2017 Fantasy Football Projections.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2017 Fantasy Football Projections!\n",
    "\n",
    "In this notebook I compute projections for the 2017 NFL season.  The general methodology is to project \"opportunities\" then to project the rate at which different things occur conditional on those opportunities.  \n",
    "\n",
    "I define opportunities as either:\n",
    "\n",
    "1. Pass attempts (QBs only)\n",
    "2. Rushing attempts (RBs and WRs)\n",
    "3. Targets (RBs, WRs, and TEs)\n",
    "\n",
    "Opportunities are the main projection problem, and we use the K-Nearest Neighbors algorithm to do the forecast.  For features, we use last season's stats + the average stats for that player since entering the league.  We also add in combine numbers and draft position, which is helpful for projecting rookies.\n",
    "\n",
    "For each opportunity there is a chance for the following:\n",
    "\n",
    "1. yards\n",
    "2. completed passes (targets only)\n",
    "3. touchdowns\n",
    "4. fumbles\n",
    "\n",
    "So the next round of models estimates the rate of these outcomes after an opportunities.  For this we use regularized linear regression.\n",
    "\n",
    "If you only care about the projections themselves, [click here to skip to the results](#2017-Projections!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(readr)\n",
    "library(dplyr)\n",
    "library(ggplot2)\n",
    "library(tidyr)\n",
    "library(StatRank)\n",
    "library(doMC)\n",
    "library(stringr)\n",
    "library(caret)\n",
    "library(ebbr)\n",
    "library(rvest)\n",
    "library(mice)\n",
    "registerDoMC(cores = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These wonderful data sets come from [Armchair Analysis](armchairanalysis.com), who provide super clean, easy to use, NFL data from 2000-2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  .default = col_character(),\n",
      "  height = col_integer(),\n",
      "  weight = col_integer(),\n",
      "  forty = col_double(),\n",
      "  bench = col_integer(),\n",
      "  vertical = col_double(),\n",
      "  broad = col_integer(),\n",
      "  shuttle = col_double(),\n",
      "  cone = col_double(),\n",
      "  arm = col_double(),\n",
      "  hand = col_double(),\n",
      "  dpos = col_integer(),\n",
      "  start = col_integer(),\n",
      "  jnum = col_integer(),\n",
      "  dcp = col_integer()\n",
      ")\n",
      "See spec(...) for full column specifications.\n",
      "Parsed with column specification:\n",
      "cols(\n",
      "  .default = col_integer(),\n",
      "  player = col_character(),\n",
      "  fp = col_double(),\n",
      "  fp2 = col_double(),\n",
      "  fp3 = col_double(),\n",
      "  team = col_character(),\n",
      "  posd = col_character()\n",
      ")\n",
      "See spec(...) for full column specifications.\n",
      "Parsed with column specification:\n",
      "cols(\n",
      "  gid = col_integer(),\n",
      "  seas = col_integer(),\n",
      "  wk = col_integer(),\n",
      "  day = col_character(),\n",
      "  v = col_character(),\n",
      "  h = col_character(),\n",
      "  stad = col_character(),\n",
      "  temp = col_integer(),\n",
      "  humd = col_integer(),\n",
      "  wspd = col_integer(),\n",
      "  wdir = col_character(),\n",
      "  cond = col_character(),\n",
      "  surf = col_character(),\n",
      "  ou = col_double(),\n",
      "  sprv = col_double(),\n",
      "  ptsv = col_integer(),\n",
      "  ptsh = col_integer()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "## One row per player, with combine, draft position, and name.\n",
    "player <- read_csv('nfl_00-16/PLAYER.csv')\n",
    "\n",
    "## One row per (player, game) with all stats for that game.\n",
    "offense <- read_csv('nfl_00-16/OFFENSE.csv')\n",
    "\n",
    "## One row per game\n",
    "game <- read_csv('nfl_00-16/GAME.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining, by = \"player\"\n",
      "Joining, by = \"gid\"\n"
     ]
    }
   ],
   "source": [
    "off.games <- offense %>%\n",
    "  inner_join(player %>% select(player, pos1)) %>%\n",
    "  filter(pos1 %in% c('WR', 'TE', 'RB', 'QB')) %>%\n",
    "  ## add the week/seas of the game\n",
    "  dplyr::select(-seas) %>%\n",
    "  inner_join(game %>% select(gid, wk, seas))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This step creates a data frame for all possible games a player could have played.  Not really necessary right now, but will come in handy for week-level projections when we need the explicit zeros for missed games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining, by = c(\"player\", \"seas\", \"wk\")\n"
     ]
    }
   ],
   "source": [
    "all.games <- off.games %>%\n",
    "  group_by(player, seas) %>%\n",
    "  do({\n",
    "    data_frame(wk = 1L:17L)\n",
    "  })\n",
    "\n",
    "off.games2 <- all.games %>%\n",
    "  left_join(off.games) %>%\n",
    "  dplyr::select(player, seas, wk, pa, py, tdp, ints, ra, ry, tdr, trg, rec, recy, tdrec, fuml) %>%\n",
    "  mutate(gms = as.integer(!is.na(trg)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "seasons <- off.games2 %>%\n",
    "  filter(gms > 0, tdr < ra + 1) %>%\n",
    "  gather(metric, value, pa:gms) %>%\n",
    "  mutate(value = coalesce(value, 0L)) %>%\n",
    "  group_by(player, seas, metric) %>%\n",
    "  summarise(value = sum(value)) %>%\n",
    "  ungroup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the stats are counts, and we'd like to compute some rates.  The rates will often be noisy, so we use empirical Bayes estimates instead of the raw estimates.  Notice the `group_by(seas, pos1)` which means the empirical Bayes smoothing is performed for each (position, season) pair.  For rate that aren't binomial (e.g. yards per target), we do a really simple smoothing method where we add 1 attempt (either target, pass attempt, or rushing attempt) with the (position, seas) average outcome.\n",
    "\n",
    "For count stats we simply aggregate them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining, by = \"player\"\n"
     ]
    }
   ],
   "source": [
    "seasons.with.rates <- seasons %>%\n",
    "  ## add the rate stats\n",
    "  spread(metric, value) %>%\n",
    "  inner_join(player %>% dplyr::select(player, pos1)) %>%\n",
    "  group_by(seas, pos1) %>%\n",
    "  do({\n",
    "    ## fumbles lost per (ra + trg + pa)\n",
    "    x4 <- add_ebb_estimate(., pmin(fuml, ra + trg + pa), pmax((ra + trg + pa), 1))\n",
    "    if (first(.$pos1) == 'QB') {\n",
    "      x1 <- data_frame(.fitted = rep(0, nrow(.)))\n",
    "      x2 <- data_frame(.fitted = rep(0, nrow(.)))\n",
    "      x5 <- add_ebb_estimate(., pmax(ints, 0), pmax(pa, 1))\n",
    "      x6 <- add_ebb_estimate(., pmax(tdp, 0), pmax(pa, 1))\n",
    "    } else {\n",
    "      x1 <- add_ebb_estimate(., rec, pmax(trg, 1))\n",
    "      x2 <- add_ebb_estimate(., tdrec, pmax(trg, 1))\n",
    "      x5 <- data_frame(.fitted = rep(0, nrow(.)))\n",
    "      x6 <- data_frame(.fitted = rep(0, nrow(.)))\n",
    "    }\n",
    "    if (first(.$pos1) %in% c('RB', 'QB')) {\n",
    "      x3 <- add_ebb_estimate(., tdr, pmax(ra, 1))\n",
    "    } else {\n",
    "      x3 <- data_frame(.fitted = rep(0, nrow(.)))\n",
    "    }\n",
    "    ypc <- with(., sum(ry) / sum(ra))\n",
    "    ypt <- with(., sum(recy) / sum(trg))\n",
    "    ypp <- with(., sum(py) / sum(pa))\n",
    "    new.cols <- data_frame(rec_trg = x1$.fitted,\n",
    "                           tdrec_trg = x2$.fitted,\n",
    "                           tdr_ra = x3$.fitted,\n",
    "                           fuml_ratrg = x4$.fitted,\n",
    "                           ry_ra = (.$ry + ypc) / (.$ra + 1),\n",
    "                           recy_trg = (.$recy + ypt) / (.$trg + 1),\n",
    "                           ints_pa = x5$.fitted,\n",
    "                           tdp_pa = x6$.fitted,\n",
    "                           py_pa = (.$py + ypp) / (.$pa+1))\n",
    "    bind_cols(., new.cols)\n",
    "  }) %>%\n",
    "  ungroup() %>%\n",
    "  dplyr::select(-pos1) %>%\n",
    "  gather(metric, value, -player, -seas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check out the distribution of all the stats we've constructed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining, by = \"player\"\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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I+ie9BBBzkiRN54440+bSjaNlR1moAUUR5zH8CCwgSbqrWOirGfHU6Btdde2wdj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\n8QzyfRn/Nq37SPQrI9n+HJ7eOgNmioM+R6KPbtoWddOnfofCr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ui33HJLx6A0lH7MzncCMHeic9OPJUag5epwigMrm+RHR8J5JZjAUXZW\ndWDUTpm+0VGMb61QYbrFygQDApsN+XUqjyb1L0rLLAd0x7MbKx8IZJwJgVdEmRBRD/gBSImP\nCSO8A42ZabrxxhudYoR9OGaVrOIgZLK8j8LCKmLL/WWGwgxPS7kq4/O891tttZWrEzNedOyY\nhaHkY9PL3ifMBHDS0ak6YcqBpyZMRS+66CJXVxxBQDfCqZNmfvLKXPbtKh18jCdA6oMpX8tt\neru9GKSq1KmIL3iH/Tffy/rrr+8mZPT9s5l0rAJn96i/0pW9beLfTtJltPuVkWz/bvfRnWyX\nKrhS+suW+95hfURe/8lkYh6UpQnzYMIThzXse2AiiT4as2BMkZtAlf67bj7+eMREF98fm9nZ\nv4JydPbZZ7vVMfr7GLA/FE95jNmkV7/GVfu8/DxS2jCWz2iEpZTVr1sK/ULeYmxn3MYkM09m\nCPmtrC/B2gazdywxGOvOOOOMDM+WjHUoZp2UR7pZfs5DRP7DtI7JcSazUN59Ocj/jv3vRTTw\ncXBP3VG2JAOQJib3f6KVSWf9S48GB3coT7xJ4f0N0yhMl1gmZq9FpwB7XOxBY9A64yW6PyUW\nt0oYTMUGegROf8m8Co68uHhQQniFhaAXwnqZfXIerlg4HQWdAB8fHTYMzKZPhHVmQXoZGFjg\nJQYIOr7555/f7Z+RiRqzRrg/RXECyuITh31rDMDYzTLzycZFZqJi4YceeqibyWMGDEETt9zQ\nDijj87z3fhlpcwR9ysLs0RFHHOHK0ek6+fnQ5tSBAYj6YYKl2dm8MrsKe38MMkwWKK2fDg99\n1AnlUu2FGWzVOik7bL0xn1AbgwdPPDFYdNFFnZe92LuxGIZiz0w0/NtpGKl+ZSTbn8kRaKVv\nXDTrdh+tfEb66vdFsf5Vqxn6zilfrJ8s84halCbMgw3kWIhg4sxYS7ngYYSxFAj5RWmq9t9K\nF7uG/Z/ihOMRih19On0WwGraYa09VhxGHAMO7MS8PwbsR+J7DvMoa8MYrtEKKytrWLcU+oW8\nxeQQk415MoPPb9AhZSxhLxl7uBnXGN8YQ/3xrZPySDfLj+xHHejjMEO8+uqrp5CDxBvh9yIa\n+DjwuMj3CfgyQCj3m4IkqnpXli6Z6feXO73XdutRAKbFDpYVhG4pLazAMAtFHphC9RNgesaM\nR2gqmFeHsviY0xEH21kf8sL9OOF9GZ/nvVcZaW/apsx0U/HzaJBXdoWzKpba/nllDusePisd\nwnNKe5XVKcRvz71HgX7uV6pQcyT66Crl6WTcqt+h+pSw/ywqU9U0zHKzuo33tm6NiZRXfVY3\n5RTGdibBWEXpFlRtw26VIwVv1bKW0S/GW1XzKCs3qy+4s4fn6fNSxjfhLCvLSJdfk/yxfFXm\n2NWngXCE8WLfkylIIZXs2ShgFDAKGAWMAkYBo4BRwChgFBizFLA9SGO26a3iRgGjgFHAKGAU\nMAoYBYwCRgGjQEgBU5BCitizUcAoYBQwChgFjAJGAaOAUcAoMGYpYArSmG16q7hRwChgFDAK\nGAWMAkYBo4BRwCgQUsAUpJAi9mwUMAoYBYwCRgGjgFHAKGAUMAqMWQqYgjRmm94qbhQwChgF\njAJGAaOAUcAoYBQwCoQUMAUppIg9GwWMAkYBo4BRwChgFDAKGAWMAmOWAv11qEwXmokzC95+\n++0uYDaU3aYAJyHPPPPM3c5mVPBz7s/7778/Knlbps0owEF2HOQ7iPDrX//anakxiHUb9DpN\nN9107ny/QaznL37xC3fY9iDWbdDrNNNMM3X1nKVu0Y+DV3/60592C73h7SIFOAtpjjnmKM1h\nzCtInKK77777DqygXcoBfRqBU8unmmqq7Morr+zTGhQX+5577skuuOCCgRW0i2vfv29///vf\nZ3PNNVd23HHH9W8lCkp+6aWXZj/60Y/caeYF0exVj1GAQxDXWmutbOedd+6xknWmOBMnTnQT\nSkyaGfQPBVBs99prr2yNNdbon0JPLimHr2611VbZnHPO2XdlH8sFHhoacn0F41gZjHkFCQLN\nPffc2U477VRGK3vfQxRgdeWqq67qoRJ1vihLLrlktv7663cesWHsGgVeeOGF7PXXX+8a/l5A\nvOqqq2bLLrtsLxTFypBIASZcBh023njjpFnhQadDP9Xv8ssv76fiTlFWLAX23nvvKcItoHcp\n8PHHH2d77rlnUgFtD1ISmSySUcAoYBQwChgFjAJGAaOAUcAoMBYoYArSWGhlq6NRwChgFDAK\nGAWMAkYBo4BRwCiQRAFTkJLIZJGMAkYBo4BRwChgFDAKGAWMAkaBsUABU5DGQitbHY0CRgGj\ngFHAKGAUMAoYBYwCRoEkCpiClEQmi2QUMAoYBYwCRgGjgFHAKGAUMAqMBQqYgjQWWtnqaBQw\nChgFjAJGAaOAUcAoYBQwCiRRwBSkJDJZJKOAUcAoYBQwChgFjAJGAaOAUWAsUMAUpLHQylZH\no4BRwChgFDAKGAWMAkYBo4BRIIkCpiAlkckiGQWMAkYBo4BRwChgFDAKGAWMAmOBAqYgjYVW\ntjoaBYwCRgGjgFHAKGAUMAoYBYwCSRT4+6RYFqlnKfDxxx9np512WrbOOutkd9xxR/bBBx9k\nCyywQLbaaqtl//RP/+TK/d///d/ZzTffnL3++uvZn//852yGGWbI1l9//ewrX/lKz9bLCtb/\nFLjllluyaaedNvvFL36Rvfjii9m4ceOylVdeOZtpppmyn/3sZ9ltt92W7bjjjm0+pcYXXXRR\nttBCC2Xf+MY3+p8AVoOuU+AnP/lJ9vzzz2fzzDNPduedd2Z///d/ny299NLZkksu6fK+6667\nXJ+33nrrtcvyb//2b9mVV16Z7bDDDtmnP/3pdnjspoiHFf9f//VfXf8Kn//jP/5jNtdcc2UT\nJkzI/uEf/kFR7GoUKKUA4/QZZ5yRrb766tk111yT/cu//Eu27bbbZozxNn6Xks8iJFIgj8/+\n7u/+Lrv33nuzp556KiMOYzBy5Sc/+ck25vvuuy977rnnsk984hPZEkss4fpZ/3074oDc2ApS\nnzfk//7v/2Z33313ts8++zimXnzxxTMG9eOOOy4bGhpytdtvv/2yhx56KFtwwQWzb37zm05Y\n3WuvvTLSGhgFukUBOtLjjz8+e/jhh7PlllsumzRpUvb9738/++1vf+sG/wceeCB7/PHH29m/\n99572dVXX22Ke5sidlNGgV/+8pfZ9ddfn02cODGbffbZs6mnnjo75phjsgcffNAlZZIIpftP\nf/pTGxVCwNtvv12qHJGgiId5/6tf/Srbfvvtsz/84Q/ZCiuskH31q1/NfvCDH2SXXHIJrw2M\nAskU+J//+R83lsO/KNd//OMfM4RWG7+TSWgREyiQx2dnnXVWdu6557o+bO65586uvfba7PDD\nD29jvPTSS7Mzzzwz++IXv5h9/etfd/e33npr+/0g3tgK0oC0KooPSg8A8zIz//LLL2czzzxz\n9vnPfz7bbbfd3Mw971lBOuCAA7Lf//732Re+8AWCDIwCXaEAs5+nn366m4VaccUVs8022yy7\n6qqrsl133TVbZpllMpQkBEuA2an55psvm2666bpSFkM6mBRgVfzAAw/MFl10UVdBhEoG8uWX\nX97NcP6///f/skcffTRbZZVV3Pv777/frfCkUqOIh1k1Gj9+fLbnnns6YRac9KusbBkYBepQ\ngH6RlSMAXrLxuw4VLU0ZBXw+ox9jYn3//fd3/SZpeb/FFls4OXL66ad3Ez8nn3yyG6N5/6Uv\nfSl77LHH3EQ8K0qDCKYgDUirLrLIIu2azDrrrNk///M/Z2+99ZZbJj3kkEOcSdMPf/hDZ+70\nyiuvuLj/9V//1U5jN0aBblCAVUt/CX7hhRfO3nzzTZcV5nasfP7Hf/yHEwIQXLfccstuFMNw\nDjAFmG2ff/752zWExzBRwtx4mmmmcQM+vIWC9OMf/9iFs6KZCkU8zMQUpiiYkLIC+u6772Yv\nvPCCW8lKxW/xjAI+Beacc872I+O4jd9tcthNByng8xljMhZHb7zxhltdVzZTTTWVG6//8pe/\nuFXNeeedV6+cAoUSNchgCtKAtK6/nwht/jOf+UwGU2NLSgeLYMCyKb9vfetbNsM5IO3e69Vg\nD5IPn/3sZx1fEoZQiwCL+efXvvY1Z6Y06B2uTwu77wwFECIZyAXwGPDRRx+5K4r4Lrvs4kw7\nUZQWW2yx7HOf+5x7l/JXxMPvvPNOtu+++7aFB/pX8n3//fdTUFsco8AUFPB508bvKchjAR2i\ngM9nmHMykclkk78axB4k9gz/7ne/y1iJ9991qBg9jcYUpJ5unvTCvfTSS25zMCnYhIxtPitJ\n7PFgRhO7eIRRQPs+bA+SI4f9dZECzKz7wIb62WabzQXR2a600krZI4884gRKlCM5FvHT2L1R\noIgC7GlDIcEMBKC/Q2HClBiYY445shlnnNHxGaZ2MkV2LxP+inj4sssuc7jZa6eV0tdeey3D\nzt/AKNCUAjZ+N6WgpU+hAHsn6bPYw84kD8Az+zXpV+lP2ceJtYe2ZWChRP930EEHDZugSsmv\nX+KYk4Z+aamScsLIeKmDgdlMhwcc9nPAzChChAMoT2xaBv7617+6q/0ZBbpFATbD33PPPRnm\nnFx5RikScM9+DZQkP1zv7WoUSKHAFVdc4fq4n/70p26jO7zEXiQB5nU4AEEp982R9b7oWsTD\nOIX4z//8T7dSj4kKAi28bH1rEUXtXSoFbPxOpZTFa0IBzIRRhJAdcabEyuXll1+eXXDBBc6Z\nDUoTk0ynnnpqhtfODz/80MVlf5y/et+kDL2Y1laQerFVapQJD05sFGaQHtdyp3z00Uc7xobx\nEQ723ntv54IWN7h4XYLRmQFg+dTAKNAtCmBGh0tl+A1TKJyF4IZegPkSijzewPx9JHpvV6NA\nGQVw1Y1CsvHGGzsFiJXInXbaaVgyHIEw2K+77rrtlZ5hEQoeingY9+E///nPM66YoLBqT/9K\nXsy4lrkRL8jWXhkF3P42G7+NEbpNAeTCI488MjvhhBOcgxCUHsze8aDIuA0cdthhzlso+4Sx\n9GCiadD3DJuC1G3OGyH8q666qjMdYVBGq/cBkxIEUzzi4HkEqLJJ2cdl90aBKhRg1umkk05y\n+z9wDxqzYWamHyEg9q5KXhZ3bFIA0zb2WdK/MbBzFlEM4DP6yapQxMOYpqD8kzf2+5/61Kcc\nes5BMjAKVKEAQid75EKw8TukiD03oUAen2GSjMdZ9iNhXifFSHnRD5599tmur2MyaCyYw5uC\npNYfgCsDdKgcqVq8k3KkMLsaBUaKAjHe47BYVjFfffVV5150pMpi+QwmBcIBnVoyYYQLWw7a\nZKWSQV5Qtk8oVNhjPCxcsbz1zq5GgaYUsPG7KQUtfSoFcPBVBGOprzMFqYgT+uQdDMsSqYFR\noJcogHlR0SwTZyogvO6xxx7tjZ+9VH4rS+9TgJnMogGblZ2dd97ZHX7I4dk+cFbcv//7v/tB\nw+6XWmopZyJXxMPDEtiDUcAoYBQwCgwMBUyq7vOmREC48cYb+7wWVvxBpACHzhXBGWec4V7L\n+1dRXHtnFIhRYNlll8345QHOau666y63PyiMc/7554dB9mwUMAoYBYwCRgFHAVOQjBGMAkaB\nUaGAKUajQvYxlymTSAZGAaOAUcAoYBSoQoG/+UGtksriGgWMAkYBo8CIUeC9997LrrnmGncu\nBZtoU4A9NpxTgRvqEP785z+7DeG4cuXcIAOjgFHAKGAUMAoYBf5GAVOQ/kYLuzMKGAWMAj1H\nAc742Wyzzdx5Udddd13G3hmda1ZUWDwOXXjhhc4rkR+P86jWWGON7I477nBnp3E8wIknnuhH\nsXujgFHAKGAUMAqMaQqYiV2r+T/++OOMGVWD/qHAX/7yl/4pbM2ScraL8WVN4o1SMg7E7SSw\ncnTJJZdkp512mjsThb7qu9/9bnbttde6aywvDoNG4YmtDHFoNKtK4Fh//fVdcg42PfDAA7O1\n117bneMTw+mHcYig8aVPkd6/p80G/UwmxgTjy97nRb+E/X6gMqv0xnN+i/b+PWNoKox5BQn3\nmQghBx98cCrNLF6PUIDTnQcV8Jz1/PPPu9+g1nFQ67X88st3rGrPPPNMhqMBDnwG8FbJmVFX\nX311roI0ceJEdxgqXtvwEOjD7373O3fA34orrtgO1sG9nJDOQadFgAvYW265JWMVyqC/KLDJ\nJpv0V4ErlHbqqafOWGk16D8KrL766v1X6FaJOVeNCTGTHfuv+RhTU2DMK0jMqMLkX//61x29\nhoaGMmbbYH6Up7oADtKHZ2mk4mNmAk0XHJSlDlA38DStBzTJO3wxpVzUgzo0qQczTWzqlzvz\njz76KPvDH/6Qkn1fxoFm8I5/bkteRaANbc1m9Lr8JvrWbSPKK15rgoPy13XeoG8GHmmCAzrX\nTf/b3/42uucnr+3Kwn/1q185F9V+PDp38qHNY7Tm9POvfOUr2bvvvusnc/ec5YNJnQ8PPPCA\nq+/ss8/uB2coU9/+9reHhc0222yuPwnPBKKPAOrwXyfS1sl7tPKlrOTdhFZV68uqYqdXNylD\nr4AOSJfL9zr9EWN2qkMPvj31mRqTymih+LFvNpaW9oJHUstE/wek9l1Vxw14FrqmyhPqj4tk\nmJ///OeuH4vVv9fDoMeHH36YzT///O2iVm2zqjStw3dV+aLOtwMv8R2k9ml8a9S9ilxZ5fsE\nN/H51kJ+5V1sbGw3oncz5hUkaLHEEktkOiMDZvrggw/ciexf+MIXPFJVuwUHQkRqZxhiZyM2\nCgBl4HT4OgCDMHA0rQcf5bTTTlunCC4NZ5HwITSpB+eVcEq9BsC33347O/roo2uXqdcTQnNm\n+bfddtvSomqgKxqIypBUHbxDfHU61RgOOtjUAT6Wnu+3iYJEPZqU4bHHHssef/zxsGi1n3/9\n619nn/vc54al/+xnP+uECr6r2LeNcpQKfEfnnXdexupCmA46hGcA0Z9ttNFGbhXLz6PqIOyn\nhdeBOn2l8iVt6uCsvBkoybsOv5GO9HXyJX/KXSdflZm6VqHXWDgKgomBhRde2DUvYydjaJXx\nEyUy/AbEK+GVsZUxCbPF8PsM4+qZfYPET1V4mByhTw8nI4QvvDLWwx9lB30qHeWnHtQ5hZcY\nI3D48sUvflEoCq8pMgyr3f0MTBhddNFF7SrQZow/X/7yl9thRTeMN/TjqTStw3dV+ULfDquy\nqQoMk2mMS6Eykld3Jvjgp+mmmy4vyhThVb5P+mfi862FtCVfrDBSwBSkFCpZHKPACFOAAYvO\niU6nDOjQmLli8E2dzQxxgoP8UgfvMD2dMKt6KLGpnWSIgwGVtKmdcpgeW3B+CPVNcMQUgzCv\nvGfKX1VQz8NFOPgYRH3QM7RuAq+88kqGUIlJ4DbbbDMFKoRLVpd84HBfBMJpppnGD86qCnN+\n4qoDuJ9WQh7lqUr3qsKJn6+EPyZs6kz8MHiHNPTx590zuCNcwOOaLMqL64fTj9heCZ8idt+v\nFGCsYhKK66KLLlpqZcFkxEsvveSc3MwxxxzOxNivO7jog3yYc845sxlmmMEPsvsxSIExryAx\n48LmTgYsgGdm2xZccMFslllmqc0SaLCsIlUdtJUh5QBYwm2CAzyqm3BXuVIPoCkOhOcm9aAM\nDPByzoCQoNlj3g0ioCSlKBvQhQ35q622Wm0FibxQrlLyi9FaM5BNcDCjzq9uGTQj3xQHfFq3\nDKJDjEZ1wpg9njRp0rCkzOKivNRVAkHGStehhx6abbDBBtkOO+wwDH8vPKDwP/3009nSSy9d\nu9/ohXpYGUaXAr/85S+dIK09fKNbGsu9KQUwyWMy52tf+5ozPWb1+6ijjsoWW2yxKGpkBBzS\nMIGz1FJLZXgBXW655dpmxrw/5JBD3ESkP7m4/fbbN1KQ6LtQsFL3ukQLb4GjToExryAhDDED\nqKX1n/3sZ9kuu+ySjRs3LuNjrAudMrH7/Oc/X2uGknJ30sRO9KlDj26Y2DGDK4G4TpkGKQ0b\n8W+44QY3q4a5qMHgUGDmmWd2DhFY7dAA/uMf/3iKfUlVavzQQw9lRx55ZLbbbrtla621VpWk\nIxZ39913z84991xn/oewYmAUqEoBTNpQsLn+6Ec/cvdVcVj83qLAsccem6255pqu70J2wyPn\nKaec4s6Ii03AohAhK+D1EwsL9p5wZAKOIdhz+Ytf/MLJSZjJhaZYdWvOqjtmyDPNNFP21ltv\n1Z5sq5u/pescBert/u9c/j2LKXa4Ys8W1go2pinw/vvvu/qjlBsMFgVWWGEFV6Err7zS7Zd5\n5513srvuussN8qop71CaUgCTNOz+x48f7yaBXn755Uw/7Mh7BW666SZXlNtuu61XimTl6DMK\n3H777c7RCFYUnAdm0N8UoO/66U9/6iZ1pAzhRAbvmz/5yU+ilWOlnL28KEcASss888yT3Xff\nfe4ZBYZV+k4pRyBlshJAGeMIBYP+pcCYX0EKm057MDDxMDAK9AMFmCEDsMk2GCwKYEbHas/h\nhx+eoQix92TChAnOsYxqykoLZiQpbu/vvvtuZ6qKgCAhQXjYj9QLLncx55Wy/+qrr6p4djUK\n5FIAUylWimQKjlKEi3wAs9d77723/S4PiTZ25733w2UCr72X/ru8e/DLy1lenDCcPWeqU/gu\nfPbLFL6LPct8PnUrAPj5VS1P0TYB6KdyxMroh+GwBvDN1lBskNmoQ6z/w7TOj6/06l+wGGJ/\nHnsr2YuE6fLmm2+eLbPMMi4v/+/88893k0kKw/KIlf1wYunFF19UFMd3OkahHejdQE/aOMTh\nRRl2K1qx1UB7UYdFiDxoKwIWRSkgvMgT4d6svPTUgUUFKa558RSuPFLrTTrqnhpf30KMtuSt\n9ypP3tUUpBzKqAFzXluwUaBnKGAKUs80RVcKwgDL2UMIJnhHCvc5Pfroo9F8mS0N32266aYZ\nv14GhBYBq6MMcnX3hAmPXQebAphbI9zKFBzhjlVVJhiWXXZZJ6giYBV5zeL7UvoyatXxJtar\nXuxwFhL2KbH6SwhOXW1hXKIdirYJsLKTkjflQdmhPcO9lyg40DYEZDj2KodeBnl+8803XXSu\nCN0c84J5OhNIHJp9/PHHZ4svvvgwlKy033///e0weIX+2J9Mhy8mtfaM8g5+Io3/vp04uEmJ\n4ydB6ZHi44cX3VeVaWnvKpCqgPk4q9a7anwUoTCNKUh+C9S8r8p8NbOxZEaBxhTQLI8UpcYI\nDUFPUiBVeOvJwlcoFAKGAKEWJYm9WAZGgVQKMH5jjoo3soUWWsgpSHhtLFKQUnFbvNGhAJMk\nMSGfto559ERpRvkK0/Ask7vDDjvMrUygXAM4e2CChj1LoYLE/ifiC37zm99kRxxxxDBvlE8+\n+aTDhyMITKEx4SvyVknZ5XRHeIuuKCGsyFHfVHfuOHFCUVCdi/DzDjmCNCi2sqgqS0OZKI/2\nyZbFRymlHYpoE+KA3qnu06kv8eEZta3wofilKuW2giSqBVctZQbB9mgU6DkKaIZE154roBXI\nKFCBAu+9956LjaMclCVmjk1BqkBAi5rBQwhCrAzMNddcjiLsX1l55ZWNOn1KAfYKoVAgvPsK\nEQpGTPHF3IuzfELTc+LrXMeYq3wUo3DlHZKFK1HwF3n4zqK0Hxgveyjnzz77rCtznqKBIB/i\nKGoe5VUlDcoA+ShtEX7eSXngmpqG8lSNT16p+Ilbpc6S32Np9A6cZWBOGnIoVIWIOSgseAAp\nQGd7zz33ZNdff70bhMuqyMwKM0nXXHNNhsvZboAUozpL3N0oj+E0CjShgL4THfqpvQdNcFra\nsUUBrUKiWKMkAczmG/QvBaaffnq3QuE7pEHpRVYL9xmpligqfnzCcejw1a9+1UXZd999204V\nlAazuDx8ipN3ZTIHwMU3h8hSNlYyDfqTAqYgFbSbBM+CKPZqDFEAt++4RcZLzWuvvZZtvfXW\n2VNPPZVLgbfffjvbaqutsjvuuCN74403nOexSy65JDd+3RdSjIxf61LQ0vUSBfBKBejsGm2o\n7qUyWll6mwLaY4KgOuuss7rC+nvberv0VroYBVjtWWmllTLGUMzAcFSAd8JVVlmlbXqF1zj2\nEQnWW289t28IpYhVFM64ZLzkzECA/Z1XXHGFU54ZP3n/+uuvu/PhhKPKVQ4sUMB0jqYpSFUo\n2FtxB8bEDk0d+0+E0nnnnTebf/7520uFdUnOBxhuCKyLy9L1PwWqnsFwzjnnuGX2Y445xlUe\nZYrDOem02VjaCcCOl44fMAWpExQ1HGUUwMwltOsnDXwYCy/DR9/tp9UsLKfeA6wg5eEV7/Me\nc4oqQBo/3yppKTOQR4sUXHl1KkqrNOSv+6L4ekc5xxLIQyOCNGZW7KcwQbX/OQBvnXj0XGON\nNZxshpzHuZUCnCgwwbLqqqu6IPYUbbjhhtlOO+3k9qOguBx00EHt/TtMeLI3jclOzOCQ93DS\nEO4/Ev6yq1a7MfmTWXCT8zTL8rP33aXAQChI2ILuv//+7mDXRRZZJOMMDTrEiy++uJGSpJn5\n7jaBYe8HCugMBvhMghhnMDCDxexU6GKUTprTtHHNLFh00UXd7BfuQTsFvlJk/NopqhqePAog\nmH/00UcZhz+HgBAeCw/jhc8S3unHAb4dPGVpEgGFKQ+vlATe67sM8ec9SznKw52XjnCVmf0Q\nTKRVBehYJ18pZnzrVdJTRimTVcvaj/GZJMW1t/YfjWvtZ6Ofhn7aY9GP9RrrZWbD/amnnuoc\nG7B/JXQ8gNOEEFB+8NzJ3iP2MfnAsQlMYOLoCPN5HOFU7Ud8fHjNA8BD3wDI3NM92F9fUWAg\nFCQ6QkyYWHrlA0BoZJaeE405JKwumMBZl3KDl04zQ75tctEZDJzQTQdOZ3vCCSe4Q+MYrLfc\ncsuoy2LMPyT8QD06c54lNOZRVB7seI/gWhY/Dw95IWzWFR5UdnDUHWDAgeBZtw4SWpvioPx1\nyyA65NG538PhD7wVha5+UWLwYBSGp9QXHkZ4l1cmvA9hFsX+AQBBIw8vExf007yvynfwKkpG\nHu6isrMygUCFEldnwgNTnDr5wpcIYeQZ22CeV2YESQlseXEGKVyWH+IJFKSXXnrJrUb6ffgg\n1Xks1SV0mFBWd1aHQuXIT8P3ESpb/vvUeylIeGfTWMCBsQb9SYGBUJBuvvlmpxDpA6BzRFmq\nM3D5zVhXSPJx2P1gUAABEL7SwKta5Z3BICFm7733zthsjqvZW2+91Q3SHOwZKiLrrrvusJno\nBRdcMJtvvvmcMKS8Yld/fwYCmzroWNyyMH81qixu3vsqs9p5OKhHEyB9Uxy+4lmlLMzUa2Cs\nks7i/h8FaDcUfWZgpUCgBBkYBZpQgL1IAN7tTEFqQklLW0QB+iomepATMOdjktQUpCKK9fa7\ngVCQ1Olddtll2QsvvOD8nrOkqs2ZfhOwPKuNdITjJ50ZSLyNAb5gBbNjqlcHEJIQFjWDVRWH\nlDMEtTomHORHGZglVd2qlkE4mN1tggP6Uo4m9aAsCPAqh1ZY6tSpTpqqZzBQX9qO5f0NNtjA\nZYmi9L3vfc+Z3oU2zihI/oolqyDMyGMCUAS+okWasvh5uMib/Hx8eXFj4fArdWamrorrTh8X\nOMi/SfqmZSA9kHqeg19+7rVqGIbbcxoFpPAzA8sKCfyQenp6Wg4WayxSYMYZZ3TVZmWffSkG\nRoGmFEAuYtz05Un4izFYYUz0sAdJz2Ge4OCX9z4WnzBkCyaSUgD8QOqknyb4OHw3VX4lDbSo\nEp8ypdabuOSRGl91DtsHPIzxqiPPRdD3ChJmAzAKyhGHSC211FLOtG6HHXbILrroooyldR/Y\nvOl7s8FXPZ2nmM2fRddspp++yn1dhcDPgwZuCqpbEzxNccj8qUkZwKFy0E6pTN4kT6VldZL8\n4beUMxh0oBmnuAvmmWced5aCzkpQONdDDjnEf3RuxDHrK1PQw9n1svjDMvEeUDzp2MMVMi9K\n4S0KKx0PK2p5Zz4UImi9BAeKaF0lT2ZPtE8THHTydc0toF/qIFFGj7H4XgMgChLKEUqSKUhj\nkRM6W2dcRAOxvrezORm2sUIB+nnGOpQgAfIaY4/CkD9xHsbeqdi4yJhZxcwX/Iz5jE+pZoYo\nRigMMmFWWfOuyL2MpZQ5VR6gj2bsZ/xOASxdmBAVnVLSMDakxpcyBc1liaA8NBGr56Jr3ytI\nErxp/FNOOcXVlf1HzMhfddVV2QEHHDCs/pg3+UoHfvRxDSmBVgI4icCp8GFIEh5gYhis7ow8\ngjiMzUeQyqRhsWAE8FSxVQ9xwPgwm8wXw/cpz3xwMGqTekiA10eOMF13pSGlzGEc/wwGHIEA\nRWcwSDFHydFHzd4Kyq13YR51nmljgX+vMLsaBfqJAlpB0jeDBzJmMg2MAk0oYApSE+pZ2lQK\nIOfojCXSMPn+xBNPOMVceypTcVm80adA3ytIaK0I3uPHj29TE80eE6aYe0XZIisyAzLxZVKj\nK+/Ruv1npUm5CmddBUnpUALqlgHFBjx10/v1bIKDMjStB2URTbmnPDyPFKBk6gwGVh3JP3YG\nA0otLkaxc4cnTzvtNOekgfisaDIzHnq8a1IHXyny75vgtLRGgdGigBQkvhOASSaz4R+t1hic\nfCW06hDiwamZ1aSXKSDTTraBmILUyy0VL9vfxYP7KxR/8/IyppJz5sFMM82kx1pXf6WpFgJL\nNFAU4AwGZog4g2Httdd2SlJ4BgMHzQn22WefbNppp3WrmRMmTHArTieddNIwEz3FrXv1lSL/\nvi4+S9ebFGCAveaaa5zrYswfyqAsPgdp4oL+nnvu6akVmpiChBmKvze0rO72vjMUwDrj+eef\ndwdpPvvss6VIU+MTD5N4VtNDKOPbMH7qMwoSE2qmIKVSrHfj0RfQb11//fXO6UZZSVP5Ejzg\n9LdglOEue68JefYmGfQfBfp+BQmSb7TRRtnRRx/t9h9xMjLuvTnzgA3yTcAEzibUG7y0zGZX\nOYOB1U3OWMDMkf1omAt1GnweRZA0GDwKcNI7q5XsZ+OMIJ5PP/10t7oSq21ZfJR4VjZnn312\nZzeOG3rwNV3ZpIwoXmeffXZtD6L+HiTqxjcHYGbH92QwMhRAqGRCCO+d7Ou97rrrsuWWWy7b\nc889owWoEh/+AB9HcPj7KMr4NppxYqD2IpiClEiwHo2GVdA222zjVmNQes8777zsqKOOynW8\nUYUvb7/9dtcPclBszMFXHZKYglSHar2TZiAUpOWXXz5j5pFDPDGLY5McHbn2itQlty981sVh\n6QaPAv6gnlI7nAb4jh1S0qTG8XnUv09Nb/F6mwLMqHNkAQrNN77xDecIA8H12muvdQJsWPqy\n+GyOPeuss9zBidtvv71LzqwpQgErSnWdU7BXERyY9S6xxBLZtttuGxYt6VkrSNqDJKcjKEgy\nV0lCZJEaUQAFhpVK+AyewMxxs802y1ZffXWnWIfIU+Kj/J544onO02yYvoxvw38gGosAAEAA\nSURBVPh1nhGoX3/99TpJLU2PUODYY4/N1lxzzWy33XZzK4KsRLL3nNX1mMl9Cl9SNZx3nH/+\n+clOBlLJIQUJ/jboPwoMhIkdZN9www3dsiuD/G233ZattdZajVvDBM7GJDQEXaaAz6P+fZez\nNfQjRIFnnnnG7WdDOQLYy7bKKqtkeOOMQVl8HIvAJ5iJClZYYQW3QsMRCXXhxRdfbHuVzCtb\nCu7XXnvNRZOCpBUkufdPwWFxmlPgsccecys8UpgxV8cLZ17bpsSfOHGim8A87rjjpihgGd9O\nkaBGAPtC8X5qXhFrEK8HkuD4iv4L2U7K0Le//W23qo7FUAxS+BLLiyOPPDLbYost3OS6cMfw\nVQ3TpI6Z2FWlXG/EH4gVJJESRwAaWBXW5GomS02oZ2lHggK+UmT8OhIUH9k8MHHSBnPljKDH\nSpCcsCica1l84iAA+B4g4SFwheZHhD/33HMkaQP7Rgj3j0Pg5VtvvdWOw76V8H37ZeQGvmXl\nnzSTJk1y5aOMPMtrJStLMZyUG+BdVcEG8xvSx/BGijksSN8atKiaL4hU32FIEx6UL2WvUm7R\nOCELFwU+Cg9U5VkrfCGelPj77befG59jTjfK+FZOi8gXXgu902JCh7tkHZRNuwCYNytMJs4/\n/vGPMxzthAAvKG74LnwW3+H1NnWvMm3Arwq/EL9KmeCr1ONFwA2geKSUCdzwXWp5iAvQZ+Tt\nm4R+oqWLXPCnfeY+X+LCmbaHL2Mmwil8ySoUFh54Pma1Pg9YBfIPQueesoftD50UhrXJVFNN\n5fo1hfn41QfF3vnxdC++Jl1qGuL6ZRKuvKvarUrfBh2ITz4poHipdQBnlToIfyyNaJhSzoFS\nkFIqXCWOOpAqaSyuUWAkKeB/7P79SJbB8uoeBRAKQpNO9uIwIDFAa4VFJSiLj2DIWRWYpHBo\nMYLRTTfd5JIjTPqAYLPlllv6QRmHHY9rne0RzsJL6MW8GQc5CCZV3fojLCEIs1oh/Ag/AMqb\nwoYVaPJDE1fgRXhjeflheYKfHyfvvkm+KEdVFCS8a/pKRl6ZCJdQHvIdz+wxCyE1ftHkZ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MIKK2S45cbTEmcVsRJz1113uQMKhZh3mJjhPr4sPnuAcDWL23CcNLACctFFF7kD+Oq4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0NSrPP/+8YUGGBkzO12nqCpcqt+eee1oGCXNG7MqV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\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dzDWE1cxizO72JxxJjBN0sY9/QjcQRkM8v5D4aYhZfUgUUZTB/1569VKLQfZ2kX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qsxanX2UiUF0EcPpINX700Uf7hCg4S0Rs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\nqZoBS70IiIAIiIAIiIAIiIAIiEDpEPg/thy3Gym2u/QAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seasons.with.rates %>% \n",
    "  inner_join(player %>% dplyr::select(player, pos1)) %>%\n",
    "  filter(pos1 == 'RB') %>%\n",
    "  ggplot(aes(x = value)) +\n",
    "  geom_density() +\n",
    "  facet_wrap('metric', scales = 'free') +\n",
    "  theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get a list of 2017 players to project\n",
    "\n",
    "First we take any player who played in 2016.  For rookies we scrape the 2017 combine/draft information from Pro Football Reference and collect a list of players, their combine stats, and their draft positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all.metrics <- with(seasons.with.rates, unique(metric))\n",
    "season.2017 <- seasons.with.rates %>%\n",
    "  filter(seas == 2016, metric == 'gms', value >= 1) %>%\n",
    "  dplyr::select(player) %>%\n",
    "  mutate(seas = 2017) %>%\n",
    "  group_by(player, seas) %>% do({\n",
    "    data_frame(metric = all.metrics, value = NA)\n",
    "  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "header <- c('player', 'pos', 'college', 'stats', 'height', 'weight', 'forty', 'vertical', 'bench', 'broad', 'threecone', 'shuttle', 'drafted')\n",
    "\n",
    "url.extract <- function(tds) {\n",
    "  results <- c()\n",
    "  for(td in tds) {\n",
    "    children <- html_children(td)\n",
    "    if (length(children) == 0) {\n",
    "      results <- c(results, NA)\n",
    "    } else{\n",
    "      results <- c(results, (html_attr(html_children(td), 'href')))\n",
    "    }\n",
    "  }\n",
    "  results\n",
    "}\n",
    "\n",
    "combine.table <- data_frame(year = 2017) %>%\n",
    "  group_by(year) %>% do({\n",
    "    url <- paste('http://www.pro-football-reference.com/draft/', .$year, '-combine.htm', sep ='')\n",
    "    html.table <- read_html(url) %>%\n",
    "      html_nodes('table') %>%\n",
    "      first\n",
    "    urls <- html.table %>%\n",
    "      html_nodes('tr td:nth-child(4)') %>%\n",
    "      url.extract\n",
    "    my.table <- html_table(html.table)\n",
    "    colnames(my.table) <- header\n",
    "    my.table <- my.table %>%\n",
    "      filter(pos != 'Pos') %>%\n",
    "      mutate(url = urls)\n",
    "    my.table\n",
    "  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "draft.header <- c('round', 'pick', 'team', 'player', 'pos', 'age', 'to', 'ap1', 'pb', 'st', 'carav', 'drav', 'games', 'pass.cmp', 'pass.att', 'pass.yds', 'pass.tds', 'pass.ints', 'rush.att', 'rush.yds', 'rush.tds', 'receptions', 'rec.yds', 'rec.tds', 'tackles', 'ints', 'sacks', 'college', 'stats')\n",
    "draft.table <- data_frame(year = 2017) %>%\n",
    "  group_by(year) %>% do({\n",
    "    url <- paste('http://www.pro-football-reference.com/years/', .$year, '/draft.htm', sep ='')\n",
    "    doc <- read_html(url)\n",
    "    html.table <- doc %>%\n",
    "      html_nodes('table') %>%\n",
    "      first\n",
    "    urls <- html.table %>%\n",
    "      html_nodes('tr td:nth-child(29)') %>%\n",
    "      url.extract\n",
    "    my.table <- html_table(html.table)\n",
    "    colnames(my.table) <- draft.header\n",
    "    my.table <- my.table %>%\n",
    "      filter(pos != 'Pos') %>%\n",
    "      mutate(url = urls)\n",
    "    my.table\n",
    "  }) %>%\n",
    "  ungroup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining, by = c(\"player\", \"pos\")\n"
     ]
    }
   ],
   "source": [
    "draft.table2 <- draft.table %>%\n",
    "  select(player, dpos = pick, pos, age)\n",
    "combine.table2 <- combine.table %>%\n",
    "  full_join(draft.table2) %>%\n",
    "  filter(pos %in% c('QB', 'RB', 'WR', 'TE'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "rookies.with.stats <- combine.table2 %>%\n",
    "  dplyr::select(year, player, pos1 = pos, height, weight, forty, vertical, bench, threecone, shuttle, broad, dpos) %>%\n",
    "  mutate(height = ifelse(is.na(height), 'NA-NA', height)) %>%\n",
    "  separate(height, c('feet', 'inches'), sep = '-', convert = TRUE) %>%\n",
    "  mutate(height = feet * 12 + inches) %>%\n",
    "  select(-feet, -inches) %>%\n",
    "  gather(metric, value, height, weight, forty, vertical, bench, threecone, shuttle, broad, dpos) %>%\n",
    "  filter(!is.na(value), value != '') %>%\n",
    "  mutate(value = as.numeric(value)) %>%\n",
    "  spread(metric, value) %>%\n",
    "  mutate(start = 2017) %>%\n",
    "  ungroup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "rookies.2017 <- rookies.with.stats %>%\n",
    "  dplyr::select(player) %>%\n",
    "    mutate(seas = 2017) %>%\n",
    "    group_by(player, seas) %>% do({\n",
    "      data_frame(metric = all.metrics, value = NA)\n",
    "  })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a bit of trickery to created \"last season\" and \"average season so far\" features for all the metrics for all the players.  For players with only one season of experience, these will be the same value, but that's ok because regularization.  In the future we could add variance or min/max features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "season.metrics <- seasons.with.rates %>%\n",
    "  bind_rows(season.2017) %>%\n",
    "  bind_rows(rookies.2017) %>%\n",
    "  group_by(player, metric) %>%\n",
    "  arrange(seas) %>%\n",
    "  mutate(career.year = 1:n(),\n",
    "         lst = coalesce(lag(value), 0),\n",
    "         avg = coalesce(lag(cumsum(value) / cumsum(rep(1, length(value)))), 0)) %>%\n",
    "  ungroup()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Impute missing player metrics\n",
    "\n",
    "We don't have combine information for all players, so we'll impute that information using MICE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " iter imp variable\n",
      "  1   1  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  1   2  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  1   3  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  1   4  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  1   5  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  2   1  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  2   2  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  2   3  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  2   4  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  2   5  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  3   1  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  3   2  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  3   3  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  3   4  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  3   5  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  4   1  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  4   2  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  4   3  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  4   4  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  4   5  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  5   1  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  5   2  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  5   3  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  5   4  bench  broad  dpos  forty  height  shuttle  vertical  weight\n",
      "  5   5  bench  broad  dpos  forty  height  shuttle  vertical  weight\n"
     ]
    }
   ],
   "source": [
    "player.metrics <-\n",
    "  bind_rows(player %>% select(player, height, weight, forty, bench, broad, shuttle, vertical, dpos, start, pos1),\n",
    "            rookies.with.stats %>% dplyr::select(-year, -threecone) %>% ungroup()) %>%\n",
    "  gather(metric, value, -player, -pos1) %>%\n",
    "  mutate(value = ifelse(value == 0.0, NA, value)) %>%\n",
    "  spread(metric, value) %>%\n",
    "  mutate(undrafted = ifelse(is.na(dpos), 1, 0))\n",
    "\n",
    "to.impute <- player.metrics %>% \n",
    "  dplyr::select(-player, -pos1)\n",
    "imputed <- mice(to.impute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    player              pos1               bench           broad      \n",
       " Length:9461        Length:9461        Min.   : 2.00   Min.   : 85.0  \n",
       " Class :character   Class :character   1st Qu.:15.00   1st Qu.:109.0  \n",
       " Mode  :character   Mode  :character   Median :20.00   Median :115.0  \n",
       "                                       Mean   :20.09   Mean   :114.5  \n",
       "                                       3rd Qu.:24.00   3rd Qu.:121.0  \n",
       "                                       Max.   :49.00   Max.   :147.0  \n",
       "      dpos           forty           height        shuttle          start     \n",
       " Min.   :  1.0   Min.   :4.160   Min.   :65.0   Min.   :3.700   Min.   :1982  \n",
       " 1st Qu.: 59.0   1st Qu.:4.530   1st Qu.:72.0   1st Qu.:4.210   1st Qu.:2002  \n",
       " Median :120.0   Median :4.690   Median :74.0   Median :4.350   Median :2008  \n",
       " Mean   :123.3   Mean   :4.765   Mean   :73.8   Mean   :4.392   Mean   :2007  \n",
       " 3rd Qu.:185.0   3rd Qu.:4.970   3rd Qu.:76.0   3rd Qu.:4.550   3rd Qu.:2013  \n",
       " Max.   :329.0   Max.   :6.030   Max.   :82.0   Max.   :7.910   Max.   :2017  \n",
       "    vertical        weight        undrafted     \n",
       " Min.   :19.0   Min.   :150.0   Min.   :0.0000  \n",
       " 1st Qu.:30.5   1st Qu.:204.0   1st Qu.:0.0000  \n",
       " Median :33.5   Median :235.0   Median :0.0000  \n",
       " Mean   :33.1   Mean   :242.3   Mean   :0.4557  \n",
       " 3rd Qu.:36.0   3rd Qu.:280.0   3rd Qu.:1.0000  \n",
       " Max.   :46.0   Max.   :394.0   Max.   :1.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "player.metrics2 <- bind_cols(player.metrics %>% dplyr::select(player, pos1), \n",
    "                             complete(imputed))\n",
    "player.metrics2 %>% summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features is a data frame that contains one row per (player, season), and includes: 1) last season 2) average season and 3) all player-level numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining, by = \"player\"\n"
     ]
    }
   ],
   "source": [
    "features <- season.metrics %>%\n",
    "  select(player, seas, metric, lst, avg) %>%\n",
    "  gather(which.lag, lag.value, lst, avg) %>%\n",
    "  unite(metric, metric, which.lag) %>%\n",
    "  spread(metric, lag.value) %>%\n",
    "  inner_join(player.metrics2) %>%\n",
    "  mutate(career.seas = seas - start) %>%\n",
    "  dplyr::select(-start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    player               seas         fuml_avg         fuml_lst      \n",
       " Length:10127       Min.   :2000   Min.   :0.0000   Min.   : 0.0000  \n",
       " Class :character   1st Qu.:2004   1st Qu.:0.0000   1st Qu.: 0.0000  \n",
       " Mode  :character   Median :2009   Median :0.0000   Median : 0.0000  \n",
       "                    Mean   :2009   Mean   :0.5253   Mean   : 0.4994  \n",
       "                    3rd Qu.:2013   3rd Qu.:0.6667   3rd Qu.: 1.0000  \n",
       "                    Max.   :2017   Max.   :9.0000   Max.   :11.0000  \n",
       " fuml_ratrg_avg     fuml_ratrg_lst        gms_avg          gms_lst      \n",
       " Min.   :0.000000   Min.   :0.000000   Min.   : 0.000   Min.   : 0.000  \n",
       " 1st Qu.:0.000000   1st Qu.:0.000000   1st Qu.: 0.000   1st Qu.: 0.000  \n",
       " Median :0.005940   Median :0.005559   Median : 8.500   Median : 8.000  \n",
       " Mean   :0.005002   Mean   :0.004818   Mean   : 7.631   Mean   : 7.758  \n",
       " 3rd Qu.:0.007162   3rd Qu.:0.007139   3rd Qu.:13.000   3rd Qu.:14.000  \n",
       " Max.   :0.047356   Max.   :0.060671   Max.   :16.000   Max.   :16.000  \n",
       "    ints_avg          ints_lst        ints_pa_avg        ints_pa_lst      \n",
       " Min.   : 0.0000   Min.   : 0.0000   Min.   :0.000000   Min.   :0.000000  \n",
       " 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.:0.000000   1st Qu.:0.000000  \n",
       " Median : 0.0000   Median : 0.0000   Median :0.000000   Median :0.000000  \n",
       " Mean   : 0.8069   Mean   : 0.7758   Mean   :0.003282   Mean   :0.003176  \n",
       " 3rd Qu.: 0.0000   3rd Qu.: 0.0000   3rd Qu.:0.000000   3rd Qu.:0.000000  \n",
       " Max.   :25.0000   Max.   :29.0000   Max.   :0.044593   Max.   :0.044593  \n",
       "     pa_avg           pa_lst           py_avg           py_lst      \n",
       " Min.   :  0.00   Min.   :  0.00   Min.   :  -4.0   Min.   :  -6.0  \n",
       " 1st Qu.:  0.00   1st Qu.:  0.00   1st Qu.:   0.0   1st Qu.:   0.0  \n",
       " Median :  0.00   Median :  0.00   Median :   0.0   Median :   0.0  \n",
       " Mean   : 26.75   Mean   : 27.19   Mean   : 184.4   Mean   : 190.4  \n",
       " 3rd Qu.:  0.00   3rd Qu.:  0.00   3rd Qu.:   0.0   3rd Qu.:   0.0  \n",
       " Max.   :627.00   Max.   :728.00   Max.   :4410.0   Max.   :5476.0  \n",
       "   py_pa_avg        py_pa_lst          ra_avg             ra_lst      \n",
       " Min.   : 0.000   Min.   :-0.450   Min.   :  0.0000   Min.   :  0.00  \n",
       " 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:  0.0000   1st Qu.:  0.00  \n",
       " Median : 5.800   Median : 5.273   Median :  0.3333   Median :  0.00  \n",
       " Mean   : 5.151   Mean   : 5.311   Mean   : 21.8077   Mean   : 21.65  \n",
       " 3rd Qu.: 8.143   3rd Qu.: 8.047   3rd Qu.: 10.0000   3rd Qu.:  7.00  \n",
       " Max.   :42.000   Max.   :42.000   Max.   :404.0000   Max.   :416.00  \n",
       "    rec_avg          rec_lst        rec_trg_avg      rec_trg_lst    \n",
       " Min.   :  0.00   Min.   :  0.00   Min.   :0.0000   Min.   :0.0000  \n",
       " 1st Qu.:  0.00   1st Qu.:  0.00   1st Qu.:0.0000   1st Qu.:0.0000  \n",
       " Median :  5.00   Median :  4.00   Median :0.5629   Median :0.5656  \n",
       " Mean   : 15.32   Mean   : 16.11   Mean   :0.4022   Mean   :0.4045  \n",
       " 3rd Qu.: 24.67   3rd Qu.: 25.00   3rd Qu.:0.6476   3rd Qu.:0.6593  \n",
       " Max.   :118.00   Max.   :143.00   Max.   :0.7739   Max.   :0.8487  \n",
       "    recy_avg         recy_lst       recy_trg_avg     recy_trg_lst    \n",
       " Min.   :  -7.5   Min.   : -24.0   Min.   :-3.100   Min.   :-12.000  \n",
       " 1st Qu.:   0.0   1st Qu.:   0.0   1st Qu.: 0.000   1st Qu.:  0.000  \n",
       " Median :  44.0   Median :  30.0   Median : 5.328   Median :  5.120  \n",
       " Mean   : 179.3   Mean   : 186.6   Mean   : 4.438   Mean   :  4.505  \n",
       " 3rd Qu.: 254.0   3rd Qu.: 249.0   3rd Qu.: 6.983   3rd Qu.:  7.250  \n",
       " Max.   :1635.0   Max.   :1964.0   Max.   :27.350   Max.   : 31.365  \n",
       "     ry_avg            ry_lst          ry_ra_avg        ry_ra_lst     \n",
       " Min.   : -16.00   Min.   : -31.00   Min.   :-3.240   Min.   :-5.746  \n",
       " 1st Qu.:   0.00   1st Qu.:   0.00   1st Qu.: 0.000   1st Qu.: 0.000  \n",
       " Median :   0.00   Median :   0.00   Median : 3.766   Median : 3.626  \n",
       " Mean   :  93.12   Mean   :  90.93   Mean   : 3.402   Mean   : 3.343  \n",
       " 3rd Qu.:  41.00   3rd Qu.:  30.00   3rd Qu.: 5.057   3rd Qu.: 5.284  \n",
       " Max.   :1709.00   Max.   :2097.00   Max.   :33.254   Max.   :33.282  \n",
       "    tdp_avg          tdp_lst         tdp_pa_avg         tdp_pa_lst      \n",
       " Min.   : 0.000   Min.   : 0.000   Min.   :0.000000   Min.   :0.000000  \n",
       " 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000000   1st Qu.:0.000000  \n",
       " Median : 0.000   Median : 0.000   Median :0.000000   Median :0.000000  \n",
       " Mean   : 1.108   Mean   : 1.161   Mean   :0.004305   Mean   :0.004383  \n",
       " 3rd Qu.: 0.000   3rd Qu.: 0.000   3rd Qu.:0.000000   3rd Qu.:0.000000  \n",
       " Max.   :34.143   Max.   :55.000   Max.   :0.054159   Max.   :0.075811  \n",
       "    tdr_avg           tdr_lst         tdr_ra_avg        tdr_ra_lst     \n",
       " Min.   : 0.0000   Min.   : 0.000   Min.   :0.00000   Min.   :0.00000  \n",
       " 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.:0.00000   1st Qu.:0.00000  \n",
       " Median : 0.0000   Median : 0.000   Median :0.00000   Median :0.00000  \n",
       " Mean   : 0.6687   Mean   : 0.649   Mean   :0.01020   Mean   :0.01017  \n",
       " 3rd Qu.: 0.2000   3rd Qu.: 0.000   3rd Qu.:0.02734   3rd Qu.:0.02688  \n",
       " Max.   :18.0000   Max.   :28.000   Max.   :0.09126   Max.   :0.09340  \n",
       "   tdrec_avg        tdrec_lst      tdrec_trg_avg     tdrec_trg_lst    \n",
       " Min.   : 0.000   Min.   : 0.000   Min.   :0.00000   Min.   :0.00000  \n",
       " 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.00000   1st Qu.:0.00000  \n",
       " Median : 0.000   Median : 0.000   Median :0.02420   Median :0.02475  \n",
       " Mean   : 1.092   Mean   : 1.132   Mean   :0.02490   Mean   :0.02499  \n",
       " 3rd Qu.: 1.400   3rd Qu.: 1.000   3rd Qu.:0.04503   3rd Qu.:0.04486  \n",
       " Max.   :15.000   Max.   :23.000   Max.   :0.09071   Max.   :0.12214  \n",
       "    trg_avg          trg_lst           pos1               bench      \n",
       " Min.   :  0.00   Min.   :  0.00   Length:10127       Min.   : 3.00  \n",
       " 1st Qu.:  0.00   1st Qu.:  0.00   Class :character   1st Qu.:14.00  \n",
       " Median :  8.00   Median :  6.00   Mode  :character   Median :18.00  \n",
       " Mean   : 24.89   Mean   : 26.01                      Mean   :18.31  \n",
       " 3rd Qu.: 38.75   3rd Qu.: 39.00                      3rd Qu.:22.00  \n",
       " Max.   :179.33   Max.   :205.00                      Max.   :43.00  \n",
       "     broad            dpos           forty           height     \n",
       " Min.   : 96.0   Min.   :  1.0   Min.   :4.160   Min.   :65.00  \n",
       " 1st Qu.:113.0   1st Qu.: 45.0   1st Qu.:4.480   1st Qu.:71.00  \n",
       " Median :118.0   Median : 97.0   Median :4.580   Median :73.00  \n",
       " Mean   :117.7   Mean   :107.4   Mean   :4.615   Mean   :73.18  \n",
       " 3rd Qu.:122.0   3rd Qu.:166.0   3rd Qu.:4.740   3rd Qu.:75.00  \n",
       " Max.   :139.0   Max.   :326.0   Max.   :5.480   Max.   :80.00  \n",
       "    shuttle         vertical         weight        undrafted    \n",
       " Min.   :3.730   Min.   :22.50   Min.   :150.0   Min.   :0.000  \n",
       " 1st Qu.:4.150   1st Qu.:32.50   1st Qu.:202.0   1st Qu.:0.000  \n",
       " Median :4.260   Median :34.50   Median :219.0   Median :0.000  \n",
       " Mean   :4.271   Mean   :34.57   Mean   :221.3   Mean   :0.282  \n",
       " 3rd Qu.:4.380   3rd Qu.:37.00   3rd Qu.:242.0   3rd Qu.:1.000  \n",
       " Max.   :5.260   Max.   :45.00   Max.   :348.0   Max.   :1.000  \n",
       "  career.seas    \n",
       " Min.   : 0.000  \n",
       " 1st Qu.: 1.000  \n",
       " Median : 3.000  \n",
       " Mean   : 3.575  \n",
       " 3rd Qu.: 5.000  \n",
       " Max.   :20.000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we construct a \"configuration\" for the models we are about to fit.  I've chosen to use KNN for the opportunity forecasting.  This is because linear models are too biased and tend to shrink the projections to the league average too aggressively, resulting in massive declines for most players.\n",
    "\n",
    "We use the `caret` package to optimize two different loss functions.  For opportunities we care about [NDCG](https://en.wikipedia.org/wiki/Discounted_cumulative_gain) because we care about getting the top of this list correct.  It's most important that we have the correct ordering of players and that the ordering is mostly right at the top.\n",
    "\n",
    "For all of the rates, we use mean absolute erorr (MAE), mostly for some additional robustness to outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "mySummary <- function(data, lev = NULL, model = NULL) {\n",
    "  out <- c(mean(abs(data$obs - data$pred)), \n",
    "           Evaluation.NDCG(order(data$pred), data$obs))\n",
    "  names(out) <- c('MAE', 'NDCG')\n",
    "  out\n",
    "}\n",
    "ctrl <- trainControl(method = 'cv',\n",
    "                     number = 10,\n",
    "                     summaryFunction = mySummary)\n",
    "\n",
    "config <- data_frame(metric = c('ra', 'trg', 'pa', 'rec_trg', 'ry_ra', 'tdr_ra', 'tdrec_trg', 'recy_trg', 'py_pa', 'tdp_pa', 'ints_pa', 'fuml_ratrg'),\n",
    "                     method = c('kknn', 'kknn', 'kknn', rep('glmnet', 9)),\n",
    "                     outcome.metric = c('NDCG', 'NDCG', 'NDCG', rep('MAE', 9)),\n",
    "                     maximize = c(TRUE, TRUE, TRUE, rep(FALSE, 9)),\n",
    "                     key = 1)\n",
    "\n",
    "positions <- data_frame(pos1 = c('WR', 'TE', 'RB', 'QB'), key = 1)\n",
    "cutoffs <- data_frame(cutoff = 2015:2016, key = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main prediction loop\n",
    "\n",
    "Here we fit a model for each (position, cutoff) pair.  The cutoffs ensure that we evaluate out of sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining, by = \"key\"\n",
      "Joining, by = \"key\"\n"
     ]
    }
   ],
   "source": [
    "preds <- config %>%\n",
    "  inner_join(positions) %>%\n",
    "  inner_join(cutoffs) %>%\n",
    "  group_by(metric, pos1, cutoff) %>% do({\n",
    "    my.metric <- first(.$metric)\n",
    "    my.pos <- first(.$pos1)\n",
    "    my.cutoff <- first(.$cutoff)\n",
    "    for.reg <- season.metrics %>%\n",
    "      filter(metric == my.metric) %>%\n",
    "      dplyr::select(player, seas, value) %>%\n",
    "      inner_join(features, by = c('player', 'seas')) %>%\n",
    "      filter(pos1 == my.pos) %>%\n",
    "      ungroup()\n",
    "    if (str_detect(my.metric, 'pa') & my.pos != 'QB') {\n",
    "      for.reg$yhat <- 0\n",
    "      return(for.reg %>% select(player, seas, yhat = yhat))\n",
    "    } else if (str_detect(my.metric, 'trg') & my.pos == 'QB') {\n",
    "      for.reg$yhat <- 0\n",
    "      return(for.reg %>% select(player, seas, yhat = yhat))\n",
    "    } else if (str_detect(my.metric, 'ra') & my.pos %in% c('TE', 'WR')) {\n",
    "      for.reg$yhat <- 0\n",
    "      return(for.reg %>% select(player, seas, yhat = yhat))\n",
    "    }\n",
    "    X <- model.matrix( ~ 0 + ., for.reg %>% dplyr::select(-player, -value, -pos1))\n",
    "    nzv <- nearZeroVar(X)\n",
    "    X <- X[, -nzv]\n",
    "    y <- with(for.reg, value)\n",
    "    trainable <- !is.na(y) & (for.reg$seas <= my.cutoff)\n",
    "    fit <- train(X[trainable,],y[trainable],\n",
    "                 metric = first(.$outcome.metric),\n",
    "                 method = first(.$method),\n",
    "                 maximize = first(.$maximize),\n",
    "                 trControl = ctrl,\n",
    "                 preProc = c(\"center\", \"scale\"))\n",
    "    for.reg$yhat <- predict(fit, X)\n",
    "    for.reg %>% dplyr::select(player, seas, yhat = yhat)\n",
    "  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining, by = c(\"pos1\", \"player\")\n",
      "Adding missing grouping variables: `cutoff`\n"
     ]
    }
   ],
   "source": [
    "rnks <- preds %>%\n",
    "  spread(metric, yhat) %>%\n",
    "  mutate(py = pa * py_pa,\n",
    "         tdp = pa * tdp_pa,\n",
    "         ints = pa * ints_pa,\n",
    "         rec = trg * rec_trg,\n",
    "         recy = trg * recy_trg,\n",
    "         ry = ra * ry_ra,\n",
    "         td = trg * tdrec_trg+ ra * tdr_ra,\n",
    "         fuml = fuml_ratrg * (ra + pa + trg),\n",
    "         fpts = py / 25 \n",
    "         + recy / 15 \n",
    "         + ry / 10 \n",
    "         + rec * 0.5 \n",
    "         + (td + tdp) * 6 \n",
    "         + ints * -2 \n",
    "         + fuml * -2) %>%\n",
    "  left_join(player %>% select(player, pos1, fname, lname)) %>%\n",
    "  dplyr::select(seas, player, fname, lname, pos1, fpts) %>%\n",
    "  group_by(cutoff, seas, pos1) %>%\n",
    "  arrange(-fpts) %>%\n",
    "  mutate(rnk = row_number())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2016 Evaluation\n",
    "\n",
    "First, we'll use `cutoff == 2015` and check out how well we projected fantasy points for the 2016 season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining, by = c(\"seas\", \"player\")\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Joining, by = c(\"seas\", \"player\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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wREQAREQATyQADlJ5aLHccgDKRiHZuH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CZuNSjGsWlhm/yzvvvONSLIQqQ/zOcPci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\nVig5gnMsREDkR6VKnGcRZjYY+6BPp44fhd6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ut2O44zGiXciJnn/+eU92uoiNaKcQSBABGUgJAqjThUCmEfjxxx9tztEXX3xhvUYUHpQI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\niDqAnAHGvfBxMM9RD8ksrCWvdfbs2eI9Y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2eVI9tzPuMV5Puokm8EVEGq0/OjZoQNW3AviYL05ptvFjdRv6eUhLVfPE64dGkbAgdotPlh\n4tlhEmSiQjHgB5uG4Gol9IFXlCKsgvSRWk/kICUhbjiCrz2uRyFcKsLfdtttMjkyUdrQPrxY\nTJpQqOPJQ3EEI7xJhGvUS1BWn3/+eQlTqdc19TqKgCLQmgigPPhouplzVq1aJaFezB9R+azV\nosY1fWyreFEI0bNMefQDj8qcOXOCe+65p83lfP12D2A/3igMdCxI7SKVV9o88sgjY1Gcu22m\n8Z4QP+aicgQXKKlK1pDGE0ivzXfffVdYc31X4PvJWkMl3wioglSn59e5c+dI17lLVEDRvigh\n8RbGNysvvPCCKCJMOLfffrt4RaC7JJ4ZFiMrr7zyihROjZOnY8+p5HWXXXaR65EDRTIvAwPv\n8eZUK1gbqcmBwsXkQSIrBAulBIUID9vee+8tLH5ggHJFyCHWOVdBZTsTKfTiaQuTNmEfKGrE\nzkO5jiIcDn1Jux/aviKgCLQOAuRbhg1q7t2TU0SdPOollVNI3PPs+yjPEzH/PgWJkgs+RYFx\n+c4777TNyut+++1XVHrcHdwPREeMpRibouo8EU6N8tdogdSiXFQIfQSDww8/vNHdrfj6GF7/\n8Ic/yHx7yy23FJXfihvK4Qk+L6m9Db6n9UgvsNfT13QQUAUpHVzbtUoYWNRk5daLYPCHqccd\nVHnPZESMsm2DmkjQqBK7DNOPbYOCqWGPC585bocddhAPExTcrrLQrrMxN1D/aJ999hFFCOps\nvDcoJ1tvvXXMFvyHMYlSJ4kYdKyEWBrxwNE24YSVCHjhJcK75ROUKDxPacrZZ58dMHlwHxZ3\nWAxherKf07y+tq0IKAKthwBGGBSFKGHsIfSbaAPG70qVJF8+Ddf6+uuvvcasqL4wLlIuwhWi\nHijL4PaJkDtCtpnvEJQjd797PnNmlPLkHpf2e7xCUThxbeYl+koIHix9eRKMocz1fId4T2kR\n1i8QDrSCoADtueee3hQDvtPUEFPJNwKqINXp+bG4J/zNJwwwTCpW+IxHCG/DL3/5S/GKvPTS\nS5L4xzFYbWgLL8vUqVOFAc+ei6eEH2dYmAwJq0BZYOLBQlfL4hzLGAoXCgseLCY4LIRMbPSr\nFkHRIgk3bG2kvyialUrUJGrbKbffHlfNK4wvJBCH74VJExe9xilXg6qeowgoAuUQIN+nX79+\nkcYhzse7TVkJogzOPfdcWeD+6Ec/Ktd0pGLCiRjzfOFFKGyu4c9eBKUHY1FY6BdzAfsIkSZ/\nFEMZcwxC8e/wuGrbYDseskYL0QL00+dtA+fLLrssgASDOTlPwvxMn1lrgDWvrAMwOJJ73Coy\na9YsoWdH0eWPZ8r3GSp39SDl/1ugClKdniFeHJh5fIKlix+aKySvwkLEQITi0a1bN9nNYApD\nCsm3sOLA6OMKibnlhElx3rx5RUtcueN9+4knZ0B0lSzahVYTa1gtAk5REx/5WlH7oq6Jp42+\nhgXv0qabblrMVQrvT+IzSpBPYaVtFDOYl1QUAUVAEUgCAQwvbikDFAzG6lLhzkQXcB5ENyzy\n8Npj/CLkC1Y4DHWwhLLQZ35BWSnFasd451sckpOKF8hVkvhMruygQYO8t08fMOotXbpUqJMZ\nr63gnfGxutImkRSEpGdByA3u1KmTYMtz4I8i6oSM82wwguZNHnzwQa/izRoAggy+Q60gfMcw\nWM+cOVMif6DWJyIFA7RK/hFQBalOzxCFJhxGYC/Ngh+q0nJCgivWtE8//VRCIqinsOaaa4ol\nh0kOIfabSa6cMJCFE2PLnePuf/rpp9soR3Yf9wJpQy3C5OGzuNEmk1/Uvqhrkr+E58n1FNEG\nODGwpSnUrIpSkNhuiSTS7IO2rQgoAs2NAAQwGNWwYFMknAU5eaCMc3gpiEqIYhTlGCz/eFwI\n3R4wYEBALiuv0HCz2MMQt2jRomCeMayhpEA8wLVcZccizDhLHmxYCMcib7Z3797SF/oJgx3b\nKiWKYBHKPEj9GeoHdjY5vswN3DcGuiuuuCJ8+YZ9JscYwx4h48OGDZNwNPqf55ITREawhvAJ\nxuCotY7v+Lxv43vHbw9Gu9NPP13JNvL+QN3+m0VaS4sJByuYwbwqDEysbcEQH8Q61wwmBTMR\nEfvW7s9YlArGulayHTOBFUylbTnXeD7atGEW+gVj3SsYb06B63A/XIvtvuvZbUbZ8l7TKHPS\njnfn/200Vr/Its2EWurUsvtM8m0hfI/02QxEBRMyUvZ83wFmQC+Y+hsF7nmrrbYqGAKIgvHG\n+Q5NfJth9JO+W9x55f5MIbmCmUhquh7nG4txTW1k7WTj7SsYAosCz6yZhN+mKdzZTLck92IW\nqgXGwrTEeFkLJmwnreYTb/fXv/51VW2aAuLyvTdGporO5/diPD3txhijqBRMqI+0xTMyCk27\nMZs5wuRRFIyCImOSUTgKxvpfMEpMcQ5hfjKKUMEYmeQatOuOZeyjHY7jzxTOLBj2toruIe7B\nDz30UMFY7YvXZyw34YGRpzOXMZaYRXu7Y0xOVMHkzhTM4rZgyHoKhsyn3TFpbDDGxYJhnpVn\nZspNFIzSKZfhN+TrZxp9qKVN+mnCxiPXF6Zwey3NJ3ou3/usC8+c72ge5gbmMH5TWRf6CaaM\nqWFhfGXMiyNYt1ta6qUgGbYg76KficYwtRVK/ZBRwgx7nUwKxmNUnBzcSYoJyniEis/ShCUU\nuKYJhyiEJzTO4/hx48YVj3ffxFGQTLy6d4BEiTFkEm5zVb03ljbBy/bdWD9FQTQhfBW3x4/F\nFOCTCcl4bAqGGrZuyhGdNdbdgglZkQUHz5p7MQw4BZNXVvG9hE9QBSmMSHY/q4JU3bNRBak0\nbjNmzPCOxYzzhoCheLKpLVQwBWRFiWEcYmw13iAZZ1FsTF0iOdbU7Ylsz51z7HsUJBPGXDCe\nf1GkmFsw0I0YMaJ47STeoFjY+cC9NsrdBx984L1ElIKE8cUQChTvk3ugbRSlNIW5kWvxxz1g\nKOP9zTffLEaGvChIH330kRgame/ts+AVDE3+dJoQVtR2qXVVRQ2leLAqSMmDqwpSQpjWQ0Ey\niaWRyhGDo7Ug+W4JqwJKDoPPKaec0mYwcgcmJiQTJ96uCZMDUzChDDJh2eOZwLbYYouCCctr\ndzwb4ihIeC1M6FpxgmGgpw+GZjsxK5gJ8yiYRNCCKWhYMCEdBZOr5e1vqY0MPkze7kBOP7Gm\nmnCSUqcmuo9+8Jy5D6y61dyLr0OqIPlQyeY2VZCqey6qIJXGzbCHRc4vjPmGAKhgQrpkvGPB\naEK9ChMmTCj07dtX5hOTL1TAa2/F0HRHzjN2DnFfGU8ZX8Nef7Yx3iUleObC16AfzGeGKdR7\nmSgFyZRdKM5d7r2wwDfF1r1t1bqR5+Dz4nF9jGZENORFQeJ7hJeQuZnnzz0QVWJqWtUKU6Ln\nq4KUKJwSWcRvKuuSlIL0/8wXWyVlBKjDQ/y3j+bUDIgSy+3rgnEPClsdSYBGOZJiqDfeeKM3\np8UM7JKPFG7HhOUJoxDJoCSFmsFMEnD79+8vxAXUGapGjCUygKp6+vTpwWOPPSb3Rw0iWJPM\nJFZNk+3O2W233QL+ahEqtsO0Z34wxWZISAZ3ahNRS6MeAia9evWSv3pcT6+hCCgC+UcA9lLG\nV7MgkTIHECWEBeIEo4x4yWuYFxh3YKljDGIeuvjii2Xco5DslltuKXXimCesuIyqdlupV/pm\n/9zjGHPJy4D+OQmJYmgl75U8pkqE4rScFxbmBeNJC/r06RPeVfNn5hqX1CjcIDleLglFeH/W\nPvO9gwQE2nVIGciHVlEEmgkBJWmow9NkomACiRIowMNFXGEign2NpNmBAwdKIiqJrJA0oOSE\nhcEeBcUnJLAy6DNZkczLhHvssccGJlY4oCZStWIsd8I+BKUltOQk9CalHFXbp/B5JqzEOykx\nES5cuLCN4hQ+Vz8rAoqAItAoBEj4hjgBwgETIhxQkNtHocw84VvsozQxV6BYIMxBGOko7Ily\nxFzCot1VjjgOpakSYdEftfA3FvySdYAquc7aa6/tPZw5BzKcSiSKZQ2MKlUQ416XOafU/Mj+\nPArfMVWO8vjktM/lEFAFqRxCCeyHtQevRZRA8z1q1KjibhjpTGK/WMVMvozQm9qBlYKjFPXD\nEogwOLEPliKTsFpsI/wGRQFLHhMplNf88R7PVKXFV8NtZ/mzCWEs2T2La8mDdKcioAgoAnVE\nAMs80QIsmlFq+OM91nrmACuwz2GYwlPkCuOeIYERpcinvDB/UIPNhNO5p8l7FLK44yLzT/fu\n3SOZU+kDxyQhzFUofWGhrz7FMXyc+xnF0zc3gAvFP9MQ6kBFGUp5tlmo25TGfWubikBeESi9\neszrXWWk3ygl0D9eeOGFAQXjogQP01NPPSW7mQhx7z/77LOBiREXGmp3IIcylNo5KFTUiCBM\nbMGCBcH5558f1bxsh5/fNziz7eqrry55bp53omi6+Nl7YUFBfY+kJm/brr4qAoqAIlArAihH\nPqMa2wzzWrF56geZPNV2HhzGdRbkbmhx8STzBgOZyV90N8l76u7huQorXOEDUUoYPw3pgNTp\n880tKDNnnXVW+NSqPxvyCJkb6RuKjKUZxyNGFEYlQk0m2nEVQfpLVEVUTSa3fbxyzMFQqFPy\nIo5QvgIK8vB8xOdrrrlGakzFaUePUQQUgTohYAa2lpa0SBpMOJuw05gBWBIYzeArr+axel8h\nPID9xUxqsh9qaOhbXYFeE5YdGNCgiCZBEvrvONK5c2fvdemPKVTXpok4JA1tTsj4B2hcjSJU\nvH+eBcQVJH/nXZSkIT9P0CxWc0HlWimiJEIrzfd/UEuC5rtLly7F8So8Z5haP3IxiHLC++xn\nxjuTvynJ/3ab+wqxAd9HV4wXQ8h7jOIQ2a5twygoBVPAvHg6FNkw5LEdhjzmPeYqo9AVj7Fv\nIC1i7oJFzig2hQceeMDuivUKeQ/zq4maKJj83JLnRJE0cBIsorvuuqswr0GeYPJyC3FYUk0O\nl9wfzH/cL3hRfsJ3r77OmRD3wh577FEwxk6hUzeFVeWwPNF854H8AFDz0E9+d0rz7fulVL+N\nsS0Jmu9kfN9m1FT5DwJ4eIYPHy4hEXZrlCWP/VixsEhBnICrnXheCvGR42PFMMZIKIMZwItW\nQTNBSSy5mXDKxmCbCVcqPJuvnG1SXrk2+8JiFEcJv2gGDwv5V+A5a9YsqTJPyCOV1rVIa/ip\n62dFQBHIAgKmLEBgGEjbeZHwevTo0UO6aEotRHYVT9M777zjzU1iXhlo8lrDYzv5roZJrc28\nFXkBs8MN3cNTbxYkEq5N/izhYr55hXBucqaYhzh/2bJlgSlJIfMlnqA4kgR5D9fZfvvtJVIj\nzjXtMRAujR49WvpPtIcVyICuvfbaAC9XOcHbVanHq1ybul8RUASSR0BD7JLHVFiBCAGIIygo\nVmlBOUJIIIUNzhVTfE9CKVxFi/dMRiNGjHAP9b6/4IIL2oQTuAcZevDix5kzZ0qSrqF+DWC4\ng8yBqtl5FjDmPqgsv3jxYlGWVDnK8xPVvisCzY2AqQMnoViMXa4QjoXxDQWIfKRSAvGPnVs4\njnP5M14TCekKn2s8GN4cn/BxfCZ/FSOT8WYFJ5xwQmDq4ohh7+CDDw6OOeYYr3JEX9hH313l\ninmPUDXC+7IgKIooceRPdTYER8yvlgTDFBtvg6ntL3OxG/pot+urIqAI5BcBVZBSeHYMpu7E\nVOoSvuOYQKBlheLVinHDFwdpu41XBubHH3/c3eR9D2MRg31YmDDJlUJI/j3uuONEEeMzbUOH\nStKqVd7YrqIIKAKKgCKQHgKmTl0wb968NrTP5LHigdl6663FaEUeUSXCGM7f0qVLhck0fC6e\npbhtoritXLkyMAVaJQ+JPr333nvhJtt8Xr58eTu2VnsABsVS8xj9Zi7C0MUfUQFpzEnk8+K9\nY06kzIapHyW5Rvvss4/M6WGl0/afVxhiVRQBRaB5EFAFKYVn2duEcFmLU7XNM2EwAVkx8c72\nbbvXON4qSCCYzMKCJQ+SBrxEeJJcyx7Hch9MqA8++GD4VP2sCCgCioAikBIC0HoT8kbo2qpV\nqyRkzuQ3ydXwbpD0X41A6nDooYeKl4drDBkyJID8ByNaOSEsjzA/VznBoMf8QTulJDy3hI+N\n2k/7eHTwPhEmzR9KEmFq7EtSYMqjH+79MQc+99xzgSkgK6GDkDmEBUNjt27dwpv1syKgCOQY\nAVWQUnh4TDqw1/kG0riXw5LnMt9B9+3mJNl22AabXTkhxymqP0wGJvFVLGZR7bjerKhjdLsi\noAgoAopAsgist9567XJM8eBceumlkWN6uR4QufD2229LLTiY1QiRiyMoJD5FhvaIcigl1Fcy\nBEPeQ8jniVLQyB/Fm0ZEA9fhj/fMWfQ9KSG0nXmS9sPCPVNonbwv5lwUIld4HldddZW7Sd9n\nAAE8gNR65I+cPhVFoBIE2v7KKzlTjy2JAHUs4iguvkaw0lFbgiRSK6eddpq4/l0lifdMOtCI\nlxNqJ/kGfs5DQepswu/C8e62TSyGvnoZdn+eX7HOkihcq8cvzxho3xUBRSB/CEAIQB5QVgQl\nYsmSJZHdQakgjJv5xFUwmO/OPvvswLDaec8lLxaFKCyM2exLSuhX1BxIf4nU2GijjYTYoWvX\nrsXL4n1DOdx5552L2/RN4xEgr41nAtU8f5tttpnkujW+Z9qDvCCgClJKT4oBlQKw1YihS5UY\nazd8AGWIMLnrrrsuIB76t7/9bQBxw8KFC4M11lij7GX2339/r7WRyYlBhCKzWPD4HBYmIkIy\nmkkIX4Rpaf311w+Y7Dp06CDYNtM96r0oAopA8yLAYr7eZDMoEVHCPrxSpeTAAw8UBYM5jLEX\nRj48ROPGjYs8DSKiKPn666+jdlW8nTm2d+/e3hpQGBHpO7LtttuKpwlSCzxvhK4rK13FcKd6\nAoRMhgpejMKsw/jjGbLt3nvvTfXa2njzIKAKUorP0leIL87loNjedNNNpRAebHbEjCOEyFHE\n7tFHH5UkUmK+S+UmudcydX8CU29ClCnOYTLAIob1684775RDZ8yYIZMW+5h82W+tfniYmkW+\n++47YXKC0c4KAyjhE7jiVRQBRUARyDoCkAiUovpOuv/MCQMGDIhsFg8S80k5wSBHPg9h6KaO\nkcxp5FRNmDDBeyokQb7wcLaZekLec6rdyPhPbhdzoBUUv5NPPjno1auX3SSvRGWg5GVdIJag\n+C/EH0SlUCTX55HL+n1U0j8Myb5QULZBx66iCMRBoL27IM5ZekwsBPDIlGLmKdcIHiSUGsIW\nIEqIQ8ZQqk2sYyT93nPPPYEpoCchDX379i1OBgz21HCiijvHrbvuugG5T756FqWuk/V9VH+n\nrpTroaPPfL7kkkuCk046Keu3oP1TBBSBFkaAGkdEBZTyroThITqABWJUqHX4+PDnPn36BBja\nooT2CaGD9McUPA9OPPFEof62x2PoI8dn7bXXDkYY6uxHHnmkGNqMskcpCl4JjXKFMdkUUQ8w\nbNkxG6XFFHcN4tZOctsr9d4UTQ/efPNNoUF/+umnJWcKQoi8RlDg3dpuu+3ke2KVItYSrCtg\nRCzlESyFU9b3+QipbJ/j5tvZ4/W1dRFQBSmlZ487l6TOWoVB7cMPPwzIaTKVyatujgmVhf88\nk+zKBAktK5Y5tjNhYQ1D8C4dffTRYkXzhdtV3YEMncgEEUVnyyQOI1O1DFEZuk3tiiKgCDQh\nAjCqUW8IIxd5FaXC2lgAYxibNGmSLIopNE4tHzzmdsEcByKMcyS5UxA1SlBebr75ZlHCYD3F\nU7Fo0aLgF7/4hRRQhVCBOYXr+pQ0tkN0gFfMVcQII3z55ZeDM844Q4rKcn2Mj7QXx2MV1d+o\n7cyFo0aNitqdq+3kdhGGaBVLOk/I/PPPPy9sgKU8grm60VBnt9lmm+A9QzvPOswVImJY+6go\nAnEQ0BC7OChVcQxUpEkoSFyaxfwLL7xQRS/+fQqTIrUdsIjZien111+XiZPEWDxFKEu44ltB\nmLCjvHFsJwdMRRFQBBSBrCEwe/ZsUQ7wtKAg3HXXXZFeAJQjDD1Tp06V8Co8NHh38JAMHDgw\noBh4HCG0jkU14XB2/og6z4Y1MWd9+umnEpo2cuTIYMqUKbJYpZ1ybTA3hQXPjq0FSBt4nwhD\nVymNwGOPPdZGObJHo4w2c+mOiy++2Eu4wXcZj6SKIhAHAVWQ4qBUxTFMXJVY6EpdglhrSASq\nlWuuuUbCE+zk5WsHytTdd989kT5Dl0rBPajBwxYc99pYtbCGYpXE2lMvOeqoo7zxycSdY1Fr\n1rCDeuGr11EEFIHkEUDR6N+/v+TjECJFXgm1dx566CEJBWPxhzB+ERaNQkTItFsugv3QhqM0\nYTgjeiBKaI8xcfXVVxfPT6n5w9cG4ztKDYnxKDVxhGtYxlS8XFdeeaUwuuIRYGGLd7+ZhDUC\nId9EdxAuyHyYpJRSRkvtS7IPjWi/+9oLAAArlklEQVQLgzBEDayb8Brxx3edbexTUQTiIKAh\ndnFQquIYlISkhImmWspw+oDnqNwExTWI24XhBYa8aoWJl4nbKodMsiTj3nrrrW28Nq+++qqw\nAmFlJOwCiyPKCef7EnKr7Y/vPKyRWGIJJWQxwUTB/ZM8TFiIiiKgCCgCWUGAsfuEE06QkCjC\nzVCI3KKkFFFlHCV0GM8SBqe5c+fKYpDx96KLLmqTC8R9/f3vfw9WrFghyhLvw8K4yDgJCcKT\nTz4podjhY+J8ZmyNuxBnEcs1YRVl/qSeIAqenbuWL18uoeavvPKKeLPId6JMAwte5hzCoyEa\nIiQdpjmUDqs0xulrvY8h9I3Cv3j0mC+5f0gizj///GD06NGJdAd2Pb4vzG+uoPhaVj53ezO9\n5/74TWAIQAitU+NnMz3hOtyLGbxaWgxjXMH8kKrCwNB8FkxYmvdcw3pDtbma/swgVjADfMFQ\noBYM80rBTI4F8wMvdOrUqWAUCbmuGVgLxlNTMLHeBaNkePty0EEHxeoH1xs2bFjBxLYXaLdS\nMdaZghnkvdcyDEDF5owVsGAsO3JvLkZc39T2KB6X5BuuaSbTgilIWGyWzzfccEPBTEYFswgo\nbs/TG8OUWDCJ2nnqctm+8j3m2fDMmkn4TZlFXDPdktyLWZwXGAvTErNILpiE/7SaT7xds+it\nqk2j3Mj33igExfMZi2mPcdIwkMn+4s7QG45ljmActeMq47HJqSmsWrVKjjaLxYJRqIr77XG+\nV841xquyxzJH8edrI+42rsOcYMLrpJ9jxoxpcx+2He5t1113lWvZa3Ku8XJJH4xxTfaZUOmC\n8UQVTGSEYGYiGUJoNf4jawTffMl9GQKFRDpoylkIrhYXcATD3Xbbrao5nk7xW+c3nwfJQz/5\nbjLf5WFuYA5jnMm60E8wZUwNC+OrSSkJb/Z+1hA7M2IkLfPnzy8WsMM7Uq3AnIO1DNaV8847\nT14JQaA6NIVj8YCQT7PjjjsKaxBVymH7CQssPHEsJxyz5pprSlgD9Slol1pLVDmPI4QIRIXU\n4cV69tlnpZk77rhDLITmG9mmWSyF1MSwFsM2O1P4QKgJtOkU2o2q4p7CZbVJRUARyAECeNQZ\nq2AirbZkQy23CZMoXm3Cn2GQYwxlzIoSQvCwlrvjJ+Mx28jXIYcIjxDsZXGEc8OeB995jOPh\nsdx3XKltzGuUm6AeH0Ikg3sf9ly22TA0e036SDgen83CSA4lIgEPDZ63rAreLt98yX1AypSE\ndDblOfCigAPeOUIVr7jiCvkO1LI2SaJv2oYikHUEql+9Z/3OGtQ/JjGK4DGQo2AQxgCldDXy\nxBNPCMX3smXL2p3ORBAeRKF8tUqTWx8CRYs6DtB3G4tV5KTHYE39ACZUO9FAMc7EhXITJjZA\ncSJ3iGRf4ntLsSkZq5jkJRnLlbAhRU28TGxcn1ASFUVAEVAEGoHAzJkzhe2N2jfGEikGL8bG\nWnJBK7kPjGyHHHKIzB0wkUGuQKgU4XKEmpGnQwgZJAx2zC41thOyRlhepXlElfS5lmNRCvba\nay8JtyYsDIWnVmE+Q8lE0cWQmCWBstynANo+8oyTko4dOwpJRlLtaTuKQKsg0DQKEgM/eS0o\nExREa0QiHpY+FBNrFaqFeY4voHFlyl8lX0YmGpJiXQWJ8ydPniw1jWCuwbKG8kWsNlYklBcE\nJju2W+WIbQziUKwec8wxYk1lErbXgJIVhQZBKURJMqEc8jn8j2tY6lasWlzXpyQZ93+Rcjzc\nhn5WBBQBRSBtBFhQT58+XQpKksvCOHXKKacEc+bMkde0r0/S/qmnnirzCOxvXJvIAMZgq+Bg\ndMO7z1jMH2Lnnaj+2XOj9mdhO1hTCJ08VP7cuYj+MY/Y+43b30Z4/8r1rZQxkXMpnKuiCCgC\njUWgKRQkBn4mEWiq8VDg8UBBMPksqaPLAI7HA2pSCveVm6TS7hCTB0oimDCJMtBiPWTiAZst\nt9yyyOzCRISHCA+QyVOSJEafVYs27777brFowgKDix52IfdYKM0J8SslNimU4rNDhw4V5cqd\n7FCOBg8eXCxcW6ot3acIKAKKQBoIYNjC6o5yhGDMgQgBJYV5xhXGQHccZB9jWjXzALVpIBsw\neSMSEo2hC88/YzfbwwpONddw+57V99wX90y5BeYuiy8KEwqS/Ryn/zDwEV5Im9YQGOe8tI8p\nR7F+wAEHVPUdSrvftG/n7Dx8/6r9LdYDR3sNiyefs44p/csDphZHX1/tPot/qdemUJBQiLAS\nYeFbY401JEcHaxsKy+abb17q/qveh4uc0Adi1CsBvOoLVnCiSaIUJYN+uRY3+55QOSZ6kwgb\n/O53vxOlhFycUvfBFw1mJOjLw8oRXUNRhA0JqnDCDF3hujNMvDWLDoTwEJiRUJhgHmLiYyI8\n7LDDgrFjx7qn6ntFQBFQBOqKAIa29ddfv801Gbvw6DNG4kW3QmHUCRMm2I/yiiedMbgSYR5x\nDXooQ4YwRxQlmM6qDdOupA9ZOpb7J+KA/FmowvlMnihKEyGF7qIyqt/k1I4YMULmQoyYWRK+\nQ7D0oYyHFV+UOubRSr9D9b6/rPfP4pGXfrKGyktf89JP0kDCOfQYX0qtde33htemUJDIjyF+\nGeUIMSxvkuxJuFhaChLKF9eNC7R0LKF/DK4M/jzoqInC9svdb98TFkfoBiF2TOZQi8YRBnKU\nURtWFz4HRYjnQOgHig4Lje22207CRShI64phZBJFloRbFh5Ya7Xwn4uQvlcEFIFGIABltg0H\ntteHvIYxlTo8bh7ShhtuKAQ59jheyXvBGx5XCOmDhCcsXA8liXyktIQxm75GjelpXJf8UkKx\n7XwUdQ2ewfDhw+XPHgONOSUjeA7u+cyJ++23n8zJ7Ntoo42kbhKRERxnvU+2nSy8Mu8efPDB\nkiPFc0CY1/FUlvMwNbL/1oNXyXe8Uf2lr1nvJ99PlCO+A3xPsyz0lXVnHvoJpowLYTISttnf\nWzmsm0JBYiFuvRP2hvnssxrhsXBjki37DQ89rjBhsbAPW37inl/rcfzgX3vtNamETTFAJlJ3\nsojTPgMH4XKVnMexDOBRwpeOMLvu3buLZ809zocvxxP2Z8V3jN1X66tVGHlmaV6n1n5Wer79\nDjbjPfHMmum+uBc7wVT6nLN8vB1D0npW9jteLwyY/MP3Yj9j3XeFUCj+XMH6j+EpjlADBy++\nHZ/C5zDeorAxphIOnSQWLBTwgOHJZ760C99wH5L+DCsrhBNc1353wtdgjhs4cGA7HMGVIuQU\n+4bhFYGowpTBkDp6fKZNuwAiqoH74jy7jWOyIPSJ509eMGHxfLfwmMX97jTqHlhXgXHW+wk+\neDqy3k+w5DfOQj7rfWUcxACRh36y1idayhaetr8Xq4zaz6Vec68g8cDwQIQtfnzGkhcWPBtU\nIbfCcSzqATOuQMaQ9GBr24uaMNy+4TJEsTvyyCOFBtbUhXB3x37PJFSJ5ZCFQ+/evQUrMLCL\nBntB7gGFpxIs7bn1euXH3YxCyGezCWGX/DWbZPn3US3WjFtp3RdegyQVg3L3yHwAO6crENvg\nOQozebrHVPoeuu1+/foFphafWDqjlCSwhbGUcZ7fOWM2iymLCe+Z9BnP2Wa3h/vD+ExbjOMc\nM8OEPROKjuC1IMSZY8Lno0hF9S18jTifwZA+mxp0xZwud95jX8+ePYMzzzzT2xwkTIsXLxZ2\nQYyd0Fe7hjs7l3pPzthG7rVv377yh+Kh5AwZe0DanZZGIPcKEgMjA3h4sc5nG3LnPmHq+3C8\nFQYo2O9+9KMf2U1lXzk2fL2yJ5U4gP5gIdx4442FDMGdLHyncTwuePrBZMGExwRZqVQy6XGN\nnXbaKTj88MMljI7QBSjM6QsTEn9M4lml5wYfnhmLCHcyrRSzrB3PPdlFT9b6Vm1/WKBh9eW3\nyfeuWYTfG/e12mqrNcstyX2gxPL7T+u+WFDXc9HLOAyBDb8tvoMINYTCeUmyo8p/eG4gpEEm\nTZoUnH/++V5jAH0gB4nxHoMfpRpMYXDx+OBhISdn2rRpMhZ37dpVFI4XX3wxOOOMM0SBsN3D\nWwPNNzVxCD+jLg41caxAKc492ras0sEYjwGSXJlKjGm2Xd8r94QiAPMpodYwBppC5/LbIMya\nvkDkU26cDkeN+K6l2xQBRUARqBoBs7jKvRgLTMG47Nvch4ldLowfP77NNt8HY70rmMnDt8u7\nzbjsCyafRqp1G9BjvZqBvmAUs4KZtLzVwY2SUTAscHI9U+9CqmubxYa3bbNgbNdfYwX0tluu\nf1Qap8p61HHGilcwMfZyv6NGjSqYCbKIiZnkCvfff3/BMNoVTCx15qsrG8+RVFY23rfiPTTD\nG2NBLZj6V81wK8V74HtGFWyeWTOJUdJzUS29UsypVm9CWSo9Lfbxb731VuHEE0+MfXytBzJG\nmBzTgvGwFIyyXnjnnXfkswmrjtW0UWgijzNKcsEUppYxl/HXeJHkWKMkyLjvjsXMCyZ0LLKt\ncjt4JsbTUmCOq0VMSFyBecftG+/pX3ib/cycZxTbdvs5x+QIC6619CnuuUa5k7EE3LMuPK+8\n9JPffB4kD/3kmTPfGQ985iFlDuM3lXWhn2Bqog/addUYKQumpE277b4NufcgmQE52GSTTcT6\nBXGCFWJ6CV9IUgjPwupF25ARPPXUU5GeJDwVWMqwgGN5JKyPWEjiprGWmR+FWMjwbJCIa+sW\nnXvuuQGFXbFgQgIxe/ZsCevA8kxbWP+w8rkCqw/x6WYhIdek7VKCRRbr3H333SeWSYgUaN8V\nPERGAXI3tXnP+RzDn4oioAgoAs2CAOMjFNuXXXaZeMXx1OPVIOyrFsHTRkjbPffcI3MWrKBE\nACB4g/CqjBs3LnjPhPfhwYG4gfykasUYvxKpKcf8RXQAeT941JizGP8pvkox9HvvvbddCB7F\nXpnL+vTpIyF7zC/MiXgZmVeMolTtbel5ioAioAjUBYGmUJBQhP7whz9Isix1fhiwGZBrmVzC\n6BPrTHuEN/Tv3z+gmB9ECdCzkjQKUQSJazDpoDRATUpoAmENhAsw6SJQYDNBoPyQlAmLDeF1\nrnQ2hVSh4ebPeMKEOY5kUxQZQtyYaFxB8aKYKxMvoRAoUkxATMhGK5bwFJQmFDbOhXmJe1h3\n3XWlGdqmQvvChQtFiTvuuOPkOu419L0ioAgoAq2CAGMtBiSSvAlvq3VBTzvMC4zPKFrWOOXi\nCbkDBc7JgWIMz5KQn0TfmO+4F4ySKHWw+0FaxP28++67AXMXcwuKE0JNJ5Sr999/X1hK2cd8\npaIIKAKKQNYR+AFupax3Mk7/brnllmDmzJlFj81pp50WkG9UTvAKYdVziRvC56BooBzNmzdP\nvFJMEljQSKpE8cCS1kyCwgQzkI2/b4Z7I8maBGestFZZbYb74p74CZOL0CyCcYPvIDmEYfKV\nPN8jBgoS8lkAN5OwYCZHCI9FGrJ8+fLAhB7HLkeQRh8qaTNcC468H5juUBIwlpFz48vX4rvB\nXJNFBSl8//w++W1mTZHz9ZPxhDm6nnls4X7E+cx6AmU8D/1kzrEG1jj31qhjGJuy3k+whMWO\n31LW5wbmMNbMeWGxw/sfNsjgAafwNyQ55aQpPEjc5PHHHx8cffTRAQvhJL9kJKbCMoNyxCRH\nyBvKkYoioAgoAoqAIlAKgcceeyz4/e9/L/MSodQjR47M/AK41P3oPkVAEVAEWgWBplGQeGCE\njyWpHKFpMrkxyZFzdPfdd2feYtYqX1y9T0VAEVAEsowANNawyRGeR3FTatyoKAKKgCKgCOQD\ngaZSkJKEHKph4qUJvevVq5fkDTVTaFaSWGlbioAioAgoAv9BAIICw6IqIb3kxDKHqCgCioAi\noAjkBwFVkDzPirwiiAruuusuKcRK1W9iGVUUAUVAEVAEFIFSCFDYFuWImj4w1XXp0qXU4bpP\nEVAEFAFFIIMIKNdm6KGQMAd7HIQPsMtBt91MCfCh29WPioAioAgoAgkiQN4qxV1hBVXlKEFg\ntSlFQBFQBOqIgHqQQmCfeeaZwpa09dZbB48//riwuYUO0Y+KgCKgCCgCioAXARiennzyyXbl\nGLwH60ZFQBFQBBSBTCKgHiTnsZgK58HEiROleB8TXNapDJ2u61tFQBFQBBSBDCAAXW+4Vl0G\nuqVdUAQUAUVAEagAAVWQ/g+sESNGBGPHjpUK5vCjp1XTo4Jno4cqAoqAIqAIKAKKgCKgCCgC\nikCdEVAFyQBOgbbLLrss2GijjaR4VMeOHev8GPRyioAioAgoAoqAIqAIKAKKgCKQBQRaPgeJ\nKtuff/550KlTp2DQoEFS8yjug/nuu+8CGO/WXHPNuKfk4rjvv/9eKr1Tv6NZhMRpnvXqq6/e\nVIV+uSekmUJ6oNjnO8g9NRO1PmPF//zP/8h3sFl+V9zHt99+K8VP11hjjVRu64svvkil3bQa\n5fs7derUipvnu0HtPXDM+tiblzmCfvI88jBHs55I6zdU8ZexxAn0EzKrPJBXMTbloZ/ffPON\nrEtYn2RZ8jKH0U++p4Q7r7baam0gZV9caXkF6Sc/+UlwyCGHCNtQpVTeL730UsDCmyKyzSR8\nobI+QVeK94cffhi8++67wU477ZSLyTLu/dnv7A9+8IO4p2T+uK+++ipYvHhx0Llz52DLLbfM\nfH8r6SATYLP9thYsWCCTe+/evSuBIvaxePZ79uwZ+/hGH3j22WeLIlxpPxifGKd22223zCvR\neZkjXn/99eDLL78M9t5778wbxvKC6aJFi0RB2nPPPSv9itf9+DxgirI5f/784Kc//Wmwyy67\n1B2jSi+YhzkM5Yg1xPrrrx9ss8027W5xyJAh7bb5NvzAPJyCb4duK4/AvvvuG/z9738PXnzx\nxfIH6xENReDqq68WdsJZs2YFO+ywQ0P7ohcvjcArr7wSHHHEEVKL7KKLLip9sO5tOAJM6kya\n5G6qVI/AsGHDpPbegw8+qPTg1cPY5swBAwYI3fqSJUvaWZLbHKgfYiOwxx57iKfz2WefjX2O\nHhiNAB7OX/3qV8H2228fzJ49O/pA3RMbgZUrVwaszw8++OBgzJgxsc8LH9g8MVThO9PPioAi\noAgoAoqAIqAIKAKKgCKgCFSIgCpIFQKmhysCioAioAgoAoqAIqAIKAKKQPMioApSDc+WvI9m\nyyeoAY5Mn8qzsn+Z7qh2rvicmimvqpkfK2OgjoO1P2Ew1DGqdhzdFiyeOpa4qNT23n5Pa2tF\nz7YI6HfUIpHcq8W01nlJc5CSeybakiKgCCgCioAioAgoAoqAIqAI5BwB9SDl/AFq9xUBRUAR\nUAQUAUVAEVAEFAFFIDkEVEFKDkttSRFQBBQBRUARUAQUAUVAEVAEco6AKkg5f4DafUVAEVAE\nFAFFQBFQBBQBRUARSA6Bli8UWy2UH3zwQfD8888Ha621lhQxzEO15mrvNU/nUZGawpVhoXYD\nVZUR6g68+uqrwbJly4IttthC6yKFwarD51WrVsnv57DDDmtztTjPRn97bSBL/QPPhPphffv2\nDX784x8Xr7dixQopvlzcYN4wHvbo0aO4id/jc889F/BKkWaKvqq0R0Bxao9JJVv0u1gJWqWP\njfq9c1a5sVe/x35so+a7ON/bcpj7r9i8W//1r38FFIBmDbfuuusGrO1++MMfFm84yTWEkjQU\nYY3/ZubMmcG0adOCXr16BR9//HHwv//7v8F1110XdOjQIX4jemQqCFC8joKL66yzTpv2p0+f\nHqy55pqiHJ1yyinBJ598IhXrWbzxAzvnnHPaHK8f0kPg22+/DQYPHiyDGr8jKwxs5Z6N/vYs\nWvV7nThxYnDnnXcGc+bMCTp27Fi88OWXXx7we+N3ZWXrrbcOhg8f/v+3dy7AVk1/HP+VJOWd\nKHrc8sgjSqKkBr1LKk16jNtjegxTjQZ5FqOEUglDJaQxk1eJGE0ojzGGIgkpIiWPq6cykxTW\n//f9+e9tn33Oufvce87pHOd+fzN1915r7bX2/qy199m//fut37JdLNY3bNgwadSoka1ojntt\n0qRJ0qpVK684/yoBckp/GHAsps/QqyHZ/R717OU49gjG/k32e4dSUeM2inlsS4W/t337dhk+\nfLi9OzRt2tQ+hsM48dhjj9nHu4y/QzhKmQhs3rzZ6Qu1W716tR134MABpy8BbtasWWWqh4Wz\nQ2Du3Llu5MiRSSvXlapd//79nT60rMymTZtc27Zt3fr165Mew4zMEfjwww9d7969Xbt27ey+\nCdYc1Te894K0sr9dUlLixo4da33Vpk0bp19BYxotLi52CxYsiEkL7owYMcLNmDHD6Rc/S543\nb57r27evvx8sW5G3ySn93udYTJ9hafd7Ks9ejuP4Pijt9w6lSxu3qTCPb7GwU/CerR9X/Yvc\nu3ev69Kli5szZ46lZfodgnOQyqh0r1y50r6iNmvWzI6sUqWKaAfJm2++WcaaWDwbBDZs2CCN\nGzdOWjW+eHfs2FFq1KhhZRo0aCBNmjRh/yUllrkMuF/cfvvt0rVrVxkwYEBcxVF9w3svDllW\nEyZPniz6qyNTpkyJawdWc7h+JLvXduzYIevWrZOePXva2j6ooHv37mZxh2sr5R8C5JT+SOBY\nTJ8haijtfo969nIcx/dB1O9d1LiNYh7fYuGnVK9eXQYNGuRf6OGHH27TJODJBcn0OwQVJB91\nahtwzTr55JNjCsPtBKY/+EZScksACtKuXbvk1ltvlV69esltt90m8P/1BP0XdBNCOva3bt3q\nFeHfLBHAwwyuWjCR48NCWKL6hvdemFh293EPTZs2TWrVqhXXENxp8LzTL6TmRtevXz+ZPXu2\nuRujsH6NtmOC91rNmjWlatWqvNcCNMkpAKOcmxyL5QQXOqy0+z3q2ctxHIKpu1G/d1HjNop5\nfIuFnwLlKOiivXPnTlFvLjnrrLPs4jP9DkEFqYxjCg+C4ERlHA4ffLws7N69u4y1sXgmCeCL\nDfoHymqPHj3sRRw3zKhRowR+wH/++aflhfsP+7jRKNklAKUIL8mJJJW+4b2XiFz20jABNpng\nQwQEX0Fxf7Vv314WL14s06dPt3Tcd5g4G5w8iww8K/EBg/IPAXJKfyRwLKbPEDWUdr9HPXs5\njuP7oLTfO5SOGrdRzONbrFgp+/fvl7vuukvgBYSP4dl4h4j/jFuxGJf5ahEJDR0RFG8f5j9K\n7ghgsp7OibBIWvhSDcGXhcGDB8vy5ctNaapcuXLC/vNc7nJ39hW75UMOOUSi+ob3Xv6MkU6d\nOlm0ujp16thJNW/eXNCHOs9IRo8ebREjvedi8KwxiZbPyX+JJBrTyCWnfxlFbXEsRhFKPz/R\nOPXub9zPifLRKsdxcvblGbdB5slrLvycPXv2mHcQ/uo8Vxt/cAfP9DsELUhlHEuIjgZLRVDQ\nSYhgF/5aGizD7ewTqFSpktSuXdvceLzWEEELLkL4woV8hCFO1H84jpI7Aqn0De+93PVPuGU8\n6zzlyMvzXB/w5RN9hZcjnUTrZdtfPCvDx8UUqGA75JR+h3Msps8wqoaoZy/HcRTB+PxUxm2i\nd5WK/q4JDyENxGUfuh955BE/YnE23iGoIMWP21JTGjZsKBrxLMYKsXbt2rh5SaVWwsysENCI\ndGYt2rJli18/FKNt27b5/QOFCf0VFEwaD88rC+Zz++AQiOob3nsHpx9SaWXhwoVyyy23xBRd\ns2aNfYSAAlS3bl2bZxa81xC0Aa7IwXlJMRVUwB1ySr/TORbTZxhVQ9Szl+M4imB8ftS4jWIe\nX2Php/zyyy+mHNWrV8+W1jn66KNjLjrT7xBUkGLwRu906NDBCs2fP99+7Ddu3ChLliyRgQMH\nRh/MElklUFRUJNWqVbPJ4pjnAOVo5syZZt3DHAlInz59ZNmyZbZILEyyL774osCXtVu3blk9\nN1YeTSCqb3jvRTM8WCVat24tK1assHlHcPtYtWqVbSOiJ+YZ4YcLLiRYfwzz//bt22drxyE/\nUdCHg3Xe+dYOOaXfIxyL6TOMqiHq2ctxHEUwPj9q3EYxj6+x8FMwxxWeCVhgHoYKfJTDPwS8\ngGT6HYILxZZjTCFqxoQJE8x9BJFKEMp26NCh5aiJh2SaAG6aiRMnWjhh1I0vCpjIV79+fb8p\nXStJsAAb/KZhOcIk8xYtWvj53Mg+AcxVQUjO4EKxaDWqb3jvZb9vwi3oehyi63XELRSL+X66\n/oR9KMKPVufOnW3BZc/VGB8p8JzEDxjSsLDfuHHj4oLchNuraPvklH6Pcyymz9CrIdn9HvXs\n5Tj2CMb/TfZ7FzVuo5jHt1S4KQjljWipiaRly5YWcRV5mXyHoIKUiHaKaTD34WsoJoZR8osA\n/FShAIVNsN5ZwmqE+RDwnabkF4FU+ob3Xn70GaxHCJGP+8gLjBI+M9xnCODAQChhMrH75BTL\no6x7HItlJVa+8lHPXo7jsnFNZdxGMS9bi4VfOlPvEFSQCn+s8ApJgARIgARIgARIgARIgARS\nJEDTR4qgWIwESIAESIAESIAESIAESKDwCVBBKvw+5hWSAAmQAAmQAAmQAAmQAAmkSIAKUoqg\nWIwESIAESIAESIAESIAESKDwCVBBKvw+5hWSAAmQAAmQAAmQAAmQAAmkSIAKUoqgWIwESIAE\nSIAESIAESIAESKDwCVBBKvw+5hWSAAmQAAmQAAmQAAmQAAmkSKBKiuVYjARIgARIoEAI7Ny5\nU3777beYq8FaRVg37IgjjpBKlSrF5KW78/vvv9t6SSeccIJgce3/gmDtEaxRgsWkk8nff/8t\nW7ZsicuuXr26HHPMMbYWW1xmigklJSW2CO9JJ52U4hEsRgIkQAIkkCkCtCBliiTrIQESIIH/\nCIE77rhDioqKYv7Vq1dPjjrqKIESM2bMmDgFKp1LW758ubX1xhtvpFNN0mMPHDgg999/v2zb\nti1pmbJmFBcXS5s2bUo9bMeOHTEMPaZgeNhhh8kll1wiS5YsKbWOZJm9evWSTp06JctOmp4N\nFkkbYwYJkAAJFCgBWpAKtGN5WSRAAiQQRWD8+PFy4oknWrG//vpLfv31V3nttdfk4Ycflm+/\n/VZeffXVjFiTatasKZdeeqkcf/zxUadUrvypU6fKuHHj5Oqrry7X8ekedP7558uQIUP8amAx\n+/7772XOnDly5ZVXChTEKGXLP/j/GxdeeKHs27cvnBy5n2sWkSfIAiRAAiTwHyBABek/0Ek8\nRRIgARLIBoGBAwfK6aefHlM1FI127dqZovTll1/K2WefHZNfnp2LLrpI3n777fIcmtIxcIXL\npYDh6NGj407hiiuukM6dO8tDDz1UZgUJSmp5JNcsynPOPIYESIAE8o0AFaR86xGeDwmQAAnk\nkECVKlWkZ8+e8t5778nHH39sCtKsWbPM9Q5ueI8++qiceeaZMmzYMKlVq5bAWvLEE09YWVih\nmjZtKiNGjLA5ON5lfP311zJ//nwZMGCAnHHGGV6yfP755/LCCy/IunXrpH79+tK9e3dTzvwC\n/99Auddff10++eQTgbKF80N5yPPPPy9vvfWWbU+bNk3OO+88GTRokO1DWXjqqadk5cqVsnfv\nXsvDuWGuVVC2bt1q1jLU07BhwxhrULBcWbc7dOhgbovgGJSPPvrIzvu7774zF72uXbsKygbl\n8ccfl/3798uoUaMsGdaoY489Vi677DKZN2+e8a5Tp45cddVV0rp1ayuTjAXmSr300kuydOlS\nwfwzKHTdunWTtm3bBpvkNgmQAAmQgEfAUUiABEiABCoUgZEjRzr9DXBfffVVwuvu06eP5ev8\nGctv1qyZUxc5pwqRq1y5sv374Ycf3I8//uh03o2rWrWq69ixo1MFx9WoUcNpYAOnSoFft7rq\nWX0vv/yynzZ79mw7DseqpcU1b97cyowdO9Yvgw0cowEkXKNGjZwqA6527dpWbsGCBVbuzjvv\ntHPA9aii4G666SZLV6XHtWjRwsqqQuB0To/TwAmuQYMGbu3atVYG/+m8JUs78sgjnSperlWr\nVu64446zOnFtpQnaQLuq+CUs9sUXX1i+usv5+XfffbfTIBj+9aiFzspcc801fhlstGzZ0iHP\nkwsuuMCpm55r3Lixw7nq/CZjDTYLFy60YslYXHfdddbmOeec49C3GvjB9h944AGvev4lARIg\nARIIEJDANjdJgARIgAQqAIFkCpJagByUmUMPPdSUIZ2TZDSgIEERuOGGG5xaYtz69estvUuX\nLk6j0rkVK1b41KB06bwm16RJE6cBAyw9rCBt2LDBlCO1hpiC4h2s7n3WzrJlyywJCgbqh+Ly\nxx9/WJpahUx5UEuP0zk6ljZhwgQ7DkqbJ0OHDrW0RYsWeUlu8+bNTq0uTi0nfhoUO7UoxSiL\nU6ZMsWPTUZB+/vln16NHD6vnnnvusfbef/99Uy779+/v1DpkaWrdMa7g+9xzz/nnlUhBQhko\ngOgDiFrmnEYddGpV848Ls9izZ48pmOhzT9DPUJbQT+BJIQESIAESiCVABSmWB/dIgARIoOAJ\neAoSlAV1m7N/sCpAMcJLuLpyuVdeecXnAAUJliN1p/PTNLy1lYV1IiywTKAeT9EJK0jXX3+9\n5avbXMyh6v5l56CBDSx9+vTpVg6KQFBWr17t1NXPwYIDCSsFu3btMgtJUHHwjr/xxhutzjVr\n1rjdu3dbubDVCkqDuvCZFck7LtFfz4KkYb19juAJSxWuH//Ulc1X7tQt0a5PQ4jHVAeuGvnO\nLEReRiIFSSPj+XV55S6++GKzCHn7YRZQkNCvsDj99NNPXjEH1sH+9DO4QQIkQAIk4DgHSX/B\nKCRAAiRQEQlgvpC6a9mlY+4R1vzBHJx+/foJIs8FBfOPqlWr5idh3hAE0dbCoi/3lqSWJmnf\nvn04W9TKZNHxMK8G85eCgjWEMGcJ8umnn4q67Mlpp50WLCKqsNm/mMTAjlqo8PFPVDmQvn37\nBnJE1Mpk+2gD+Sh37rnnxpTBmlBgg7lPqQii83nXjPJYA0ld+SwtOLcIzJCOMOBBAVe0p4pf\nMDluG32gLokx6agLwTSSCfpXFVILg47+VVdGwZwnhBFH9D0KCZAACZBAPAEqSPFMmEICJEAC\nFYIAoquFo9glu/CwwoQ1gCBYOyksWGwWgjV5Esn27dttnSAoZWFRtz1brBbpOsfJ3w6XK20f\n9UOwKK1avmKKIrgD/kFx8K7BO99gQZ2HFNwtdVutOPLMM8+UWgaZaC8RL+ThHJLxQj4EymNY\nsKgvlLzSRF0GLcw6AmVgLapJkybZPwSzePLJJyVRP5RWH/NIgARIoNAJxP86FfoV8/pIgARI\ngATSJnDKKadYHZs2bYqry0uDpSeRaMAFiyw3ceLEOAUNkee8F3ZYs9555x1THNRNzK+qpKRE\nFi9ebNapU0891U/3NlA/BMoflIKgINIeLEQQnZNkf7FmUVjUHS2clPY+mOl8rYT1gFkyXgkP\nKEMirhmLzsJyBGUKUfXUrVCefvppi/iXyMpXhupZlARIgAQKjkDsp7WCuzxeEAmQAAmQQDYI\nINQ3wk4j5HTYgjF37lxrMtkLPywuELygB+Wzzz4zS8qYMWMsGSG9EaJaI9YFi9m6Qtdee63v\nLucpPAiLDYGCpNHuLLQ13OiCgsVk4QIH5QjublgoN3weUI4Q5jzTguuGFQnKXVDgWgd3QoQo\nT1fCLDw3xRkzZljVsDhpRDx/3SYomxQSIAESIIFYAlSQYnlwjwRIgARIIAUCcAmDqxbWJurd\nu7d88MEHsmrVKtFw1aYA3HvvvTFrIQWrRBkoWA8++KApOxp221zUNLqbKUjjx4+34oMHD7Z1\nmLAIKxQxKFCTJ0+WmTNn2sKr3jo+UNQg9913n2hYcIG1aerUqbZGE+bavPvuu4K1hzRAg60/\npIElTDnCMVhvCEoE1mjC+WNBW6zHBKtLpgVzgaCUDRkyxFzbMCcJyh8WlIW1DOeXroRZQEmF\nhQjcoCRpcAp59tlnBdY79CGsShQSIAESIIEQAf3yRyEBEiABEqhABLwodsnWQQqj0JdsW6co\nnI59tRZZSHD9abGobVhzKLy+TjiKHY5DBDisa6TudH7ENw3G4HSODLJ9UQuHhflGFD2vDVXI\nXDASHOrCOSJfFRD/WF041SK8ecehLYT/9kKGewV1Ho7DeaOcWmDc8OHDXXFxccpR7JKtg+TV\nH/yLUOSXX365RQVEe1g3CuHSEYI8KImi2GkwiWAR2wYLRM3zJBEL9DPq8zhgHSbwQhh1CgmQ\nAAmQQDyBSkjShyaFBEiABEiABMpNQMN+29weDRceV4eGDBddy0jwF9aSoMAt7ptvvhFdi0hw\nLFzAEomGpJaNGzdK3bp1rWyiMhre2yLtIThDUOBGBte2oqIii4oXzAtuYx4QItIlCtoQLJeJ\nbVwP2sMcquD8qkzUjToSsUDwCl2fyTh40Qsz1R7rIQESIIFCIkAFqZB6k9dCAiRAAnlIAHOS\ndA0gm9fTpk2bPDxDnhIJkAAJkAAJ/EuAUez+ZcEtEiABEiCBDBKA1ebmm2+WpUuXWsjt8HpD\nGWyKVZEACZAACZBAxggwSEPGULIiEiABEiCBIAG43SEoAMJtL1q0KOkaQMFjuE0CJEACJEAC\nuSZAF7tc9wDbJwESIAESIAESIAESIAESyBsCtCDlTVfwREiABEiABEiABEiABEiABHJNgApS\nrnuA7ZMACZAACZAACZAACZAACeQNASpIedMVPBESIAESIAESIAESIAESIIFcE6CClOseYPsk\nQAIkQAIkQAIkQAIkQAJ5Q4AKUt50BU+EBEiABEiABEiABEiABEgg1wSoIOW6B9g+CZAACZAA\nCZAACZAACZBA3hCggpQ3XcETIQESIAESIAESIAESIAESyDUBKki57gG2TwIkQAIkQAIkQAIk\nQAIkkDcE/gdeZ/CFRwHQlgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "truth <- season.metrics %>%\n",
    "  filter(seas == 2016) %>%\n",
    "  dplyr::select(-career.year, -lst, -avg) %>%\n",
    "  spread(metric, value) %>%\n",
    "  mutate(fpts = py / 25 \n",
    "         + recy / 15 \n",
    "         + ry / 10 \n",
    "         + rec * 0.5 \n",
    "         + (tdr + tdrec + tdp) * 6 \n",
    "         + ints * -2 \n",
    "         + fuml * -2) %>%\n",
    "  arrange(-fpts) %>%\n",
    "  mutate(rnk = row_number())\n",
    "\n",
    "rnks %>%\n",
    "  filter(cutoff == 2015, seas == 2016) %>%\n",
    "  inner_join(truth %>% dplyr::select(player, seas, fpts.true = fpts)) %>%\n",
    "  ggplot(aes(x = fpts, y = fpts.true)) + \n",
    "  geom_point() +\n",
    "  facet_wrap('pos1', scales = 'free') +\n",
    "  theme_bw() +\n",
    "  geom_abline() +\n",
    "  xlab('Projected Points') +\n",
    "  ylab('Actual Points')\n",
    "\n",
    "comparison <- rnks %>%\n",
    "  filter(cutoff == 2015) %>%\n",
    "  inner_join(truth %>% dplyr::select(player, seas, fpts.true = fpts)) %>%\n",
    "  group_by(pos1) %>%\n",
    "  arrange(-fpts.true) %>%\n",
    "  mutate(rnk.true = row_number())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparisons\n",
    "\n",
    "Here we compare the performance of my method to various 2016 ranks from Yahoo, fantrax, CBS, ESPN. My method beats all of them for ordering the picks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  Rank = col_integer(),\n",
      "  name = col_character(),\n",
      "  pos = col_character(),\n",
      "  team = col_character(),\n",
      "  bye = col_integer(),\n",
      "  FFC = col_integer(),\n",
      "  ESPN = col_integer(),\n",
      "  CBS = col_integer(),\n",
      "  AVG = col_integer(),\n",
      "  YAHOO = col_integer(),\n",
      "  NFL = col_integer(),\n",
      "  dw = col_integer(),\n",
      "  fantrax = col_integer(),\n",
      "  other = col_character()\n",
      ")\n",
      "Joining, by = \"player\"\n",
      "Joining, by = \"name\"\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zkxuXPnjkuTVHascQ+TxkESSY0WaTTo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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "md <- read_csv('mock_drafts/fantasy_pros_2016.csv') %>%\n",
    "  filter(Rank <= 200) %>%\n",
    "  mutate(year = 2016) %>%\n",
    "  dplyr::select(-pos, -team, -bye) %>%\n",
    "  gather(method, rnk, -year, -name) %>%\n",
    "  mutate(rnk = as.numeric(rnk)) %>%\n",
    "  filter(!is.na(name), !is.na(rnk)) %>%\n",
    "  ## make name more joinable\n",
    "  mutate(name = str_replace_all(name, \"[.']\", ''),\n",
    "         name = str_to_lower(name))\n",
    "\n",
    "my.method <- rnks %>%\n",
    "  ungroup() %>%\n",
    "  filter(cutoff == 2015, seas == 2016) %>%\n",
    "  mutate(name = paste(fname, lname),\n",
    "         name = str_replace_all(name, \"[.']\", ''),\n",
    "         name = str_to_lower(name),\n",
    "         method = 'Sean') %>%\n",
    "  dplyr::select(name, year = seas, method, rnk)\n",
    "\n",
    "options(repr.plot.height = 6)\n",
    "truth %>%\n",
    "  inner_join(player %>% dplyr::select(player, fname, lname, pos1)) %>%\n",
    "  mutate(name = paste(fname, lname),\n",
    "         name = str_replace_all(name, \"[.']\", ''),\n",
    "         name = str_to_lower(name)) %>%\n",
    "  dplyr::select(name, pos1, rnk.true = rnk) %>%\n",
    "  inner_join(md %>% bind_rows(my.method)) %>%\n",
    "  group_by(pos1, method) %>%\n",
    "  summarise(rho = cor(rnk, rnk.true, method = 'spearman')) %>%\n",
    "  ggplot(aes(x = reorder(method, rho), y = rho, scale = 'free')) + \n",
    "  geom_bar(stat = 'identity') + \n",
    "  facet_wrap('pos1', nrow = 4) +\n",
    "  theme_bw() +\n",
    "  xlab('Method') + \n",
    "  ylab('Rank Correlation') + \n",
    "  coord_flip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2017 Projections!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quarterbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>cutoff</th><th scope=col>seas</th><th scope=col>player</th><th scope=col>fname</th><th scope=col>lname</th><th scope=col>pos1</th><th scope=col>fpts</th><th scope=col>rnk</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>DB-3800       </td><td>Drew          </td><td>Brees         </td><td>QB            </td><td>368.5694      </td><td> 1            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>MR-2500       </td><td>Matt          </td><td>Ryan          </td><td>QB            </td><td>349.6652      </td><td> 2            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>AR-1300       </td><td>Aaron         </td><td>Rodgers       </td><td>QB            </td><td>334.9376      </td><td> 3            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>MS-4100       </td><td>Matthew       </td><td>Stafford      </td><td>QB            </td><td>332.1406      </td><td> 4            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>TB-2300       </td><td>Tom           </td><td>Brady         </td><td>QB            </td><td>330.5391      </td><td> 5            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>KC-2350       </td><td>Kirk          </td><td>Cousins       </td><td>QB            </td><td>317.3565      </td><td> 6            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>PR-0300       </td><td>Philip        </td><td>Rivers        </td><td>QB            </td><td>315.3590      </td><td> 7            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>AD-0100       </td><td>Andy          </td><td>Dalton        </td><td>QB            </td><td>314.8137      </td><td> 8            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>CN-0500       </td><td>Cam           </td><td>Newton        </td><td>QB            </td><td>313.6420      </td><td> 9            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>EM-0200       </td><td>Eli           </td><td>Manning       </td><td>QB            </td><td>311.1295      </td><td>10            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>JW-5650       </td><td>Jameis        </td><td>Winston       </td><td>QB            </td><td>309.9050      </td><td>11            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>DC-0725       </td><td>Derek         </td><td>Carr          </td><td>QB            </td><td>305.7381      </td><td>12            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>RT-0150       </td><td>Ryan          </td><td>Tannehill     </td><td>QB            </td><td>301.3228      </td><td>13            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>DP-2037       </td><td>Dak           </td><td>Prescott      </td><td>QB            </td><td>296.3097      </td><td>14            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>BB-2425       </td><td>Blake         </td><td>Bortles       </td><td>QB            </td><td>289.7969      </td><td>15            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>JF-1900       </td><td>Joe           </td><td>Flacco        </td><td>QB            </td><td>287.8281      </td><td>16            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>TT-0500       </td><td>Tyrod         </td><td>Taylor        </td><td>QB            </td><td>282.7517      </td><td>17            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>BR-1100       </td><td>Ben           </td><td>Roethlisberger</td><td>QB            </td><td>276.5516      </td><td>18            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>AL-1100       </td><td>Andrew        </td><td>Luck          </td><td>QB            </td><td>259.5401      </td><td>19            </td></tr>\n",
       "\t<tr><td>2016          </td><td>2017          </td><td>CP-0500       </td><td>Carson        </td><td>Palmer        </td><td>QB            </td><td>253.4203      </td><td>20            </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " cutoff & seas & player & fname & lname & pos1 & fpts & rnk\\\\\n",
       "\\hline\n",
       "\t 2016           & 2017           & DB-3800        & Drew           & Brees          & QB             & 368.5694       &  1            \\\\\n",
       "\t 2016           & 2017           & MR-2500        & Matt           & Ryan           & QB             & 349.6652       &  2            \\\\\n",
       "\t 2016           & 2017           & AR-1300        & Aaron          & Rodgers        & QB             & 334.9376       &  3            \\\\\n",
       "\t 2016           & 2017           & MS-4100        & Matthew        & Stafford       & QB             & 332.1406       &  4            \\\\\n",
       "\t 2016           & 2017           & TB-2300        & Tom            & Brady          & QB             & 330.5391       &  5            \\\\\n",
       "\t 2016           & 2017           & KC-2350        & Kirk           & Cousins        & QB             & 317.3565       &  6            \\\\\n",
       "\t 2016           & 2017           & PR-0300        & Philip         & Rivers         & QB             & 315.3590       &  7            \\\\\n",
       "\t 2016           & 2017           & AD-0100        & Andy           & Dalton         & QB             & 314.8137       &  8            \\\\\n",
       "\t 2016           & 2017           & CN-0500        & Cam            & Newton         & QB             & 313.6420       &  9            \\\\\n",
       "\t 2016           & 2017           & EM-0200        & Eli            & Manning        & QB             & 311.1295       & 10            \\\\\n",
       "\t 2016           & 2017           & JW-5650        & Jameis         & Winston        & QB             & 309.9050       & 11            \\\\\n",
       "\t 2016           & 2017           & DC-0725        & Derek          & Carr           & QB             & 305.7381       & 12            \\\\\n",
       "\t 2016           & 2017           & RT-0150        & Ryan           & Tannehill      & QB             & 301.3228       & 13            \\\\\n",
       "\t 2016           & 2017           & DP-2037        & Dak            & Prescott       & QB             & 296.3097       & 14            \\\\\n",
       "\t 2016           & 2017           & BB-2425        & Blake          & Bortles        & QB             & 289.7969       & 15            \\\\\n",
       "\t 2016           & 2017           & JF-1900        & Joe            & Flacco         & QB             & 287.8281       & 16            \\\\\n",
       "\t 2016           & 2017           & TT-0500        & Tyrod          & Taylor         & QB             & 282.7517       & 17            \\\\\n",
       "\t 2016           & 2017           & BR-1100        & Ben            & Roethlisberger & QB             & 276.5516       & 18            \\\\\n",
       "\t 2016           & 2017           & AL-1100        & Andrew         & Luck           & QB             & 259.5401       & 19            \\\\\n",
       "\t 2016           & 2017           & CP-0500        & Carson         & Palmer         & QB             & 253.4203       & 20            \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "cutoff | seas | player | fname | lname | pos1 | fpts | rnk | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 2016           | 2017           | DB-3800        | Drew           | Brees          | QB             | 368.5694       |  1             | \n",
       "| 2016           | 2017           | MR-2500        | Matt           | Ryan           | QB             | 349.6652       |  2             | \n",
       "| 2016           | 2017           | AR-1300        | Aaron          | Rodgers        | QB             | 334.9376       |  3             | \n",
       "| 2016           | 2017           | MS-4100        | Matthew        | Stafford       | QB             | 332.1406       |  4             | \n",
       "| 2016           | 2017           | TB-2300        | Tom            | Brady          | QB             | 330.5391       |  5             | \n",
       "| 2016           | 2017           | KC-2350        | Kirk           | Cousins        | QB             | 317.3565       |  6             | \n",
       "| 2016           | 2017           | PR-0300        | Philip         | Rivers         | QB             | 315.3590       |  7             | \n",
       "| 2016           | 2017           | AD-0100        | Andy           | Dalton         | QB             | 314.8137       |  8             | \n",
       "| 2016           | 2017           | CN-0500        | Cam            | Newton         | QB             | 313.6420       |  9             | \n",
       "| 2016           | 2017           | EM-0200        | Eli            | Manning        | QB             | 311.1295       | 10             | \n",
       "| 2016           | 2017           | JW-5650        | Jameis         | Winston        | QB             | 309.9050       | 11             | \n",
       "| 2016           | 2017           | DC-0725        | Derek          | Carr           | QB             | 305.7381       | 12             | \n",
       "| 2016           | 2017           | RT-0150        | Ryan           | Tannehill      | QB             | 301.3228       | 13             | \n",
       "| 2016           | 2017           | DP-2037        | Dak            | Prescott       | QB             | 296.3097       | 14             | \n",
       "| 2016           | 2017           | BB-2425        | Blake          | Bortles        | QB             | 289.7969       | 15             | \n",
       "| 2016           | 2017           | JF-1900        | Joe            | Flacco         | QB             | 287.8281       | 16             | \n",
       "| 2016           | 2017           | TT-0500        | Tyrod          | Taylor         | QB             | 282.7517       | 17             | \n",
       "| 2016           | 2017           | BR-1100        | Ben            | Roethlisberger | QB             | 276.5516       | 18             | \n",
       "| 2016           | 2017           | AL-1100        | Andrew         | Luck           | QB             | 259.5401       | 19             | \n",
       "| 2016           | 2017           | CP-0500        | Carson         | Palmer         | QB             | 253.4203       | 20             | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   cutoff seas player  fname   lname          pos1 fpts     rnk\n",
       "1  2016   2017 DB-3800 Drew    Brees          QB   368.5694  1 \n",
       "2  2016   2017 MR-2500 Matt    Ryan           QB   349.6652  2 \n",
       "3  2016   2017 AR-1300 Aaron   Rodgers        QB   334.9376  3 \n",
       "4  2016   2017 MS-4100 Matthew Stafford       QB   332.1406  4 \n",
       "5  2016   2017 TB-2300 Tom     Brady          QB   330.5391  5 \n",
       "6  2016   2017 KC-2350 Kirk    Cousins        QB   317.3565  6 \n",
       "7  2016   2017 PR-0300 Philip  Rivers         QB   315.3590  7 \n",
       "8  2016   2017 AD-0100 Andy    Dalton         QB   314.8137  8 \n",
       "9  2016   2017 CN-0500 Cam     Newton         QB   313.6420  9 \n",
       "10 2016   2017 EM-0200 Eli     Manning        QB   311.1295 10 \n",
       "11 2016   2017 JW-5650 Jameis  Winston        QB   309.9050 11 \n",
       "12 2016   2017 DC-0725 Derek   Carr           QB   305.7381 12 \n",
       "13 2016   2017 RT-0150 Ryan    Tannehill      QB   301.3228 13 \n",
       "14 2016   2017 DP-2037 Dak     Prescott       QB   296.3097 14 \n",
       "15 2016   2017 BB-2425 Blake   Bortles        QB   289.7969 15 \n",
       "16 2016   2017 JF-1900 Joe     Flacco         QB   287.8281 16 \n",
       "17 2016   2017 TT-0500 Tyrod   Taylor         QB   282.7517 17 \n",
       "18 2016   2017 BR-1100 Ben     Roethlisberger QB   276.5516 18 \n",
       "19 2016   2017 AL-1100 Andrew  Luck           QB   259.5401 19 \n",
       "20 2016   2017 CP-0500 Carson  Palmer         QB   253.4203 20 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rnks %>% \n",
    "  filter(seas == 2017, cutoff == 2016, pos1 == 'QB') %>%\n",
    "  head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wide Receivers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>cutoff</th><th scope=col>seas</th><th scope=col>player</th><th scope=col>fname</th><th scope=col>lname</th><th scope=col>pos1</th><th scope=col>fpts</th><th scope=col>rnk</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>AB-3500    </td><td>Antonio    </td><td>Brown      </td><td>WR         </td><td>193.5945   </td><td> 1         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>OB-0075    </td><td>Odell      </td><td>Beckham Jr.</td><td>WR         </td><td>188.7067   </td><td> 2         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>JN-0900    </td><td>Jordy      </td><td>Nelson     </td><td>WR         </td><td>173.7585   </td><td> 3         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>ME-0600    </td><td>Mike       </td><td>Evans      </td><td>WR         </td><td>172.0226   </td><td> 4         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>JJ-4700    </td><td>Julio      </td><td>Jones      </td><td>WR         </td><td>171.4137   </td><td> 5         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>DH-3950    </td><td>DeAndre    </td><td>Hopkins    </td><td>WR         </td><td>164.8889   </td><td> 6         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>TH-1850    </td><td>Ty         </td><td>Hilton     </td><td>WR         </td><td>153.2773   </td><td> 7         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>BM-0300    </td><td>Brandon    </td><td>Marshall   </td><td>WR         </td><td>150.5808   </td><td> 8         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>DT-0900    </td><td>Demaryius  </td><td>Thomas     </td><td>WR         </td><td>147.0305   </td><td> 9         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>DB-5300    </td><td>Dez        </td><td>Bryant     </td><td>WR         </td><td>141.7774   </td><td>10         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>ES-0200    </td><td>Emmanuel   </td><td>Sanders    </td><td>WR         </td><td>141.4799   </td><td>11         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>KB-0950    </td><td>Kelvin     </td><td>Benjamin   </td><td>WR         </td><td>139.8689   </td><td>12         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>LF-0200    </td><td>Larry      </td><td>Fitzgerald </td><td>WR         </td><td>136.0268   </td><td>13         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>AC-2350    </td><td>Amari      </td><td>Cooper     </td><td>WR         </td><td>132.0906   </td><td>14         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>SS-1250    </td><td>Sterling   </td><td>Shepard    </td><td>WR         </td><td>130.5920   </td><td>15         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>MT-0875    </td><td>Michael    </td><td>Thomas     </td><td>WR         </td><td>130.4849   </td><td>16         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>JL-0215    </td><td>Jarvis     </td><td>Landry     </td><td>WR         </td><td>129.3361   </td><td>17         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>PG-0100    </td><td>Pierre     </td><td>Garcon     </td><td>WR         </td><td>129.0795   </td><td>18         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>RM-1500    </td><td>Rishard    </td><td>Matthews   </td><td>WR         </td><td>128.1243   </td><td>19         </td></tr>\n",
       "\t<tr><td>2016       </td><td>2017       </td><td>MC-2900    </td><td>Michael    </td><td>Crabtree   </td><td>WR         </td><td>127.1738   </td><td>20         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " cutoff & seas & player & fname & lname & pos1 & fpts & rnk\\\\\n",
       "\\hline\n",
       "\t 2016        & 2017        & AB-3500     & Antonio     & Brown       & WR          & 193.5945    &  1         \\\\\n",
       "\t 2016        & 2017        & OB-0075     & Odell       & Beckham Jr. & WR          & 188.7067    &  2         \\\\\n",
       "\t 2016        & 2017        & JN-0900     & Jordy       & Nelson      & WR          & 173.7585    &  3         \\\\\n",
       "\t 2016        & 2017        & ME-0600     & Mike        & Evans       & WR          & 172.0226    &  4         \\\\\n",
       "\t 2016        & 2017        & JJ-4700     & Julio       & Jones       & WR          & 171.4137    &  5         \\\\\n",
       "\t 2016        & 2017        & DH-3950     & DeAndre     & Hopkins     & WR          & 164.8889    &  6         \\\\\n",
       "\t 2016        & 2017        & TH-1850     & Ty          & Hilton      & WR          & 153.2773    &  7         \\\\\n",
       "\t 2016        & 2017        & BM-0300     & Brandon     & Marshall    & WR          & 150.5808    &  8         \\\\\n",
       "\t 2016        & 2017        & DT-0900     & Demaryius   & Thomas      & WR          & 147.0305    &  9         \\\\\n",
       "\t 2016        & 2017        & DB-5300     & Dez         & Bryant      & WR          & 141.7774    & 10         \\\\\n",
       "\t 2016        & 2017        & ES-0200     & Emmanuel    & Sanders     & WR          & 141.4799    & 11         \\\\\n",
       "\t 2016        & 2017        & KB-0950     & Kelvin      & Benjamin    & WR          & 139.8689    & 12         \\\\\n",
       "\t 2016        & 2017        & LF-0200     & Larry       & Fitzgerald  & WR          & 136.0268    & 13         \\\\\n",
       "\t 2016        & 2017        & AC-2350     & Amari       & Cooper      & WR          & 132.0906    & 14         \\\\\n",
       "\t 2016        & 2017        & SS-1250     & Sterling    & Shepard     & WR          & 130.5920    & 15         \\\\\n",
       "\t 2016        & 2017        & MT-0875     & Michael     & Thomas      & WR          & 130.4849    & 16         \\\\\n",
       "\t 2016        & 2017        & JL-0215     & Jarvis      & Landry      & WR          & 129.3361    & 17         \\\\\n",
       "\t 2016        & 2017        & PG-0100     & Pierre      & Garcon      & WR          & 129.0795    & 18         \\\\\n",
       "\t 2016        & 2017        & RM-1500     & Rishard     & Matthews    & WR          & 128.1243    & 19         \\\\\n",
       "\t 2016        & 2017        & MC-2900     & Michael     & Crabtree    & WR          & 127.1738    & 20         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "cutoff | seas | player | fname | lname | pos1 | fpts | rnk | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 2016        | 2017        | AB-3500     | Antonio     | Brown       | WR          | 193.5945    |  1          | \n",
       "| 2016        | 2017        | OB-0075     | Odell       | Beckham Jr. | WR          | 188.7067    |  2          | \n",
       "| 2016        | 2017        | JN-0900     | Jordy       | Nelson      | WR          | 173.7585    |  3          | \n",
       "| 2016        | 2017        | ME-0600     | Mike        | Evans       | WR          | 172.0226    |  4          | \n",
       "| 2016        | 2017        | JJ-4700     | Julio       | Jones       | WR          | 171.4137    |  5          | \n",
       "| 2016        | 2017        | DH-3950     | DeAndre     | Hopkins     | WR          | 164.8889    |  6          | \n",
       "| 2016        | 2017        | TH-1850     | Ty          | Hilton      | WR          | 153.2773    |  7          | \n",
       "| 2016        | 2017        | BM-0300     | Brandon     | Marshall    | WR          | 150.5808    |  8          | \n",
       "| 2016        | 2017        | DT-0900     | Demaryius   | Thomas      | WR          | 147.0305    |  9          | \n",
       "| 2016        | 2017        | DB-5300     | Dez         | Bryant      | WR          | 141.7774    | 10          | \n",
       "| 2016        | 2017        | ES-0200     | Emmanuel    | Sanders     | WR          | 141.4799    | 11          | \n",
       "| 2016        | 2017        | KB-0950     | Kelvin      | Benjamin    | WR          | 139.8689    | 12          | \n",
       "| 2016        | 2017        | LF-0200     | Larry       | Fitzgerald  | WR          | 136.0268    | 13          | \n",
       "| 2016        | 2017        | AC-2350     | Amari       | Cooper      | WR          | 132.0906    | 14          | \n",
       "| 2016        | 2017        | SS-1250     | Sterling    | Shepard     | WR          | 130.5920    | 15          | \n",
       "| 2016        | 2017        | MT-0875     | Michael     | Thomas      | WR          | 130.4849    | 16          | \n",
       "| 2016        | 2017        | JL-0215     | Jarvis      | Landry      | WR          | 129.3361    | 17          | \n",
       "| 2016        | 2017        | PG-0100     | Pierre      | Garcon      | WR          | 129.0795    | 18          | \n",
       "| 2016        | 2017        | RM-1500     | Rishard     | Matthews    | WR          | 128.1243    | 19          | \n",
       "| 2016        | 2017        | MC-2900     | Michael     | Crabtree    | WR          | 127.1738    | 20          | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   cutoff seas player  fname     lname       pos1 fpts     rnk\n",
       "1  2016   2017 AB-3500 Antonio   Brown       WR   193.5945  1 \n",
       "2  2016   2017 OB-0075 Odell     Beckham Jr. WR   188.7067  2 \n",
       "3  2016   2017 JN-0900 Jordy     Nelson      WR   173.7585  3 \n",
       "4  2016   2017 ME-0600 Mike      Evans       WR   172.0226  4 \n",
       "5  2016   2017 JJ-4700 Julio     Jones       WR   171.4137  5 \n",
       "6  2016   2017 DH-3950 DeAndre   Hopkins     WR   164.8889  6 \n",
       "7  2016   2017 TH-1850 Ty        Hilton      WR   153.2773  7 \n",
       "8  2016   2017 BM-0300 Brandon   Marshall    WR   150.5808  8 \n",
       "9  2016   2017 DT-0900 Demaryius Thomas      WR   147.0305  9 \n",
       "10 2016   2017 DB-5300 Dez       Bryant      WR   141.7774 10 \n",
       "11 2016   2017 ES-0200 Emmanuel  Sanders     WR   141.4799 11 \n",
       "12 2016   2017 KB-0950 Kelvin    Benjamin    WR   139.8689 12 \n",
       "13 2016   2017 LF-0200 Larry     Fitzgerald  WR   136.0268 13 \n",
       "14 2016   2017 AC-2350 Amari     Cooper      WR   132.0906 14 \n",
       "15 2016   2017 SS-1250 Sterling  Shepard     WR   130.5920 15 \n",
       "16 2016   2017 MT-0875 Michael   Thomas      WR   130.4849 16 \n",
       "17 2016   2017 JL-0215 Jarvis    Landry      WR   129.3361 17 \n",
       "18 2016   2017 PG-0100 Pierre    Garcon      WR   129.0795 18 \n",
       "19 2016   2017 RM-1500 Rishard   Matthews    WR   128.1243 19 \n",
       "20 2016   2017 MC-2900 Michael   Crabtree    WR   127.1738 20 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rnks %>% \n",
    "  filter(seas == 2017, cutoff == 2016, pos1 == 'WR') %>%\n",
    "  head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Backs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>cutoff</th><th scope=col>seas</th><th scope=col>player</th><th scope=col>fname</th><th scope=col>lname</th><th scope=col>pos1</th><th scope=col>fpts</th><th scope=col>rnk</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>DJ-1325          </td><td>David            </td><td>Johnson          </td><td>RB               </td><td>209.4883         </td><td> 1               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>MF-1300          </td><td>Matt             </td><td>Forte            </td><td>RB               </td><td>190.4054         </td><td> 2               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>LM-1000          </td><td>LeSean           </td><td>McCoy            </td><td>RB               </td><td>189.6376         </td><td> 3               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>DF-1875          </td><td>Devonta          </td><td>Freeman          </td><td>RB               </td><td>186.2265         </td><td> 4               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>FG-0200          </td><td>Frank            </td><td>Gore             </td><td>RB               </td><td>181.7967         </td><td> 5               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>EE-0250          </td><td>Ezekiel          </td><td>Elliott          </td><td>RB               </td><td>171.9824         </td><td> 6               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>DM-4300          </td><td>DeMarco          </td><td>Murray           </td><td>RB               </td><td>166.4855         </td><td> 7               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>LB-0250          </td><td>Le'Veon          </td><td>Bell             </td><td>RB               </td><td>162.1650         </td><td> 8               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>Leonard Fournette</td><td>NA               </td><td>NA               </td><td>RB               </td><td>155.5248         </td><td> 9               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>MG-1150          </td><td>Melvin           </td><td>Gordon           </td><td>RB               </td><td>149.4913         </td><td>10               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>MI-0100          </td><td>Mark             </td><td>Ingram           </td><td>RB               </td><td>147.7702         </td><td>11               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>DM-0450          </td><td>Doug             </td><td>Martin           </td><td>RB               </td><td>137.4707         </td><td>12               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>JH-5575          </td><td>Jordan           </td><td>Howard           </td><td>RB               </td><td>136.5513         </td><td>13               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>KD-1050          </td><td>Kenneth          </td><td>Dixon            </td><td>RB               </td><td>134.8842         </td><td>14               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>LM-1850          </td><td>Latavius         </td><td>Murray           </td><td>RB               </td><td>134.7452         </td><td>15               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>CH-5000          </td><td>Carlos           </td><td>Hyde             </td><td>RB               </td><td>134.1729         </td><td>16               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>LB-0500          </td><td>LeGarrette       </td><td>Blount           </td><td>RB               </td><td>131.8789         </td><td>17               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>DB-2950          </td><td>Devontae         </td><td>Booker           </td><td>RB               </td><td>130.0696         </td><td>18               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>DW-3600          </td><td>DeAngelo         </td><td>Williams         </td><td>RB               </td><td>127.8926         </td><td>19               </td></tr>\n",
       "\t<tr><td>2016             </td><td>2017             </td><td>CJ-1700          </td><td>Chris            </td><td>Johnson          </td><td>RB               </td><td>127.8128         </td><td>20               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " cutoff & seas & player & fname & lname & pos1 & fpts & rnk\\\\\n",
       "\\hline\n",
       "\t 2016              & 2017              & DJ-1325           & David             & Johnson           & RB                & 209.4883          &  1               \\\\\n",
       "\t 2016              & 2017              & MF-1300           & Matt              & Forte             & RB                & 190.4054          &  2               \\\\\n",
       "\t 2016              & 2017              & LM-1000           & LeSean            & McCoy             & RB                & 189.6376          &  3               \\\\\n",
       "\t 2016              & 2017              & DF-1875           & Devonta           & Freeman           & RB                & 186.2265          &  4               \\\\\n",
       "\t 2016              & 2017              & FG-0200           & Frank             & Gore              & RB                & 181.7967          &  5               \\\\\n",
       "\t 2016              & 2017              & EE-0250           & Ezekiel           & Elliott           & RB                & 171.9824          &  6               \\\\\n",
       "\t 2016              & 2017              & DM-4300           & DeMarco           & Murray            & RB                & 166.4855          &  7               \\\\\n",
       "\t 2016              & 2017              & LB-0250           & Le'Veon           & Bell              & RB                & 162.1650          &  8               \\\\\n",
       "\t 2016              & 2017              & Leonard Fournette & NA                & NA                & RB                & 155.5248          &  9               \\\\\n",
       "\t 2016              & 2017              & MG-1150           & Melvin            & Gordon            & RB                & 149.4913          & 10               \\\\\n",
       "\t 2016              & 2017              & MI-0100           & Mark              & Ingram            & RB                & 147.7702          & 11               \\\\\n",
       "\t 2016              & 2017              & DM-0450           & Doug              & Martin            & RB                & 137.4707          & 12               \\\\\n",
       "\t 2016              & 2017              & JH-5575           & Jordan            & Howard            & RB                & 136.5513          & 13               \\\\\n",
       "\t 2016              & 2017              & KD-1050           & Kenneth           & Dixon             & RB                & 134.8842          & 14               \\\\\n",
       "\t 2016              & 2017              & LM-1850           & Latavius          & Murray            & RB                & 134.7452          & 15               \\\\\n",
       "\t 2016              & 2017              & CH-5000           & Carlos            & Hyde              & RB                & 134.1729          & 16               \\\\\n",
       "\t 2016              & 2017              & LB-0500           & LeGarrette        & Blount            & RB                & 131.8789          & 17               \\\\\n",
       "\t 2016              & 2017              & DB-2950           & Devontae          & Booker            & RB                & 130.0696          & 18               \\\\\n",
       "\t 2016              & 2017              & DW-3600           & DeAngelo          & Williams          & RB                & 127.8926          & 19               \\\\\n",
       "\t 2016              & 2017              & CJ-1700           & Chris             & Johnson           & RB                & 127.8128          & 20               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "cutoff | seas | player | fname | lname | pos1 | fpts | rnk | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 2016              | 2017              | DJ-1325           | David             | Johnson           | RB                | 209.4883          |  1                | \n",
       "| 2016              | 2017              | MF-1300           | Matt              | Forte             | RB                | 190.4054          |  2                | \n",
       "| 2016              | 2017              | LM-1000           | LeSean            | McCoy             | RB                | 189.6376          |  3                | \n",
       "| 2016              | 2017              | DF-1875           | Devonta           | Freeman           | RB                | 186.2265          |  4                | \n",
       "| 2016              | 2017              | FG-0200           | Frank             | Gore              | RB                | 181.7967          |  5                | \n",
       "| 2016              | 2017              | EE-0250           | Ezekiel           | Elliott           | RB                | 171.9824          |  6                | \n",
       "| 2016              | 2017              | DM-4300           | DeMarco           | Murray            | RB                | 166.4855          |  7                | \n",
       "| 2016              | 2017              | LB-0250           | Le'Veon           | Bell              | RB                | 162.1650          |  8                | \n",
       "| 2016              | 2017              | Leonard Fournette | NA                | NA                | RB                | 155.5248          |  9                | \n",
       "| 2016              | 2017              | MG-1150           | Melvin            | Gordon            | RB                | 149.4913          | 10                | \n",
       "| 2016              | 2017              | MI-0100           | Mark              | Ingram            | RB                | 147.7702          | 11                | \n",
       "| 2016              | 2017              | DM-0450           | Doug              | Martin            | RB                | 137.4707          | 12                | \n",
       "| 2016              | 2017              | JH-5575           | Jordan            | Howard            | RB                | 136.5513          | 13                | \n",
       "| 2016              | 2017              | KD-1050           | Kenneth           | Dixon             | RB                | 134.8842          | 14                | \n",
       "| 2016              | 2017              | LM-1850           | Latavius          | Murray            | RB                | 134.7452          | 15                | \n",
       "| 2016              | 2017              | CH-5000           | Carlos            | Hyde              | RB                | 134.1729          | 16                | \n",
       "| 2016              | 2017              | LB-0500           | LeGarrette        | Blount            | RB                | 131.8789          | 17                | \n",
       "| 2016              | 2017              | DB-2950           | Devontae          | Booker            | RB                | 130.0696          | 18                | \n",
       "| 2016              | 2017              | DW-3600           | DeAngelo          | Williams          | RB                | 127.8926          | 19                | \n",
       "| 2016              | 2017              | CJ-1700           | Chris             | Johnson           | RB                | 127.8128          | 20                | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   cutoff seas player            fname      lname    pos1 fpts     rnk\n",
       "1  2016   2017 DJ-1325           David      Johnson  RB   209.4883  1 \n",
       "2  2016   2017 MF-1300           Matt       Forte    RB   190.4054  2 \n",
       "3  2016   2017 LM-1000           LeSean     McCoy    RB   189.6376  3 \n",
       "4  2016   2017 DF-1875           Devonta    Freeman  RB   186.2265  4 \n",
       "5  2016   2017 FG-0200           Frank      Gore     RB   181.7967  5 \n",
       "6  2016   2017 EE-0250           Ezekiel    Elliott  RB   171.9824  6 \n",
       "7  2016   2017 DM-4300           DeMarco    Murray   RB   166.4855  7 \n",
       "8  2016   2017 LB-0250           Le'Veon    Bell     RB   162.1650  8 \n",
       "9  2016   2017 Leonard Fournette NA         NA       RB   155.5248  9 \n",
       "10 2016   2017 MG-1150           Melvin     Gordon   RB   149.4913 10 \n",
       "11 2016   2017 MI-0100           Mark       Ingram   RB   147.7702 11 \n",
       "12 2016   2017 DM-0450           Doug       Martin   RB   137.4707 12 \n",
       "13 2016   2017 JH-5575           Jordan     Howard   RB   136.5513 13 \n",
       "14 2016   2017 KD-1050           Kenneth    Dixon    RB   134.8842 14 \n",
       "15 2016   2017 LM-1850           Latavius   Murray   RB   134.7452 15 \n",
       "16 2016   2017 CH-5000           Carlos     Hyde     RB   134.1729 16 \n",
       "17 2016   2017 LB-0500           LeGarrette Blount   RB   131.8789 17 \n",
       "18 2016   2017 DB-2950           Devontae   Booker   RB   130.0696 18 \n",
       "19 2016   2017 DW-3600           DeAngelo   Williams RB   127.8926 19 \n",
       "20 2016   2017 CJ-1700           Chris      Johnson  RB   127.8128 20 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rnks %>% \n",
    "  filter(seas == 2017, cutoff == 2016, pos1 == 'RB') %>%\n",
    "  head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tight Ends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>cutoff</th><th scope=col>seas</th><th scope=col>player</th><th scope=col>fname</th><th scope=col>lname</th><th scope=col>pos1</th><th scope=col>fpts</th><th scope=col>rnk</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>GO-0100   </td><td>Greg      </td><td>Olsen     </td><td>TE        </td><td>146.42042 </td><td> 1        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>ZE-0100   </td><td>Zach      </td><td>Ertz      </td><td>TE        </td><td>130.63426 </td><td> 2        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>RG-2200   </td><td>Rob       </td><td>Gronkowski</td><td>TE        </td><td>126.88084 </td><td> 3        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>AG-0500   </td><td>Antonio   </td><td>Gates     </td><td>TE        </td><td>122.56548 </td><td> 4        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>DW-0500   </td><td>Delanie   </td><td>Walker    </td><td>TE        </td><td>122.47415 </td><td> 5        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>JG-2900   </td><td>Jimmy     </td><td>Graham    </td><td>TE        </td><td>119.02849 </td><td> 6        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>JR-1150   </td><td>Jordan    </td><td>Reed      </td><td>TE        </td><td>117.61401 </td><td> 7        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>HH-0225   </td><td>Hunter    </td><td>Henry     </td><td>TE        </td><td>116.09537 </td><td> 8        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>JW-6000   </td><td>Jason     </td><td>Witten    </td><td>TE        </td><td>115.74999 </td><td> 9        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>TE-0300   </td><td>Tyler     </td><td>Eifert    </td><td>TE        </td><td>100.51851 </td><td>10        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>CC-2100   </td><td>Charles   </td><td>Clay      </td><td>TE        </td><td> 92.28116 </td><td>11        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>MB-1800   </td><td>Martellus </td><td>Bennett   </td><td>TE        </td><td> 89.25252 </td><td>12        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>AH-3650   </td><td>Austin    </td><td>Hooper    </td><td>TE        </td><td> 89.12048 </td><td>13        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>JC-0400   </td><td>Jordan    </td><td>Cameron   </td><td>TE        </td><td> 88.66733 </td><td>14        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>TK-0150   </td><td>Travis    </td><td>Kelce     </td><td>TE        </td><td> 88.57494 </td><td>15        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>CF-0650   </td><td>Coby      </td><td>Fleener   </td><td>TE        </td><td> 87.32912 </td><td>16        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>JC-4300   </td><td>Jared     </td><td>Cook      </td><td>TE        </td><td> 84.94412 </td><td>17        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>KR-1200   </td><td>Kyle      </td><td>Rudolph   </td><td>TE        </td><td> 84.18583 </td><td>18        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>VD-0100   </td><td>Vernon    </td><td>Davis     </td><td>TE        </td><td> 73.31119 </td><td>19        </td></tr>\n",
       "\t<tr><td>2016      </td><td>2017      </td><td>LK-0200   </td><td>Lance     </td><td>Kendricks </td><td>TE        </td><td> 70.80278 </td><td>20        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " cutoff & seas & player & fname & lname & pos1 & fpts & rnk\\\\\n",
       "\\hline\n",
       "\t 2016       & 2017       & GO-0100    & Greg       & Olsen      & TE         & 146.42042  &  1        \\\\\n",
       "\t 2016       & 2017       & ZE-0100    & Zach       & Ertz       & TE         & 130.63426  &  2        \\\\\n",
       "\t 2016       & 2017       & RG-2200    & Rob        & Gronkowski & TE         & 126.88084  &  3        \\\\\n",
       "\t 2016       & 2017       & AG-0500    & Antonio    & Gates      & TE         & 122.56548  &  4        \\\\\n",
       "\t 2016       & 2017       & DW-0500    & Delanie    & Walker     & TE         & 122.47415  &  5        \\\\\n",
       "\t 2016       & 2017       & JG-2900    & Jimmy      & Graham     & TE         & 119.02849  &  6        \\\\\n",
       "\t 2016       & 2017       & JR-1150    & Jordan     & Reed       & TE         & 117.61401  &  7        \\\\\n",
       "\t 2016       & 2017       & HH-0225    & Hunter     & Henry      & TE         & 116.09537  &  8        \\\\\n",
       "\t 2016       & 2017       & JW-6000    & Jason      & Witten     & TE         & 115.74999  &  9        \\\\\n",
       "\t 2016       & 2017       & TE-0300    & Tyler      & Eifert     & TE         & 100.51851  & 10        \\\\\n",
       "\t 2016       & 2017       & CC-2100    & Charles    & Clay       & TE         &  92.28116  & 11        \\\\\n",
       "\t 2016       & 2017       & MB-1800    & Martellus  & Bennett    & TE         &  89.25252  & 12        \\\\\n",
       "\t 2016       & 2017       & AH-3650    & Austin     & Hooper     & TE         &  89.12048  & 13        \\\\\n",
       "\t 2016       & 2017       & JC-0400    & Jordan     & Cameron    & TE         &  88.66733  & 14        \\\\\n",
       "\t 2016       & 2017       & TK-0150    & Travis     & Kelce      & TE         &  88.57494  & 15        \\\\\n",
       "\t 2016       & 2017       & CF-0650    & Coby       & Fleener    & TE         &  87.32912  & 16        \\\\\n",
       "\t 2016       & 2017       & JC-4300    & Jared      & Cook       & TE         &  84.94412  & 17        \\\\\n",
       "\t 2016       & 2017       & KR-1200    & Kyle       & Rudolph    & TE         &  84.18583  & 18        \\\\\n",
       "\t 2016       & 2017       & VD-0100    & Vernon     & Davis      & TE         &  73.31119  & 19        \\\\\n",
       "\t 2016       & 2017       & LK-0200    & Lance      & Kendricks  & TE         &  70.80278  & 20        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "cutoff | seas | player | fname | lname | pos1 | fpts | rnk | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 2016       | 2017       | GO-0100    | Greg       | Olsen      | TE         | 146.42042  |  1         | \n",
       "| 2016       | 2017       | ZE-0100    | Zach       | Ertz       | TE         | 130.63426  |  2         | \n",
       "| 2016       | 2017       | RG-2200    | Rob        | Gronkowski | TE         | 126.88084  |  3         | \n",
       "| 2016       | 2017       | AG-0500    | Antonio    | Gates      | TE         | 122.56548  |  4         | \n",
       "| 2016       | 2017       | DW-0500    | Delanie    | Walker     | TE         | 122.47415  |  5         | \n",
       "| 2016       | 2017       | JG-2900    | Jimmy      | Graham     | TE         | 119.02849  |  6         | \n",
       "| 2016       | 2017       | JR-1150    | Jordan     | Reed       | TE         | 117.61401  |  7         | \n",
       "| 2016       | 2017       | HH-0225    | Hunter     | Henry      | TE         | 116.09537  |  8         | \n",
       "| 2016       | 2017       | JW-6000    | Jason      | Witten     | TE         | 115.74999  |  9         | \n",
       "| 2016       | 2017       | TE-0300    | Tyler      | Eifert     | TE         | 100.51851  | 10         | \n",
       "| 2016       | 2017       | CC-2100    | Charles    | Clay       | TE         |  92.28116  | 11         | \n",
       "| 2016       | 2017       | MB-1800    | Martellus  | Bennett    | TE         |  89.25252  | 12         | \n",
       "| 2016       | 2017       | AH-3650    | Austin     | Hooper     | TE         |  89.12048  | 13         | \n",
       "| 2016       | 2017       | JC-0400    | Jordan     | Cameron    | TE         |  88.66733  | 14         | \n",
       "| 2016       | 2017       | TK-0150    | Travis     | Kelce      | TE         |  88.57494  | 15         | \n",
       "| 2016       | 2017       | CF-0650    | Coby       | Fleener    | TE         |  87.32912  | 16         | \n",
       "| 2016       | 2017       | JC-4300    | Jared      | Cook       | TE         |  84.94412  | 17         | \n",
       "| 2016       | 2017       | KR-1200    | Kyle       | Rudolph    | TE         |  84.18583  | 18         | \n",
       "| 2016       | 2017       | VD-0100    | Vernon     | Davis      | TE         |  73.31119  | 19         | \n",
       "| 2016       | 2017       | LK-0200    | Lance      | Kendricks  | TE         |  70.80278  | 20         | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   cutoff seas player  fname     lname      pos1 fpts      rnk\n",
       "1  2016   2017 GO-0100 Greg      Olsen      TE   146.42042  1 \n",
       "2  2016   2017 ZE-0100 Zach      Ertz       TE   130.63426  2 \n",
       "3  2016   2017 RG-2200 Rob       Gronkowski TE   126.88084  3 \n",
       "4  2016   2017 AG-0500 Antonio   Gates      TE   122.56548  4 \n",
       "5  2016   2017 DW-0500 Delanie   Walker     TE   122.47415  5 \n",
       "6  2016   2017 JG-2900 Jimmy     Graham     TE   119.02849  6 \n",
       "7  2016   2017 JR-1150 Jordan    Reed       TE   117.61401  7 \n",
       "8  2016   2017 HH-0225 Hunter    Henry      TE   116.09537  8 \n",
       "9  2016   2017 JW-6000 Jason     Witten     TE   115.74999  9 \n",
       "10 2016   2017 TE-0300 Tyler     Eifert     TE   100.51851 10 \n",
       "11 2016   2017 CC-2100 Charles   Clay       TE    92.28116 11 \n",
       "12 2016   2017 MB-1800 Martellus Bennett    TE    89.25252 12 \n",
       "13 2016   2017 AH-3650 Austin    Hooper     TE    89.12048 13 \n",
       "14 2016   2017 JC-0400 Jordan    Cameron    TE    88.66733 14 \n",
       "15 2016   2017 TK-0150 Travis    Kelce      TE    88.57494 15 \n",
       "16 2016   2017 CF-0650 Coby      Fleener    TE    87.32912 16 \n",
       "17 2016   2017 JC-4300 Jared     Cook       TE    84.94412 17 \n",
       "18 2016   2017 KR-1200 Kyle      Rudolph    TE    84.18583 18 \n",
       "19 2016   2017 VD-0100 Vernon    Davis      TE    73.31119 19 \n",
       "20 2016   2017 LK-0200 Lance     Kendricks  TE    70.80278 20 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rnks %>% \n",
    "  filter(seas == 2017, cutoff == 2016, pos1 == 'TE') %>%\n",
    "  head(20)"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "R",
   "name": "ir"
  },
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The link to the data seems to be broken.

Cool! Just in case you hadn't noticed Leonard Fournette is in the wrong column

The only "huh" projection: Le'Veon Bell is way rated 8th instead of top 3. (usually among the top 3 fantasy foodball picks) -- why so?

How does your method perform on a weekly ranking?

Or is there a way to create weekly rankings based on previous performance?

These may be relevant for getting data:

https://github.com/immersinn/nfl_analytics
https://github.com/immersinn/sports_data_scraping

Rough, but maybe a nice start for ppl.