superbowl-scores.py

import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


d = pd.read_html('http://www.espn.com/nfl/superbowl/history/winners', header=1)
scores = []
for s in d[0]['RESULT']:
    parts = s.split(',')
    scores.append([int(parts[0].split(' ')[-1]), int(parts[1].split(' ')[-1])])

scores = np.array(scores)
# sum_scores = scores.sum(axis=1)

dates = []
for s in d[0]['DATE']:
    dates.append(datetime.datetime.strptime(s, '%b. %d, %Y'))

df = pd.DataFrame(data=[[d, w, l] for d, (w, l) in zip(dates, scores)], columns=['date', 'winner', 'looser'])
df['total'] = df['winner'] + df['looser']

df.plot(x='date', grid=True)

In [2]: df.describe()
Out[2]:
          winner     looser      total
count  53.000000  53.000000  53.000000
mean   30.094340  16.132075  46.226415
std     9.859131   7.465402  14.057083
min    13.000000   3.000000  16.000000
25%    23.000000  10.000000  37.000000
50%    30.000000  17.000000  46.000000
75%    35.000000  21.000000  56.000000
max    55.000000  33.000000  75.000000

In [3]: df
Out[3]:
         date  winner  looser  total
0  1967-01-15      35      10     45
1  1968-01-14      33      14     47
2  1969-01-12      16       7     23
3  1970-01-11      23       7     30
4  1971-01-17      16      13     29
5  1972-01-16      24       3     27
6  1973-01-14      14       7     21
7  1974-01-13      24       7     31
8  1975-01-12      16       6     22
9  1976-01-18      21      17     38
10 1977-01-09      32      14     46
11 1978-01-15      27      10     37
12 1979-01-21      35      31     66
13 1980-01-20      31      19     50
14 1981-01-25      27      10     37
15 1982-01-24      26      21     47
16 1983-01-30      27      17     44
17 1984-01-22      38       9     47
18 1985-01-20      38      16     54
19 1986-01-26      46      10     56
20 1987-01-25      39      20     59
21 1988-01-31      42      10     52
22 1989-01-22      20      16     36
23 1990-01-28      55      10     65
24 1991-01-27      20      19     39
25 1992-01-26      37      24     61
26 1993-01-31      52      17     69
27 1994-01-30      30      13     43
28 1995-01-29      49      26     75
29 1996-01-28      27      17     44
30 1997-01-26      35      21     56
31 1998-01-25      31      24     55
32 1999-01-31      34      19     53
33 2000-01-30      23      16     39
34 2001-01-28      34       7     41
35 2002-02-03      20      17     37
36 2003-01-26      48      21     69
37 2004-02-01      32      29     61
38 2005-02-06      24      21     45
39 2006-02-05      21      10     31
40 2007-02-04      29      17     46
41 2008-02-03      17      14     31
42 2009-02-01      27      23     50
43 2010-02-07      31      17     48
44 2011-02-06      31      25     56
45 2012-02-05      21      17     38
46 2013-02-03      34      31     65
47 2014-02-02      43       8     51
48 2015-02-01      28      24     52
49 2016-02-07      24      10     34
50 2017-02-05      34      28     62
51 2018-02-04      41      33     74
52 2019-02-03      13       3     16

In [4]: