7.0 \n",
"Dallas Cowboys 204.0 142.0 35.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 35.0 5 2 \n",
"Pittsburgh Steelers 20.0 2 5 \n",
"New England Patriots 7.0 6 1 \n",
"Tampa Bay Buccaneers 17.0 2 5 \n",
"Philadelphia Eagles 13.0 4 4 \n",
"St. Louis Rams 38.0 4 3 \n",
"Atlanta Falcons 27.0 5 2 \n",
"Cleveland Browns 13.0 2 5 \n",
"Cincinnati Bengals 29.0 4 3 \n",
"Oakland Raiders 13.0 2 5 \n",
"Buffalo Bills 28.0 2 5 \n",
"New York Giants 3.0 5 2 \n",
"Detroit Lions 31.0 1 6 \n",
"Carolina Panthers 35.0 4 4 \n",
"San Francisco 49ers 41.0 2 5 \n",
"Indianapolis Colts 31.0 7 0 \n",
"Seattle Seahawks 35.0 4 3 \n",
"Arizona Cardinals 31.0 1 7 \n",
"Houston Texans 28.0 2 5 \n",
"Tennessee Titans 22.0 2 5 \n",
"Jacksonville Jaguars 6.0 4 3 \n",
"Chicago Bears 10.0 7 0 \n",
"San Diego Chargers 24.0 5 2 \n",
"Miami Dolphins 34.0 1 6 \n",
"New York Jets 20.0 4 4 \n",
"Baltimore Ravens 22.0 5 2 \n",
"Kansas City Chiefs 28.0 4 3 \n",
"Denver Broncos 34.0 5 2 \n",
"Washington Redskins 36.0 2 5 \n",
"Green Bay Packers 14.0 3 4 \n",
"Minnesota Vikings 31.0 4 3 \n",
"Dallas Cowboys 14.0 4 3 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 198.0 159.0 31.0 \n",
"Pittsburgh Steelers 177.0 176.0 20.0 \n",
"New England Patriots 187.0 114.0 20.0 \n",
"Tampa Bay Buccaneers 102.0 173.0 14.0 \n",
"Philadelphia Eagles 206.0 160.0 6.0 \n",
"St. Louis Rams 180.0 197.0 17.0 \n",
"Atlanta Falcons 167.0 164.0 14.0 \n",
"Cleveland Browns 133.0 171.0 25.0 \n",
"Cincinnati Bengals 175.0 168.0 20.0 \n",
"Oakland Raiders 92.0 164.0 0.0 \n",
"Buffalo Bills 124.0 163.0 24.0 \n",
"New York Giants 194.0 144.0 14.0 \n",
"Detroit Lions 162.0 203.0 30.0 \n",
"Carolina Panthers 137.0 163.0 14.0 \n",
"San Francisco 49ers 143.0 238.0 9.0 \n",
"Indianapolis Colts 232.0 173.0 27.0 \n",
"Seattle Seahawks 165.0 177.0 16.0 \n",
"Arizona Cardinals 134.0 196.0 14.0 \n",
"Houston Texans 131.0 196.0 10.0 \n",
"Tennessee Titans 120.0 216.0 7.0 \n",
"Jacksonville Jaguars 175.0 114.0 37.0 \n",
"Chicago Bears 234.0 100.0 13.0 \n",
"San Diego Chargers 248.0 134.0 32.0 \n",
"Miami Dolphins 133.0 158.0 31.0 \n",
"New York Jets 160.0 193.0 13.0 \n",
"Baltimore Ravens 171.0 111.0 26.0 \n",
"Kansas City Chiefs 183.0 169.0 31.0 \n",
"Denver Broncos 141.0 98.0 31.0 \n",
"Washington Redskins 162.0 190.0 22.0 \n",
"Green Bay Packers 162.0 200.0 10.0 \n",
"Minnesota Vikings 130.0 135.0 3.0 \n",
"Dallas Cowboys 223.0 164.0 19.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 14.0 6 2 \n",
"Pittsburgh Steelers 31.0 2 6 \n",
"New England Patriots 27.0 6 2 \n",
"Tampa Bay Buccaneers 31.0 2 6 \n",
"Philadelphia Eagles 13.0 4 4 \n",
"St. Louis Rams 31.0 4 4 \n",
"Atlanta Falcons 30.0 5 3 \n",
"Cleveland Browns 32.0 2 6 \n",
"Cincinnati Bengals 26.0 4 4 \n",
"Oakland Raiders 16.0 2 6 \n",
"Buffalo Bills 10.0 3 5 \n",
"New York Giants 10.0 6 2 \n",
"Detroit Lions 14.0 2 6 \n",
"Carolina Panthers 35.0 4 4 \n",
"San Francisco 49ers 3.0 3 5 \n",
"Indianapolis Colts 20.0 8 0 \n",
"Seattle Seahawks 0.0 5 3 \n",
"Arizona Cardinals 31.0 1 7 \n",
"Houston Texans 14.0 2 6 \n",
"Tennessee Titans 37.0 2 6 \n",
"Jacksonville Jaguars 7.0 5 3 \n",
"Chicago Bears 31.0 7 1 \n",
"San Diego Chargers 25.0 6 2 \n",
"Miami Dolphins 13.0 2 6 \n",
"New York Jets 20.0 4 4 \n",
"Baltimore Ravens 20.0 6 2 \n",
"Kansas City Chiefs 17.0 5 3 \n",
"Denver Broncos 20.0 6 2 \n",
"Washington Redskins 19.0 3 5 \n",
"Green Bay Packers 24.0 3 5 \n",
"Minnesota Vikings 9.0 4 4 \n",
"Dallas Cowboys 22.0 4 4 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 229.0 197.0 31.0 \n",
"Pittsburgh Steelers 215.0 207.0 38.0 \n",
"New England Patriots 201.0 131.0 14.0 \n",
"Tampa Bay Buccaneers 112.0 197.0 10.0 \n",
"Philadelphia Eagles 233.0 163.0 27.0 \n",
"St. Louis Rams 202.0 221.0 22.0 \n",
"Atlanta Falcons 180.0 181.0 13.0 \n",
"Cleveland Browns 150.0 184.0 17.0 \n",
"Cincinnati Bengals 216.0 217.0 41.0 \n",
"Oakland Raiders 105.0 181.0 13.0 \n",
"Buffalo Bills 140.0 180.0 16.0 \n",
"New York Giants 214.0 182.0 20.0 \n",
"Detroit Lions 175.0 222.0 13.0 \n",
"Carolina Panthers 161.0 173.0 24.0 \n",
"San Francisco 49ers 162.0 251.0 19.0 \n",
"Indianapolis Colts 249.0 189.0 17.0 \n",
"Seattle Seahawks 189.0 199.0 24.0 \n",
"Arizona Cardinals 144.0 223.0 10.0 \n",
"Houston Texans 144.0 206.0 13.0 \n",
"Tennessee Titans 146.0 243.0 26.0 \n",
"Jacksonville Jaguars 185.0 127.0 10.0 \n",
"Chicago Bears 272.0 120.0 38.0 \n",
"San Diego Chargers 297.0 175.0 49.0 \n",
"Miami Dolphins 146.0 168.0 13.0 \n",
"New York Jets 177.0 207.0 17.0 \n",
"Baltimore Ravens 198.0 137.0 27.0 \n",
"Kansas City Chiefs 193.0 182.0 10.0 \n",
"Denver Broncos 158.0 111.0 17.0 \n",
"Washington Redskins 165.0 217.0 3.0 \n",
"Green Bay Packers 185.0 217.0 23.0 \n",
"Minnesota Vikings 147.0 158.0 17.0 \n",
"Dallas Cowboys 250.0 174.0 27.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 38.0 6 3 \n",
"Pittsburgh Steelers 31.0 3 6 \n",
"New England Patriots 17.0 6 3 \n",
"Tampa Bay Buccaneers 24.0 2 7 \n",
"Philadelphia Eagles 3.0 5 4 \n",
"St. Louis Rams 24.0 4 5 \n",
"Atlanta Falcons 17.0 5 4 \n",
"Cleveland Browns 13.0 3 6 \n",
"Cincinnati Bengals 49.0 4 5 \n",
"Oakland Raiders 17.0 2 7 \n",
"Buffalo Bills 17.0 3 6 \n",
"New York Giants 38.0 6 3 \n",
"Detroit Lions 19.0 2 7 \n",
"Carolina Panthers 10.0 5 4 \n",
"San Francisco 49ers 13.0 4 5 \n",
"Indianapolis Colts 16.0 9 0 \n",
"Seattle Seahawks 22.0 6 3 \n",
"Arizona Cardinals 27.0 1 8 \n",
"Houston Texans 10.0 3 6 \n",
"Tennessee Titans 27.0 2 7 \n",
"Jacksonville Jaguars 13.0 5 4 \n",
"Chicago Bears 20.0 8 1 \n",
"San Diego Chargers 41.0 7 2 \n",
"Miami Dolphins 10.0 3 6 \n",
"New York Jets 14.0 5 4 \n",
"Baltimore Ravens 26.0 7 2 \n",
"Kansas City Chiefs 13.0 5 4 \n",
"Denver Broncos 13.0 7 2 \n",
"Washington Redskins 27.0 3 6 \n",
"Green Bay Packers 17.0 4 5 \n",
"Minnesota Vikings 23.0 4 5 \n",
"Dallas Cowboys 10.0 5 4 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 245.0 228.0 16.0 \n",
"Pittsburgh Steelers 239.0 227.0 24.0 \n",
"New England Patriots 236.0 131.0 35.0 \n",
"Tampa Bay Buccaneers 132.0 214.0 20.0 \n",
"Philadelphia Eagles 246.0 194.0 13.0 \n",
"St. Louis Rams 202.0 236.0 0.0 \n",
"Atlanta Falcons 190.0 205.0 10.0 \n",
"Cleveland Browns 170.0 208.0 20.0 \n",
"Cincinnati Bengals 247.0 233.0 31.0 \n",
"Oakland Raiders 118.0 198.0 13.0 \n",
"Buffalo Bills 164.0 201.0 24.0 \n",
"New York Giants 224.0 208.0 10.0 \n",
"Detroit Lions 185.0 239.0 10.0 \n",
"Carolina Panthers 176.0 173.0 15.0 \n",
"San Francisco 49ers 182.0 265.0 20.0 \n",
"Indianapolis Colts 263.0 210.0 14.0 \n",
"Seattle Seahawks 203.0 219.0 14.0 \n",
"Arizona Cardinals 161.0 233.0 17.0 \n",
"Houston Texans 165.0 230.0 21.0 \n",
"Tennessee Titans 177.0 256.0 31.0 \n",
"Jacksonville Jaguars 211.0 137.0 26.0 \n",
"Chicago Bears 282.0 120.0 10.0 \n",
"San Diego Chargers 332.0 202.0 35.0 \n",
"Miami Dolphins 170.0 188.0 24.0 \n",
"New York Jets 177.0 217.0 0.0 \n",
"Baltimore Ravens 222.0 147.0 24.0 \n",
"Kansas City Chiefs 210.0 195.0 17.0 \n",
"Denver Broncos 185.0 146.0 27.0 \n",
"Washington Redskins 182.0 237.0 17.0 \n",
"Green Bay Packers 185.0 252.0 0.0 \n",
"Minnesota Vikings 167.0 182.0 20.0 \n",
"Dallas Cowboys 271.0 188.0 21.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 31.0 6 4 \n",
"Pittsburgh Steelers 20.0 4 6 \n",
"New England Patriots 0.0 7 3 \n",
"Tampa Bay Buccaneers 17.0 3 7 \n",
"Philadelphia Eagles 31.0 5 5 \n",
"St. Louis Rams 15.0 4 6 \n",
"Atlanta Falcons 24.0 5 5 \n",
"Cleveland Browns 24.0 3 7 \n",
"Cincinnati Bengals 16.0 5 5 \n",
"Oakland Raiders 17.0 2 8 \n",
"Buffalo Bills 21.0 4 6 \n",
"New York Giants 26.0 6 4 \n",
"Detroit Lions 17.0 2 8 \n",
"Carolina Panthers 0.0 6 4 \n",
"San Francisco 49ers 14.0 5 5 \n",
"Indianapolis Colts 21.0 9 1 \n",
"Seattle Seahawks 20.0 6 4 \n",
"Arizona Cardinals 10.0 2 8 \n",
"Houston Texans 24.0 3 7 \n",
"Tennessee Titans 13.0 3 7 \n",
"Jacksonville Jaguars 10.0 6 4 \n",
"Chicago Bears 0.0 9 1 \n",
"San Diego Chargers 27.0 8 2 \n",
"Miami Dolphins 20.0 4 6 \n",
"New York Jets 10.0 5 5 \n",
"Baltimore Ravens 10.0 8 2 \n",
"Kansas City Chiefs 13.0 6 4 \n",
"Denver Broncos 35.0 7 3 \n",
"Washington Redskins 20.0 3 7 \n",
"Green Bay Packers 35.0 4 6 \n",
"Minnesota Vikings 24.0 4 6 \n",
"Dallas Cowboys 14.0 6 4 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 276.0 241.0 31.0 \n",
"Pittsburgh Steelers 239.0 254.0 0.0 \n",
"New England Patriots 253.0 144.0 17.0 \n",
"Tampa Bay Buccaneers 142.0 252.0 10.0 \n",
"Philadelphia Eagles 267.0 239.0 21.0 \n",
"St. Louis Rams 222.0 253.0 20.0 \n",
"Atlanta Falcons 203.0 236.0 13.0 \n",
"Cleveland Browns 170.0 238.0 0.0 \n",
"Cincinnati Bengals 277.0 233.0 30.0 \n",
"Oakland Raiders 132.0 219.0 14.0 \n",
"Buffalo Bills 191.0 225.0 27.0 \n",
"New York Giants 245.0 232.0 21.0 \n",
"Detroit Lions 195.0 266.0 10.0 \n",
"Carolina Panthers 189.0 190.0 13.0 \n",
"San Francisco 49ers 199.0 285.0 17.0 \n",
"Indianapolis Colts 308.0 231.0 45.0 \n",
"Seattle Seahawks 237.0 243.0 34.0 \n",
"Arizona Cardinals 187.0 264.0 26.0 \n",
"Houston Texans 176.0 256.0 11.0 \n",
"Tennessee Titans 201.0 277.0 24.0 \n",
"Jacksonville Jaguars 235.0 164.0 24.0 \n",
"Chicago Bears 295.0 137.0 13.0 \n",
"San Diego Chargers 353.0 216.0 21.0 \n",
"Miami Dolphins 197.0 198.0 27.0 \n",
"New York Jets 203.0 228.0 26.0 \n",
"Baltimore Ravens 249.0 147.0 27.0 \n",
"Kansas City Chiefs 229.0 205.0 19.0 \n",
"Denver Broncos 195.0 165.0 10.0 \n",
"Washington Redskins 199.0 250.0 17.0 \n",
"Green Bay Packers 209.0 286.0 24.0 \n",
"Minnesota Vikings 198.0 208.0 31.0 \n",
"Dallas Cowboys 309.0 198.0 38.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 13.0 7 4 \n",
"Pittsburgh Steelers 27.0 4 7 \n",
"New England Patriots 13.0 8 3 \n",
"Tampa Bay Buccaneers 38.0 3 8 \n",
"Philadelphia Eagles 45.0 5 6 \n",
"St. Louis Rams 17.0 5 6 \n",
"Atlanta Falcons 31.0 5 6 \n",
"Cleveland Browns 30.0 3 8 \n",
"Cincinnati Bengals 0.0 6 5 \n",
"Oakland Raiders 21.0 2 9 \n",
"Buffalo Bills 24.0 5 6 \n",
"New York Giants 24.0 6 5 \n",
"Detroit Lions 27.0 2 9 \n",
"Carolina Panthers 17.0 6 5 \n",
"San Francisco 49ers 20.0 5 6 \n",
"Indianapolis Colts 21.0 10 1 \n",
"Seattle Seahawks 24.0 7 4 \n",
"Arizona Cardinals 31.0 2 9 \n",
"Houston Texans 26.0 3 8 \n",
"Tennessee Titans 21.0 4 7 \n",
"Jacksonville Jaguars 27.0 6 5 \n",
"Chicago Bears 17.0 9 2 \n",
"San Diego Chargers 14.0 9 2 \n",
"Miami Dolphins 10.0 5 6 \n",
"New York Jets 11.0 6 5 \n",
"Baltimore Ravens 0.0 9 2 \n",
"Kansas City Chiefs 10.0 7 4 \n",
"Denver Broncos 19.0 7 4 \n",
"Washington Redskins 13.0 4 7 \n",
"Green Bay Packers 34.0 4 7 \n",
"Minnesota Vikings 26.0 5 6 \n",
"Dallas Cowboys 10.0 7 4 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 310.0 251.0 34.0 \n",
"Pittsburgh Steelers 259.0 257.0 20.0 \n",
"New England Patriots 281.0 165.0 28.0 \n",
"Tampa Bay Buccaneers 145.0 272.0 3.0 \n",
"Philadelphia Eagles 294.0 263.0 27.0 \n",
"St. Louis Rams 242.0 287.0 20.0 \n",
"Atlanta Falcons 227.0 250.0 24.0 \n",
"Cleveland Browns 201.0 266.0 31.0 \n",
"Cincinnati Bengals 290.0 240.0 13.0 \n",
"Oakland Raiders 146.0 242.0 14.0 \n",
"Buffalo Bills 212.0 249.0 21.0 \n",
"New York Giants 265.0 255.0 20.0 \n",
"Detroit Lions 216.0 294.0 21.0 \n",
"Carolina Panthers 213.0 217.0 24.0 \n",
"San Francisco 49ers 209.0 319.0 10.0 \n",
"Indianapolis Colts 325.0 251.0 17.0 \n",
"Seattle Seahawks 260.0 263.0 23.0 \n",
"Arizona Cardinals 221.0 284.0 34.0 \n",
"Houston Texans 199.0 270.0 23.0 \n",
"Tennessee Titans 221.0 294.0 20.0 \n",
"Jacksonville Jaguars 259.0 174.0 24.0 \n",
"Chicago Bears 318.0 150.0 23.0 \n",
"San Diego Chargers 377.0 237.0 24.0 \n",
"Miami Dolphins 207.0 222.0 10.0 \n",
"New York Jets 241.0 238.0 38.0 \n",
"Baltimore Ravens 256.0 160.0 7.0 \n",
"Kansas City Chiefs 257.0 236.0 28.0 \n",
"Denver Broncos 215.0 188.0 20.0 \n",
"Washington Redskins 213.0 274.0 14.0 \n",
"Green Bay Packers 219.0 324.0 10.0 \n",
"Minnesota Vikings 211.0 231.0 13.0 \n",
"Dallas Cowboys 332.0 218.0 23.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 10.0 8 4 \n",
"Pittsburgh Steelers 3.0 5 7 \n",
"New England Patriots 21.0 9 3 \n",
"Tampa Bay Buccaneers 20.0 3 9 \n",
"Philadelphia Eagles 24.0 6 6 \n",
"St. Louis Rams 34.0 5 7 \n",
"Atlanta Falcons 14.0 6 6 \n",
"Cleveland Browns 28.0 4 8 \n",
"Cincinnati Bengals 7.0 7 5 \n",
"Oakland Raiders 23.0 2 10 \n",
"Buffalo Bills 24.0 5 7 \n",
"New York Giants 23.0 6 6 \n",
"Detroit Lions 28.0 2 10 \n",
"Carolina Panthers 27.0 6 6 \n",
"San Francisco 49ers 34.0 5 7 \n",
"Indianapolis Colts 20.0 10 2 \n",
"Seattle Seahawks 20.0 8 4 \n",
"Arizona Cardinals 20.0 3 9 \n",
"Houston Texans 14.0 4 8 \n",
"Tennessee Titans 17.0 5 7 \n",
"Jacksonville Jaguars 10.0 7 5 \n",
"Chicago Bears 13.0 10 2 \n",
"San Diego Chargers 21.0 10 2 \n",
"Miami Dolphins 24.0 5 7 \n",
"New York Jets 10.0 7 5 \n",
"Baltimore Ravens 13.0 9 3 \n",
"Kansas City Chiefs 31.0 7 5 \n",
"Denver Broncos 23.0 7 5 \n",
"Washington Redskins 24.0 4 8 \n",
"Green Bay Packers 38.0 4 8 \n",
"Minnesota Vikings 23.0 5 7 \n",
"Dallas Cowboys 20.0 8 4 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 352.0 268.0 42.0 \n",
"Pittsburgh Steelers 286.0 264.0 27.0 \n",
"New England Patriots 281.0 186.0 0.0 \n",
"Tampa Bay Buccaneers 151.0 289.0 6.0 \n",
"Philadelphia Eagles 315.0 282.0 21.0 \n",
"St. Louis Rams 269.0 329.0 27.0 \n",
"Atlanta Falcons 244.0 256.0 17.0 \n",
"Cleveland Browns 208.0 293.0 7.0 \n",
"Cincinnati Bengals 317.0 250.0 27.0 \n",
"Oakland Raiders 156.0 269.0 10.0 \n",
"Buffalo Bills 243.0 262.0 31.0 \n",
"New York Giants 292.0 268.0 27.0 \n",
"Detroit Lions 236.0 324.0 20.0 \n",
"Carolina Panthers 226.0 244.0 13.0 \n",
"San Francisco 49ers 228.0 349.0 19.0 \n",
"Indianapolis Colts 342.0 295.0 17.0 \n",
"Seattle Seahawks 281.0 290.0 21.0 \n",
"Arizona Cardinals 248.0 305.0 27.0 \n",
"Houston Texans 219.0 296.0 20.0 \n",
"Tennessee Titans 247.0 314.0 26.0 \n",
"Jacksonville Jaguars 303.0 191.0 44.0 \n",
"Chicago Bears 360.0 177.0 42.0 \n",
"San Diego Chargers 425.0 257.0 48.0 \n",
"Miami Dolphins 228.0 222.0 21.0 \n",
"New York Jets 254.0 269.0 13.0 \n",
"Baltimore Ravens 276.0 170.0 20.0 \n",
"Kansas City Chiefs 267.0 256.0 10.0 \n",
"Denver Broncos 235.0 236.0 20.0 \n",
"Washington Redskins 232.0 295.0 19.0 \n",
"Green Bay Packers 249.0 343.0 30.0 \n",
"Minnesota Vikings 241.0 251.0 30.0 \n",
"Dallas Cowboys 349.0 260.0 17.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 17.0 9 4 \n",
"Pittsburgh Steelers 7.0 6 7 \n",
"New England Patriots 21.0 9 4 \n",
"Tampa Bay Buccaneers 17.0 3 10 \n",
"Philadelphia Eagles 19.0 7 6 \n",
"St. Louis Rams 42.0 5 8 \n",
"Atlanta Falcons 6.0 7 6 \n",
"Cleveland Browns 27.0 4 9 \n",
"Cincinnati Bengals 10.0 8 5 \n",
"Oakland Raiders 27.0 2 11 \n",
"Buffalo Bills 13.0 6 7 \n",
"New York Giants 13.0 7 6 \n",
"Detroit Lions 30.0 2 11 \n",
"Carolina Panthers 27.0 6 7 \n",
"San Francisco 49ers 30.0 5 8 \n",
"Indianapolis Colts 44.0 10 3 \n",
"Seattle Seahawks 27.0 8 5 \n",
"Arizona Cardinals 21.0 4 9 \n",
"Houston Texans 26.0 4 9 \n",
"Tennessee Titans 20.0 6 7 \n",
"Jacksonville Jaguars 17.0 8 5 \n",
"Chicago Bears 27.0 11 2 \n",
"San Diego Chargers 20.0 11 2 \n",
"Miami Dolphins 0.0 6 7 \n",
"New York Jets 31.0 7 6 \n",
"Baltimore Ravens 10.0 10 3 \n",
"Kansas City Chiefs 20.0 7 6 \n",
"Denver Broncos 48.0 7 6 \n",
"Washington Redskins 21.0 4 9 \n",
"Green Bay Packers 19.0 5 8 \n",
"Minnesota Vikings 20.0 6 7 \n",
"Dallas Cowboys 42.0 8 5 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 362.0 284.0 10.0 \n",
"Pittsburgh Steelers 323.0 267.0 37.0 \n",
"New England Patriots 321.0 193.0 40.0 \n",
"Tampa Bay Buccaneers 182.0 323.0 31.0 \n",
"Philadelphia Eagles 351.0 304.0 36.0 \n",
"St. Louis Rams 289.0 329.0 20.0 \n",
"Atlanta Falcons 272.0 294.0 28.0 \n",
"Cleveland Browns 225.0 320.0 17.0 \n",
"Cincinnati Bengals 333.0 284.0 16.0 \n",
"Oakland Raiders 156.0 289.0 0.0 \n",
"Buffalo Bills 264.0 262.0 21.0 \n",
"New York Giants 314.0 304.0 22.0 \n",
"Detroit Lions 245.0 341.0 9.0 \n",
"Carolina Panthers 229.0 281.0 3.0 \n",
"San Francisco 49ers 252.0 363.0 24.0 \n",
"Indianapolis Colts 376.0 311.0 34.0 \n",
"Seattle Seahawks 295.0 314.0 14.0 \n",
"Arizona Cardinals 268.0 342.0 20.0 \n",
"Houston Texans 226.0 336.0 7.0 \n",
"Tennessee Titans 271.0 331.0 24.0 \n",
"Jacksonville Jaguars 320.0 215.0 17.0 \n",
"Chicago Bears 394.0 208.0 34.0 \n",
"San Diego Chargers 445.0 266.0 20.0 \n",
"Miami Dolphins 228.0 243.0 0.0 \n",
"New York Jets 280.0 282.0 26.0 \n",
"Baltimore Ravens 303.0 187.0 27.0 \n",
"Kansas City Chiefs 276.0 276.0 9.0 \n",
"Denver Broncos 272.0 256.0 37.0 \n",
"Washington Redskins 248.0 305.0 16.0 \n",
"Green Bay Packers 266.0 352.0 17.0 \n",
"Minnesota Vikings 254.0 277.0 13.0 \n",
"Dallas Cowboys 387.0 288.0 38.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 16.0 9 5 \n",
"Pittsburgh Steelers 3.0 7 7 \n",
"New England Patriots 7.0 10 4 \n",
"Tampa Bay Buccaneers 34.0 3 11 \n",
"Philadelphia Eagles 22.0 8 6 \n",
"St. Louis Rams 0.0 6 8 \n",
"Atlanta Falcons 38.0 7 7 \n",
"Cleveland Browns 27.0 4 10 \n",
"Cincinnati Bengals 34.0 8 6 \n",
"Oakland Raiders 20.0 2 12 \n",
"Buffalo Bills 0.0 7 7 \n",
"New York Giants 36.0 7 7 \n",
"Detroit Lions 17.0 2 12 \n",
"Carolina Panthers 37.0 6 8 \n",
"San Francisco 49ers 14.0 6 8 \n",
"Indianapolis Colts 16.0 11 3 \n",
"Seattle Seahawks 24.0 8 6 \n",
"Arizona Cardinals 37.0 4 10 \n",
"Houston Texans 40.0 4 10 \n",
"Tennessee Titans 17.0 7 7 \n",
"Jacksonville Jaguars 24.0 8 6 \n",
"Chicago Bears 31.0 12 2 \n",
"San Diego Chargers 9.0 12 2 \n",
"Miami Dolphins 21.0 6 8 \n",
"New York Jets 13.0 8 6 \n",
"Baltimore Ravens 17.0 11 3 \n",
"Kansas City Chiefs 20.0 7 7 \n",
"Denver Broncos 20.0 8 6 \n",
"Washington Redskins 10.0 5 9 \n",
"Green Bay Packers 9.0 6 8 \n",
"Minnesota Vikings 26.0 6 8 \n",
"Dallas Cowboys 28.0 9 5 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 392.0 291.0 30.0 \n",
"Pittsburgh Steelers 330.0 298.0 7.0 \n",
"New England Patriots 345.0 214.0 24.0 \n",
"Tampa Bay Buccaneers 204.0 330.0 22.0 \n",
"Philadelphia Eagles 374.0 311.0 23.0 \n",
"St. Louis Rams 326.0 360.0 37.0 \n",
"Atlanta Falcons 275.0 304.0 3.0 \n",
"Cleveland Browns 232.0 342.0 7.0 \n",
"Cincinnati Bengals 356.0 308.0 23.0 \n",
"Oakland Raiders 165.0 309.0 9.0 \n",
"Buffalo Bills 293.0 292.0 29.0 \n",
"New York Giants 321.0 334.0 7.0 \n",
"Detroit Lions 266.0 367.0 21.0 \n",
"Carolina Panthers 239.0 284.0 10.0 \n",
"San Francisco 49ers 272.0 389.0 20.0 \n",
"Indianapolis Colts 400.0 338.0 24.0 \n",
"Seattle Seahawks 312.0 334.0 17.0 \n",
"Arizona Cardinals 294.0 362.0 26.0 \n",
"Houston Texans 253.0 360.0 27.0 \n",
"Tennessee Titans 301.0 360.0 30.0 \n",
"Jacksonville Jaguars 341.0 239.0 21.0 \n",
"Chicago Bears 420.0 229.0 26.0 \n",
"San Diego Chargers 465.0 283.0 20.0 \n",
"Miami Dolphins 238.0 256.0 10.0 \n",
"New York Jets 293.0 292.0 13.0 \n",
"Baltimore Ravens 334.0 194.0 31.0 \n",
"Kansas City Chiefs 296.0 285.0 20.0 \n",
"Denver Broncos 296.0 279.0 24.0 \n",
"Washington Redskins 279.0 342.0 31.0 \n",
"Green Bay Packers 275.0 359.0 9.0 \n",
"Minnesota Vikings 261.0 286.0 7.0 \n",
"Dallas Cowboys 394.0 311.0 7.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 7.0 10 5 \n",
"Pittsburgh Steelers 31.0 7 8 \n",
"New England Patriots 21.0 11 4 \n",
"Tampa Bay Buccaneers 7.0 4 11 \n",
"Philadelphia Eagles 7.0 9 6 \n",
"St. Louis Rams 31.0 7 8 \n",
"Atlanta Falcons 10.0 7 8 \n",
"Cleveland Browns 22.0 4 11 \n",
"Cincinnati Bengals 24.0 8 7 \n",
"Oakland Raiders 20.0 2 13 \n",
"Buffalo Bills 30.0 7 8 \n",
"New York Giants 30.0 7 8 \n",
"Detroit Lions 26.0 2 13 \n",
"Carolina Panthers 3.0 7 8 \n",
"San Francisco 49ers 26.0 6 9 \n",
"Indianapolis Colts 27.0 11 4 \n",
"Seattle Seahawks 20.0 8 7 \n",
"Arizona Cardinals 20.0 5 10 \n",
"Houston Texans 24.0 5 10 \n",
"Tennessee Titans 29.0 8 7 \n",
"Jacksonville Jaguars 24.0 8 7 \n",
"Chicago Bears 21.0 13 2 \n",
"San Diego Chargers 17.0 13 2 \n",
"Miami Dolphins 13.0 6 9 \n",
"New York Jets 10.0 9 6 \n",
"Baltimore Ravens 7.0 12 3 \n",
"Kansas City Chiefs 9.0 8 7 \n",
"Denver Broncos 23.0 9 6 \n",
"Washington Redskins 37.0 5 10 \n",
"Green Bay Packers 7.0 7 8 \n",
"Minnesota Vikings 9.0 6 9 \n",
"Dallas Cowboys 23.0 9 6 , points_forced points_allowed points_forced_last \\\n",
"New Orleans Saints 413.0 322.0 21.0 \n",
"Pittsburgh Steelers 353.0 315.0 23.0 \n",
"New England Patriots 385.0 237.0 40.0 \n",
"Tampa Bay Buccaneers 211.0 353.0 7.0 \n",
"Philadelphia Eagles 398.0 328.0 24.0 \n",
"St. Louis Rams 367.0 381.0 41.0 \n",
"Atlanta Falcons 292.0 328.0 17.0 \n",
"Cleveland Browns 238.0 356.0 6.0 \n",
"Cincinnati Bengals 373.0 331.0 17.0 \n",
"Oakland Raiders 168.0 332.0 3.0 \n",
"Buffalo Bills 300.0 311.0 7.0 \n",
"New York Giants 355.0 362.0 34.0 \n",
"Detroit Lions 305.0 398.0 39.0 \n",
"Carolina Panthers 270.0 305.0 31.0 \n",
"San Francisco 49ers 298.0 412.0 26.0 \n",
"Indianapolis Colts 427.0 360.0 27.0 \n",
"Seattle Seahawks 335.0 341.0 23.0 \n",
"Arizona Cardinals 314.0 389.0 20.0 \n",
"Houston Texans 267.0 366.0 14.0 \n",
"Tennessee Titans 324.0 400.0 23.0 \n",
"Jacksonville Jaguars 371.0 274.0 30.0 \n",
"Chicago Bears 427.0 255.0 7.0 \n",
"San Diego Chargers 492.0 303.0 27.0 \n",
"Miami Dolphins 260.0 283.0 22.0 \n",
"New York Jets 316.0 295.0 23.0 \n",
"Baltimore Ravens 353.0 201.0 19.0 \n",
"Kansas City Chiefs 331.0 315.0 35.0 \n",
"Denver Broncos 319.0 305.0 23.0 \n",
"Washington Redskins 307.0 376.0 28.0 \n",
"Green Bay Packers 301.0 366.0 26.0 \n",
"Minnesota Vikings 282.0 327.0 21.0 \n",
"Dallas Cowboys 425.0 350.0 31.0 \n",
"\n",
" points_allowed_last win loss \n",
"New Orleans Saints 31.0 10 6 \n",
"Pittsburgh Steelers 17.0 8 8 \n",
"New England Patriots 23.0 12 4 \n",
"Tampa Bay Buccaneers 23.0 4 12 \n",
"Philadelphia Eagles 17.0 10 6 \n",
"St. Louis Rams 21.0 8 8 \n",
"Atlanta Falcons 24.0 7 9 \n",
"Cleveland Browns 14.0 4 12 \n",
"Cincinnati Bengals 23.0 8 8 \n",
"Oakland Raiders 23.0 2 14 \n",
"Buffalo Bills 19.0 7 9 \n",
"New York Giants 28.0 8 8 \n",
"Detroit Lions 31.0 3 13 \n",
"Carolina Panthers 21.0 8 8 \n",
"San Francisco 49ers 23.0 7 9 \n",
"Indianapolis Colts 22.0 12 4 \n",
"Seattle Seahawks 7.0 9 7 \n",
"Arizona Cardinals 27.0 5 11 \n",
"Houston Texans 6.0 6 10 \n",
"Tennessee Titans 40.0 8 8 \n",
"Jacksonville Jaguars 35.0 8 8 \n",
"Chicago Bears 26.0 13 3 \n",
"San Diego Chargers 20.0 14 2 \n",
"Miami Dolphins 27.0 6 10 \n",
"New York Jets 3.0 10 6 \n",
"Baltimore Ravens 7.0 13 3 \n",
"Kansas City Chiefs 30.0 9 7 \n",
"Denver Broncos 26.0 9 7 \n",
"Washington Redskins 34.0 5 11 \n",
"Green Bay Packers 7.0 8 8 \n",
"Minnesota Vikings 41.0 6 10 \n",
"Dallas Cowboys 39.0 9 7 ]\n"
]
}
],
"source": [
"year = 2006\n",
"reg_games, po_games = get_game_data(year)\n",
"reg_history = get_regular_season_history(year)\n",
"print(reg_history)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"\n",
"vegas_line_dict = sort_vegas_lines()\n",
"\n",
"def get_features_and_labels(years):\n",
" # takes in list of years\n",
" # label is 1 if home team wins, 0 otherwise (including ties)\n",
" # week 17 excluded from the data\n",
" \n",
" data_X , data_Y, data_des = [], [], []\n",
"\n",
" for year in years:\n",
" reg_games, po_games = get_game_data(year)\n",
" reg_history = get_regular_season_history(year)\n",
" for index, row in reg_games.iterrows():\n",
" if row['Week'] == 17:\n",
" break\n",
" home_features = list(reg_history[row['Week']-1].loc[row['Home']])\n",
" \n",
" away_features = list(reg_history[row['Week']-1].loc[row['Away']])\n",
" num_games_played_home = sum(home_features[2:4]) #wins and losses!\n",
" num_games_played_away = sum(away_features[2:4])\n",
" normalize_points = False\n",
" if num_games_played_home>0 and normalize_points:\n",
" home_features[0:2] = [x / num_games_played_home for x in home_features[0:2]]\n",
" if num_games_played_away>0 and normalize_points:\n",
" away_features[0:2] = [x / num_games_played_away for x in away_features[0:2]]\n",
" \n",
" vegas_line = 0\n",
" try:\n",
" vegas_line = vegas_line_dict[year][row['Week']][row['Home']]\n",
" except:\n",
" pass\n",
" data_X.append(home_features[:-1] + away_features[:-1] + [vegas_line]) \n",
" data_Y.append(int(row['Winner/tie'] == row['Home']))\n",
" data_des.append(row)\n",
" \n",
" data_X = np.array(data_X)\n",
" data_Y = np.array(data_Y).reshape(-1, 1)\n",
"\n",
" return data_X, data_Y, data_des"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"tr_x,tr_y,z = get_features_and_labels([2013])\n",
"#val_x,val_y,z = get_features_and_labels([2012])"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 269. 362. 17. 24. 3. 298. 214. 28. 35. 9.\n",
" -3.5]\n"
]
}
],
"source": [
"print(tr_x[200])"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Buffalo Bills': -3.0, 'Washington Redskins': 3.0, 'St. Louis Rams': 1.5, 'New York Jets': -6.5, 'San Diego Chargers': 7.0, 'San Francisco 49ers': 3.5, 'Minnesota Vikings': 3.0, 'Cincinnati Bengals': 3.0, 'Indianapolis Colts': 5.5, 'Seattle Seahawks': 6.0, 'Houston Texans': 3.0, 'Green Bay Packers': -3.5, 'Cleveland Browns': -5.5, 'Dallas Cowboys': 6.5, 'Jacksonville Jaguars': 7.5, 'Oakland Raiders': 3.0}\n"
]
}
],
"source": [
"print(vegas_line_dict[2007][1])"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"#====== my code here ========"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0, 1)\n"
]
}
],
"source": [
"x = [0,1]\n",
"a,b = x\n",
"print(a,b)"
]
},
{
"cell_type": "code",
"execution_count": 253,
"metadata": {},
"outputs": [],
"source": [
"xt,yt,_ = get_features_and_labels([2000,2001])"
]
},
{
"cell_type": "code",
"execution_count": 255,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
" [ 30. 27. 30. 27. 1. 23. 0. 23. 0. 1.]\n",
" [ 0. 0. 0. 0. 0. 7. 27. 7. 27. 0.]\n",
" [ 27. 14. 27. 14. 1. 9. 6. 9. 6. 1.]\n",
" [ 16. 13. 16. 13. 1. 16. 20. 16. 20. 0.]\n",
" [ 21. 16. 21. 16. 1. 27. 30. 27. 30. 0.]\n",
" [ 41. 14. 41. 14. 1. 21. 16. 21. 16. 1.]\n",
" [ 16. 0. 16. 0. 1. 27. 7. 27. 7. 1.]\n",
" [ 13. 16. 13. 16. 0. 14. 27. 14. 27. 0.]\n",
" [ 0. 23. 0. 23. 0. 41. 36. 41. 36. 1.]\n",
" [ 6. 9. 6. 9. 0. 10. 14. 10. 14. 0.]\n",
" [ 28. 36. 28. 36. 0. 17. 20. 17. 20. 0.]\n",
" [ 14. 10. 14. 10. 1. 20. 17. 20. 17. 1.]\n",
" [ 36. 41. 36. 41. 0. 36. 28. 36. 28. 1.]\n",
" [ 16. 21. 16. 21. 0. 14. 41. 14. 41. 0.]\n",
" [ 20. 16. 20. 16. 1. 16. 21. 16. 21. 0.]\n",
" [ 63. 46. 36. 39. 1. 7. 24. 7. 24. 0.]\n",
" [ 34. 47. 18. 27. 0. 59. 47. 18. 33. 1.]\n",
" [ 55. 42. 38. 22. 1. 50. 70. 14. 42. 1.]\n",
" [ 78. 70. 37. 34. 2. 50. 74. 22. 38. 0.]\n",
" [ 31. 34. 24. 7. 1. 0. 16. 0. 16. 0.]\n",
" [ 40. 35. 20. 19. 2. 43. 31. 27. 18. 2.]\n",
" [ 29. 20. 15. 10. 2. 62. 16. 41. 0. 2.]\n",
" [ 27. 71. 0. 41. 0. 54. 34. 33. 18. 2.]\n",
" [ 47. 37. 38. 31. 2. 78. 55. 42. 14. 1.]\n",
" [ 28. 44. 14. 17. 0. 33. 37. 27. 28. 0.]]\n"
]
}
],
"source": [
"print(xt[10:40])\n",
"#for i in range(len())"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"# Partition into training and test years\n",
"\n",
"# train_years_1 = [1997, 2001, 2005, 2009, 2013]\n",
"# train_years_2 = [1998, 2002, 2006, 2010, 2014]\n",
"# train_years_3 = [1999, 2003, 2007, 2011, 2015]\n",
"# test_years = [2000, 2004, 2008, 2012, 2016]\n",
"\n",
"train_years_1 = [2009, 2013,2016]\n",
"train_years_2 = [2006, 2010, 2014]\n",
"train_years_3 = [2007, 2011, 2015]\n",
"test_years = [2000, 2004, 2008, 2012]\n",
"\n",
"X_train, Y_train = [None]*3, [None]*3\n",
"X_train[0], Y_train[0], _ = get_features_and_labels(train_years_1)\n",
"X_train[1], Y_train[1], _ = get_features_and_labels(train_years_2)\n",
"X_train[2], Y_train[2], _ = get_features_and_labels(train_years_3)\n",
"X_test, Y_test, _ = get_features_and_labels(test_years)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 47. 50. 20. 23. 1. 73. 64. 23. 13. 2. 5.5]\n"
]
}
],
"source": [
"print(X_train[0][60])"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Scale of 0 disables regularizer.\n",
"Initializing variables...\n",
"\n",
"Batch loss at step 0: 0.752607\n",
"Batch training accuracy: 50.0%\n",
"Full training accuracy: 56.3%\n",
"Validation accuracy: 58.0%\n",
"\n",
"Batch loss at step 100: 0.728479\n",
"Batch training accuracy: 53.0%\n",
"Full training accuracy: 56.3%\n",
"Validation accuracy: 58.0%\n",
"\n",
"Batch loss at step 200: 0.693150\n",
"Batch training accuracy: 60.0%\n",
"Full training accuracy: 57.1%\n",
"Validation accuracy: 57.1%\n",
"\n",
"Batch loss at step 300: 0.692230\n",
"Batch training accuracy: 54.0%\n",
"Full training accuracy: 56.0%\n",
"Validation accuracy: 55.5%\n",
"\n",
"Batch loss at step 400: 0.761270\n",
"Batch training accuracy: 53.0%\n",
"Full training accuracy: 56.6%\n",
"Validation accuracy: 56.0%\n",
"\n",
"Batch loss at step 500: 0.737284\n",
"Batch training accuracy: 50.0%\n",
"Full training accuracy: 56.6%\n",
"Validation accuracy: 56.9%\n",
"\n",
"Batch loss at step 600: 0.717703\n",
"Batch training accuracy: 54.0%\n",
"Full training accuracy: 56.2%\n",
"Validation accuracy: 56.9%\n",
"\n",
"Batch loss at step 700: 0.693540\n",
"Batch training accuracy: 59.0%\n",
"Full training accuracy: 56.0%\n",
"Validation accuracy: 56.8%\n",
"\n",
"Batch loss at step 800: 0.692699\n",
"Batch training accuracy: 58.0%\n",
"Full training accuracy: 56.1%\n",
"Validation accuracy: 56.9%\n",
"\n",
"Batch loss at step 900: 0.709974\n",
"Batch training accuracy: 57.0%\n",
"Full training accuracy: 56.1%\n",
"Validation accuracy: 57.0%\n",
"\n",
"Batch loss at step 1000: 0.742629\n",
"Batch training accuracy: 49.0%\n",
"Full training accuracy: 56.3%\n",
"Validation accuracy: 56.9%\n",
"\n",
"Batch loss at step 1100: 0.665799\n",
"Batch training accuracy: 61.0%\n",
"Full training accuracy: 56.5%\n",
"Validation accuracy: 57.3%\n",
"\n",
"Batch loss at step 1200: 0.686617\n",
"Batch training accuracy: 59.0%\n",
"Full training accuracy: 56.4%\n",
"Validation accuracy: 57.3%\n",
"\n",
"Batch loss at step 1300: 0.716881\n",
"Batch training accuracy: 57.0%\n",
"Full training accuracy: 56.5%\n",
"Validation accuracy: 57.2%\n",
"\n",
"Batch loss at step 1400: 0.649270\n",
"Batch training accuracy: 63.0%\n",
"Full training accuracy: 56.6%\n",
"Validation accuracy: 57.4%\n",
"\n",
"Batch loss at step 1500: 0.709802\n",
"Batch training accuracy: 54.0%\n",
"Full training accuracy: 56.7%\n",
"Validation accuracy: 57.3%\n",
"\n",
"Batch loss at step 1600: 0.688328\n",
"Batch training accuracy: 58.0%\n",
"Full training accuracy: 56.6%\n",
"Validation accuracy: 57.4%\n",
"\n",
"Batch loss at step 1700: 0.664140\n",
"Batch training accuracy: 59.0%\n",
"Full training accuracy: 56.7%\n",
"Validation accuracy: 57.3%\n",
"\n",
"Batch loss at step 1800: 0.697892\n",
"Batch training accuracy: 56.0%\n",
"Full training accuracy: 57.0%\n",
"Validation accuracy: 57.5%\n",
"\n",
"Batch loss at step 1900: 0.631405\n",
"Batch training accuracy: 64.0%\n",
"Full training accuracy: 57.0%\n",
"Validation accuracy: 57.5%\n",
"\n",
"Batch loss at step 2000: 0.683110\n",
"Batch training accuracy: 53.0%\n",
"Full training accuracy: 56.9%\n",
"Validation accuracy: 57.6%\n",
"\n",
"Batch loss at step 2100: 0.701136\n",
"Batch training accuracy: 53.0%\n",
"Full training accuracy: 57.0%\n",
"Validation accuracy: 57.7%\n",
"\n",
"Batch loss at step 2200: 0.678213\n",
"Batch training accuracy: 55.0%\n",
"Full training accuracy: 57.2%\n",
"Validation accuracy: 57.6%\n",
"\n",
"Batch loss at step 2300: 0.681789\n",
"Batch training accuracy: 58.0%\n",
"Full training accuracy: 57.2%\n",
"Validation accuracy: 57.7%\n",
"\n",
"Batch loss at step 2400: 0.618764\n",
"Batch training accuracy: 66.0%\n",
"Full training accuracy: 57.2%\n",
"Validation accuracy: 57.9%\n",
"\n",
"Batch loss at step 2500: 0.665986\n",
"Batch training accuracy: 62.0%\n",
"Full training accuracy: 57.4%\n",
"Validation accuracy: 58.2%\n",
"\n",
"Batch loss at step 2600: 0.698866\n",
"Batch training accuracy: 51.0%\n",
"Full training accuracy: 57.5%\n",
"Validation accuracy: 58.2%\n",
"\n",
"Batch loss at step 2700: 0.686642\n",
"Batch training accuracy: 52.0%\n",
"Full training accuracy: 57.4%\n",
"Validation accuracy: 58.1%\n",
"\n",
"Batch loss at step 2800: 0.683523\n",
"Batch training accuracy: 57.0%\n",
"Full training accuracy: 57.4%\n",
"Validation accuracy: 58.0%\n",
"\n",
"Batch loss at step 2900: 0.608903\n",
"Batch training accuracy: 68.0%\n",
"Full training accuracy: 57.5%\n",
"Validation accuracy: 58.6%\n",
"\n",
"Batch loss at step 3000: 0.688730\n",
"Batch training accuracy: 57.0%\n",
"Full training accuracy: 57.6%\n",
"Validation accuracy: 58.2%\n",
"('valaccs: ', [57.96125, 57.96125, 57.118786, 55.518116, 56.02359, 56.866051, 56.866051, 56.781803, 56.866051, 57.034538, 56.866051, 57.287281, 57.287281, 57.20303, 57.371529, 57.287281, 57.371529, 57.287281, 57.455772, 57.540016, 57.624264, 57.708508, 57.624264, 57.708508, 57.876999, 58.213985, 58.213985, 58.129738, 58.045494, 58.550972, 58.213985])\n",
"('Final validation accuracy: ', 58.213985)\n",
"('Finished training. Total time taken:', 5.527379989624023)\n",
"INFO:tensorflow:Scale of 0 disables regularizer.\n",
"Initializing variables...\n",
"\n",
"Batch loss at step 0: 0.936003\n",
"Batch training accuracy: 39.0%\n",
"Full training accuracy: 44.3%\n",
"Validation accuracy: 42.5%\n",
"\n",
"Batch loss at step 100: 0.864339\n",
"Batch training accuracy: 41.0%\n",
"Full training accuracy: 44.3%\n",
"Validation accuracy: 42.5%\n",
"\n",
"Batch loss at step 200: 0.757732\n",
"Batch training accuracy: 49.0%\n",
"Full training accuracy: 43.8%\n",
"Validation accuracy: 43.0%\n",
"\n",
"Batch loss at step 300: 0.776932\n",
"Batch training accuracy: 49.0%\n",
"Full training accuracy: 43.9%\n",
"Validation accuracy: 41.9%\n",
"\n",
"Batch loss at step 400: 0.773716\n",
"Batch training accuracy: 51.0%\n",
"Full training accuracy: 45.2%\n",
"Validation accuracy: 43.7%\n",
"\n",
"Batch loss at step 500: 0.839884\n",
"Batch training accuracy: 45.0%\n",
"Full training accuracy: 45.7%\n",
"Validation accuracy: 44.7%\n",
"\n",
"Batch loss at step 600: 0.837758\n",
"Batch training accuracy: 41.0%\n",
"Full training accuracy: 46.4%\n",
"Validation accuracy: 44.9%\n",
"\n",
"Batch loss at step 700: 0.749941\n",
"Batch training accuracy: 49.0%\n",
"Full training accuracy: 46.7%\n",
"Validation accuracy: 44.4%\n",
"\n",
"Batch loss at step 800: 0.777320\n",
"Batch training accuracy: 50.0%\n",
"Full training accuracy: 46.5%\n",
"Validation accuracy: 45.0%\n",
"\n",
"Batch loss at step 900: 0.736048\n",
"Batch training accuracy: 49.0%\n",
"Full training accuracy: 47.1%\n",
"Validation accuracy: 45.8%\n",
"\n",
"Batch loss at step 1000: 0.764839\n",
"Batch training accuracy: 47.0%\n",
"Full training accuracy: 48.2%\n",
"Validation accuracy: 46.1%\n",
"\n",
"Batch loss at step 1100: 0.771561\n",
"Batch training accuracy: 54.0%\n",
"Full training accuracy: 49.2%\n",
"Validation accuracy: 48.4%\n",
"\n",
"Batch loss at step 1200: 0.700677\n",
"Batch training accuracy: 53.0%\n",
"Full training accuracy: 49.3%\n",
"Validation accuracy: 47.5%\n",
"\n",
"Batch loss at step 1300: 0.715845\n",
"Batch training accuracy: 58.0%\n",
"Full training accuracy: 49.5%\n",
"Validation accuracy: 47.8%\n",
"\n",
"Batch loss at step 1400: 0.728818\n",
"Batch training accuracy: 52.0%\n",
"Full training accuracy: 49.7%\n",
"Validation accuracy: 47.4%\n",
"\n",
"Batch loss at step 1500: 0.752970\n",
"Batch training accuracy: 48.0%\n",
"Full training accuracy: 49.8%\n",
"Validation accuracy: 47.6%\n",
"\n",
"Batch loss at step 1600: 0.737375\n",
"Batch training accuracy: 51.0%\n",
"Full training accuracy: 49.7%\n",
"Validation accuracy: 47.5%\n",
"\n",
"Batch loss at step 1700: 0.722005\n",
"Batch training accuracy: 48.0%\n",
"Full training accuracy: 50.0%\n",
"Validation accuracy: 47.4%\n",
"\n",
"Batch loss at step 1800: 0.721826\n",
"Batch training accuracy: 50.0%\n",
"Full training accuracy: 50.5%\n",
"Validation accuracy: 47.7%\n",
"\n",
"Batch loss at step 1900: 0.722078\n",
"Batch training accuracy: 53.0%\n",
"Full training accuracy: 50.3%\n",
"Validation accuracy: 47.9%\n",
"\n",
"Batch loss at step 2000: 0.745341\n",
"Batch training accuracy: 45.0%\n",
"Full training accuracy: 50.6%\n",
"Validation accuracy: 47.8%\n",
"\n",
"Batch loss at step 2100: 0.724539\n",
"Batch training accuracy: 49.0%\n",
"Full training accuracy: 50.9%\n",
"Validation accuracy: 47.9%\n",
"\n",
"Batch loss at step 2200: 0.707039\n",
"Batch training accuracy: 51.0%\n",
"Full training accuracy: 51.1%\n",
"Validation accuracy: 47.4%\n",
"\n",
"Batch loss at step 2300: 0.720049\n",
"Batch training accuracy: 48.0%\n",
"Full training accuracy: 51.2%\n",
"Validation accuracy: 47.8%\n",
"\n",
"Batch loss at step 2400: 0.743228\n",
"Batch training accuracy: 46.0%\n",
"Full training accuracy: 51.2%\n",
"Validation accuracy: 48.0%\n",
"\n",
"Batch loss at step 2500: 0.760176\n",
"Batch training accuracy: 44.0%\n",
"Full training accuracy: 51.1%\n",
"Validation accuracy: 48.3%\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Batch loss at step 2600: 0.697881\n",
"Batch training accuracy: 53.0%\n",
"Full training accuracy: 51.2%\n",
"Validation accuracy: 48.6%\n",
"\n",
"Batch loss at step 2700: 0.700325\n",
"Batch training accuracy: 52.0%\n",
"Full training accuracy: 51.3%\n",
"Validation accuracy: 48.9%\n",
"\n",
"Batch loss at step 2800: 0.746273\n",
"Batch training accuracy: 41.0%\n",
"Full training accuracy: 51.5%\n",
"Validation accuracy: 48.8%\n",
"\n",
"Batch loss at step 2900: 0.702589\n",
"Batch training accuracy: 53.0%\n",
"Full training accuracy: 51.6%\n",
"Validation accuracy: 49.1%\n",
"\n",
"Batch loss at step 3000: 0.752972\n",
"Batch training accuracy: 46.0%\n",
"Full training accuracy: 51.9%\n",
"Validation accuracy: 49.3%\n",
"('valaccs: ', [42.544228, 42.544228, 42.965462, 41.870262, 43.723671, 44.734623, 44.903118, 44.39764, 44.987362, 45.829823, 46.082561, 48.441448, 47.514744, 47.767483, 47.430496, 47.598988, 47.514744, 47.430496, 47.683235, 47.851727, 47.767483, 47.851727, 47.430496, 47.767483, 48.020218, 48.272957, 48.609943, 48.946926, 48.778431, 49.115417, 49.283909])\n",
"('Final validation accuracy: ', 49.283909)\n",
"('Finished training. Total time taken:', 5.580216884613037)\n",
"INFO:tensorflow:Scale of 0 disables regularizer.\n",
"Initializing variables...\n",
"\n",
"Batch loss at step 0: 0.751531\n",
"Batch training accuracy: 61.0%\n",
"Full training accuracy: 41.9%\n",
"Validation accuracy: 41.8%\n",
"\n",
"Batch loss at step 100: 0.735365\n",
"Batch training accuracy: 61.0%\n",
"Full training accuracy: 42.1%\n",
"Validation accuracy: 41.8%\n",
"\n",
"Batch loss at step 200: 0.750056\n",
"Batch training accuracy: 58.0%\n",
"Full training accuracy: 48.7%\n",
"Validation accuracy: 45.9%\n",
"\n",
"Batch loss at step 300: 0.707216\n",
"Batch training accuracy: 58.0%\n",
"Full training accuracy: 55.3%\n",
"Validation accuracy: 51.7%\n",
"\n",
"Batch loss at step 400: 0.731285\n",
"Batch training accuracy: 59.0%\n",
"Full training accuracy: 57.4%\n",
"Validation accuracy: 55.9%\n",
"\n",
"Batch loss at step 500: 0.779659\n",
"Batch training accuracy: 51.0%\n",
"Full training accuracy: 57.8%\n",
"Validation accuracy: 57.0%\n",
"\n",
"Batch loss at step 600: 0.734061\n",
"Batch training accuracy: 61.0%\n",
"Full training accuracy: 58.4%\n",
"Validation accuracy: 56.5%\n",
"\n",
"Batch loss at step 700: 0.681873\n",
"Batch training accuracy: 61.0%\n",
"Full training accuracy: 58.6%\n",
"Validation accuracy: 57.0%\n",
"\n",
"Batch loss at step 800: 0.662641\n",
"Batch training accuracy: 65.0%\n",
"Full training accuracy: 58.3%\n",
"Validation accuracy: 56.9%\n",
"\n",
"Batch loss at step 900: 0.692121\n",
"Batch training accuracy: 62.0%\n",
"Full training accuracy: 58.3%\n",
"Validation accuracy: 56.9%\n",
"\n",
"Batch loss at step 1000: 0.776042\n",
"Batch training accuracy: 59.0%\n",
"Full training accuracy: 58.4%\n",
"Validation accuracy: 57.3%\n",
"\n",
"Batch loss at step 1100: 0.753391\n",
"Batch training accuracy: 55.0%\n",
"Full training accuracy: 58.5%\n",
"Validation accuracy: 57.2%\n",
"\n",
"Batch loss at step 1200: 0.701342\n",
"Batch training accuracy: 56.0%\n",
"Full training accuracy: 58.6%\n",
"Validation accuracy: 57.4%\n",
"\n",
"Batch loss at step 1300: 0.677065\n",
"Batch training accuracy: 62.0%\n",
"Full training accuracy: 58.5%\n",
"Validation accuracy: 57.5%\n",
"\n",
"Batch loss at step 1400: 0.711617\n",
"Batch training accuracy: 61.0%\n",
"Full training accuracy: 58.4%\n",
"Validation accuracy: 57.5%\n",
"\n",
"Batch loss at step 1500: 0.723216\n",
"Batch training accuracy: 61.0%\n",
"Full training accuracy: 58.5%\n",
"Validation accuracy: 57.3%\n",
"\n",
"Batch loss at step 1600: 0.788287\n",
"Batch training accuracy: 50.0%\n",
"Full training accuracy: 58.4%\n",
"Validation accuracy: 57.2%\n",
"\n",
"Batch loss at step 1700: 0.720712\n",
"Batch training accuracy: 54.0%\n",
"Full training accuracy: 58.4%\n",
"Validation accuracy: 57.1%\n",
"\n",
"Batch loss at step 1800: 0.703029\n",
"Batch training accuracy: 53.0%\n",
"Full training accuracy: 58.4%\n",
"Validation accuracy: 57.4%\n",
"\n",
"Batch loss at step 1900: 0.746208\n",
"Batch training accuracy: 56.0%\n",
"Full training accuracy: 58.4%\n",
"Validation accuracy: 57.2%\n",
"\n",
"Batch loss at step 2000: 0.733420\n",
"Batch training accuracy: 55.0%\n",
"Full training accuracy: 58.6%\n",
"Validation accuracy: 57.3%\n",
"\n",
"Batch loss at step 2100: 0.759131\n",
"Batch training accuracy: 51.0%\n",
"Full training accuracy: 58.6%\n",
"Validation accuracy: 57.1%\n",
"\n",
"Batch loss at step 2200: 0.737057\n",
"Batch training accuracy: 50.0%\n",
"Full training accuracy: 58.7%\n",
"Validation accuracy: 57.6%\n",
"\n",
"Batch loss at step 2300: 0.697182\n",
"Batch training accuracy: 57.0%\n",
"Full training accuracy: 58.6%\n",
"Validation accuracy: 57.7%\n",
"\n",
"Batch loss at step 2400: 0.756082\n",
"Batch training accuracy: 50.0%\n",
"Full training accuracy: 58.6%\n",
"Validation accuracy: 57.6%\n",
"\n",
"Batch loss at step 2500: 0.703891\n",
"Batch training accuracy: 58.0%\n",
"Full training accuracy: 58.7%\n",
"Validation accuracy: 57.8%\n",
"\n",
"Batch loss at step 2600: 0.776378\n",
"Batch training accuracy: 47.0%\n",
"Full training accuracy: 58.8%\n",
"Validation accuracy: 57.5%\n",
"\n",
"Batch loss at step 2700: 0.738485\n",
"Batch training accuracy: 48.0%\n",
"Full training accuracy: 59.1%\n",
"Validation accuracy: 57.6%\n",
"\n",
"Batch loss at step 2800: 0.701409\n",
"Batch training accuracy: 56.0%\n",
"Full training accuracy: 59.0%\n",
"Validation accuracy: 57.4%\n",
"\n",
"Batch loss at step 2900: 0.696694\n",
"Batch training accuracy: 57.0%\n",
"Full training accuracy: 59.1%\n",
"Validation accuracy: 57.3%\n",
"\n",
"Batch loss at step 3000: 0.703226\n",
"Batch training accuracy: 55.0%\n",
"Full training accuracy: 59.3%\n",
"Validation accuracy: 57.4%\n",
"('valaccs: ', [41.786015, 41.786015, 45.91407, 51.727043, 55.855095, 57.034538, 56.529064, 56.950294, 56.866051, 56.866051, 57.287281, 57.20303, 57.371529, 57.540016, 57.540016, 57.287281, 57.20303, 57.118786, 57.371529, 57.20303, 57.287281, 57.118786, 57.624264, 57.708508, 57.624264, 57.792751, 57.540016, 57.624264, 57.371529, 57.287281, 57.371529])\n",
"('Final validation accuracy: ', 57.371529)\n",
"('Finished training. Total time taken:', 5.574479818344116)\n",
"('average validation accuracy: ', 54.956474304199219)\n"
]
}
],
"source": [
"'''\n",
"NN CODE\n",
"'''\n",
"\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import sys\n",
"import math\n",
"import time\n",
"\n",
"\n",
"NUM_LABELS = 2\n",
"\n",
"BATCH_SIZE = 100\n",
"NUM_TRAINING_STEPS = 3001\n",
"LEARNING_RATE = 0.0001\n",
"\n",
"L2_CONST = 0.00 # Set to > 0 to use L2 regularization\n",
"DROPOUT_RATE = 0.# Set to > 0 to use dropout\n",
"BN = True # Set to True to use batch normalization\n",
"\n",
"class NFL_net:\n",
" def __init__(self, train_data,val_data):\n",
" '''Initialize the class by loading the required datasets \n",
" and building the graph'''\n",
"# train_data_in = [[1,0],[2,0],[3,0],[1,1],[2,1],[3,1],[1,2],[2,2],[3,2],[11,2],[11,1]]\n",
"# train_data_out = [[1,0],[1,0],[1,0],[0,1],[0,1],[0,1],[1,0],[1,0],[1,0],[1,0],[0,1]]\n",
"# val_data_in = [[4,0],[5,0],[8,0],[4,1],[5,1],[7,1],[4,2],[5,2],[8,2]]\n",
"# val_data_out = [[1,0],[1,0],[1,0],[0,1],[0,1],[0,1],[1,0],[1,0],[1,0]]\n",
"\n",
" #train_x, train_y,_ = get_features_and_labels(train_years)\n",
" train_x,train_y = train_data\n",
" train_data_in = train_x\n",
" train_data_out = [[0,1] if i==0 else [1,0] for i in train_y]\n",
" self.train_X = np.array(train_data_in)\n",
" self.train_Y = np.array(train_data_out)\n",
" \n",
" \n",
" #val_x, val_y,_ = get_features_and_labels(val_years)\n",
" val_x,val_y = val_data\n",
" val_data_in = val_x\n",
" val_data_out = [[0,1] if i==0 else [1,0] for i in val_y]\n",
" #print(val_data_out[:10])\n",
" self.val_X = np.array(val_data_in)\n",
" #print(self.val_X)\n",
" self.val_Y = np.array(val_data_out)\n",
"\n",
" self.graph = tf.Graph()\n",
" self.build_graph()\n",
"\n",
"\n",
" def build_graph(self):\n",
" with self.graph.as_default():\n",
" # Input data\n",
" self.images = tf.placeholder(tf.float32, shape=(None,len(self.train_X[0])))\n",
" self.labels = tf.placeholder(tf.float32, shape=(None,2))\n",
" self.training = tf.placeholder(tf.bool)\n",
"\n",
" # Network\n",
" regularizer = tf.contrib.layers.l2_regularizer(scale=L2_CONST)\n",
"\n",
" fc1 = tf.layers.dense(inputs=self.images, units=32, activation=tf.nn.relu, kernel_regularizer=regularizer)\n",
" if BN:\n",
" fc1 = tf.layers.batch_normalization(inputs=fc1, axis=1, training=self.training)\n",
" #fc1 = tf.layers.dropout(inputs=fc1, rate=DROPOUT_RATE, training=self.training)\n",
"\n",
"# fc1 = tf.layers.dense(inputs=fc1, units=256, activation=tf.nn.relu, kernel_regularizer=regularizer)\n",
"# if BN:\n",
"# fc1 = tf.layers.batch_normalization(inputs=fc1, axis=1, training=self.training)\n",
" #fc1 = tf.layers.batch_normalization(inputs=fc1, axis=1, training=self.training)\n",
" #fc1 = tf.layers.dropout(inputs=fc1, rate=DROPOUT_RATE, training=self.training)\n",
"\n",
" #fc1 = tf.layers.dense(inputs=fc1, units=64, activation=tf.nn.relu, kernel_regularizer=regularizer)\n",
"# if BN:\n",
"# fc1 = tf.layers.batch_normalization(inputs=fc1, axis=1, training=self.training)\n",
" #fc1 = tf.layers.dropout(inputs=fc1, rate=DROPOUT_RATE, training=self.training)\n",
" #fc1 = tf.layers.dense(inputs=fc1, units=64, activation=tf.nn.relu, kernel_regularizer=regularizer)\n",
"# if BN:\n",
"# fc1 = tf.layers.batch_normalization(inputs=fc1, axis=1, training=self.training)\n",
" fc1 = tf.layers.dropout(inputs=fc1, rate=DROPOUT_RATE, training=self.training)\n",
" fc1 = tf.layers.dense(inputs=fc1, units=32, activation=tf.nn.relu, kernel_regularizer=regularizer)\n",
"\n",
" logits = tf.layers.dense(inputs=fc1, units=NUM_LABELS, activation=None, kernel_regularizer=regularizer)\n",
" # Compute loss\n",
" self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=self.labels))\n",
" self.loss += tf.losses.get_regularization_loss()\n",
" \n",
" update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
" with tf.control_dependencies(update_ops):\n",
" self.optimizer = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(self.loss)\n",
"\n",
" self.preds = tf.argmax(logits, 1)\n",
" self.acc = 100*tf.reduce_mean(tf.cast(tf.equal(tf.argmax(self.labels, 1), self.preds), dtype=tf.float32))\n",
"\n",
"\n",
" def train_model(self, num_steps=NUM_TRAINING_STEPS):\n",
" '''Train the model with minibatches in a tensorflow session'''\n",
" with tf.Session(graph=self.graph) as session:\n",
" session.run(tf.global_variables_initializer())\n",
" print('Initializing variables...')\n",
" valaccs = []\n",
" for step in range(num_steps):\n",
" offset = (step * BATCH_SIZE) % (self.train_Y.shape[0] - BATCH_SIZE)\n",
" #print(offset,BATCH_SIZE)\n",
" #print(self.train_X)\n",
" batch_data = self.train_X[offset:(offset + BATCH_SIZE)]\n",
" batch_labels = self.train_Y[offset:(offset + BATCH_SIZE)]\n",
" \n",
" # Data to feed into the placeholder variables in the tensorflow graph\n",
" feed_dict = {self.images: batch_data, self.labels: batch_labels, self.training: True}\n",
" _, l, acc = session.run([self.optimizer, self.loss, self.acc], feed_dict=feed_dict)\n",
" \n",
" if (step % 100 == 0):\n",
"\n",
" val_acc = session.run(self.acc, feed_dict={self.images: self.val_X, self.labels: self.val_Y, self.training: False})\n",
" train_acc = session.run(self.acc, feed_dict={self.images: self.train_X, self.labels: self.train_Y, self.training: False})\n",
" print('')\n",
" print('Batch loss at step %d: %f' % (step, l))\n",
" print('Batch training accuracy: %.1f%%' % acc)\n",
" print('Full training accuracy: %.1f%%' % train_acc)\n",
" print('Validation accuracy: %.1f%%' % val_acc)\n",
" valaccs += [val_acc]\n",
" print(\"valaccs: \",valaccs)\n",
" print(\"Final validation accuracy: \",valaccs[-1])\n",
" return valaccs[-1]\n",
"\n",
"if __name__ == '__main__':\n",
"\n",
" \n",
"# train_years_1 = [1997, 2001, 2005, 2009, 2013]\n",
"# train_years_2 = [1998, 2002, 2006, 2010, 2014]\n",
"# train_years_3 = [1999, 2003, 2007, 2011, 2015]\n",
"# test_years = [2000, 2004, 2008, 2012, 2016]\n",
"\n",
"# X_train, Y_train = [None]*3, [None]*3\n",
"# X_train[0], Y_train[0], _ = get_features_and_labels(train_years_1)\n",
"# X_train[1], Y_train[1], _ = get_features_and_labels(train_years_2)\n",
"# X_train[2], Y_train[2], _ = get_features_and_labels(train_years_3)\n",
"# X_test, Y_test, _ = get_features_and_labels(test_years)\n",
" validation_accs = [0,0,0]\n",
" for i in range(3):\n",
" t1 = time.time()\n",
"# training_x = np.concatenate((X_train[i],X_train[(i+1)%3]),axis=0)\n",
" \n",
"# training_y = np.concatenate((Y_train[i],Y_train[(i+1)%3]),axis=0)\n",
"\n",
" \n",
"# validation_x = X_train[(i+2)%3]\n",
"# validation_y = Y_train[(i+2)%3]\n",
"# conv_net = NFL_net([training_x,training_y],[validation_x,validation_y])\n",
" \n",
" decay = 0.92\n",
" X_train_ewma,Y_train_ewma,X_test_EWMA,Y_test_EWMA = get_EWMA_train_test(decay)\n",
" training_x = np.concatenate((X_train_ewma[i],X_train_ewma[(i+1)%3]),axis=0)\n",
" training_y = np.concatenate((Y_train_ewma[i],Y_train_ewma[(i+1)%3]),axis=0)\n",
" #print(training_x[0])\n",
" validation_x = X_train_ewma[(i+2)%3]\n",
" validation_y = Y_train_ewma[(i+2)%3]\n",
" #print(validation_x[0])\n",
" conv_net = NFL_net([training_x,training_y],[validation_x,validation_y])\n",
" \n",
" validation_accs[i] = conv_net.train_model()\n",
" t2 = time.time()\n",
" print(\"Finished training. Total time taken:\", t2-t1)\n",
" print(\"average validation accuracy: \",sum(validation_accs)/3.)\n"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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74h1KTLR0RDAC2AwsBhabWT1NzF3snHOpprEc9YxFNXGOJDaaTQRmNgr4HNAV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\nngiccy7N/X+hiFdhhS23+wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ad02d1b10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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73Ja9tLRpz62FDm6Hj5+BRf8PkvvXSzDcWrwtlNSWOFUpPlUqxTXFnapwspOz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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ad88996d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lr_vals = [61.5,60.9,63.4,63.2,62.1,62.2,57.2]\n",
"\n",
"plt.plot([-1,-1.5,-2,-2.5,-3,-3.5,-4],lr_vals)\n",
"plt.gca().invert_xaxis()\n",
"plt.xlabel(\"-log(Learning Rate)\")\n",
"plt.ylabel(\"Validation Accuracy\")\n",
"plt.title(\"Learning Rate Tuning\")\n",
"plt.show()\n",
"\n",
"reg_1 = [43.97641, 45.408592, 54.338669, 55.855095, 58.213985, 59.393429, 60.572876, 61.920811, 61.836563, 62.173546, 62.679024, 62.25779, 62.679024, 63.268745, 63.352989, 63.437237, 63.352989, 63.437237, 63.521481, 63.437237, 63.437237, 63.184502, 63.521481, 63.437237, 63.352989, 63.352989, 63.437237, 63.521481, 63.521481, 63.268745, 63.858467]\n",
"reg_2 = [42.544228, 51.895535, 54.844147, 58.045494, 57.96125, 58.887951, 59.224937, 59.224937, 59.224937, 59.477676, 59.56192, 59.646164, 59.730412, 59.898907, 60.067398, 60.151642, 59.646164, 59.98315, 60.404377, 60.488628, 60.404377, 60.404377, 60.572876, 60.65712, 60.404377, 60.404377, 60.404377, 60.572876, 60.572876, 60.572876, 60.572876]\n",
"reg_3 = [47.96125, 53.96125, 57.118786, 55.518116, 56.02359, 56.866051, 56.866051, 56.781803, 56.866051, 57.034538, 56.866051, 57.287281, 57.287281, 57.20303, 57.371529, 57.287281, 57.371529, 57.287281, 57.455772, 57.540016, 57.624264, 57.708508, 57.624264, 57.708508, 57.876999, 58.213985, 58.213985, 58.129738, 58.045494, 58.550972, 58.213985]\n",
"reg_4 = [57.287281, 52.232521, 50.294857, 54.001682, 56.613308, 57.287281, 57.792751, 58.129738, 58.213985, 58.213985, 58.382477, 58.803707, 59.14069, 59.730412, 59.56192, 59.56192, 59.730412, 59.98315, 59.898907, 59.814663, 59.730412, 59.814663, 59.898907, 59.646164, 59.814663, 59.814663, 59.814663, 59.814663, 59.98315, 60.067398, 59.898907]\n",
"step_range = range(0,3001,100)\n",
"plt.plot(step_range,reg_2,label=\"$\\lambda$ = 0.1\")\n",
"plt.plot(step_range,reg_1,label=\"$\\lambda$ = 0.01\")\n",
"plt.plot(step_range,reg_3,label=\"$\\lambda$ = 0.001\")\n",
"plt.legend()\n",
"plt.xlabel(\"Training Steps\")\n",
"plt.ylabel(\"Validation Accuracy\")\n",
"plt.title(\"Regularization Tuning\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"def guess_victory_margin(home_team,away_team,week,victory_algorithm,point_spread = False):\n",
" #result = 1 if we predict home team to win, 0 otherwise\n",
" result = 0\n",
" \n",
" home_wins = feature_matrix[week-1][\"win\"][home_team] \n",
" home_losses = feature_matrix[week-1][\"loss\"][home_team] \n",
" away_wins = feature_matrix[week-1][\"win\"][away_team]\n",
" away_losses = feature_matrix[week-1][\"loss\"][away_team]\n",
"\n",
" if victory_algorithm == \"home_team\":\n",
" result = 1\n",
" if victory_algorithm == \"biased_home\":\n",
" wl_diff = home_wins + away_losses - (home_losses + away_wins)\n",
" result = 1 if wl_diff >= -2.2 else 0\n",
" \n",
" if victory_algorithm == \"win_percentage\":\n",
" if week == 1:\n",
" #assume home teams always win week 1\n",
" result = 1\n",
" else:\n",
" \n",
" home_win_percentage = home_wins/(home_wins + home_losses)\n",
" away_win_percentage = away_wins/(away_wins + away_losses)\n",
" if point_spread:\n",
" #random thing here based on win percentage difference\n",
" result = (home_win_percentage - away_win_percentage) * 10\n",
" else:\n",
" result = 1 if home_win_percentage >= away_win_percentage else 0\n",
" if victory_algorithm == \"expected_win_percentage\":\n",
" home_points_for = feature_matrix[week-1][\"points_forced\"][home_team]\n",
" home_points_vs = feature_matrix[week-1][\"points_allowed\"][home_team] \n",
" away_points_for = feature_matrix[week-1][\"points_forced\"][away_team]\n",
" away_points_vs = feature_matrix[week-1][\"points_allowed\"][away_team] \n",
" \n",
" #This constant has been empirically found to be accurate - check \n",
" pythag_c = 2.37\n",
" \n",
" expected_home_win_percentage = home_points_for**pythag_c/ (home_points_for**pythag_c + home_points_vs**pythag_c)\n",
" \n",
" expected_away_win_percentage = away_points_for**pythag_c/ (away_points_for**pythag_c + away_points_vs**pythag_c)\n",
" if point_spread:\n",
" #random thing here based on win percentage difference\n",
" result = (expected_home_win_percentage - expected_away_win_percentage) * 10\n",
" else:\n",
" result = 1 if expected_home_win_percentage >= expected_away_win_percentage else 0\n",
" if victory_algorithm == \"logistic_regression\":\n",
" pass\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"year_range = range(1997,2017)\n",
"year_data = {}\n",
"for year in year_range:\n",
" reg_season,playoff = get_game_data(year)\n",
" feature_set = get_regular_season_history(year)\n",
" year_data[year] = [reg_season,feature_set]\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ Week Day Date Time Winner/tie \\\n",
"0 1 Sun August 31 1:00PM Cincinnati Bengals \n",
"1 1 Sun August 31 1:00PM New York Giants \n",
"2 1 Sun August 31 1:00PM Dallas Cowboys \n",
"3 1 Sun August 31 1:00PM St. Louis Rams \n",
"4 1 Sun August 31 1:00PM Minnesota Vikings \n",
"5 1 Sun August 31 1:00PM New England Patriots \n",
"6 1 Sun August 31 1:00PM Tennessee Oilers \n",
"7 1 Sun August 31 1:00PM Miami Dolphins \n",
"8 1 Sun August 31 1:00PM Detroit Lions \n",
"9 1 Sun August 31 4:00PM Tampa Bay Buccaneers \n",
"10 1 Sun August 31 4:00PM Denver Broncos \n",
"11 1 Sun August 31 4:00PM New York Jets \n",
"12 1 Sun August 31 4:00PM Jacksonville Jaguars \n",
"13 1 Sun August 31 8:00PM Washington Redskins \n",
"14 1 Mon September 1 9:00PM Green Bay Packers \n",
"15 2 Sun September 7 1:00PM Minnesota Vikings \n",
"16 2 Sun September 7 1:00PM San Francisco 49ers \n",
"17 2 Sun September 7 1:00PM Carolina Panthers \n",
"18 2 Sun September 7 1:00PM Baltimore Ravens \n",
"19 2 Sun September 7 1:00PM Miami Dolphins \n",
"20 2 Sun September 7 1:00PM San Diego Chargers \n",
"21 2 Sun September 7 1:00PM Buffalo Bills \n",
"22 2 Sun September 7 1:00PM Tampa Bay Buccaneers \n",
"23 2 Sun September 7 1:00PM New England Patriots \n",
"24 2 Sun September 7 1:00PM Pittsburgh Steelers \n",
"25 2 Sun September 7 4:00PM Philadelphia Eagles \n",
"26 2 Sun September 7 4:00PM Jacksonville Jaguars \n",
"27 2 Sun September 7 4:00PM Denver Broncos \n",
"28 2 Sun September 7 8:00PM Arizona Cardinals \n",
"29 2 Mon September 8 9:00PM Kansas City Chiefs \n",
".. ... ... ... ... ... \n",
"210 16 Sat December 13 12:30PM New York Giants \n",
"211 16 Sat December 13 4:00PM Pittsburgh Steelers \n",
"212 16 Sun December 14 1:00PM New York Jets \n",
"213 16 Sun December 14 1:00PM Detroit Lions \n",
"214 16 Sun December 14 1:00PM Jacksonville Jaguars \n",
"215 16 Sun December 14 1:00PM Indianapolis Colts \n",
"216 16 Sun December 14 1:00PM Atlanta Falcons \n",
"217 16 Sun December 14 1:00PM Baltimore Ravens \n",
"218 16 Sun December 14 1:00PM Cincinnati Bengals \n",
"219 16 Sun December 14 4:00PM Green Bay Packers \n",
"220 16 Sun December 14 4:00PM Seattle Seahawks \n",
"221 16 Sun December 14 4:00PM Kansas City Chiefs \n",
"222 16 Sun December 14 4:00PM New Orleans Saints \n",
"223 16 Sun December 14 8:00PM Chicago Bears \n",
"224 16 Mon December 15 9:00PM San Francisco 49ers \n",
"225 17 Sat December 20 12:30PM Green Bay Packers \n",
"226 17 Sat December 20 4:00PM St. Louis Rams \n",
"227 17 Sun December 21 1:00PM Kansas City Chiefs \n",
"228 17 Sun December 21 1:00PM Tennessee Oilers \n",
"229 17 Sun December 21 1:00PM Cincinnati Bengals \n",
"230 17 Sun December 21 1:00PM Washington Redskins \n",
"231 17 Sun December 21 1:00PM Tampa Bay Buccaneers \n",
"232 17 Sun December 21 1:00PM Minnesota Vikings \n",
"233 17 Sun December 21 1:00PM New York Giants \n",
"234 17 Sun December 21 4:00PM Jacksonville Jaguars \n",
"235 17 Sun December 21 4:00PM Arizona Cardinals \n",
"236 17 Sun December 21 4:00PM Denver Broncos \n",
"237 17 Sun December 21 4:00PM Detroit Lions \n",
"238 17 Sun December 21 8:00PM Seattle Seahawks \n",
"239 17 Mon December 22 9:00PM New England Patriots \n",
"\n",
" Home Loser/tie PtsW PtsL YdsW TOW \\\n",
"0 Cincinnati Bengals Arizona Cardinals 24.0 21.0 317.0 1.0 \n",
"1 New York Giants Philadelphia Eagles 31.0 17.0 290.0 0.0 \n",
"2 Pittsburgh Steelers Pittsburgh Steelers 37.0 7.0 380.0 0.0 \n",
"3 St. Louis Rams New Orleans Saints 38.0 24.0 403.0 1.0 \n",
"4 Buffalo Bills Buffalo Bills 34.0 13.0 385.0 1.0 \n",
"5 New England Patriots San Diego Chargers 41.0 7.0 424.0 0.0 \n",
"6 Tennessee Oilers Oakland Raiders 24.0 21.0 419.0 2.0 \n",
"7 Miami Dolphins Indianapolis Colts 16.0 10.0 202.0 1.0 \n",
"8 Detroit Lions Atlanta Falcons 28.0 17.0 221.0 3.0 \n",
"9 Tampa Bay Buccaneers San Francisco 49ers 13.0 6.0 271.0 2.0 \n",
"10 Denver Broncos Kansas City Chiefs 19.0 3.0 378.0 0.0 \n",
"11 Seattle Seahawks Seattle Seahawks 41.0 3.0 434.0 0.0 \n",
"12 Baltimore Ravens Baltimore Ravens 28.0 27.0 411.0 1.0 \n",
"13 Carolina Panthers Carolina Panthers 24.0 10.0 323.0 0.0 \n",
"14 Green Bay Packers Chicago Bears 38.0 24.0 315.0 2.0 \n",
"15 Chicago Bears Chicago Bears 27.0 24.0 378.0 1.0 \n",
"16 St. Louis Rams St. Louis Rams 15.0 12.0 228.0 4.0 \n",
"17 Atlanta Falcons Atlanta Falcons 9.0 6.0 321.0 2.0 \n",
"18 Baltimore Ravens Cincinnati Bengals 23.0 10.0 412.0 3.0 \n",
"19 Miami Dolphins Tennessee Oilers 16.0 13.0 354.0 1.0 \n",
"20 New Orleans Saints New Orleans Saints 20.0 6.0 230.0 2.0 \n",
"21 New York Jets New York Jets 28.0 22.0 269.0 3.0 \n",
"22 Detroit Lions Detroit Lions 24.0 17.0 272.0 0.0 \n",
"23 Indianapolis Colts Indianapolis Colts 31.0 6.0 382.0 0.0 \n",
"24 Pittsburgh Steelers Washington Redskins 14.0 13.0 295.0 1.0 \n",
"25 Philadelphia Eagles Green Bay Packers 10.0 9.0 258.0 1.0 \n",
"26 Jacksonville Jaguars New York Giants 40.0 13.0 375.0 1.0 \n",
"27 Seattle Seahawks Seattle Seahawks 35.0 14.0 331.0 1.0 \n",
"28 Arizona Cardinals Dallas Cowboys 25.0 22.0 286.0 4.0 \n",
"29 Oakland Raiders Oakland Raiders 28.0 27.0 428.0 3.0 \n",
".. ... ... ... ... ... ... \n",
"210 New York Giants Washington Redskins 30.0 10.0 240.0 2.0 \n",
"211 New England Patriots New England Patriots 24.0 21.0 404.0 2.0 \n",
"212 New York Jets Tampa Bay Buccaneers 31.0 0.0 234.0 1.0 \n",
"213 Minnesota Vikings Minnesota Vikings 14.0 13.0 390.0 1.0 \n",
"214 Buffalo Bills Buffalo Bills 20.0 14.0 388.0 1.0 \n",
"215 Indianapolis Colts Miami Dolphins 41.0 0.0 401.0 1.0 \n",
"216 Atlanta Falcons Philadelphia Eagles 20.0 17.0 386.0 1.0 \n",
"217 Baltimore Ravens Tennessee Oilers 21.0 19.0 246.0 1.0 \n",
"218 Cincinnati Bengals Dallas Cowboys 31.0 24.0 419.0 2.0 \n",
"219 Carolina Panthers Carolina Panthers 31.0 10.0 458.0 1.0 \n",
"220 Oakland Raiders Oakland Raiders 22.0 21.0 381.0 2.0 \n",
"221 San Diego Chargers San Diego Chargers 29.0 7.0 239.0 0.0 \n",
"222 New Orleans Saints Arizona Cardinals 27.0 10.0 382.0 1.0 \n",
"223 St. Louis Rams St. Louis Rams 13.0 10.0 309.0 5.0 \n",
"224 San Francisco 49ers Denver Broncos 34.0 17.0 323.0 1.0 \n",
"225 Green Bay Packers Buffalo Bills 31.0 21.0 272.0 3.0 \n",
"226 Carolina Panthers Carolina Panthers 30.0 18.0 297.0 0.0 \n",
"227 Kansas City Chiefs New Orleans Saints 25.0 13.0 176.0 0.0 \n",
"228 Tennessee Oilers Pittsburgh Steelers 16.0 6.0 241.0 1.0 \n",
"229 Cincinnati Bengals Baltimore Ravens 16.0 14.0 302.0 0.0 \n",
"230 Washington Redskins Philadelphia Eagles 35.0 32.0 221.0 2.0 \n",
"231 Tampa Bay Buccaneers Chicago Bears 31.0 15.0 269.0 1.0 \n",
"232 Minnesota Vikings Indianapolis Colts 39.0 28.0 345.0 3.0 \n",
"233 Dallas Cowboys Dallas Cowboys 20.0 7.0 283.0 1.0 \n",
"234 Oakland Raiders Oakland Raiders 20.0 9.0 417.0 0.0 \n",
"235 Arizona Cardinals Atlanta Falcons 29.0 26.0 297.0 2.0 \n",
"236 Denver Broncos San Diego Chargers 38.0 3.0 451.0 1.0 \n",
"237 Detroit Lions New York Jets 13.0 10.0 301.0 0.0 \n",
"238 Seattle Seahawks San Francisco 49ers 38.0 9.0 407.0 0.0 \n",
"239 Miami Dolphins Miami Dolphins 14.0 12.0 207.0 2.0 \n",
"\n",
" YdsL TOL Away \n",
"0 323.0 2.0 Arizona Cardinals \n",
"1 447.0 2.0 Philadelphia Eagles \n",
"2 174.0 2.0 Dallas Cowboys \n",
"3 241.0 5.0 New Orleans Saints \n",
"4 426.0 4.0 Minnesota Vikings \n",
"5 227.0 3.0 San Diego Chargers \n",
"6 332.0 1.0 Oakland Raiders \n",
"7 298.0 3.0 Indianapolis Colts \n",
"8 323.0 4.0 Atlanta Falcons \n",
"9 191.0 1.0 San Francisco 49ers \n",
"10 241.0 1.0 Kansas City Chiefs \n",
"11 247.0 2.0 New York Jets \n",
"12 373.0 3.0 Jacksonville Jaguars \n",
"13 262.0 4.0 Washington Redskins \n",
"14 336.0 3.0 Chicago Bears \n",
"15 305.0 2.0 Minnesota Vikings \n",
"16 203.0 4.0 San Francisco 49ers \n",
"17 210.0 2.0 Carolina Panthers \n",
"18 339.0 2.0 Cincinnati Bengals \n",
"19 294.0 1.0 Tennessee Oilers \n",
"20 243.0 6.0 San Diego Chargers \n",
"21 202.0 2.0 Buffalo Bills \n",
"22 346.0 1.0 Tampa Bay Buccaneers \n",
"23 255.0 1.0 New England Patriots \n",
"24 354.0 3.0 Washington Redskins \n",
"25 380.0 2.0 Green Bay Packers \n",
"26 206.0 3.0 New York Giants \n",
"27 289.0 1.0 Denver Broncos \n",
"28 327.0 1.0 Dallas Cowboys \n",
"29 389.0 2.0 Kansas City Chiefs \n",
".. ... ... ... \n",
"210 320.0 6.0 Washington Redskins \n",
"211 253.0 2.0 Pittsburgh Steelers \n",
"212 111.0 3.0 Tampa Bay Buccaneers \n",
"213 257.0 0.0 Detroit Lions \n",
"214 335.0 3.0 Jacksonville Jaguars \n",
"215 183.0 2.0 Miami Dolphins \n",
"216 277.0 1.0 Philadelphia Eagles \n",
"217 390.0 4.0 Tennessee Oilers \n",
"218 402.0 2.0 Dallas Cowboys \n",
"219 172.0 1.0 Green Bay Packers \n",
"220 275.0 2.0 Seattle Seahawks \n",
"221 282.0 2.0 Kansas City Chiefs \n",
"222 265.0 4.0 Arizona Cardinals \n",
"223 165.0 5.0 Chicago Bears \n",
"224 237.0 3.0 Denver Broncos \n",
"225 325.0 4.0 Buffalo Bills \n",
"226 327.0 3.0 St. Louis Rams \n",
"227 252.0 5.0 New Orleans Saints \n",
"228 269.0 3.0 Pittsburgh Steelers \n",
"229 355.0 0.0 Baltimore Ravens \n",
"230 401.0 3.0 Philadelphia Eagles \n",
"231 194.0 3.0 Chicago Bears \n",
"232 298.0 5.0 Indianapolis Colts \n",
"233 184.0 3.0 New York Giants \n",
"234 300.0 1.0 Jacksonville Jaguars \n",
"235 356.0 1.0 Atlanta Falcons \n",
"236 203.0 1.0 San Diego Chargers \n",
"237 315.0 3.0 New York Jets \n",
"238 324.0 2.0 San Francisco 49ers \n",
"239 295.0 1.0 New England Patriots \n",
"\n",
"[240 rows x 14 columns], [ points_forced points_allowed win loss tie\n",
"New Orleans Saints 0 0 0 0 0\n",
"St. Louis Rams 0 0 0 0 0\n",
"New England Patriots 0 0 0 0 0\n",
"Tampa Bay Buccaneers 0 0 0 0 0\n",
"Philadelphia Eagles 0 0 0 0 0\n",
"Pittsburgh Steelers 0 0 0 0 0\n",
"Atlanta Falcons 0 0 0 0 0\n",
"Cincinnati Bengals 0 0 0 0 0\n",
"Oakland Raiders 0 0 0 0 0\n",
"Buffalo Bills 0 0 0 0 0\n",
"New York Giants 0 0 0 0 0\n",
"Detroit Lions 0 0 0 0 0\n",
"Tennessee Oilers 0 0 0 0 0\n",
"Carolina Panthers 0 0 0 0 0\n",
"San Francisco 49ers 0 0 0 0 0\n",
"Indianapolis Colts 0 0 0 0 0\n",
"Washington Redskins 0 0 0 0 0\n",
"Seattle Seahawks 0 0 0 0 0\n",
"Arizona Cardinals 0 0 0 0 0\n",
"Jacksonville Jaguars 0 0 0 0 0\n",
"Chicago Bears 0 0 0 0 0\n",
"San Diego Chargers 0 0 0 0 0\n",
"Miami Dolphins 0 0 0 0 0\n",
"New York Jets 0 0 0 0 0\n",
"Baltimore Ravens 0 0 0 0 0\n",
"Kansas City Chiefs 0 0 0 0 0\n",
"Denver Broncos 0 0 0 0 0\n",
"Green Bay Packers 0 0 0 0 0\n",
"Minnesota Vikings 0 0 0 0 0\n",
"Dallas Cowboys 0 0 0 0 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 24.0 38.0 0 1 0\n",
"St. Louis Rams 38.0 24.0 1 0 0\n",
"New England Patriots 41.0 7.0 1 0 0\n",
"Tampa Bay Buccaneers 13.0 6.0 1 0 0\n",
"Philadelphia Eagles 17.0 31.0 0 1 0\n",
"Pittsburgh Steelers 7.0 37.0 0 1 0\n",
"Atlanta Falcons 17.0 28.0 0 1 0\n",
"Cincinnati Bengals 24.0 21.0 1 0 0\n",
"Oakland Raiders 21.0 24.0 0 1 0\n",
"Buffalo Bills 13.0 34.0 0 1 0\n",
"New York Giants 31.0 17.0 1 0 0\n",
"Detroit Lions 28.0 17.0 1 0 0\n",
"Tennessee Oilers 24.0 21.0 1 0 0\n",
"Carolina Panthers 10.0 24.0 0 1 0\n",
"San Francisco 49ers 6.0 13.0 0 1 0\n",
"Indianapolis Colts 10.0 16.0 0 1 0\n",
"Washington Redskins 24.0 10.0 1 0 0\n",
"Seattle Seahawks 3.0 41.0 0 1 0\n",
"Arizona Cardinals 21.0 24.0 0 1 0\n",
"Jacksonville Jaguars 28.0 27.0 1 0 0\n",
"Chicago Bears 24.0 38.0 0 1 0\n",
"San Diego Chargers 7.0 41.0 0 1 0\n",
"Miami Dolphins 16.0 10.0 1 0 0\n",
"New York Jets 41.0 3.0 1 0 0\n",
"Baltimore Ravens 27.0 28.0 0 1 0\n",
"Kansas City Chiefs 3.0 19.0 0 1 0\n",
"Denver Broncos 19.0 3.0 1 0 0\n",
"Green Bay Packers 38.0 24.0 1 0 0\n",
"Minnesota Vikings 34.0 13.0 1 0 0\n",
"Dallas Cowboys 37.0 7.0 1 0 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 30.0 58.0 0 2 0\n",
"St. Louis Rams 50.0 39.0 1 1 0\n",
"New England Patriots 72.0 13.0 2 0 0\n",
"Tampa Bay Buccaneers 37.0 23.0 2 0 0\n",
"Philadelphia Eagles 27.0 40.0 1 1 0\n",
"Pittsburgh Steelers 21.0 50.0 1 1 0\n",
"Atlanta Falcons 23.0 37.0 0 2 0\n",
"Cincinnati Bengals 34.0 44.0 1 1 0\n",
"Oakland Raiders 48.0 52.0 0 2 0\n",
"Buffalo Bills 41.0 56.0 1 1 0\n",
"New York Giants 44.0 57.0 1 1 0\n",
"Detroit Lions 45.0 41.0 1 1 0\n",
"Tennessee Oilers 37.0 37.0 1 1 0\n",
"Carolina Panthers 19.0 30.0 1 1 0\n",
"San Francisco 49ers 21.0 25.0 1 1 0\n",
"Indianapolis Colts 16.0 47.0 0 2 0\n",
"Washington Redskins 37.0 24.0 1 1 0\n",
"Seattle Seahawks 17.0 76.0 0 2 0\n",
"Arizona Cardinals 46.0 46.0 1 1 0\n",
"Jacksonville Jaguars 68.0 40.0 2 0 0\n",
"Chicago Bears 48.0 65.0 0 2 0\n",
"San Diego Chargers 27.0 47.0 1 1 0\n",
"Miami Dolphins 32.0 23.0 2 0 0\n",
"New York Jets 63.0 31.0 1 1 0\n",
"Baltimore Ravens 50.0 38.0 1 1 0\n",
"Kansas City Chiefs 31.0 46.0 1 1 0\n",
"Denver Broncos 54.0 17.0 2 0 0\n",
"Green Bay Packers 47.0 34.0 1 1 0\n",
"Minnesota Vikings 61.0 37.0 2 0 0\n",
"Dallas Cowboys 59.0 32.0 1 1 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 37.0 91.0 0 3 0\n",
"St. Louis Rams 64.0 74.0 1 2 0\n",
"New England Patriots 99.0 37.0 3 0 0\n",
"Tampa Bay Buccaneers 65.0 37.0 3 0 0\n",
"Philadelphia Eagles 47.0 61.0 1 2 0\n",
"Pittsburgh Steelers 21.0 50.0 1 1 0\n",
"Atlanta Falcons 54.0 73.0 0 3 0\n",
"Cincinnati Bengals 34.0 44.0 1 1 0\n",
"Oakland Raiders 84.0 83.0 1 2 0\n",
"Buffalo Bills 57.0 78.0 1 2 0\n",
"New York Giants 67.0 81.0 1 2 0\n",
"Detroit Lions 77.0 48.0 2 1 0\n",
"Tennessee Oilers 37.0 37.0 1 1 0\n",
"Carolina Panthers 45.0 37.0 2 1 0\n",
"San Francisco 49ers 54.0 32.0 2 1 0\n",
"Indianapolis Colts 19.0 78.0 0 3 0\n",
"Washington Redskins 56.0 37.0 2 1 0\n",
"Seattle Seahawks 48.0 79.0 1 2 0\n",
"Arizona Cardinals 59.0 65.0 1 2 0\n",
"Jacksonville Jaguars 68.0 40.0 2 0 0\n",
"Chicago Bears 55.0 97.0 0 3 0\n",
"San Diego Chargers 34.0 73.0 1 2 0\n",
"Miami Dolphins 50.0 46.0 2 1 0\n",
"New York Jets 87.0 58.0 1 2 0\n",
"Baltimore Ravens 74.0 61.0 2 1 0\n",
"Kansas City Chiefs 53.0 62.0 2 1 0\n",
"Denver Broncos 89.0 31.0 3 0 0\n",
"Green Bay Packers 70.0 52.0 2 1 0\n",
"Minnesota Vikings 75.0 65.0 2 1 0\n",
"Dallas Cowboys 80.0 52.0 2 1 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 72.0 108.0 1 3 0\n",
"St. Louis Rams 77.0 77.0 2 2 0\n",
"New England Patriots 130.0 40.0 4 0 0\n",
"Tampa Bay Buccaneers 96.0 58.0 4 0 0\n",
"Philadelphia Eagles 47.0 61.0 1 2 0\n",
"Pittsburgh Steelers 42.0 80.0 1 2 0\n",
"Atlanta Falcons 61.0 107.0 0 4 0\n",
"Cincinnati Bengals 54.0 82.0 1 2 0\n",
"Oakland Raiders 106.0 106.0 1 3 0\n",
"Buffalo Bills 94.0 113.0 2 2 0\n",
"New York Giants 70.0 94.0 1 3 0\n",
"Detroit Lions 94.0 83.0 2 2 0\n",
"Tennessee Oilers 47.0 73.0 1 2 0\n",
"Carolina Panthers 59.0 72.0 2 2 0\n",
"San Francisco 49ers 88.0 39.0 3 1 0\n",
"Indianapolis Colts 54.0 115.0 0 4 0\n",
"Washington Redskins 56.0 37.0 2 1 0\n",
"Seattle Seahawks 74.0 101.0 2 2 0\n",
"Arizona Cardinals 59.0 65.0 1 2 0\n",
"Jacksonville Jaguars 98.0 61.0 3 0 0\n",
"Chicago Bears 58.0 128.0 0 4 0\n",
"San Diego Chargers 56.0 99.0 1 3 0\n",
"Miami Dolphins 71.0 77.0 2 2 0\n",
"New York Jets 110.0 80.0 2 2 0\n",
"Baltimore Ravens 110.0 71.0 3 1 0\n",
"Kansas City Chiefs 88.0 76.0 3 1 0\n",
"Denver Broncos 127.0 51.0 4 0 0\n",
"Green Bay Packers 108.0 84.0 3 1 0\n",
"Minnesota Vikings 107.0 103.0 2 2 0\n",
"Dallas Cowboys 80.0 52.0 2 1 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 81.0 122.0 1 4 0\n",
"St. Louis Rams 94.0 112.0 2 3 0\n",
"New England Patriots 130.0 40.0 4 0 0\n",
"Tampa Bay Buccaneers 115.0 76.0 5 0 0\n",
"Philadelphia Eagles 66.0 89.0 1 3 0\n",
"Pittsburgh Steelers 79.0 104.0 2 2 0\n",
"Atlanta Falcons 82.0 136.0 0 5 0\n",
"Cincinnati Bengals 68.0 113.0 1 3 0\n",
"Oakland Raiders 141.0 123.0 2 3 0\n",
"Buffalo Bills 94.0 113.0 2 2 0\n",
"New York Giants 84.0 103.0 2 3 0\n",
"Detroit Lions 120.0 98.0 3 2 0\n",
"Tennessee Oilers 71.0 110.0 1 3 0\n",
"Carolina Panthers 80.0 106.0 2 3 0\n",
"San Francisco 49ers 122.0 60.0 4 1 0\n",
"Indianapolis Colts 54.0 115.0 0 4 0\n",
"Washington Redskins 80.0 49.0 3 1 0\n",
"Seattle Seahawks 91.0 121.0 2 3 0\n",
"Arizona Cardinals 77.0 84.0 1 3 0\n",
"Jacksonville Jaguars 110.0 85.0 3 1 0\n",
"Chicago Bears 61.0 155.0 0 5 0\n",
"San Diego Chargers 77.0 116.0 2 3 0\n",
"Miami Dolphins 71.0 77.0 2 2 0\n",
"New York Jets 141.0 94.0 3 2 0\n",
"Baltimore Ravens 127.0 92.0 3 2 0\n",
"Kansas City Chiefs 108.0 93.0 4 1 0\n",
"Denver Broncos 156.0 72.0 5 0 0\n",
"Green Bay Packers 123.0 110.0 3 2 0\n",
"Minnesota Vikings 135.0 122.0 3 2 0\n",
"Dallas Cowboys 107.0 55.0 3 1 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 101.0 139.0 2 4 0\n",
"St. Louis Rams 94.0 112.0 2 3 0\n",
"New England Patriots 143.0 74.0 4 1 0\n",
"Tampa Bay Buccaneers 131.0 97.0 5 1 0\n",
"Philadelphia Eagles 90.0 99.0 2 3 0\n",
"Pittsburgh Steelers 121.0 138.0 3 2 0\n",
"Atlanta Falcons 82.0 136.0 0 5 0\n",
"Cincinnati Bengals 81.0 134.0 1 4 0\n",
"Oakland Raiders 151.0 148.0 2 4 0\n",
"Buffalo Bills 116.0 126.0 3 2 0\n",
"New York Giants 104.0 120.0 3 3 0\n",
"Detroit Lions 133.0 120.0 3 3 0\n",
"Tennessee Oilers 84.0 126.0 1 4 0\n",
"Carolina Panthers 80.0 106.0 2 3 0\n",
"San Francisco 49ers 122.0 60.0 4 1 0\n",
"Indianapolis Colts 66.0 131.0 0 5 0\n",
"Washington Redskins 90.0 73.0 3 2 0\n",
"Seattle Seahawks 107.0 134.0 3 3 0\n",
"Arizona Cardinals 96.0 104.0 1 4 0\n",
"Jacksonville Jaguars 131.0 98.0 4 1 0\n",
"Chicago Bears 78.0 175.0 0 6 0\n",
"San Diego Chargers 102.0 126.0 3 3 0\n",
"Miami Dolphins 88.0 91.0 3 2 0\n",
"New York Jets 157.0 106.0 4 2 0\n",
"Baltimore Ravens 161.0 134.0 3 3 0\n",
"Kansas City Chiefs 122.0 110.0 4 2 0\n",
"Denver Broncos 190.0 85.0 6 0 0\n",
"Green Bay Packers 144.0 126.0 4 2 0\n",
"Minnesota Vikings 155.0 141.0 4 2 0\n",
"Dallas Cowboys 124.0 75.0 3 2 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 118.0 162.0 2 5 0\n",
"St. Louis Rams 104.0 142.0 2 4 0\n",
"New England Patriots 176.0 80.0 5 1 0\n",
"Tampa Bay Buccaneers 140.0 124.0 5 2 0\n",
"Philadelphia Eagles 111.0 137.0 2 4 0\n",
"Pittsburgh Steelers 145.0 160.0 4 2 0\n",
"Atlanta Falcons 105.0 153.0 1 5 0\n",
"Cincinnati Bengals 88.0 164.0 1 5 0\n",
"Oakland Raiders 151.0 148.0 2 4 0\n",
"Buffalo Bills 122.0 159.0 3 3 0\n",
"New York Giants 131.0 133.0 4 3 0\n",
"Detroit Lions 160.0 129.0 4 3 0\n",
"Tennessee Oilers 114.0 133.0 2 4 0\n",
"Carolina Panthers 94.0 127.0 2 4 0\n",
"San Francisco 49ers 152.0 70.0 5 1 0\n",
"Indianapolis Colts 88.0 155.0 0 6 0\n",
"Washington Redskins 111.0 89.0 4 2 0\n",
"Seattle Seahawks 107.0 134.0 3 3 0\n",
"Arizona Cardinals 109.0 131.0 1 5 0\n",
"Jacksonville Jaguars 169.0 119.0 5 1 0\n",
"Chicago Bears 101.0 199.0 0 7 0\n",
"San Diego Chargers 102.0 126.0 3 3 0\n",
"Miami Dolphins 119.0 111.0 4 2 0\n",
"New York Jets 177.0 137.0 4 3 0\n",
"Baltimore Ravens 161.0 134.0 3 3 0\n",
"Kansas City Chiefs 122.0 110.0 4 2 0\n",
"Denver Broncos 190.0 85.0 6 0 0\n",
"Green Bay Packers 168.0 149.0 5 2 0\n",
"Minnesota Vikings 176.0 155.0 5 2 0\n",
"Dallas Cowboys 140.0 96.0 3 3 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 118.0 175.0 2 6 0\n",
"St. Louis Rams 113.0 159.0 2 5 0\n",
"New England Patriots 195.0 104.0 5 2 0\n",
"Tampa Bay Buccaneers 140.0 124.0 5 2 0\n",
"Philadelphia Eagles 124.0 147.0 3 4 0\n",
"Pittsburgh Steelers 171.0 170.0 5 2 0\n",
"Atlanta Falcons 133.0 188.0 1 6 0\n",
"Cincinnati Bengals 98.0 190.0 1 6 0\n",
"Oakland Raiders 179.0 173.0 3 4 0\n",
"Buffalo Bills 131.0 165.0 4 3 0\n",
"New York Giants 157.0 153.0 5 3 0\n",
"Detroit Lions 180.0 155.0 4 4 0\n",
"Tennessee Oilers 142.0 147.0 3 4 0\n",
"Carolina Panthers 107.0 127.0 3 4 0\n",
"San Francisco 49ers 187.0 98.0 6 1 0\n",
"Indianapolis Colts 94.0 164.0 0 7 0\n",
"Washington Redskins 125.0 117.0 4 3 0\n",
"Seattle Seahawks 124.0 143.0 4 3 0\n",
"Arizona Cardinals 119.0 144.0 1 6 0\n",
"Jacksonville Jaguars 191.0 145.0 5 2 0\n",
"Chicago Bears 101.0 199.0 0 7 0\n",
"San Diego Chargers 105.0 157.0 3 4 0\n",
"Miami Dolphins 143.0 124.0 5 2 0\n",
"New York Jets 201.0 156.0 5 3 0\n",
"Baltimore Ravens 174.0 158.0 3 4 0\n",
"Kansas City Chiefs 153.0 113.0 5 2 0\n",
"Denver Broncos 215.0 113.0 6 1 0\n",
"Green Bay Packers 168.0 149.0 5 2 0\n",
"Minnesota Vikings 176.0 155.0 5 2 0\n",
"Dallas Cowboys 166.0 118.0 4 3 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 118.0 198.0 2 7 0\n",
"St. Louis Rams 133.0 187.0 2 6 0\n",
"New England Patriots 205.0 132.0 5 3 0\n",
"Tampa Bay Buccaneers 146.0 134.0 5 3 0\n",
"Philadelphia Eagles 137.0 159.0 4 4 0\n",
"Pittsburgh Steelers 194.0 187.0 6 2 0\n",
"Atlanta Falcons 145.0 209.0 1 7 0\n",
"Cincinnati Bengals 125.0 219.0 1 7 0\n",
"Oakland Raiders 213.0 218.0 3 5 0\n",
"Buffalo Bills 151.0 188.0 4 4 0\n",
"New York Giants 186.0 180.0 6 3 0\n",
"Detroit Lions 180.0 155.0 4 4 0\n",
"Tennessee Oilers 183.0 161.0 4 4 0\n",
"Carolina Panthers 128.0 139.0 4 4 0\n",
"San Francisco 49ers 210.0 98.0 7 1 0\n",
"Indianapolis Colts 113.0 199.0 0 8 0\n",
"Washington Redskins 142.0 137.0 4 4 0\n",
"Seattle Seahawks 169.0 177.0 5 3 0\n",
"Arizona Cardinals 133.0 185.0 1 7 0\n",
"Jacksonville Jaguars 208.0 168.0 5 3 0\n",
"Chicago Bears 137.0 232.0 1 7 0\n",
"San Diego Chargers 140.0 176.0 4 4 0\n",
"Miami Dolphins 176.0 160.0 5 3 0\n",
"New York Jets 201.0 156.0 5 3 0\n",
"Baltimore Ravens 194.0 175.0 4 4 0\n",
"Kansas City Chiefs 181.0 133.0 6 2 0\n",
"Denver Broncos 238.0 133.0 7 1 0\n",
"Green Bay Packers 196.0 159.0 6 2 0\n",
"Minnesota Vikings 186.0 161.0 6 2 0\n",
"Dallas Cowboys 178.0 131.0 4 4 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 118.0 198.0 2 7 0\n",
"St. Louis Rams 164.0 221.0 2 7 0\n",
"New England Patriots 223.0 155.0 5 4 0\n",
"Tampa Bay Buccaneers 177.0 162.0 6 3 0\n",
"Philadelphia Eagles 158.0 190.0 4 5 0\n",
"Pittsburgh Steelers 204.0 200.0 6 3 0\n",
"Atlanta Falcons 179.0 240.0 2 7 0\n",
"Cincinnati Bengals 163.0 250.0 2 7 0\n",
"Oakland Raiders 227.0 256.0 3 6 0\n",
"Buffalo Bills 160.0 194.0 5 4 0\n",
"New York Giants 186.0 180.0 6 3 0\n",
"Detroit Lions 190.0 175.0 4 5 0\n",
"Tennessee Oilers 207.0 191.0 4 5 0\n",
"Carolina Panthers 166.0 153.0 5 4 0\n",
"San Francisco 49ers 227.0 108.0 8 1 0\n",
"Indianapolis Colts 141.0 230.0 0 9 0\n",
"Washington Redskins 173.0 145.0 5 4 0\n",
"Seattle Seahawks 196.0 207.0 5 4 0\n",
"Arizona Cardinals 164.0 206.0 2 7 0\n",
"Jacksonville Jaguars 238.0 192.0 6 3 0\n",
"Chicago Bears 145.0 263.0 1 8 0\n",
"San Diego Chargers 171.0 214.0 4 5 0\n",
"Miami Dolphins 182.0 169.0 5 4 0\n",
"New York Jets 220.0 172.0 6 3 0\n",
"Baltimore Ravens 210.0 194.0 4 5 0\n",
"Kansas City Chiefs 194.0 143.0 7 2 0\n",
"Denver Broncos 268.0 160.0 8 1 0\n",
"Green Bay Packers 216.0 169.0 7 2 0\n",
"Minnesota Vikings 209.0 179.0 7 2 0\n",
"Dallas Cowboys 188.0 148.0 4 5 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 131.0 208.0 3 7 0\n",
"St. Louis Rams 171.0 238.0 2 8 0\n",
"New England Patriots 254.0 165.0 6 4 0\n",
"Tampa Bay Buccaneers 208.0 172.0 7 3 0\n",
"Philadelphia Eagles 170.0 214.0 4 6 0\n",
"Pittsburgh Steelers 241.0 200.0 7 3 0\n",
"Atlanta Falcons 189.0 271.0 2 8 0\n",
"Cincinnati Bengals 191.0 263.0 3 7 0\n",
"Oakland Raiders 237.0 269.0 3 7 0\n",
"Buffalo Bills 170.0 225.0 5 5 0\n",
"New York Giants 192.0 190.0 6 4 0\n",
"Detroit Lions 197.0 205.0 4 6 0\n",
"Tennessee Oilers 217.0 197.0 5 5 0\n",
"Carolina Panthers 166.0 187.0 5 5 0\n",
"San Francisco 49ers 251.0 120.0 9 1 0\n",
"Indianapolis Colts 154.0 258.0 0 10 0\n",
"Washington Redskins 203.0 152.0 6 4 0\n",
"Seattle Seahawks 233.0 238.0 6 4 0\n",
"Arizona Cardinals 170.0 230.0 2 8 0\n",
"Jacksonville Jaguars 262.0 202.0 7 3 0\n",
"Chicago Bears 167.0 292.0 1 9 0\n",
"San Diego Chargers 202.0 251.0 4 6 0\n",
"Miami Dolphins 206.0 186.0 6 4 0\n",
"New York Jets 237.0 196.0 6 4 0\n",
"Baltimore Ravens 210.0 231.0 4 6 0\n",
"Kansas City Chiefs 204.0 167.0 7 3 0\n",
"Denver Broncos 302.0 160.0 9 1 0\n",
"Green Bay Packers 233.0 176.0 8 2 0\n",
"Minnesota Vikings 238.0 201.0 8 2 0\n",
"Dallas Cowboys 212.0 154.0 5 5 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 151.0 225.0 4 7 0\n",
"St. Louis Rams 192.0 265.0 2 9 0\n",
"New England Patriots 261.0 192.0 6 5 0\n",
"Tampa Bay Buccaneers 235.0 179.0 8 3 0\n",
"Philadelphia Eagles 180.0 224.0 4 6 1\n",
"Pittsburgh Steelers 261.0 203.0 8 3 0\n",
"Atlanta Falcons 216.0 292.0 3 8 0\n",
"Cincinnati Bengals 194.0 283.0 3 8 0\n",
"Oakland Raiders 275.0 282.0 4 7 0\n",
"Buffalo Bills 183.0 255.0 5 6 0\n",
"New York Giants 211.0 200.0 7 4 0\n",
"Detroit Lions 235.0 220.0 5 6 0\n",
"Tennessee Oilers 226.0 214.0 5 6 0\n",
"Carolina Panthers 185.0 214.0 5 6 0\n",
"San Francisco 49ers 278.0 139.0 10 1 0\n",
"Indianapolis Colts 195.0 296.0 1 10 0\n",
"Washington Redskins 217.0 169.0 6 5 0\n",
"Seattle Seahawks 250.0 258.0 6 5 0\n",
"Arizona Cardinals 180.0 249.0 2 9 0\n",
"Jacksonville Jaguars 279.0 211.0 8 3 0\n",
"Chicago Bears 182.0 315.0 1 10 0\n",
"San Diego Chargers 215.0 289.0 4 7 0\n",
"Miami Dolphins 236.0 199.0 7 4 0\n",
"New York Jets 260.0 211.0 7 4 0\n",
"Baltimore Ravens 220.0 241.0 4 6 1\n",
"Kansas City Chiefs 228.0 189.0 8 3 0\n",
"Denver Broncos 324.0 184.0 9 2 0\n",
"Green Bay Packers 271.0 217.0 8 3 0\n",
"Minnesota Vikings 253.0 239.0 8 3 0\n",
"Dallas Cowboys 229.0 168.0 6 5 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 154.0 245.0 4 8 0\n",
"St. Louis Rams 202.0 281.0 2 10 0\n",
"New England Patriots 288.0 216.0 7 5 0\n",
"Tampa Bay Buccaneers 242.0 192.0 8 4 0\n",
"Philadelphia Eagles 203.0 244.0 5 6 1\n",
"Pittsburgh Steelers 281.0 226.0 8 4 0\n",
"Atlanta Falcons 236.0 295.0 4 8 0\n",
"Cincinnati Bengals 225.0 309.0 4 8 0\n",
"Oakland Raiders 278.0 313.0 4 8 0\n",
"Buffalo Bills 197.0 286.0 5 7 0\n",
"New York Giants 218.0 207.0 7 4 1\n",
"Detroit Lions 267.0 230.0 6 6 0\n",
"Tennessee Oilers 257.0 228.0 6 6 0\n",
"Carolina Panthers 201.0 224.0 6 6 0\n",
"San Francisco 49ers 295.0 149.0 11 1 0\n",
"Indianapolis Colts 205.0 328.0 1 11 0\n",
"Washington Redskins 224.0 176.0 6 5 1\n",
"Seattle Seahawks 264.0 277.0 6 6 0\n",
"Arizona Cardinals 196.0 262.0 3 9 0\n",
"Jacksonville Jaguars 305.0 242.0 8 4 0\n",
"Chicago Bears 195.0 322.0 2 10 0\n",
"San Diego Chargers 225.0 306.0 4 8 0\n",
"Miami Dolphins 260.0 226.0 7 5 0\n",
"New York Jets 283.0 232.0 8 4 0\n",
"Baltimore Ravens 233.0 257.0 4 7 1\n",
"Kansas City Chiefs 247.0 203.0 9 3 0\n",
"Denver Broncos 355.0 187.0 10 2 0\n",
"Green Bay Packers 316.0 234.0 9 3 0\n",
"Minnesota Vikings 274.0 262.0 8 4 0\n",
"Dallas Cowboys 246.0 213.0 6 6 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 170.0 258.0 5 8 0\n",
"St. Louis Rams 225.0 301.0 3 10 0\n",
"New England Patriots 308.0 233.0 8 5 0\n",
"Tampa Bay Buccaneers 262.0 200.0 9 4 0\n",
"Philadelphia Eagles 247.0 286.0 6 6 1\n",
"Pittsburgh Steelers 307.0 246.0 9 4 0\n",
"Atlanta Falcons 260.0 312.0 5 8 0\n",
"Cincinnati Bengals 267.0 353.0 4 9 0\n",
"Oakland Raiders 294.0 347.0 4 9 0\n",
"Buffalo Bills 217.0 296.0 6 7 0\n",
"New York Giants 226.0 227.0 7 5 1\n",
"Detroit Lions 322.0 250.0 7 6 0\n",
"Tennessee Oilers 284.0 242.0 7 6 0\n",
"Carolina Panthers 214.0 240.0 6 7 0\n",
"San Francisco 49ers 304.0 193.0 11 2 0\n",
"Indianapolis Colts 222.0 348.0 1 12 0\n",
"Washington Redskins 244.0 199.0 6 6 1\n",
"Seattle Seahawks 281.0 301.0 6 7 0\n",
"Arizona Cardinals 216.0 288.0 3 10 0\n",
"Jacksonville Jaguars 334.0 269.0 9 4 0\n",
"Chicago Bears 215.0 377.0 2 11 0\n",
"San Diego Chargers 253.0 344.0 4 9 0\n",
"Miami Dolphins 294.0 242.0 8 5 0\n",
"New York Jets 293.0 252.0 8 5 0\n",
"Baltimore Ravens 260.0 286.0 4 8 1\n",
"Kansas City Chiefs 291.0 212.0 10 3 0\n",
"Denver Broncos 393.0 215.0 11 2 0\n",
"Green Bay Packers 343.0 245.0 10 3 0\n",
"Minnesota Vikings 285.0 289.0 8 5 0\n",
"Dallas Cowboys 260.0 240.0 6 7 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 197.0 292.0 5 9 0\n",
"St. Louis Rams 259.0 328.0 4 10 0\n",
"New England Patriots 334.0 253.0 9 5 0\n",
"Tampa Bay Buccaneers 268.0 217.0 9 5 0\n",
"Philadelphia Eagles 268.0 317.0 6 7 1\n",
"Pittsburgh Steelers 342.0 270.0 10 4 0\n",
"Atlanta Falcons 274.0 315.0 6 8 0\n",
"Cincinnati Bengals 308.0 367.0 5 9 0\n",
"Oakland Raiders 294.0 377.0 4 10 0\n",
"Buffalo Bills 220.0 316.0 6 8 0\n",
"New York Giants 257.0 248.0 8 5 1\n",
"Detroit Lions 352.0 283.0 7 7 0\n",
"Tennessee Oilers 298.0 283.0 7 7 0\n",
"Carolina Panthers 237.0 253.0 7 7 0\n",
"San Francisco 49ers 332.0 210.0 12 2 0\n",
"Indianapolis Colts 244.0 362.0 2 12 0\n",
"Washington Redskins 282.0 227.0 7 6 1\n",
"Seattle Seahawks 305.0 332.0 6 8 0\n",
"Arizona Cardinals 244.0 326.0 3 11 0\n",
"Jacksonville Jaguars 354.0 295.0 9 5 0\n",
"Chicago Bears 235.0 380.0 3 11 0\n",
"San Diego Chargers 256.0 358.0 4 10 0\n",
"Miami Dolphins 327.0 272.0 9 5 0\n",
"New York Jets 307.0 274.0 8 6 0\n",
"Baltimore Ravens 291.0 310.0 5 8 1\n",
"Kansas City Chiefs 321.0 212.0 11 3 0\n",
"Denver Broncos 417.0 250.0 11 3 0\n",
"Green Bay Packers 360.0 251.0 11 3 0\n",
"Minnesota Vikings 302.0 317.0 8 6 0\n",
"Dallas Cowboys 273.0 263.0 6 8 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 224.0 302.0 6 9 0\n",
"St. Louis Rams 269.0 341.0 4 11 0\n",
"New England Patriots 355.0 277.0 9 6 0\n",
"Tampa Bay Buccaneers 268.0 248.0 9 6 0\n",
"Philadelphia Eagles 285.0 337.0 6 8 1\n",
"Pittsburgh Steelers 366.0 291.0 11 4 0\n",
"Atlanta Falcons 294.0 332.0 7 8 0\n",
"Cincinnati Bengals 339.0 391.0 6 9 0\n",
"Oakland Raiders 315.0 399.0 4 11 0\n",
"Buffalo Bills 234.0 336.0 6 9 0\n",
"New York Giants 287.0 258.0 9 5 1\n",
"Detroit Lions 366.0 296.0 8 7 0\n",
"Tennessee Oilers 317.0 304.0 7 8 0\n",
"Carolina Panthers 247.0 284.0 7 8 0\n",
"San Francisco 49ers 366.0 227.0 13 2 0\n",
"Indianapolis Colts 285.0 362.0 3 12 0\n",
"Washington Redskins 292.0 257.0 7 7 1\n",
"Seattle Seahawks 327.0 353.0 7 8 0\n",
"Arizona Cardinals 254.0 353.0 3 12 0\n",
"Jacksonville Jaguars 374.0 309.0 10 5 0\n",
"Chicago Bears 248.0 390.0 4 11 0\n",
"San Diego Chargers 263.0 387.0 4 11 0\n",
"Miami Dolphins 327.0 313.0 9 6 0\n",
"New York Jets 338.0 274.0 9 6 0\n",
"Baltimore Ravens 312.0 329.0 6 8 1\n",
"Kansas City Chiefs 350.0 219.0 12 3 0\n",
"Denver Broncos 434.0 284.0 11 4 0\n",
"Green Bay Packers 391.0 261.0 12 3 0\n",
"Minnesota Vikings 315.0 331.0 8 7 0\n",
"Dallas Cowboys 297.0 294.0 6 9 0, points_forced points_allowed win loss tie\n",
"New Orleans Saints 237.0 327.0 6 10 0\n",
"St. Louis Rams 299.0 359.0 5 11 0\n",
"New England Patriots 369.0 289.0 10 6 0\n",
"Tampa Bay Buccaneers 299.0 263.0 10 6 0\n",
"Philadelphia Eagles 317.0 372.0 6 9 1\n",
"Pittsburgh Steelers 372.0 307.0 11 5 0\n",
"Atlanta Falcons 320.0 361.0 7 9 0\n",
"Cincinnati Bengals 355.0 405.0 7 9 0\n",
"Oakland Raiders 324.0 419.0 4 12 0\n",
"Buffalo Bills 255.0 367.0 6 10 0\n",
"New York Giants 307.0 265.0 10 5 1\n",
"Detroit Lions 379.0 306.0 9 7 0\n",
"Tennessee Oilers 333.0 310.0 8 8 0\n",
"Carolina Panthers 265.0 314.0 7 9 0\n",
"San Francisco 49ers 375.0 265.0 13 3 0\n",
"Indianapolis Colts 313.0 401.0 3 13 0\n",
"Washington Redskins 327.0 289.0 8 7 1\n",
"Seattle Seahawks 365.0 362.0 8 8 0\n",
"Arizona Cardinals 283.0 379.0 4 12 0\n",
"Jacksonville Jaguars 394.0 318.0 11 5 0\n",
"Chicago Bears 263.0 421.0 4 12 0\n",
"San Diego Chargers 266.0 425.0 4 12 0\n",
"Miami Dolphins 339.0 327.0 9 7 0\n",
"New York Jets 348.0 287.0 9 7 0\n",
"Baltimore Ravens 326.0 345.0 6 9 1\n",
"Kansas City Chiefs 375.0 232.0 13 3 0\n",
"Denver Broncos 472.0 287.0 12 4 0\n",
"Green Bay Packers 422.0 282.0 13 3 0\n",
"Minnesota Vikings 354.0 359.0 9 7 0\n",
"Dallas Cowboys 304.0 314.0 6 10 0]]\n"
]
}
],
"source": [
"print(year_data[1997])"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1997, 0.60444444444444445)\n",
"(1998, 0.65333333333333332)\n",
"(1999, 0.61802575107296143)\n",
"(2000, 0.60085836909871249)\n",
"(2001, 0.62660944206008584)\n",
"(2002, 0.60833333333333328)\n",
"(2003, 0.62916666666666665)\n",
"(2004, 0.63749999999999996)\n",
"(2005, 0.65833333333333333)\n",
"(2006, 0.5625)\n",
"(2007, 0.65000000000000002)\n",
"(2008, 0.63749999999999996)\n",
"(2009, 0.625)\n",
"(2010, 0.60833333333333328)\n",
"(2011, 0.63749999999999996)\n",
"(2012, 0.60833333333333328)\n",
"(2013, 0.6166666666666667)\n",
"(2014, 0.6333333333333333)\n",
"(2015, 0.62083333333333335)\n",
"(2016, 0.64583333333333337)\n",
"OVERALL ACCURACY:\n",
"0.624121900334\n"
]
}
],
"source": [
"#leave out week 17 because playoff teams rest their starters, weird stuff happens, etc. Vegas does similar.\n",
"\n",
"#leave out predicing week 1\n",
"weeks = range(17)\n",
"year_by_year_acc = [0]*len(year_range)\n",
"\n",
"#Victory alg options: home_team, biased_home, win_percentage, expected_win_percentage\n",
"victory_alg = \"biased_home\"\n",
"for year in year_range:\n",
" guess_right_wrong = []\n",
" guess_right_wrong = [guess_right_wrong + [0,0] for i in range(len(weeks))]\n",
" game_list,feature_matrix = year_data[year]\n",
" counter=0\n",
" for index, game in game_list.iterrows():\n",
" counter+= 1\n",
" week = int(game[\"Week\"])\n",
" if week in weeks:\n",
" home_team = game[\"Home\"]\n",
" away_team = game[\"Away\"]\n",
" prediction = guess_victory_margin(home_team,away_team,week,victory_alg)\n",
" actual = 1 if game[\"Home\"] == game[\"Winner/tie\"] else 0\n",
" if prediction == actual:\n",
" guess_right_wrong[week - weeks[0]][0] += 1\n",
" else:\n",
" guess_right_wrong[week - weeks[0]][1] += 1\n",
" #print(guess_right_wrong)\n",
" seasonal = np.sum(guess_right_wrong,axis=0)\n",
" #print(seasonal)\n",
" acc = seasonal[0]*1. / (seasonal[0] + seasonal[1])\n",
" print(year, acc)\n",
" year_by_year_acc[year - year_range[0]] = acc\n",
"print(\"OVERALL ACCURACY:\")\n",
"print(sum(year_by_year_acc)/len(year_by_year_acc))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#LOGISTIC REGRESSION CODE\n",
"\n",
"from sklearn import linear_model\n",
"def logistic_regression_win_loss(train_years,validation_years):\n",
" train_data_x = []\n",
" train_data_y = []\n",
" for year in train_years:\n",
" game_list,feature_matrix = year_data[year]\n",
" #get relevant game list year\n",
" for index, game in game_list.iterrows():\n",
" #add data point. Consider removing week 1\n",
" week = int(game[\"Week\"])\n",
" if week != 1:\n",
" home_team = game[\"Home\"]\n",
" away_team = game[\"Away\"]\n",
" home_wins = feature_matrix[week-1][\"win\"][home_team] \n",
" home_losses = feature_matrix[week-1][\"loss\"][home_team] \n",
" away_wins = feature_matrix[week-1][\"win\"][away_team] \n",
" away_losses = feature_matrix[week-1][\"loss\"][away_team] \n",
"\n",
" x = home_wins + away_losses - (home_losses + away_wins)\n",
" y = 1 if game[\"Home\"] == game[\"Winner/tie\"] else 0\n",
" train_data_x += [[x]]\n",
" train_data_y += [y]\n",
" #experiment with different values of C\n",
" assert len(train_data_x) == len(train_data_y)\n",
" log_regression_predictor = linear_model.LogisticRegression(penalty = \"l1\", C = 1)\n",
" log_regression_predictor.fit(train_data_x,train_data_y)\n",
" \n",
" #calculate train error\n",
" train_predictions = log_regression_predictor.predict(train_data_x)\n",
" print(train_predictions[1])\n",
" assert len(train_data_y) == len(train_predictions)\n",
" train_errors = sum([abs(train_data_y[i] - train_predictions[i]) for i in range(len(train_predictions))])\n",
" train_error_rate = train_errors*1./len(train_predictions)\n",
" \n",
" val_data_x = []\n",
" val_data_y = []\n",
" for year in validation_years:\n",
" #get relevant game list year\n",
" game_list,feature_matrix = year_data[year]\n",
" for index, game in game_list.iterrows():\n",
" #add data point. Consider removing week 1. \n",
" week = int(game[\"Week\"])\n",
" if week != 17:\n",
" home_team = game[\"Home\"]\n",
" away_team = game[\"Away\"]\n",
" home_wins = feature_matrix[week-1][\"win\"][home_team] \n",
" home_losses = feature_matrix[week-1][\"loss\"][home_team] \n",
" away_wins = feature_matrix[week-1][\"win\"][away_team] \n",
" away_losses = feature_matrix[week-1][\"loss\"][away_team]\n",
" x = home_wins + away_losses - (home_losses + away_wins)\n",
" y = 1 if game[\"Home\"] == game[\"Winner/tie\"] else 0\n",
" val_data_x+=[[x]]\n",
" val_data_y+=[y]\n",
" assert len(val_data_x) == len(val_data_y)\n",
" \n",
" #calculate validation error\n",
" validation_predictions = log_regression_predictor.predict(val_data_x)\n",
" print(len(validation_predictions),len(val_data_y))\n",
"\n",
" assert len(val_data_y) == len(validation_predictions)\n",
" print(sum(validation_predictions)*1./len(val_data_y))\n",
" validation_errors = sum([abs(val_data_y[i] - validation_predictions[i]) for i in range(len(val_data_y))])\n",
" validation_error_rate = validation_errors*1./len(val_data_y)\n",
" \n",
" return train_error_rate,validation_error_rate,log_regression_predictor\n",
"\n",
"\n",
"train_error_rate,validation_error_rate,log_regression_predictor = logistic_regression_win_loss([2012,2014,2016],[2013,2015])\n",
"\n",
"print(train_error_rate,validation_error_rate,log_regression_predictor.get_params())\n",
"xx = np.linspace(-6.0, 6.0, num=50)\n",
"print(xx)\n",
"for i in xx:\n",
" print(i,log_regression_predictor.predict([[i]]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#============== master code here ==============="
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"#GET VEGAS LINES IN USABLE FORM\n",
"\n",
"def sort_vegas_lines():\n",
" team_name_dict = {}\n",
" team_list =get_teams(2006)\n",
" for team in team_list:\n",
" team_data = team.rsplit(\" \",1)\n",
" team_name_dict[team_data[0]] = team_data[1]\n",
" \n",
" xx = pickle.load(open(\"betting_lines.pickle\"))\n",
" sorted_vegas_lines = sorted(xx,key = lambda x: x[1])\n",
" vegas_line_dict = {}\n",
" vegas_years = range(2006,2018)\n",
" for year in vegas_years:\n",
" year_dict = {}\n",
" for week in range(1,18):\n",
" year_dict[week] = {}\n",
" vegas_line_dict[year] = year_dict\n",
"\n",
"\n",
" for game in sorted_vegas_lines:\n",
" week = game[0]\n",
" year = game[1]\n",
" home_team = str(game[2])\n",
" if \"London\" in home_team:\n",
" break\n",
" elif home_team =='NY Giants':\n",
" home_team = \"New York Giants\"\n",
" elif home_team =='NY Jets':\n",
" home_team = \"New York Jets\"\n",
" else:\n",
" home_team = home_team + \" \" + team_name_dict[home_team]\n",
" points_favored = game[4]\n",
" vegas_line_dict[year][week][home_team] = points_favored\n",
" return vegas_line_dict\n",
"\n",
"vegas_line_dict = sort_vegas_lines()\n",
"\n",
"\n",
"def get_game_data(year):\n",
" \"\"\"\n",
" param\n",
" ---------------\n",
" year: int, between 1997 and 2016 for now\n",
"\n",
" returns\n",
" ---------------\n",
" regular_season, playoffs: pd.DataFrame, consists of game-by-game data\n",
" from the regular season and playoffs in that year, has columns ['Week', \n",
" 'Day', 'Date', 'Time', 'Winner/tie', Loser/tie', 'Home', 'Away', 'PtsW', 'PtsL', \n",
" 'YdsW', 'TOW', 'YdsL', 'TOL']\n",
" \"\"\"\n",
" df = pd.read_csv('game_data_csv/'+str(year)+\".csv\")\n",
" df = df.drop('Unnamed: 7', axis=1)\n",
" df.rename(columns={'Unnamed: 5': 'at'}, inplace=True)\n",
" df.insert(loc=7, column='Home', value=None)\n",
" df.insert(loc=8, column='Away', value=None)\n",
" df.insert(loc=9, column='Vegas', value=None)\n",
" playoff_cutoff = None\n",
" for index, row in df.iterrows():\n",
" if row['at'] == '@':\n",
" df.loc[index, 'Home'] = row['Loser/tie']\n",
" df.loc[index,'Away'] = row['Winner/tie']\n",
" else:\n",
" df.loc[index, 'Home'] = row['Winner/tie']\n",
" df.loc[index,'Away'] = row['Loser/tie']\n",
" if row['Date'] == 'Playoffs':\n",
" playoff_cutoff = index\n",
" if playoff_cutoff is None:\n",
" df.loc[index, 'Week'] = int(row['Week'])\n",
" vegas_line = 0\n",
" try:\n",
" vegas_line = vegas_line_dict[year][df.loc[index, 'Week']][df.loc[index, 'Home']]\n",
" except:\n",
" pass\n",
" df.loc[index,'Vegas'] = vegas_line\n",
" \n",
"\n",
" df = df.drop('at', axis=1)\n",
"\n",
" regular_season = df.loc[:playoff_cutoff-1]\n",
" playoffs = df.loc[playoff_cutoff+1:]\n",
" return regular_season, playoffs"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"def get_teams(year):\n",
" \"\"\"\n",
" param\n",
" ---------------\n",
" year: int, between 1997 and 2016\n",
"\n",
" returns\n",
" ---------------\n",
" teams: list of team names\n",
" \"\"\"\n",
" regular_season, playoffs = get_game_data(year)\n",
" teams = regular_season['Winner/tie'].tolist() + regular_season['Loser/tie'].tolist()\n",
" teams = list(set(teams))\n",
" return teams\n",
"\n",
"def get_regular_season_history(year):\n",
" \"\"\"\n",
" param\n",
" --------------\n",
" year: int, between 1997 and 2016\n",
" \n",
" returns\n",
" --------------\n",
" history: list of pd.DataFrame objects, indexed by week number (note index 0 entry has\n",
" all attributes set to 0). pd.DataFrame objects have columns ['points_forced', 'points_\n",
" allowed', 'win', 'loss']. All stats are cumulative up to the week number. Ties are counted\n",
" as 0.5 win and 0.5 loss.\n",
" \"\"\"\n",
" weeks = list(range(0,18))\n",
" teams = get_teams(year)\n",
" regular_season, playoff = get_game_data(year)\n",
" \n",
" res = pd.DataFrame(index=teams, columns=['points_forced', 'points_allowed','points_forced_last','points_allowed_last', 'win', 'loss'])\n",
" #res = pd.DataFrame(index=teams, columns=['points_forced', 'points_allowed','win', 'loss'])\n",
" res = res.fillna(0)\n",
" history = []\n",
"\n",
" for week in weeks:\n",
" for index, row in regular_season.iterrows():\n",
" if row['Week'] == week:\n",
" res.loc[row['Winner/tie'], 'points_forced'] += row['PtsW']\n",
" res.loc[row['Winner/tie'], 'points_allowed'] += row['PtsL']\n",
" \n",
" res.loc[row['Loser/tie'], 'points_forced'] += row['PtsL']\n",
" res.loc[row['Loser/tie'], 'points_allowed'] += row['PtsW']\n",
" \n",
" res.loc[row['Winner/tie'], 'points_forced_last'] = row['PtsW']\n",
" res.loc[row['Winner/tie'], 'points_allowed_last'] = row['PtsL']\n",
" \n",
" res.loc[row['Loser/tie'], 'points_forced_last'] = row['PtsL']\n",
" res.loc[row['Loser/tie'], 'points_allowed_last'] = row['PtsW']\n",
" \n",
"# res.loc[row['Winner/tie'], 'vegas_line'] = row['Vegas'] \n",
"# res.loc[row['Loser/tie'], 'vegas_line'] = row['Vegas']\n",
" \n",
" if row['PtsW'] == row['PtsL']:\n",
" res.loc[row['Winner/tie'], 'win'] += 0.5\n",
" res.loc[row['Winner/tie'], 'loss'] += 0.5\n",
" res.loc[row['Loser/tie'], 'win'] += 0.5\n",
" res.loc[row['Loser/tie'], 'loss'] += 0.5\n",
" else:\n",
" res.loc[row['Winner/tie'], 'win'] += 1\n",
" res.loc[row['Loser/tie'], 'loss'] += 1\n",
" weekly_res = res.copy()\n",
" history.append(weekly_res)\n",
" return history"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"new_names = {'Tennessee Oilers': 'Tennessee Titans', 'St. Louis Rams': 'Los Angeles Rams'}\n",
"name_changes = lambda x: new_names.get(x, x)\n",
"\n",
"def get_continuous_game_data(start=1997, end=2016):\n",
" '''\n",
" Compiles game data from start year to end year into a DataFrame\n",
" '''\n",
" data = None\n",
" for year in range(start, end+1):\n",
" df = pd.read_csv('game_data_csv/'+str(year)+\".csv\")\n",
" df = df.drop('Unnamed: 7', axis=1)\n",
" df = df.applymap(name_changes)\n",
" df = df.rename(columns={'Unnamed: 5': 'at'})\n",
" df.insert(loc=7, column='Home', value=None)\n",
" df.insert(loc=8, column='Away', value=None)\n",
" df.insert(loc=0, column='Year', value=year)\n",
" df.insert(loc=0, column='Week_id', value=year)\n",
" df['Week'] = pd.to_numeric(df['Week'], errors='coerce').fillna(0).astype(int)\n",
"\n",
" for index, row in df.iterrows():\n",
" if row['Week'] == 17:\n",
" df = df.loc[:index-1]\n",
" break\n",
" if row['at'] == '@':\n",
" df.loc[index, 'Home'] = row['Loser/tie']\n",
" df.loc[index,'Away'] = row['Winner/tie']\n",
" else:\n",
" df.loc[index, 'Home'] = row['Winner/tie']\n",
" df.loc[index,'Away'] = row['Loser/tie']\n",
" df.loc[index, 'Week_id'] = (row['Year'] - start) * 16 + row['Week']\n",
" df = df.drop('at', axis=1)\n",
" data = df if data is None else data.append(df)\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [],
"source": [
"games = get_continuous_game_data() # preprocess this step so that processing the EWMAs can be faster"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"def average_pts_yds(games):\n",
" pts, yds = 0, 0\n",
" for index, row in games.iterrows():\n",
" pts += row['PtsW'] + row['PtsL']\n",
" yds += row['YdsW'] + row['YdsL']\n",
" pts /= len(games) * 2\n",
" yds /= len(games) * 2\n",
" return pts, yds\n",
"\n",
"def generate_EWMA(games, decay_rate):\n",
" '''\n",
" Generates EWMA using game data \n",
" Returns a list of DataFrames containing weekly EWMA stats of each team\n",
" List is indexed by \"Week_id\", i.e cumulative week number starting from 1997, where zeroth item has every entry initialized to the average of each stat \n",
" '''\n",
" ewma = []\n",
" teams = get_teams(2016)\n",
" avg_pts, avg_yds = average_pts_yds(games)\n",
" initialization = {'yards_forced': avg_yds, 'yards_allowed': avg_yds, 'points_forced': avg_pts, 'points_allowed': avg_pts, 'win': 0.5, 'loss': 0.5}\n",
" curr = pd.DataFrame(index=teams, columns=['yards_forced', 'yards_allowed', 'points_forced', 'points_allowed', 'win', 'loss'], data=initialization)\n",
" week_id = 0\n",
"\n",
" for index, row in games.iterrows():\n",
" if week_id != row['Week_id']:\n",
" assert week_id + 1 == row['Week_id']\n",
" ewma.append(curr.copy())\n",
" week_id = row['Week_id']\n",
" curr.loc[row['Winner/tie'], 'points_forced'] *= decay_rate\n",
" curr.loc[row['Winner/tie'], 'points_forced'] += row['PtsW'] * (1 - decay_rate)\n",
" curr.loc[row['Winner/tie'], 'points_allowed'] *= decay_rate\n",
" curr.loc[row['Winner/tie'], 'points_allowed'] += row['PtsL'] * (1 - decay_rate)\n",
" curr.loc[row['Winner/tie'], 'yards_forced'] *= decay_rate\n",
" curr.loc[row['Winner/tie'], 'yards_forced'] += row['YdsW'] * (1 - decay_rate)\n",
" curr.loc[row['Winner/tie'], 'yards_allowed'] *= decay_rate\n",
" curr.loc[row['Winner/tie'], 'yards_allowed'] += row['YdsL'] * (1 - decay_rate)\n",
" curr.loc[row['Loser/tie'], 'points_forced'] *= decay_rate\n",
" curr.loc[row['Loser/tie'], 'points_forced'] += row['PtsL'] * (1 - decay_rate)\n",
" curr.loc[row['Loser/tie'], 'points_allowed'] *= decay_rate\n",
" curr.loc[row['Loser/tie'], 'points_allowed'] += row['PtsW'] * (1 - decay_rate)\n",
" curr.loc[row['Loser/tie'], 'yards_forced'] *= decay_rate\n",
" curr.loc[row['Loser/tie'], 'yards_forced'] += row['YdsL'] * (1 - decay_rate)\n",
" curr.loc[row['Loser/tie'], 'yards_allowed'] *= decay_rate\n",
" curr.loc[row['Loser/tie'], 'yards_allowed'] += row['YdsW'] * (1 - decay_rate)\n",
"\n",
" if row['PtsW'] == row['PtsL']:\n",
" curr.loc[row['Winner/tie'], 'win'] *= decay_rate\n",
" curr.loc[row['Winner/tie'], 'win'] += 0.5 * (1 - decay_rate)\n",
" curr.loc[row['Winner/tie'], 'loss'] *= decay_rate\n",
" curr.loc[row['Winner/tie'], 'loss'] += 0.5 * (1 - decay_rate)\n",
" curr.loc[row['Loser/tie'], 'win'] *= decay_rate\n",
" curr.loc[row['Loser/tie'], 'win'] += 0.5 * (1 - decay_rate)\n",
" curr.loc[row['Loser/tie'], 'loss'] *= decay_rate\n",
" curr.loc[row['Loser/tie'], 'loss'] += 0.5 * (1 - decay_rate)\n",
" else:\n",
" curr.loc[row['Winner/tie'], 'win'] *= decay_rate\n",
" curr.loc[row['Winner/tie'], 'win'] += 1 * (1 - decay_rate)\n",
" curr.loc[row['Winner/tie'], 'loss'] *= decay_rate\n",
" curr.loc[row['Winner/tie'], 'loss'] += 0 * (1 - decay_rate)\n",
" curr.loc[row['Loser/tie'], 'win'] *= decay_rate\n",
" curr.loc[row['Loser/tie'], 'loss'] += 0 * (1 - decay_rate)\n",
" curr.loc[row['Loser/tie'], 'loss'] *= decay_rate\n",
" curr.loc[row['Loser/tie'], 'loss'] += 1 * (1 - decay_rate)\n",
" ewma.append(curr.copy())\n",
" return ewma"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished EWMA with decay = 0.8\n",
"Finished EWMA with decay = 0.9\n",
"Finished EWMA with decay = 0.92\n",
"Finished EWMA with decay = 0.95\n",
"Finished EWMA with decay = 0.98\n"
]
}
],
"source": [
"# Customizable step to preprocess the EWMAs\n",
"ewma = {}\n",
"for decay in [0.8, 0.9, 0.92, 0.95, 0.98]:\n",
" ewma[decay] = generate_EWMA(games, decay)\n",
" print('Finished EWMA with decay = {}'.format(decay))"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"def get_continuous_X_y(games, stats, yvar='binary'):\n",
" '''\n",
" Takes in 'games' (returned by get_continuous_game_data), 'stats' (e.g. EWMAs), 'yvar' is one of 'binary', 'one_hot', or 'score_diff'\n",
" Returns features X and outcomes y as np.arrays\n",
" Binary y value is 1 if home wins; one_hot y vector is [1, 0] if home wins; score_diff is positive if home wins\n",
" '''\n",
" X, y = [], []\n",
" for _, row in games.iterrows():\n",
" home_features = list(stats[int(row['Week_id']-1)].loc[row['Home']])\n",
" away_features = list(stats[int(row['Week_id']-1)].loc[row['Away']])\n",
" X.append(home_features + away_features)\n",
" if yvar == 'binary':\n",
" y.append(int(row['Winner/tie'] == row['Home']))\n",
" elif yvar == 'one_hot':\n",
" y.append([int(row['Winner/tie'] == row['Home']), int(row['Winner/tie'] != row['Home'])])\n",
" elif yvar == 'score_diff':\n",
" y.append((row['PtsW'] - row['PtsL']) * (1 if row['Winner/tie'] == row['Home'] else -1))\n",
" else:\n",
" raise Exception('Outcome type not supported.')\n",
" X = np.array(X)\n",
" y = np.array(y)\n",
" return X, y"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [],
"source": [
"def get_EWMA_train_test(decay, yvar='binary'):\n",
" '''\n",
" Generates EWMA features with the given decay rate\n",
" REQUIRES a preprocessed ewma dictionary that contains the given decay value\n",
" This method should run pretty fast given preprocessed ewma dict\n",
" Returns X_train, a list of size three (containing 3 disjoint training sets); Y_train, its corresponding labels\n",
" X_test, the test set; Y_test, its corresponding labels\n",
" (Note that the data is shuffled and evenly partitioned into quarters)\n",
" '''\n",
" assert decay in ewma\n",
" X, y = get_continuous_X_y(games, ewma[decay], yvar=yvar)\n",
" #print(X[1:10])\n",
" c = list(zip(X, y))\n",
"\n",
" random.shuffle(c)\n",
"\n",
" X, y = zip(*c)\n",
" #X,y = shuffle(X, y)\n",
"\n",
" num = len(X) // 4\n",
" X_train, Y_train = [None]*3, [None]*3\n",
" for i in range(3):\n",
" X_train[i] = X[i * num: (i+1) * num]\n",
" Y_train[i] = y[i * num: (i+1) * num]\n",
"\n",
" X_test = X[3 * num: ]\n",
" Y_test = y[3 * num: ]\n",
" return X_train, Y_train, X_test, Y_test"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [],
"source": [
"X, y = get_continuous_X_y(games, ewma[0.9], yvar='binary')"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 3.09687031e+02 3.27391331e+02 1.98887291e+01 2.21451291e+01\n",
" 4.00950000e-01 5.99050000e-01 3.16511931e+02 3.28228731e+02\n",
" 2.05714291e+01 2.32660291e+01 4.28050000e-01 5.71950000e-01]\n",
" [ 3.33356931e+02 3.26998731e+02 2.22040291e+01 2.15191291e+01\n",
" 4.90950000e-01 5.09050000e-01 3.41383531e+02 3.28962431e+02\n",
" 2.35750291e+01 2.15854291e+01 5.90950000e-01 4.09050000e-01]\n",
" [ 3.36598145e+02 3.41954145e+02 2.06282546e+01 2.33552546e+01\n",
" 4.45500000e-01 5.54500000e-01 3.27610631e+02 3.37705331e+02\n",
" 2.34367291e+01 2.13225291e+01 5.00950000e-01 4.99050000e-01]\n",
" [ 3.06028331e+02 3.27362631e+02 1.91661291e+01 2.26207291e+01\n",
" 4.09050000e-01 5.90950000e-01 3.42293731e+02 3.38380931e+02\n",
" 2.37971291e+01 2.01270291e+01 5.99050000e-01 4.00950000e-01]\n",
" [ 3.26726931e+02 3.42075231e+02 2.21725291e+01 2.06179291e+01\n",
" 5.99050000e-01 4.00950000e-01 3.31145331e+02 3.17329631e+02\n",
" 2.09485291e+01 2.24997291e+01 5.18050000e-01 4.81950000e-01]\n",
" [ 3.57131831e+02 3.45813131e+02 2.33637291e+01 2.33134291e+01\n",
" 4.18050000e-01 5.81950000e-01 3.13941731e+02 3.06726931e+02\n",
" 2.05080291e+01 2.06204291e+01 5.00950000e-01 4.99050000e-01]\n",
" [ 3.29621145e+02 3.14645145e+02 2.28612546e+01 2.06012546e+01\n",
" 5.45500000e-01 4.54500000e-01 3.12019431e+02 3.47501531e+02\n",
" 1.88294291e+01 2.51430291e+01 3.28050000e-01 6.71950000e-01]\n",
" [ 3.49724531e+02 3.41960431e+02 2.33314291e+01 2.34175291e+01\n",
" 4.81950000e-01 5.18050000e-01 3.34238145e+02 3.27101145e+02\n",
" 2.00612546e+01 2.12052546e+01 4.54500000e-01 5.45500000e-01]\n",
" [ 3.23970831e+02 3.13606731e+02 1.94038291e+01 2.05714291e+01\n",
" 4.99050000e-01 5.00950000e-01 3.19511931e+02 2.98058931e+02\n",
" 2.22282291e+01 1.74555291e+01 5.99050000e-01 4.00950000e-01]\n",
" [ 3.40864358e+02 3.48542838e+02 2.31174162e+01 2.02143262e+01\n",
" 5.39145000e-01 4.60855000e-01 3.21379631e+02 3.33006031e+02\n",
" 2.14401291e+01 2.30561291e+01 5.09050000e-01 4.90950000e-01]]\n"
]
}
],
"source": [
"print(X[60:70])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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