analysis_model.ipynb

{
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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     "end_time": "2017-12-14T17:01:47.352785Z",
     "start_time": "2017-12-14T17:01:46.733813Z"
    },
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle\n",
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import gc\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load in data\n",
    "Player_stats and Opp_stats files have been pulled in from the [nflgame API](http://pdoc.burntsushi.net/nflgame) via the nflgame_acquire.ipynb scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:01:48.669582Z",
     "start_time": "2017-12-14T17:01:47.356220Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = './data/'\n",
    "files = os.listdir(path)\n",
    "player_stats_files = sorted([(int(re.sub('[^0-9]', '', f)), path + f)\n",
    "                             for f in files if \"player\" in f], key=lambda x: x[0])\n",
    "opp_stats_files = sorted([(int(re.sub('[^0-9]', '', f)), path + f)\n",
    "                          for f in files if \"opp\" in f], key=lambda x: x[0])\n",
    "\n",
    "player_dfs = {}\n",
    "for year, fn in player_stats_files:\n",
    "    df = pd.read_csv(fn, index_col=0)\n",
    "    df['team'] = df['team'].str.replace('JAX','JAC')\n",
    "    df['team'] = df['team'].str.replace('LAC','SD')\n",
    "    df['team'] = df['team'].str.replace('STL','LA')\n",
    "    del df['position_fill']\n",
    "    df['year'] = year\n",
    "    player_dfs[year] = df\n",
    "\n",
    "opp_dfs = {}\n",
    "for year, fn in opp_stats_files:\n",
    "    df = pd.read_csv(fn, index_col=0)\n",
    "    df['year'] = year\n",
    "    df['opp_TEAM'] = df['opp_TEAM'].str.replace('JAX','JAC')\n",
    "    df['opp_OPP'] = df['opp_OPP'].str.replace('JAX','JAC')\n",
    "    df['opp_TEAM'] = df['opp_TEAM'].str.replace('LAC','SD')\n",
    "    df['opp_OPP'] = df['opp_OPP'].str.replace('LAC','SD')\n",
    "    df['opp_TEAM'] = df['opp_TEAM'].str.replace('STL','LA')\n",
    "    df['opp_OPP'] = df['opp_OPP'].str.replace('STL','LA')\n",
    "    opp_dfs[year] = df\n",
    "\n",
    "new_opp_cols = ['offense', 'defense', 'opp_first_downs', 'opp_points',\n",
    "       'opp_passing_yds', 'opp_penalty_cnt', 'opp_penalty_yds', 'opp_pos_time',\n",
    "       'opp_punt_avg', 'opp_punt_cnt', 'opp_punt_yds', 'opp_rushing_yds',\n",
    "       'opp_total_yds', 'opp_turnovers', 'week', 'year']\n",
    "\n",
    "for year, df in opp_dfs.items():\n",
    "    df.columns = new_opp_cols\n",
    "    opp_dfs[year] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:01:48.677588Z",
     "start_time": "2017-12-14T17:01:48.672917Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13827, 79)\n",
      "(14124, 79)\n",
      "(14151, 79)\n",
      "(14149, 79)\n",
      "(10592, 79)\n"
     ]
    }
   ],
   "source": [
    "for year in range(2013,2018):\n",
    "    print(player_dfs[year].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## clean data and create target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:01:48.977323Z",
     "start_time": "2017-12-14T17:01:48.679310Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# missing positions\n",
    "data = pd.concat(player_dfs.values())\n",
    "missing_positions = data[data.position.isnull()]\n",
    "means = missing_positions.groupby(['id','full_name'], as_index=False).mean()\n",
    "means = means[['id','full_name','passing_att', 'rushing_att', 'receiving_rec']]\n",
    "means.columns = ['id','full_name','QB','RB','WRTE']\n",
    "means = means[(means.QB != 0) | (means.RB != 0) | (means.WRTE != 0)]\n",
    "means['position_fill'] = means[['QB','RB','WRTE']].idxmax(axis=1)\n",
    "means = means[['id','position_fill']]\n",
    "means['position_fill'] = means['position_fill'].apply(lambda x: np.nan if x == 'WRTE' else x)\n",
    "# means[means.position=='WRTE'].full_name.unique()\n",
    "# missing_positions_urls = means.profile_url.unique().tolist()\n",
    "# with open('data/missing_positions_fills.pkl', 'wb') as f:\n",
    "#     pickle.dump(means, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:01:49.061978Z",
     "start_time": "2017-12-14T17:01:48.980199Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from data.missing_plyrs import missing_WRTE\n",
    "\n",
    "def fill_positions(name):\n",
    "    \"\"\"Fill missing positions for players who are retired. The nflgame players.json \n",
    "    will not update these positions, nor are the positions available by scraping each\n",
    "    player's nfl.com profile url.\"\"\"\n",
    "    try:\n",
    "        pos = missing_WRTE[name]\n",
    "        return pos\n",
    "    except KeyError:\n",
    "        return ''\n",
    "\n",
    "def clean(player_stats, year, imputed):\n",
    "    \"\"\"Create fantasy_points (the target variable) according to a standard scoring regime.\n",
    "       Create pass/rush/reception ratios to be included in feature set for model.\n",
    "       Trim the dataset to include the four main offensive positions: QB, RB, WR, TE.\"\"\"\n",
    "    \n",
    "    player_stats['fantasy_points'] = (player_stats['passing_tds'] * 4) +\\\n",
    "    (player_stats['passing_yds'] * 0.04) +\\\n",
    "    (player_stats['passing_twoptm'] * 2) +\\\n",
    "    (player_stats['passing_ints'] * -2) +\\\n",
    "    (player_stats['rushing_tds'] * 6) +\\\n",
    "    (player_stats['rushing_yds'] * 0.1) +\\\n",
    "    (player_stats['rushing_twoptm'] * 2) +\\\n",
    "    (player_stats['receiving_tds'] * 6) +\\\n",
    "    (player_stats['receiving_yds'] * 0.1) +\\\n",
    "    (player_stats['receiving_twoptm'] * 2) +\\\n",
    "    (player_stats['kickret_tds'] * 6) +\\\n",
    "    (player_stats['puntret_tds'] * 6) +\\\n",
    "    (player_stats['fumbles_lost'] * -2)\n",
    "    \n",
    "    player_stats['passer_ratio'] = player_stats['passing_cmp']/player_stats['passing_att']\n",
    "    player_stats['PassRushRatio_Att'] = player_stats['rushing_att'] / player_stats['passing_att']\n",
    "    player_stats['PassRushRatio_Yds'] = player_stats['rushing_yds'] / player_stats['passing_yds']\n",
    "    player_stats['PassRushRatio_Tds'] = player_stats['rushing_tds'] / player_stats['passing_tds']\n",
    "    player_stats['RushRecRatio_AttRec'] = player_stats['rushing_att'] / player_stats['receiving_rec']\n",
    "    player_stats['RushRecRatio_Tds'] = player_stats['rushing_tds'] / player_stats['receiving_tds']\n",
    "    player_stats['RushRecRatio_Yds'] = player_stats['rushing_yds'] / player_stats['receiving_yds']\n",
    "    \n",
    "    player_stats = player_stats.merge(imputed, how='left', on='id')\n",
    "    player_stats['position'].fillna(player_stats['position_fill'], inplace=True)\n",
    "    player_stats['position_fill'] = player_stats['full_name'].apply(lambda x: fill_positions(x))\n",
    "    player_stats['position'].fillna(player_stats['position_fill'], inplace=True)\n",
    " \n",
    "    include_positions = ['QB', 'TE', 'WR', 'RB']\n",
    "    player_stats['position'] = player_stats['position'].str.replace('FB','RB')\n",
    "    player_stats = player_stats[player_stats['position'].isin(include_positions)]\n",
    "    return player_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:01:49.521951Z",
     "start_time": "2017-12-14T17:01:49.063329Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4186, 88)\n",
      "(4499, 88)\n",
      "(4764, 88)\n",
      "(4908, 88)\n",
      "(3740, 88)\n"
     ]
    }
   ],
   "source": [
    "# Create dictionary with keys as year of the NFL season and values \n",
    "# as a dataframe containing game summary stats for all players in that season\n",
    "player_dfs = {year:clean(df, year, means) for year, df in player_dfs.items()}\n",
    "\n",
    "for year in range(2013,2018):\n",
    "    print(player_dfs[year].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:01:49.530017Z",
     "start_time": "2017-12-14T17:01:49.523355Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# These are the game summary stats relevant to QB, RB, WR, and TE positions.\n",
    "\n",
    "stat_cols = ['fumbles_lost', 'fumbles_rcv', 'fumbles_tot','fumbles_trcv', 'fumbles_yds', \n",
    "       'passing_att', 'passing_cmp', 'passing_ints', 'passing_tds', 'passer_ratio',\n",
    "       'passing_twopta', 'passing_twoptm', 'passing_yds',\n",
    "       'puntret_tds','puntret_avg', 'puntret_lng', 'puntret_lngtd', 'puntret_ret',\n",
    "       'receiving_lng', 'receiving_lngtd','receiving_rec', 'receiving_tds', 'receiving_twopta',\n",
    "       'receiving_twoptm', 'receiving_yds', 'rushing_att', 'rushing_lng',\n",
    "       'rushing_lngtd', 'rushing_tds', 'rushing_twopta', 'rushing_twoptm',\n",
    "       'rushing_yds','fantasy_points',\n",
    "        'PassRushRatio_Att','PassRushRatio_Yds','PassRushRatio_Tds','RushRecRatio_AttRec',\n",
    "        'RushRecRatio_Tds','RushRecRatio_Yds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:01:50.770366Z",
     "start_time": "2017-12-14T17:01:49.533146Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def trim_sort(df):\n",
    "    df = df.sort_values(['id','week'])\n",
    "    df = df[stat_cols+['id','week','team','position','full_name']]\n",
    "    return df\n",
    "\n",
    "def get_trend(df):\n",
    "    \n",
    "    \"\"\"Compute a three-week trend for each game statistic, for each player.\"\"\"\n",
    "    \n",
    "    # compute 3-week and 2-week points deltas\n",
    "    deltas = df.groupby(['id']).pct_change()\n",
    "    deltas = deltas.add_prefix('chg_')\n",
    "    deltas = pd.concat([df, deltas], axis=1)\n",
    "    deltas2 = deltas.groupby(['id'])[deltas.columns].shift(1).fillna(0)\n",
    "    deltas3 = deltas.groupby(['id'])[deltas.columns].shift(2).fillna(0)\n",
    "    deltas2 = deltas2.add_prefix('per2_')\n",
    "    deltas3 = deltas3.add_prefix('per3_')\n",
    "    trend_df = pd.concat([deltas, deltas2, deltas3], axis=1)\n",
    "    # average prior three deltas to get trend\n",
    "    for col in stat_cols:\n",
    "        name = 'trend_'+col\n",
    "        trend_df[name] = trend_df[['chg_'+col,'per2_chg_'+col,'per3_chg_'+col]].mean(axis=1).fillna(0)\n",
    "    return trend_df\n",
    "\n",
    "def get_cumul_mean_stats(df, weeks):\n",
    "    \n",
    "    \"\"\"Create a rolling mean for each statistic by player, by week.\"\"\"\n",
    "    \n",
    "    weeks_stats_mean = []\n",
    "    for week in weeks:\n",
    "        tmp = df[df.week <= week]\n",
    "        tmp = tmp.groupby(['id'])[stat_cols].mean().reset_index()\n",
    "        tmp = tmp.add_suffix('_mean')\n",
    "        tmp['week'] = week\n",
    "        weeks_stats_mean.append(tmp)\n",
    "    cumavg_stats = pd.concat(weeks_stats_mean)\n",
    "    cumavg_stats = cumavg_stats.rename(columns={'id_mean':'id'})\n",
    "    return cumavg_stats\n",
    "\n",
    "def get_cumul_stats_time_weighted(df, weeks):\n",
    "    \n",
    "    \"\"\"Create a rolling time-wegihted mean for each statistic by player, by week.\"\"\"\n",
    "    \n",
    "    weeks_stats_mean_wgt = []\n",
    "    for week in weeks:\n",
    "        tmp1 = df[df.week <= week]\n",
    "        mult = lambda x: np.asarray(x) * np.asarray(tmp1.week)\n",
    "        tmp = tmp1[['id']+stat_cols].set_index('id').apply(mult).reset_index()\n",
    "        tmp = tmp.groupby(['id'])[stat_cols].mean().reset_index()\n",
    "        tmp = tmp.add_suffix('_wgtmean')\n",
    "        tmp['week'] = week\n",
    "        weeks_stats_mean_wgt.append(tmp)\n",
    "    cumavg_stats_wgt = pd.concat(weeks_stats_mean_wgt)\n",
    "    cumavg_stats_wgt = cumavg_stats_wgt.rename(columns={'id_wgtmean':'id'})\n",
    "    return cumavg_stats_wgt\n",
    "\n",
    "def defensive_ptsallow(matchups, weeks, weighted=False):\n",
    "    \n",
    "    \"\"\" Compute the mean weekly points given up by each defense to each position.\n",
    "        Parameters:\n",
    "                    matchups: dataframe of matchups between offensive player, and\n",
    "                              defensive opponent.\n",
    "                    weeks:    list of weeks in the season.\n",
    "                    weighted: boolean. If true, compute weekly points allowed according\n",
    "                              to player-weighted fantasy points.\n",
    "    \"\"\"\n",
    "    \n",
    "    agg_col = 'fantasy_points'\n",
    "    output_name = 'defensive_matchup_allowed'\n",
    "    if weighted:\n",
    "        agg_col = 'weighted_fantasy_points'\n",
    "        output_name = 'defensive_matchup_allowed_wgt'\n",
    "    # compute weekly cumulative mean points allowed by each defense\n",
    "    defense_ranks_dfs = []\n",
    "    for week in weeks:\n",
    "        matchweek = matchups[matchups.week <= week]\n",
    "        # weekly sum of pts allowed by a given defense to each position\n",
    "        weekly_sums = matchweek.groupby(['week','defense','position'])[agg_col].sum().reset_index()\n",
    "        # season-to-date mean of weekly sums for each position\n",
    "        defense_pts_allowed = weekly_sums.groupby(['defense','position'])[agg_col].mean().reset_index()\n",
    "        defense_pts_allowed = defense_pts_allowed.rename(columns={agg_col:output_name})\n",
    "        defense_pts_allowed['week'] = week\n",
    "        defense_ranks_dfs.append(defense_pts_allowed)\n",
    "    defense_ranks = pd.concat(defense_ranks_dfs)\n",
    "    return defense_ranks\n",
    "\n",
    "def weekly_player_weights(matchups, weeks):\n",
    "    \n",
    "    \"\"\"Calculate season-to-date (STD) weekly fantasy points rankings by position.\"\"\"\n",
    "    \n",
    "    player_weights = []\n",
    "    for week in weeks:\n",
    "        mask = (matchups.week <= week)\n",
    "        # each player's mean fantasy points STD\n",
    "        std_mean = matchups[mask][['id','team','position','fantasy_points','defense']]\n",
    "        std_mean = std_mean.groupby(['position','id'], as_index=False).mean()\n",
    "\n",
    "        # each player's weight in a given week with respect to their position.\n",
    "        # This is the STD mean max-normalized for the current week.\n",
    "        week_max_position = std_mean.groupby('position', as_index=False).max()\n",
    "        week_max_position = week_max_position[['position','fantasy_points']]\n",
    "        week_max_position.columns = ['position','fp_max']\n",
    "        weekly_weights = std_mean.merge(week_max_position,how='left',on='position')\n",
    "        weekly_weights['player_weight'] = weekly_weights['fantasy_points'] / weekly_weights['fp_max']\n",
    "        weekly_weights['week'] = week\n",
    "        player_weights.append(weekly_weights)\n",
    "    player_weights = pd.concat(player_weights)\n",
    "    return player_weights[['id','week','position','player_weight']]\n",
    "\n",
    "def create_nfl_features(player_stats_orig, opp_stats_orig):\n",
    "    \n",
    "    \"\"\"Wrapper function that calls all helpers to create custom player and team\n",
    "       defense stats. This function will return a new dataframe that has merged\n",
    "       all of the custom stats described in the helper functions for each player.\n",
    "       \n",
    "       Parameters:\n",
    "                   player_stats_orig: game summary actuals for each player weekly\n",
    "                   opp_stats_orig:    matchups for each game (can substitute with\n",
    "                                      a schedule with player_id, offense, defense, week)\"\"\"\n",
    "    \n",
    "    player_stats_trimmed = trim_sort(player_stats_orig)\n",
    "    weeks = sorted(player_stats_trimmed.week.unique().tolist())\n",
    "\n",
    "    # create offensive player stats\n",
    "    trend_df         = get_trend(player_stats_trimmed)\n",
    "    cumavg_stats     = get_cumul_mean_stats(player_stats_trimmed, weeks)\n",
    "    cumavg_stats_wgt = get_cumul_stats_time_weighted(player_stats_trimmed, weeks)\n",
    "\n",
    "    # create matchups and defensive opponent stats\n",
    "    matchup_cols = ['id', 'week', 'team','position', 'full_name', 'offense', 'defense','fantasy_points']\n",
    "    sched             = opp_stats_orig[['offense','defense','week']]\n",
    "    matchups          = player_stats_trimmed.merge(sched, how='left',\n",
    "                                                   left_on=['week','team'],\n",
    "                                                   right_on=['week','offense'])[matchup_cols]\n",
    "    defense_ranks     = defensive_ptsallow(matchups, weeks)\n",
    "    player_weights    = weekly_player_weights(matchups, weeks)\n",
    "    player_weights['inverse'] = 1/player_weights.player_weight\n",
    "    matchups_wgts     = matchups.merge(player_weights, how='left', on=['id','week','position'])\n",
    "    matchups_wgts['weighted_fantasy_points'] = matchups_wgts['fantasy_points'] * matchups_wgts['inverse']\n",
    "    defense_ranks_wgt = defensive_ptsallow(matchups_wgts, weeks, weighted=True)\n",
    "    \n",
    "    ## merge features\n",
    "\n",
    "    # shift target variable, week, and defensive opponent\n",
    "    matchups['target_defense'] = matchups.sort_values(['id','week']).groupby('id')['defense'].shift(-1)\n",
    "    matchups['target'] = matchups.sort_values(['id','week']).groupby('id')['fantasy_points'].shift(-1)\n",
    "    matchups['target_week'] = matchups.sort_values(['id','week']).groupby('id')['week'].shift(-1)\n",
    "\n",
    "    # drop week 1\n",
    "    matchups.dropna(inplace=True)\n",
    "\n",
    "#     # fill in zeros for players with missing historical stats\n",
    "#     matchups.fillna(0, inplace=True)\n",
    "\n",
    "    # merge player weights to player performances\n",
    "    matchups = matchups.merge(player_weights, on=['id','week','position'])\n",
    "\n",
    "    # merge defense rankings to player performances\n",
    "    defense_ranks_all = defense_ranks.merge(defense_ranks_wgt, on=['defense', 'position', 'week'])\n",
    "    defense_ranks_all = defense_ranks_all.rename(columns={'defense':'target_defense'})\n",
    "    matchups = matchups.merge(defense_ranks_all, how='left', on=['target_defense','position','week']).dropna()\n",
    "\n",
    "    # merge trend, average, and weighted avg stats to player performances\n",
    "    avgs = cumavg_stats.merge(cumavg_stats_wgt,how='inner',on=['id','week'])\n",
    "    matchups = matchups.merge(avgs, how='left', on=['id','week'])\n",
    "    trend_cols = ['id','week']+[col for col in trend_df if col not in matchups.columns]\n",
    "    td = trend_df[trend_cols]\n",
    "    matchups = matchups.merge(td, how='inner', on=['id','week'])\n",
    "    for col in trend_df.columns:\n",
    "        matchups[col].fillna(0, inplace=True)\n",
    "\n",
    "    # create extra player attributes and merge to make model-ready df\n",
    "    attribs = ['id','birthdate','years_pro','height','weight','position','profile_url','last_name','number']\n",
    "    player_attributes = player_stats_orig[attribs]\n",
    "    player_attributes.drop_duplicates(['id'],inplace=True)\n",
    "    player_attributes['birthdate'] = pd.to_datetime(player_attributes['birthdate'])\n",
    "    player_attributes['age'] = player_attributes['birthdate'].apply(lambda x: (datetime.today() - x).days/365)\n",
    "    position_dummies = pd.get_dummies(player_attributes['position'])\n",
    "    player_attributes = pd.concat([position_dummies, player_attributes], axis=1).drop(['position'],axis=1)\n",
    "\n",
    "    # final cleaning\n",
    "    model_df = player_attributes.merge(matchups, how='right',on='id')\n",
    "    model_df.replace([-np.inf,np.inf], 0, inplace=True)\n",
    "    return model_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:06.919987Z",
     "start_time": "2017-12-14T17:01:50.771694Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rjh336/anaconda2/envs/metis/lib/python3.5/site-packages/ipykernel/__main__.py:175: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/rjh336/anaconda2/envs/metis/lib/python3.5/site-packages/ipykernel/__main__.py:176: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/home/rjh336/anaconda2/envs/metis/lib/python3.5/site-packages/ipykernel/__main__.py:177: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "nfl_dfs = {year:create_nfl_features(df, opp_dfs[year]) \n",
    "                         for year, df in player_dfs.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## merge FanDuel salaries\n",
    "I used FanDuel salaries because it was the only DFS service which has weekly historical salaries that go back to 2013 season. I scraped salaries from the [RotoGuru repo](http://rotoguru1.com/cgi-bin/fyday.pl?game=fd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:06.989599Z",
     "start_time": "2017-12-14T17:03:06.921682Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = './data/FanDuel/'\n",
    "files = os.listdir(path)\n",
    "fanduel_dfs = {}\n",
    "for fn in files:\n",
    "    yr = int(re.sub('[^0-9]', '', fn))\n",
    "    fanduel_dfs[yr] = pd.read_csv(path+fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:07.104049Z",
     "start_time": "2017-12-14T17:03:06.991314Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_salaries(df, year):\n",
    "    positions = ['QB', 'RB', 'WR', 'TE']\n",
    "    df['FirstName'] = df['FirstName'].str.strip()\n",
    "    df['LastName'] = df['LastName'].str.strip()\n",
    "    df['full_name'] = df['LastName']+' '+df['FirstName']\n",
    "    df = df[df.Pos.isin(positions)].fillna(0)[['Week','Team','full_name','fd_points','fd_salary']]\n",
    "    df.columns = ['week','team','full_name','fd_points','fd_salary']\n",
    "    df['week'] = pd.to_numeric(df['week'])\n",
    "    df['fd_points'] = pd.to_numeric(df['fd_points'])\n",
    "    df['fd_salary'] = pd.to_numeric(df['fd_salary'])\n",
    "    df['team'] = df['team'].str.upper()\n",
    "    df['team'] = df['team'].str.replace('KAN','KC')\n",
    "    df['team'] = df['team'].str.replace('GNB','GB')\n",
    "    df['team'] = df['team'].str.replace('LAR','LA')\n",
    "    df['team'] = df['team'].str.replace('STL','LA')\n",
    "    df['team'] = df['team'].str.replace('TAM','TB')\n",
    "    df['team'] = df['team'].str.replace('SFO','SF')\n",
    "    df['team'] = df['team'].str.replace('NOR','NO')\n",
    "    df['team'] = df['team'].str.replace('NWE','NE')\n",
    "    df['team'] = df['team'].str.replace('SDG','SD')\n",
    "    df['team'] = df['team'].str.replace('LAC','SD')\n",
    "    return df\n",
    "\n",
    "def merge_salaries(players, salaries):\n",
    "    new_players = {}\n",
    "    for year in players.keys():\n",
    "        players_year = players[year]\n",
    "        salaries_year = clean_salaries(salaries[year], year)\n",
    "        players_teams = set(players_year.team)\n",
    "        salaries_teams = set(salaries_year.team)\n",
    "        # check that there are no team naming conflicts\n",
    "        print(year, salaries_teams ^ players_teams)\n",
    "        salary_merge = players_year.merge(salaries_year,how='left',on=['week','full_name','team'])\n",
    "        new_players[year] = salary_merge.fillna(0)\n",
    "    return new_players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:08.133382Z",
     "start_time": "2017-12-14T17:03:07.112731Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016 set()\n",
      "2017 set()\n",
      "2013 set()\n",
      "2014 set()\n",
      "2015 set()\n"
     ]
    }
   ],
   "source": [
    "nfl_fanduel_dfs = merge_salaries(nfl_dfs, fanduel_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:08.153864Z",
     "start_time": "2017-12-14T17:03:08.141057Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Year column got lost when I called create_nfl_features() on \n",
    "# each dataframe, so I am adding it back\n",
    "\n",
    "def add_year(df, yr):\n",
    "    df['year'] = yr\n",
    "    return df\n",
    "\n",
    "for year in nfl_fanduel_dfs.keys():\n",
    "    df_year = nfl_fanduel_dfs[year]\n",
    "    nfl_fanduel_dfs[year] = add_year(df_year, year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## merge snap counts\n",
    "Snap counts are the number of times a player is on the field for a play. [Football Outsiders](http://www.footballoutsiders.com/stats/snapcounts) has a historical database of weekly snap counts for each player."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:08.224089Z",
     "start_time": "2017-12-14T17:03:08.155566Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def merge_snap_counts(dfs):\n",
    "    path = './data/FO_data/SnapCounts/'\n",
    "    SC_files = os.listdir(path)\n",
    "\n",
    "    SC_dfs = []\n",
    "    for fn in SC_files:\n",
    "        week = int(fn[:2])\n",
    "        year = int(fn[3:7])\n",
    "        df = pd.read_csv(path+fn)\n",
    "        df['week'] = week\n",
    "        df['year'] = year\n",
    "        SC_dfs.append(df)\n",
    "    sc = pd.concat(SC_dfs)\n",
    "\n",
    "    sc['number'] = pd.to_numeric(sc.Player.str.replace('[^0-9]',''))\n",
    "    sc['last_name'] = sc.Player.str.replace('[A-Za-z\\s]+\\.','')\n",
    "    sc['last_name'] = sc.last_name.str.replace('[^A-Za-z\\s]','').str.strip()\n",
    "    sc['started'] = sc['Started'].apply(lambda x: 1 if x=='YES' else 0)\n",
    "    sc['offensive_snap_pct'] = pd.to_numeric(sc['Off Snap Pct'].str.replace('[^0-9]',''), \n",
    "                                             downcast='float')\n",
    "    sc['offensive_snap_tot'] = sc['Off Snaps']\n",
    "    sc['position'] = sc.Position.str.replace('FB','RB').str.strip()\n",
    "    sc['team'] = sc['Team'].str.replace('LARM/STL','LA')\n",
    "    sc['team'] = sc['team'].str.replace('LAR','LA')\n",
    "    sc['team'] = sc['team'].str.replace('LAC','SD').str.strip()\n",
    "\n",
    "    sc = sc[sc.position.isin(['QB','RB','WR','TE'])]\n",
    "    sc = sc[['number','last_name','started','offensive_snap_pct',\n",
    "             'offensive_snap_tot','position','team','week','year']]\n",
    "    new_dfs = {}\n",
    "    for year, df in dfs.items():\n",
    "        merge_df = df.merge(sc, on=['last_name','team','week','year','position'])\n",
    "        new_dfs[year] = merge_df\n",
    "    return new_dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:11.426499Z",
     "start_time": "2017-12-14T17:03:08.226494Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nfl_fd_sc_dfs = merge_snap_counts(nfl_fanduel_dfs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## merge gameday weather forecasts\n",
    "Gameday weather forecasts were scraped from [http://nflweather.com/](http://nflweather.com/). I only care about wind speeds as this is factor that can impact the offense's decision to throw the ball versus running the ball."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:11.493291Z",
     "start_time": "2017-12-14T17:03:11.428076Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def merge_weather(dfs):\n",
    "    from data.nfl_teams import team_dict\n",
    "    path = './data/NflWeather/'\n",
    "    weather_files = os.listdir(path)\n",
    "    weather_dfs = []\n",
    "    for fn in weather_files:\n",
    "        week = re.findall('_[0-9]+\\.',fn)\n",
    "        week = re.sub('[^0-9]','',str(week))\n",
    "        year = fn[:4]\n",
    "        df = pd.read_csv(path+fn)\n",
    "        df['week'] = int(week)\n",
    "        df['year'] = int(year)\n",
    "        weather_dfs.append(df)\n",
    "    weather = pd.concat(weather_dfs)\n",
    "\n",
    "    weather['team1'] = weather['team1'].apply(lambda x: team_dict[x])\n",
    "    weather['team2'] = weather['team2'].apply(lambda x: team_dict[x])\n",
    "\n",
    "    weather['wind_conditions'] = pd.to_numeric(weather['wind_conditions'].str.replace('[^0-9]',''))\n",
    "    weather['indoor_outdoor'] = weather['weather_forecast'].apply(lambda x: 1 if 'DOME' in x else 0)\n",
    "\n",
    "    weather1 = weather[['team1','wind_conditions','indoor_outdoor','week','year']]\n",
    "    weather1.columns = ['team','wind_conditions','indoor_outdoor','week','year']\n",
    "    weather2 = weather[['team2','wind_conditions','indoor_outdoor','week','year']]\n",
    "    weather2.columns = ['team','wind_conditions','indoor_outdoor','week','year']\n",
    "    weather = pd.concat([weather1,weather2])\n",
    "    \n",
    "    new_dfs = {}\n",
    "    for year, df in dfs.items():\n",
    "        merge_df = df.merge(weather,on=['team','week','year'])\n",
    "        new_dfs[year] = merge_df\n",
    "    return new_dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:11.866532Z",
     "start_time": "2017-12-14T17:03:11.496380Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nfl_fd_sc_weather_dfs = merge_weather(nfl_fd_sc_dfs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train and test sets\n",
    "Now that all features have been cleaned and merged to a dataframe for each season, I will merge historical seasons to be used for training/validating my regression model. I will use the current season (2017) as a holdout test set.\n",
    "- \"model_df\" = train/validation set (2013-2016 seasons)\n",
    "- \"holdout\" = test set (2017 / current season)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:11.873612Z",
     "start_time": "2017-12-14T17:03:11.868107Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3332, 397)\n",
      "(3450, 397)\n",
      "(4018, 397)\n",
      "(4326, 397)\n",
      "(3245, 397)\n"
     ]
    }
   ],
   "source": [
    "for year in range(2013,2018):\n",
    "    print(nfl_fd_sc_weather_dfs[year].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:11.972363Z",
     "start_time": "2017-12-14T17:03:11.878525Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "max_year = max(list(nfl_fd_sc_weather_dfs.keys()))\n",
    "holdout = nfl_fd_sc_weather_dfs[2017]\n",
    "model_dfs = [nfl_fd_sc_weather_dfs[yr] for yr in nfl_fd_sc_weather_dfs.keys() if yr != max_year]\n",
    "model_df = pd.concat(model_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:12.033763Z",
     "start_time": "2017-12-14T17:03:11.973884Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:12.054207Z",
     "start_time": "2017-12-14T17:03:12.036943Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holdout.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:12.063000Z",
     "start_time": "2017-12-14T17:03:12.058106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15126, 397)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:12.074998Z",
     "start_time": "2017-12-14T17:03:12.064533Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3245, 397)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holdout.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## convert target to target rank\n",
    "I experimented with changing the target variable to a weekly rank instead of just using the fantasy points for that week, but I did not end up using this in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:12.187054Z",
     "start_time": "2017-12-14T17:03:12.077368Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_df = model_df.sort_values(['year','position','week','target'], ascending=False).reset_index(drop=True)\n",
    "model_df['target_rank'] = model_df.groupby(['year','position','week'])['target'].\\\n",
    "                                    rank(method=\"dense\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:12.222302Z",
     "start_time": "2017-12-14T17:03:12.189575Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "holdout.sort_values(['year','position','week','target'], ascending=False, inplace=True)\n",
    "holdout['target_rank'] = holdout.groupby(['year','position','week'])['target'].\\\n",
    "                                    rank(method=\"dense\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:12.532396Z",
     "start_time": "2017-12-14T17:03:12.224095Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xab73b50c>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U1SeHnjoC7Om29wBPD7XvTnJdkluBrcDzCzdkSdJc9PnR/CHgXwPHk3yza/st4FHgcJIH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\neklqnEEvSY0z6CWpcQa9JDXOoJekxv1/w4B/+rmjsSUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabb6d8ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_df['target_rank'].hist(bins=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## merge ESPN benchmarks\n",
    "I scraped fantasy point projections from [ESPN](http://games.espn.com/ffl/tools/projections?) for each week in the current season and merge to holdout test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:13.077770Z",
     "start_time": "2017-12-14T17:03:12.534382Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "proj = pd.read_json(\"./data/espn_projections.json\")\n",
    "\n",
    "clean_team_pos = proj['team'].str.replace('[^a-zA-Z\\s]', '').str.upper().str.split(expand=True)\n",
    "proj.drop(['team','position'],axis=1,inplace=True)\n",
    "clean_team_pos.columns= ['team', 'position']\n",
    "\n",
    "proj = pd.concat([proj, clean_team_pos], axis=1)\n",
    "\n",
    "proj['team'] = proj['team'].str.replace('LAR','LA')\n",
    "proj['team'] = proj['team'].str.replace('WSH','WAS')\n",
    "proj['team'] = proj['team'].str.replace('LAC','SD')\n",
    "proj['team'] = proj['team'].str.replace('JAX','JAC')\n",
    "\n",
    "proj = proj[['name','position','team','week','proj_pts']]\n",
    "\n",
    "proj.columns = ['full_name','position','team','target_week','proj_pts']\n",
    "proj = proj[proj.proj_pts != '--']\n",
    "proj['proj_pts'] = pd.to_numeric(proj['proj_pts'])\n",
    "proj = proj[~proj.proj_pts.isnull()]\n",
    "proj['full_name'] = proj.full_name.str.replace('Jr.','').str.strip()\n",
    "proj['full_name'] = proj.full_name.str.replace('Sr.','').str.strip()\n",
    "proj['full_name'] = proj.full_name.str.replace('III','').str.strip()\n",
    "proj['full_name'] = proj.full_name.str.replace('II','').str.strip()\n",
    "convert_names = {'Joshua Bellamy':'Josh Bellamy',\n",
    "                 'TJ Jones':'T.J. Jones',\n",
    "                 'Will Fuller V':'Will Fuller',\n",
    "                 'Matthew Dayes':'Matt Dayes'}\n",
    "proj['full_name'] = proj.full_name.replace(convert_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:13.168013Z",
     "start_time": "2017-12-14T17:03:13.079451Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# merge ESPN projections to holdout set\n",
    "merge_cols = ['full_name','position','team','target_week']\n",
    "holdout = holdout.merge(proj, how='left', on=merge_cols)\n",
    "holdout.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:13.198245Z",
     "start_time": "2017-12-14T17:03:13.169818Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a rank for ESPN projections\n",
    "holdout.sort_values(['year','position','week','proj_pts'], ascending=False, inplace=True)\n",
    "holdout['espn_rank'] = holdout.groupby(['year','position','week'])['proj_pts'].\\\n",
    "                                    rank(method=\"dense\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:13.206606Z",
     "start_time": "2017-12-14T17:03:13.199975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3159, 400)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holdout.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## trim outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:13.515959Z",
     "start_time": "2017-12-14T17:03:13.213057Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15126, 398)\n",
      "(14054, 398)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xab8c2fcc>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4yWZgc/dyNskzI+7qLODHSzOqle2PrbWX+lRr/nLeTY5a6wL6rjbL8e/6WwvZaLlDYAY4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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab91f08c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# trim players who scored zero in a given week because they \n",
    "# are skewing the distribution for my target variable.\n",
    "\n",
    "thresh = 0\n",
    "\n",
    "tmp = model_df[model_df.target > thresh]\n",
    "print(model_df.shape)\n",
    "print(tmp.shape)\n",
    "tmp.target.hist(bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:13.834994Z",
     "start_time": "2017-12-14T17:03:13.518189Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3159, 400)\n",
      "(2941, 400)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xaacb7fec>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xaac8c36c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = holdout[holdout.target > thresh]\n",
    "print(holdout.shape)\n",
    "print(tmp.shape)\n",
    "tmp.target.hist(bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.137452Z",
     "start_time": "2017-12-14T17:03:13.836686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3159, 400)\n",
      "(3102, 400)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xaaa000ec>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8diewE6DT6Vy9uLg49PGXl5eZm5vj4OHjI+ewGXXOhaNvrncUZ5c5bxzbtpw/1f2f/FzP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\nf5OV6/oSjdORAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab8c0f2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp = holdout[holdout.proj_pts > thresh]\n",
    "print(holdout.shape)\n",
    "print(tmp.shape)\n",
    "tmp.proj_pts.hist(bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.202071Z",
     "start_time": "2017-12-14T17:03:14.139492Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_df_trimmed = model_df[model_df.target > thresh].reset_index(drop=True)\n",
    "holdout_trimmed = holdout[(holdout.target > thresh) & \n",
    "                          (holdout.proj_pts > thresh)].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DO NOT RUN: boxcox transform\n",
    "The target distribution is pretty skewed, so I use a boxcox transform to normalize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.215505Z",
     "start_time": "2017-12-14T17:03:14.211233Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from scipy.stats import boxcox\n",
    "\n",
    "# model_df_trimmed['target'] = pd.Series(boxcox(model_df_trimmed.target)[0])\n",
    "# holdout_trimmed['target'] = pd.Series(boxcox(holdout_trimmed.target)[0])\n",
    "# holdout_trimmed['proj_pts'] = pd.Series(boxcox(holdout_trimmed.proj_pts)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.226336Z",
     "start_time": "2017-12-14T17:03:14.217803Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# model_df_trimmed.target.hist(bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.231063Z",
     "start_time": "2017-12-14T17:03:14.227965Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# holdout_trimmed.target.hist(bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.242735Z",
     "start_time": "2017-12-14T17:03:14.233142Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# holdout_trimmed.proj_pts.hist(bins=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## player stat interactions\n",
    "The following merges serve the purpose to inform a given player of what type of offense their team employs. For instance, merging the mean QB pass/rush attempts ratio to that QB's WR gives an idea of how often the QB runs versus throwing, thus affecting the possible targets thrown to that WR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.300338Z",
     "start_time": "2017-12-14T17:03:14.244977Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "numeric_df_columns = model_df_trimmed.select_dtypes(include=['float32','int32','int64','float64','uint8']).columns\n",
    "fumble_features = [c for c in numeric_df_columns if \"fumble\" in c and \"per\" not in c]\n",
    "\n",
    "passing_features = ['passing_att_mean','passing_cmp_mean','passing_ints_mean','passing_yds_mean',\n",
    "                    'passing_tds_mean','passer_ratio','PassRushRatio_Att_mean','PassRushRatio_Yds_mean',\n",
    "                    'PassRushRatio_Tds_mean']\n",
    "\n",
    "rushing_features = ['rushing_yds_mean','rushing_att_mean','rushing_tds_mean',\n",
    "                    'RushRecRatio_AttRec_mean','RushRecRatio_Tds_mean','RushRecRatio_Yds_mean']\n",
    "\n",
    "receiving_features = ['receiving_rec_mean','receiving_tds_mean','receiving_yds_mean']\n",
    "\n",
    "sharedfeats = ['full_name','team','years_pro','age','weight','height','player_weight','year','week','target',\n",
    "               'target_rank','fantasy_points_mean','target_week','defensive_matchup_allowed','offensive_snap_pct',\n",
    "               'offensive_snap_tot','fd_salary','started','wind_conditions','indoor_outdoor']+fumble_features\n",
    "\n",
    "QB_features = sharedfeats+passing_features+rushing_features\n",
    "RB_features = sharedfeats+rushing_features+receiving_features\n",
    "WR_features = sharedfeats+receiving_features\n",
    "TE_features = sharedfeats+receiving_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.432700Z",
     "start_time": "2017-12-14T17:03:14.305629Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QB_df = model_df_trimmed[model_df_trimmed.position == 'QB'][QB_features].reset_index().drop('index',axis=1)\n",
    "QB_stats = QB_df.groupby(['year','week','team']).mean()[passing_features].reset_index().add_prefix('QBMEAN')\n",
    "QB_stats=QB_stats.rename(columns={'QBMEANyear':'year', 'QBMEANweek':'week', 'QBMEANteam':'team'})\n",
    "QB_merge_stats = ['QBMEANPassRushRatio_Att','QBMEANPassRushRatio_Yds','QBMEANPassRushRatio_Tds']\n",
    "\n",
    "RB_df = model_df_trimmed[model_df_trimmed.position == 'RB'][RB_features].reset_index().drop('index',axis=1)\n",
    "RB_stats = RB_df.groupby(['year','week','team']).mean()[rushing_features].reset_index().add_prefix('RBMEAN')\n",
    "RB_stats=RB_stats.rename(columns={'RBMEANyear':'year', 'RBMEANweek':'week', 'RBMEANteam':'team'})\n",
    "\n",
    "WR_df = model_df_trimmed[model_df_trimmed.position == 'WR'][WR_features].reset_index().drop('index',axis=1)\n",
    "WR_stats = WR_df.groupby(['year','week','team']).mean()[receiving_features].reset_index().add_prefix('WRMEAN')\n",
    "WR_stats=WR_stats.rename(columns={'WRMEANyear':'year', 'WRMEANweek':'week', 'WRMEANteam':'team'})\n",
    "\n",
    "TE_df = model_df_trimmed[model_df_trimmed.position == 'TE'][TE_features].reset_index().drop('index',axis=1)\n",
    "TE_stats = TE_df.groupby(['year','week','team']).mean()[receiving_features].reset_index().add_prefix('TEMEAN')\n",
    "TE_stats=TE_stats.rename(columns={'TEMEANyear':'year', 'TEMEANweek':'week', 'TEMEANteam':'team'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.443754Z",
     "start_time": "2017-12-14T17:03:14.435141Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QB: (1902, 60)\n",
      "RB: (4638, 54)\n",
      "WR: (5222, 48)\n",
      "TE: (2292, 48)\n"
     ]
    }
   ],
   "source": [
    "print(\"QB:\", QB_df.shape)\n",
    "print(\"RB:\", RB_df.shape)\n",
    "print(\"WR:\", WR_df.shape)\n",
    "print(\"TE:\", TE_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.501130Z",
     "start_time": "2017-12-14T17:03:14.445588Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QB_df = QB_df.merge(RB_stats, how='inner', on=['year','week','team'])\n",
    "\n",
    "RB_df = RB_df.merge(QB_stats, how='inner', on=['year','week','team'])\n",
    "\n",
    "WR_df = WR_df.merge(QB_stats, how='inner', on=['year','week','team'])\n",
    "WR_df = WR_df.merge(RB_stats, how='inner', on=['year','week','team'])\n",
    "\n",
    "TE_df = TE_df.merge(QB_stats, how='inner', on=['year','week','team'])\n",
    "TE_df = TE_df.merge(RB_stats, how='inner', on=['year','week','team'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.510737Z",
     "start_time": "2017-12-14T17:03:14.504597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QB: (1869, 66)\n",
      "RB: (4467, 63)\n",
      "WR: (5046, 63)\n",
      "TE: (2218, 63)\n"
     ]
    }
   ],
   "source": [
    "print(\"QB:\", QB_df.shape)\n",
    "print(\"RB:\", RB_df.shape)\n",
    "print(\"WR:\", WR_df.shape)\n",
    "print(\"TE:\", TE_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.523732Z",
     "start_time": "2017-12-14T17:03:14.513513Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QB_features = QB_features+['espn_rank','proj_pts']\n",
    "RB_features = RB_features+['espn_rank','proj_pts']\n",
    "WR_features = WR_features+['espn_rank','proj_pts']\n",
    "TE_features = TE_features+['espn_rank','proj_pts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.647687Z",
     "start_time": "2017-12-14T17:03:14.525313Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QB_df_holdout = holdout_trimmed[holdout_trimmed.position == 'QB'][QB_features].reset_index().drop('index',axis=1)\n",
    "QB_stats = QB_df_holdout.groupby(['year','week','team']).mean()[passing_features].reset_index().add_prefix('QBMEAN')\n",
    "QB_stats=QB_stats.rename(columns={'QBMEANyear':'year', 'QBMEANweek':'week', 'QBMEANteam':'team'})\n",
    "QB_merge_stats = ['QBMEANPassRushRatio_Att','QBMEANPassRushRatio_Yds','QBMEANPassRushRatio_Tds']\n",
    "\n",
    "RB_df_holdout = holdout_trimmed[holdout_trimmed.position == 'RB'][RB_features].reset_index().drop('index',axis=1)\n",
    "RB_stats = RB_df_holdout.groupby(['year','week','team']).mean()[rushing_features].reset_index().add_prefix('RBMEAN')\n",
    "RB_stats=RB_stats.rename(columns={'RBMEANyear':'year', 'RBMEANweek':'week', 'RBMEANteam':'team'})\n",
    "\n",
    "WR_df_holdout = holdout_trimmed[holdout_trimmed.position == 'WR'][WR_features].reset_index().drop('index',axis=1)\n",
    "WR_stats = WR_df_holdout.groupby(['year','week','team']).mean()[receiving_features].reset_index().add_prefix('WRMEAN')\n",
    "WR_stats=WR_stats.rename(columns={'WRMEANyear':'year', 'WRMEANweek':'week', 'WRMEANteam':'team'})\n",
    "\n",
    "TE_df_holdout = holdout_trimmed[holdout_trimmed.position == 'TE'][TE_features].reset_index().drop('index',axis=1)\n",
    "TE_stats = TE_df_holdout.groupby(['year','week','team']).mean()[receiving_features].reset_index().add_prefix('TEMEAN')\n",
    "TE_stats=TE_stats.rename(columns={'TEMEANyear':'year', 'TEMEANweek':'week', 'TEMEANteam':'team'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.686584Z",
     "start_time": "2017-12-14T17:03:14.659493Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QB: (350, 62)\n",
      "RB: (874, 56)\n",
      "WR: (1174, 50)\n",
      "TE: (508, 50)\n"
     ]
    }
   ],
   "source": [
    "print(\"QB:\", QB_df_holdout.shape)\n",
    "print(\"RB:\", RB_df_holdout.shape)\n",
    "print(\"WR:\", WR_df_holdout.shape)\n",
    "print(\"TE:\", TE_df_holdout.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.840322Z",
     "start_time": "2017-12-14T17:03:14.690407Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QB_df_holdout = QB_df_holdout.merge(RB_stats, how='inner', on=['year','week','team'])\n",
    "\n",
    "RB_df_holdout = RB_df_holdout.merge(QB_stats, how='inner', on=['year','week','team'])\n",
    "\n",
    "WR_df_holdout = WR_df_holdout.merge(RB_stats, how='inner', on=['year','week','team'])\n",
    "WR_df_holdout = WR_df_holdout.merge(QB_stats, how='inner', on=['year','week','team'])\n",
    "\n",
    "TE_df_holdout = TE_df_holdout.merge(QB_stats, how='inner', on=['year','week','team'])\n",
    "TE_df_holdout = TE_df_holdout.merge(RB_stats, how='inner', on=['year','week','team'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:03:14.875043Z",
     "start_time": "2017-12-14T17:03:14.847924Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QB: (350, 68)\n",
      "RB: (828, 65)\n",
      "WR: (1113, 65)\n",
      "TE: (480, 65)\n"
     ]
    }
   ],
   "source": [
    "print(\"QB:\", QB_df_holdout.shape)\n",
    "print(\"RB:\", RB_df_holdout.shape)\n",
    "print(\"WR:\", WR_df_holdout.shape)\n",
    "print(\"TE:\", TE_df_holdout.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## interactions by position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:05:10.061421Z",
     "start_time": "2017-12-14T17:03:14.883822Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "create_interactions = [('QB',QB_df), ('RB',RB_df), ('WR',WR_df), ('TE',TE_df)]\n",
    "interactions_positions = {}\n",
    "for pos, data in create_interactions:\n",
    "    numerics = data.select_dtypes(include=['float32','int32','int64','float64','uint8']).columns.tolist()\n",
    "    data = data[numerics]\n",
    "    poly = PolynomialFeatures(2, interaction_only=True, include_bias=True)\n",
    "    interactions_arr = poly.fit_transform(data)\n",
    "    interactions_names = poly.get_feature_names(data.columns)\n",
    "    interactions = pd.DataFrame(interactions_arr, columns=interactions_names)\n",
    "    interactions_positions[pos] = interactions\n",
    "\n",
    "def explore_interaction_corrs(pos, interactions):\n",
    "    target_col = interactions['target']\n",
    "    nontarget_interactions = interactions[[c for c in interactions.columns if \"target\" not in c]]\n",
    "    explore = pd.concat([target_col, nontarget_interactions], axis=1)\n",
    "    corr_interactions = explore.corr()\n",
    "    return corr_interactions[['target']].sort_values('target',ascending=False).reset_index()\n",
    "\n",
    "QB_inters = explore_interaction_corrs('QB', interactions_positions['QB'])\n",
    "RB_inters = explore_interaction_corrs('RB', interactions_positions['RB'])\n",
    "WR_inters = explore_interaction_corrs('WR', interactions_positions['WR'])\n",
    "TE_inters = explore_interaction_corrs('TE', interactions_positions['TE'])\n",
    "\n",
    "# QB_inters.to_csv('data/interactions_QB.csv')\n",
    "# RB_inters.to_csv('data/interactions_RB.csv')\n",
    "# WR_inters.to_csv('data/interactions_WR.csv')\n",
    "# TE_inters.to_csv('data/interactions_TE.csv')\n",
    "\n",
    "# QB_inters = pd.read_csv('data/interactions_QB.csv')\n",
    "# RB_inters = pd.read_csv('data/interactions_RB.csv')\n",
    "# WR_inters = pd.read_csv('data/interactions_WR.csv')\n",
    "# TE_inters = pd.read_csv('data/interactions_TE.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:05:10.095236Z",
     "start_time": "2017-12-14T17:05:10.066113Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1785</th>\n",
       "      <td>fd_salary chg_fumbles_tot</td>\n",
       "      <td>-0.091792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1784</th>\n",
       "      <td>fumbles_rcv_wgtmean fumbles_trcv_wgtmean</td>\n",
       "      <td>-0.087180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1783</th>\n",
       "      <td>fumbles_rcv_wgtmean fumbles_tot_wgtmean</td>\n",
       "      <td>-0.087158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1782</th>\n",
       "      <td>fumbles_yds_wgtmean rushing_yds_mean</td>\n",
       "      <td>-0.087091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1781</th>\n",
       "      <td>chg_fumbles_lost passing_tds_mean</td>\n",
       "      <td>-0.086891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         index    target\n",
       "1785                 fd_salary chg_fumbles_tot -0.091792\n",
       "1784  fumbles_rcv_wgtmean fumbles_trcv_wgtmean -0.087180\n",
       "1783   fumbles_rcv_wgtmean fumbles_tot_wgtmean -0.087158\n",
       "1782      fumbles_yds_wgtmean rushing_yds_mean -0.087091\n",
       "1781         chg_fumbles_lost passing_tds_mean -0.086891"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QB_inters.sort_values('target',ascending=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### RB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:05:10.119620Z",
     "start_time": "2017-12-14T17:05:10.102970Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>fantasy_points_mean chg_fumbles_tot</td>\n",
       "      <td>-0.135982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>offensive_snap_tot chg_fumbles_tot</td>\n",
       "      <td>-0.135726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>player_weight chg_fumbles_tot</td>\n",
       "      <td>-0.135288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>chg_fumbles_tot rushing_yds_mean</td>\n",
       "      <td>-0.132689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>started chg_fumbles_tot</td>\n",
       "      <td>-0.132444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index    target\n",
       "1687  fantasy_points_mean chg_fumbles_tot -0.135982\n",
       "1686   offensive_snap_tot chg_fumbles_tot -0.135726\n",
       "1685        player_weight chg_fumbles_tot -0.135288\n",
       "1684     chg_fumbles_tot rushing_yds_mean -0.132689\n",
       "1683              started chg_fumbles_tot -0.132444"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RB_inters.sort_values('target',ascending=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:05:10.140761Z",
     "start_time": "2017-12-14T17:05:10.121366Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1664</th>\n",
       "      <td>chg_fumbles_tot receiving_yds_mean</td>\n",
       "      <td>-0.115394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1663</th>\n",
       "      <td>fantasy_points_mean chg_fumbles_tot</td>\n",
       "      <td>-0.111021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1662</th>\n",
       "      <td>player_weight chg_fumbles_tot</td>\n",
       "      <td>-0.109755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1661</th>\n",
       "      <td>chg_fumbles_tot receiving_rec_mean</td>\n",
       "      <td>-0.107877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1660</th>\n",
       "      <td>offensive_snap_tot chg_fumbles_tot</td>\n",
       "      <td>-0.096767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    index    target\n",
       "1664   chg_fumbles_tot receiving_yds_mean -0.115394\n",
       "1663  fantasy_points_mean chg_fumbles_tot -0.111021\n",
       "1662        player_weight chg_fumbles_tot -0.109755\n",
       "1661   chg_fumbles_tot receiving_rec_mean -0.107877\n",
       "1660   offensive_snap_tot chg_fumbles_tot -0.096767"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WR_inters.sort_values('target',ascending=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:05:10.179104Z",
     "start_time": "2017-12-14T17:05:10.147715Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>target</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>height receiving_yds_mean</td>\n",
       "      <td>0.380983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>receiving_yds_mean</td>\n",
       "      <td>0.380126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>year receiving_yds_mean</td>\n",
       "      <td>0.380116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>weight receiving_yds_mean</td>\n",
       "      <td>0.379562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>age receiving_yds_mean</td>\n",
       "      <td>0.373749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>height receiving_rec_mean</td>\n",
       "      <td>0.364913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>receiving_rec_mean receiving_yds_mean</td>\n",
       "      <td>0.364261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   index    target\n",
       "0                                 target  1.000000\n",
       "1              height receiving_yds_mean  0.380983\n",
       "2                     receiving_yds_mean  0.380126\n",
       "3                year receiving_yds_mean  0.380116\n",
       "4              weight receiving_yds_mean  0.379562\n",
       "5                 age receiving_yds_mean  0.373749\n",
       "6              height receiving_rec_mean  0.364913\n",
       "7  receiving_rec_mean receiving_yds_mean  0.364261"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TE_inters.sort_values('target',ascending=False).head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## compute interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:05:10.233051Z",
     "start_time": "2017-12-14T17:05:10.182753Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QB_df.reset_index(inplace=True,drop=True)\n",
    "RB_df.reset_index(inplace=True,drop=True)\n",
    "WR_df.reset_index(inplace=True,drop=True)\n",
    "TE_df.reset_index(inplace=True,drop=True)\n",
    "\n",
    "QB_df['chg_fumbles_yds trend_fumbles_tot'] = QB_df['trend_fumbles_tot']*QB_df['chg_fumbles_yds']\n",
    "QB_df['age player_weight'] = QB_df['age']*QB_df['player_weight']\n",
    "RB_df['player_weight offensive_snap_pct'] = RB_df['player_weight']*RB_df['offensive_snap_pct']\n",
    "RB_df['fantasy_points_mean offensive_snap_tot'] = RB_df['offensive_snap_tot']*RB_df['fantasy_points_mean']\n",
    "RB_df['offensive_snap_tot chg_fumbles_tot'] = RB_df['offensive_snap_tot']*RB_df['chg_fumbles_tot']\n",
    "WR_df['chg_fumbles_tot receiving_yds_mean'] = WR_df['chg_fumbles_tot']*WR_df['receiving_yds_mean']\n",
    "WR_df['age receiving_yds_mean'] = WR_df['age']*WR_df['receiving_yds_mean']\n",
    "TE_df['trend_fumbles_tot RBMEANRushRecRatio_AttRec_mean'] = TE_df['trend_fumbles_tot']*TE_df['RBMEANRushRecRatio_AttRec_mean']\n",
    "TE_df['age receiving_yds_mean'] = TE_df['age']*TE_df['receiving_yds_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:05:10.308934Z",
     "start_time": "2017-12-14T17:05:10.245134Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QB_df_holdout.reset_index(inplace=True,drop=True)\n",
    "RB_df_holdout.reset_index(inplace=True,drop=True)\n",
    "WR_df_holdout.reset_index(inplace=True,drop=True)\n",
    "TE_df_holdout.reset_index(inplace=True,drop=True)\n",
    "\n",
    "QB_df_holdout['chg_fumbles_yds trend_fumbles_tot'] = QB_df_holdout['trend_fumbles_tot']*QB_df_holdout['chg_fumbles_yds']\n",
    "QB_df_holdout['age player_weight'] = QB_df_holdout['age']*QB_df_holdout['player_weight']\n",
    "RB_df_holdout['player_weight offensive_snap_pct'] = RB_df_holdout['player_weight']*RB_df_holdout['offensive_snap_pct']\n",
    "RB_df_holdout['fantasy_points_mean offensive_snap_tot'] = RB_df_holdout['offensive_snap_tot']*RB_df_holdout['fantasy_points_mean']\n",
    "RB_df_holdout['offensive_snap_tot chg_fumbles_tot'] = RB_df_holdout['offensive_snap_tot']*RB_df_holdout['chg_fumbles_tot']\n",
    "WR_df_holdout['chg_fumbles_tot receiving_yds_mean'] = WR_df_holdout['chg_fumbles_tot']*WR_df_holdout['receiving_yds_mean']\n",
    "WR_df_holdout['age receiving_yds_mean'] = WR_df_holdout['age']*WR_df_holdout['receiving_yds_mean']\n",
    "TE_df_holdout['trend_fumbles_tot RBMEANRushRecRatio_AttRec_mean'] = TE_df_holdout['trend_fumbles_tot']*TE_df_holdout['RBMEANRushRecRatio_AttRec_mean']\n",
    "TE_df_holdout['age receiving_yds_mean'] = TE_df_holdout['age']*TE_df_holdout['receiving_yds_mean']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## memory mgmt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:05:10.447304Z",
     "start_time": "2017-12-14T17:05:10.317642Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "991"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del player_dfs\n",
    "del opp_dfs\n",
    "del nfl_dfs\n",
    "del fanduel_dfs\n",
    "del nfl_fanduel_dfs\n",
    "del nfl_fd_sc_dfs\n",
    "del nfl_fd_sc_weather_dfs\n",
    "del QB_inters\n",
    "del RB_inters\n",
    "del WR_inters\n",
    "del TE_inters\n",
    "del interactions_positions\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:05:10.515394Z",
     "start_time": "2017-12-14T17:05:10.449218Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.metrics as metrics\n",
    "import sklearn.linear_model as lin\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from pprint import pprint\n",
    "import matplotlib\n",
    "from math import exp\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:07:53.040151Z",
     "start_time": "2017-12-14T17:07:53.037518Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RESPONSE_VAR = 'target'\n",
    "BENCHMARK = 'proj_pts'\n",
    "CURR_WEEK = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:22.448580Z",
     "start_time": "2017-12-14T17:12:22.170581Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def regression_tts(input_df, week, response):\n",
    "    \n",
    "    \"\"\"Run a regression for each position (QB,RB,WR,TE),\n",
    "       Train on first 14 weeks of each season 2013-2016,\n",
    "       Test on last 3 weeks of each season 2013-2016\"\"\"\n",
    "    \n",
    "    features = input_df.select_dtypes(include=['float32','int32','int64','float64','uint8']).columns.tolist()\n",
    "    features.remove('year')\n",
    "    features.remove('target')\n",
    "    features.remove('target_rank')\n",
    "    features.remove('target_week')\n",
    "    \n",
    "    est = GradientBoostingRegressor(n_estimators=30, learning_rate=0.1)\n",
    "#     est = lin.LassoCV()\n",
    "    \n",
    "    df_train = input_df[input_df.target_week <= week]\n",
    "#     df_train.sort_values('fantasy_points_mean',ascending=True,inplace=True)\n",
    "    df_test = input_df[input_df.target_week > week]\n",
    "    \n",
    "    X_train = df_train[features]\n",
    "    y_train = df_train[response]\n",
    "    X_test = df_test[features]\n",
    "    y_test = df_test[response]\n",
    "    \n",
    "    ss = StandardScaler()\n",
    "    X_train = ss.fit_transform(X_train)\n",
    "    X_test = ss.fit_transform(X_test)\n",
    "    \n",
    "#     sw = np.linspace(.01,1,X_train.shape[0])\n",
    "    est.fit(X_train, y_train)\n",
    "\n",
    "    y_pred = est.predict(X_train)\n",
    "    train_results = (metrics.mean_squared_error(y_train, y_pred))**(0.5)\n",
    "#     train_results = metrics.r2_score(y_train, y_pred)\n",
    "\n",
    "    y_pred = est.predict(X_test)\n",
    "    test_results = (metrics.mean_squared_error(y_test, y_pred))**(0.5)\n",
    "#     test_results = metrics.r2_score(y_test, y_pred)\n",
    "    \n",
    "    df_test = df_test.reset_index()[['target_week','fantasy_points_mean',response]]\n",
    "    df_test['MODEL_PRED'] = pd.Series(y_pred)\n",
    "    df_test['RESIDUALS'] = df_test['MODEL_PRED'] - df_test[response]\n",
    "    \n",
    "    coefs = None\n",
    "    if 'sklearn.linear_model' in est.__module__:\n",
    "        coef_ranks = list(zip(abs(est.coef_), est.coef_, features))\n",
    "        coefs = sorted(coef_ranks, key=lambda x: x[0], reverse=True)\n",
    "    else:\n",
    "        coef_ranks = list(zip(est.feature_importances_, features))\n",
    "        coefs = sorted(coef_ranks, key=lambda x: x[0], reverse=True)\n",
    "        \n",
    "    return test_results, train_results, est, coefs, df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:25.049248Z",
     "start_time": "2017-12-14T17:12:22.474564Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QB\n",
      "train results: 6.71983298369\n",
      "test  results: 7.80652086888\n",
      "RB\n",
      "train results: 5.47723068253\n",
      "test  results: 5.8084739927\n",
      "WR\n",
      "train results: 5.52691685336\n",
      "test  results: 5.23941922685\n",
      "TE\n",
      "train results: 4.08313886523\n",
      "test  results: 4.21756593754\n"
     ]
    }
   ],
   "source": [
    "coefs_all = {}\n",
    "ests_positions = {}\n",
    "resids_positions = []\n",
    "\n",
    "for pos, data in [('QB',QB_df),('RB',RB_df),('WR',WR_df),('TE',TE_df)]:\n",
    "    print(pos)\n",
    "    results_test, results_train, est, coefs, resids = regression_tts(data, CURR_WEEK, RESPONSE_VAR)\n",
    "    \n",
    "    print('train results:',results_train)\n",
    "    print('test  results:',results_test)\n",
    "    coefs_all[pos] = coefs\n",
    "    ests_positions[pos] = est\n",
    "    resids['position'] = pos\n",
    "    resids_positions.append(resids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:28.638476Z",
     "start_time": "2017-12-14T17:12:28.626577Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QB\n",
      "[(0.14300964366336238, 'fd_salary'),\n",
      " (0.12806194933898976, 'player_weight'),\n",
      " (0.062770992765597194, 'passing_yds_mean'),\n",
      " (0.061638734002154257, 'offensive_snap_tot')]\n",
      "WR\n",
      "[(0.15186476231912494, 'receiving_yds_mean'),\n",
      " (0.102624103918001, 'fd_salary'),\n",
      " (0.073241829343239764, 'fantasy_points_mean'),\n",
      " (0.059645807624520333, 'player_weight')]\n",
      "RB\n",
      "[(0.29475084797471401, 'fantasy_points_mean offensive_snap_tot'),\n",
      " (0.08790939907612913, 'offensive_snap_tot'),\n",
      " (0.077457489887096775, 'rushing_yds_mean'),\n",
      " (0.071614353944568221, 'rushing_att_mean')]\n",
      "TE\n",
      "[(0.15096438758136266, 'receiving_rec_mean'),\n",
      " (0.11158696628327316, 'receiving_yds_mean'),\n",
      " (0.098962671106620312, 'fd_salary'),\n",
      " (0.070063382861525345, 'age receiving_yds_mean')]\n"
     ]
    }
   ],
   "source": [
    "for pos in coefs_all.keys():\n",
    "    print(pos)\n",
    "    pprint(coefs_all[pos][:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:31.183975Z",
     "start_time": "2017-12-14T17:12:31.170066Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QB_coefs_plot = [x[0] for x in coefs_all['QB']][:3]\n",
    "RB_coefs_plot = [x[0] for x in coefs_all['RB']][:3]\n",
    "WR_coefs_plot = [x[0] for x in coefs_all['WR']][:3]\n",
    "TE_coefs_plot = [x[0] for x in coefs_all['TE']][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:32.486560Z",
     "start_time": "2017-12-14T17:12:31.389467Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uf7dobnOPDQAAAACwEvM8RCqZXP15SyZXhZ6QyVO2zkvy5oXwdCnd\nfUlV3ZfkTUnemeT+JJ9O8sbFAWd3P1BVxyd5a5KTkjw5yTeSnJHk/Y9mbAAAAACAlZjrClQAAAAA\ngNVonj1QAQAAAABWJQEqAAAAAMAIASoAAAAAwAgBKgAAAADACAEqAAAAAMAIASoAAAAAwAgBKgAA\nAADACAEqAAAAAMAIASoAAAAAwAgBKgAAAADAiP8faazUxeP6G98AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabc799ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'size'   : 18}\n",
    "matplotlib.rc('font', **font)\n",
    "fig, (ax1,ax2,ax3,ax4) = plt.subplots(1,4,figsize=(23,5), sharey=True)\n",
    "\n",
    "for ax, title, pos_coefs in [(ax1,'QB',QB_coefs_plot), (ax2,'RB',RB_coefs_plot),\n",
    "                      (ax3,'WR',WR_coefs_plot), (ax4,'TE',TE_coefs_plot)]:\n",
    "    ax.bar(range(len(WR_coefs_plot)),WR_coefs_plot)\n",
    "    ax.set_title(title)\n",
    "    ax.axes.xaxis.set_ticklabels([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## model output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:10:19.622214Z",
     "start_time": "2017-12-14T17:10:18.378367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JkiRJUsPYkJQkSZIkSZLUMDYkJUmSJEmSJDWMDUlJkiRJkiRJDWNDUpIkSZIkSVLD2JCU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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac60aacc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble.partial_dependence import plot_partial_dependence\n",
    "font = {'size'   : 16}\n",
    "matplotlib.rc('font', **font)\n",
    "\n",
    "features = RB_df.select_dtypes(include=['float32','int32','int64','float64','uint8']).columns.tolist()\n",
    "features.remove('year')\n",
    "features.remove('target')\n",
    "features.remove('target_rank')\n",
    "features.remove('target_week')\n",
    "\n",
    "fp_mean = features.index('fantasy_points_mean')\n",
    "fd_sal = features.index('fd_salary')\n",
    "pr = features.index('receiving_rec_mean')\n",
    "pw = features.index('player_weight')\n",
    "dpa = features.index('defensive_matchup_allowed')\n",
    "snaps = features.index('offensive_snap_tot')\n",
    "X_train = RB_df[features]\n",
    "X_train = X_train[X_train.week < CURR_WEEK].as_matrix()\n",
    "\n",
    "features_idx = [fp_mean, fd_sal, dpa, pr, snaps, pw]\n",
    "fig, axs = plot_partial_dependence(ests_positions['RB'], X_train, features_idx,\n",
    "                                   feature_names=features,\n",
    "                                   n_jobs=-1, grid_resolution=50, figsize=(20,10))\n",
    "fig.suptitle('Partial dependence for RBs')\n",
    "plt.subplots_adjust(top=0.9) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:10:20.680672Z",
     "start_time": "2017-12-14T17:10:20.656931Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_results = pd.concat(resids_positions)\n",
    "model_results = model_results.sort_values('MODEL_PRED').reset_index()\n",
    "model_results_QB = resids_positions[0].sort_values('MODEL_PRED').reset_index()\n",
    "model_results_RB = resids_positions[1].sort_values('MODEL_PRED').reset_index()\n",
    "model_results_WR = resids_positions[2].sort_values('MODEL_PRED').reset_index()\n",
    "model_results_TE = resids_positions[3].sort_values('MODEL_PRED').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:02.331140Z",
     "start_time": "2017-12-14T17:12:01.841798Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xabbe6c6c>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TGeDaJWdj/lln4KdvDiCZNjqtfPNnB8y5mzOjPicxRY0JkxNc5MQWEbf5r93/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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab7beeac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(model_results.MODEL_PRED, model_results.RESIDUALS, s=3)\n",
    "plt.ylabel('Residual')\n",
    "plt.xlabel('Predicted Fantasy Points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:10:22.476341Z",
     "start_time": "2017-12-14T17:10:22.178469Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xaa68b3ec>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7mgAAAFVW2Jg3M/sVSX8s6ZPu/v4eu+2W9ISkqzu2X6NG1u0rRZUPAAAgRYUs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\nBgAAkJD/D4ZbhcgrSnVRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabde078c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(model_results_QB.MODEL_PRED, model_results_QB.RESIDUALS, s=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:10:23.175596Z",
     "start_time": "2017-12-14T17:10:22.869041Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xaa548c4c>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q9Sivfmfx9Tk77VdUhb5IVShDFvSR6lzW65y6RRnq8tkDSKwUkarsgC6vlRaY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\n9rMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa9c9b0c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(model_results_RB.MODEL_PRED, model_results_RB.RESIDUALS, s=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:10:23.603035Z",
     "start_time": "2017-12-14T17:10:23.234813Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xaa4fd5ec>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WaZaybn1yFkP3uybeaxUUVDj5dqY5UlkGVVkH4N6ypZnXz3S0bBbStv4679/y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\n8ItvM60AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa654c8c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(model_results_WR.MODEL_PRED, model_results_WR.RESIDUALS, s=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:10:23.982289Z",
     "start_time": "2017-12-14T17:10:23.604897Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xaa45a42c>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KViOxNP3U74d+mhTrP1Pe2xQA0B0qaG10eyJr11Q5OT2nj3xxZ8sBAfWvdd7py7Xn6UNR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\nAAAiQ4IGAAAQmf8Pd7Hzop1f2LoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa6c39ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(model_results_TE.MODEL_PRED, model_results_TE.RESIDUALS, s=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:10:24.401711Z",
     "start_time": "2017-12-14T17:10:23.983779Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.103422662492\n",
      "1.4348994053474649\n"
     ]
    },
    {
     "data": {
      "image/png": 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MRwdORMRKlEe2/wkcQVkrsC3VnsRz2qz7deDrbUf8xGvnUmZJS5IkqUGdMYS7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\nB/eeskFL3x7nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa46004c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'size'   : 20}\n",
    "matplotlib.rc('font', **font)\n",
    "plt.figure(figsize=(10,6))\n",
    "model_results.RESIDUALS.hist(bins=50)\n",
    "plt.xlabel(\"Residual Value\")\n",
    "plt.ylabel(\"Number of Observations\")\n",
    "print(model_results.RESIDUALS.mean())\n",
    "print(model_results.RESIDUALS.median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## holdout test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:07.690442Z",
     "start_time": "2017-12-14T17:12:07.684474Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# weeks = sorted(model_df.target_week.unique().tolist())\n",
    "# weeks.remove(1.0)\n",
    "# weeks.remove(2.0)\n",
    "weeks_predict = list(range(2,CURR_WEEK))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:41.214827Z",
     "start_time": "2017-12-14T17:12:41.100270Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def weekly_regression_predict(input_df, weeks, est, response, espn_response):\n",
    "\n",
    "    features = input_df.select_dtypes(include=['float32','int32','int64','float64','uint8']).columns.tolist()\n",
    "    features.remove('year')\n",
    "    features.remove('target')\n",
    "    features.remove('target_rank')\n",
    "    features.remove('proj_pts')\n",
    "    features.remove('espn_rank')\n",
    "    \n",
    "    week_nums = []\n",
    "    scores = []\n",
    "    bmk_scores = []\n",
    "    predictions = []\n",
    "\n",
    "    for week in weeks:\n",
    "        week_nums.append(week)\n",
    "       \n",
    "        df_cv = input_df[input_df.target_week == week]\n",
    "        X = df_cv[features]\n",
    "        y = df_cv[response]\n",
    "        y_benchmark = df_cv[espn_response]\n",
    "#         X.sort_values('fantasy_points_mean',ascending=True,inplace=True)\n",
    "        ss = StandardScaler()\n",
    "        X = ss.fit_transform(X)\n",
    "        \n",
    "        y_pred = est.predict(X)\n",
    "\n",
    "#         score = metrics.r2_score(y, y_pred)\n",
    "#         score = (metrics.mean_squared_error(y, y_pred))**(0.5)\n",
    "        score = metrics.mean_absolute_error(y, y_pred)\n",
    "        scores.append(score)\n",
    "    \n",
    "#         bmk_score = metrics.r2_score(y, y_benchmark)\n",
    "#         bmk_score = (metrics.mean_squared_error(y, y_benchmark))**(0.5)\n",
    "        bmk_score = metrics.mean_absolute_error(y, y_benchmark)\n",
    "        bmk_scores.append(bmk_score)\n",
    "        \n",
    "        predicts = df_cv.reset_index()[['full_name','target_week', response, espn_response]]\n",
    "        predicts['PREDICTION'] = pd.Series(y_pred)\n",
    "        predicts['ERROR'] = predicts['PREDICTION'] - predicts[response]\n",
    "        predicts['ESPN_ERROR'] = predicts[espn_response] - predicts[response]\n",
    "        predictions.append(predicts)\n",
    "        \n",
    "    return week_nums, scores, bmk_scores, pd.concat(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:47.774759Z",
     "start_time": "2017-12-14T17:12:41.357137Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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XvPnmmwwZMoTAwEBatmxJ165d6dSpE/fccw9169a1+jwbNmywOsLu7e1NTExMvl9zfshI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DF2YT/M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\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syLkQF7BwvGgXrPvEu3jCWUkRbMtyjylJFwkZnibp1trXrLVnWmt7WmsTrLWJ1tpB1to7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabc9710c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_dfs = {}\n",
    "\n",
    "f, (ax2, ax3, ax4, ax5) = plt.subplots(4,figsize=(12,30), sharex=True)\n",
    "for pos, data, ax in [('QB',QB_df_holdout,ax2),\n",
    "                      ('RB',RB_df_holdout,ax3),\n",
    "                      ('WR',WR_df_holdout,ax4),\n",
    "                      ('TE',TE_df_holdout,ax5)]:\n",
    "    \n",
    "    weeks, scores, bmk_scores, predictions = weekly_regression_predict(data, weeks_predict, \n",
    "                                                                       ests_positions[pos],\n",
    "                                                                       response=RESPONSE_VAR,\n",
    "                                                                       espn_response=BENCHMARK)\n",
    "    predictions['position'] = pos\n",
    "    prediction_dfs[pos] = predictions\n",
    "    \n",
    "    ax.plot(weeks, scores, label=\"Linear Model RMSE\", linewidth=4)\n",
    "    ax.plot(weeks, bmk_scores, label=\"ESPN RMSE\", linewidth=4)\n",
    "    ax.set_xticks(weeks)\n",
    "    ax.set_ylabel(\"RMSE\")\n",
    "    ax.set_title(pos)\n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:52.876494Z",
     "start_time": "2017-12-14T17:12:52.865041Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "holdout_results = pd.concat(list(prediction_dfs.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:53.919230Z",
     "start_time": "2017-12-14T17:12:53.125369Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.62152838173\n",
      "6.12655248797\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xabb74dac>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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699Dj2Hjss+fcsU2SJEmSpFrOIpJqVkYmnPogtOsbj79+C0x5NpmcJEmSJElS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bc68HJqv0g8PBzvm5hwfBcP6kL3UP/qdUWVzn9W7TMxIyGEEKaweMFlr9cxU0eHnx6FX56Sp8aO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\nSZIkaZRJJEmSJEmSJI36E5XftJKCmXIwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac57ef2c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weeks_all = []\n",
    "all_scores = []\n",
    "all_bmk_scores = []\n",
    "for week in weeks_predict:\n",
    "    weeks_all.append(week)\n",
    "    weeks_results = holdout_results[holdout_results.target_week == week]\n",
    "    y = weeks_results[RESPONSE_VAR]\n",
    "    y_pred = weeks_results['PREDICTION']\n",
    "    y_bmk = weeks_results[BENCHMARK]\n",
    "#     score = metrics.r2_score(y, y_pred)\n",
    "#     bmk_score = metrics.r2_score(y, y_bmk)\n",
    "    score = (metrics.mean_squared_error(y, y_pred))**(0.5)\n",
    "    bmk_score = (metrics.mean_squared_error(y, y_bmk))**(0.5)\n",
    "    all_scores.append(score)\n",
    "    all_bmk_scores.append(bmk_score)\n",
    "    \n",
    "print(np.mean(all_scores))\n",
    "print(np.mean(all_bmk_scores))\n",
    "plt.figure(figsize=(20,12))\n",
    "plt.plot(weeks_all, all_scores, label=\"Linear Model\", linewidth=4)\n",
    "plt.plot(weeks_all, all_bmk_scores, label=\"ESPN\", linewidth=4)\n",
    "plt.xticks(weeks_all)\n",
    "# plt.ylabel(\"RMSE\")\n",
    "# plt.xlabel(\"Week\")\n",
    "# plt.title(\"Holdout error for all positions (2017 season)\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:57.227944Z",
     "start_time": "2017-12-14T17:12:57.176888Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_name</th>\n",
       "      <th>target_week</th>\n",
       "      <th>target</th>\n",
       "      <th>proj_pts</th>\n",
       "      <th>PREDICTION</th>\n",
       "      <th>ERROR</th>\n",
       "      <th>ESPN_ERROR</th>\n",
       "      <th>position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>14.5</td>\n",
       "      <td>12.701790</td>\n",
       "      <td>8.401790</td>\n",
       "      <td>10.2</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.634204</td>\n",
       "      <td>2.734204</td>\n",
       "      <td>10.1</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>4</td>\n",
       "      <td>10.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>11.749000</td>\n",
       "      <td>0.949000</td>\n",
       "      <td>8.9</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>10.135325</td>\n",
       "      <td>1.235325</td>\n",
       "      <td>10.3</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>20.8</td>\n",
       "      <td>13.789225</td>\n",
       "      <td>-8.410775</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>8</td>\n",
       "      <td>23.3</td>\n",
       "      <td>21.9</td>\n",
       "      <td>13.892795</td>\n",
       "      <td>-9.407205</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>21.9</td>\n",
       "      <td>18.986945</td>\n",
       "      <td>16.486945</td>\n",
       "      <td>19.4</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.402418</td>\n",
       "      <td>7.402418</td>\n",
       "      <td>15.0</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>11</td>\n",
       "      <td>24.6</td>\n",
       "      <td>19.8</td>\n",
       "      <td>9.777387</td>\n",
       "      <td>-14.822613</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>12</td>\n",
       "      <td>7.9</td>\n",
       "      <td>20.1</td>\n",
       "      <td>11.828062</td>\n",
       "      <td>3.928062</td>\n",
       "      <td>12.2</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>13</td>\n",
       "      <td>10.2</td>\n",
       "      <td>18.7</td>\n",
       "      <td>14.617490</td>\n",
       "      <td>4.417490</td>\n",
       "      <td>8.5</td>\n",
       "      <td>RB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       full_name target_week  target  proj_pts  PREDICTION      ERROR  \\\n",
       "9   LeSean McCoy           2     4.3      14.5   12.701790   8.401790   \n",
       "3   LeSean McCoy           3     6.9      17.0    9.634204   2.734204   \n",
       "3   LeSean McCoy           4    10.8      19.7   11.749000   0.949000   \n",
       "4   LeSean McCoy           5     8.9      19.2   10.135325   1.235325   \n",
       "60  LeSean McCoy           7    22.2      20.8   13.789225  -8.410775   \n",
       "2   LeSean McCoy           8    23.3      21.9   13.892795  -9.407205   \n",
       "2   LeSean McCoy           9     2.5      21.9   18.986945  16.486945   \n",
       "0   LeSean McCoy          10     6.0      21.0   13.402418   7.402418   \n",
       "2   LeSean McCoy          11    24.6      19.8    9.777387 -14.822613   \n",
       "1   LeSean McCoy          12     7.9      20.1   11.828062   3.928062   \n",
       "4   LeSean McCoy          13    10.2      18.7   14.617490   4.417490   \n",
       "\n",
       "    ESPN_ERROR position  \n",
       "9         10.2       RB  \n",
       "3         10.1       RB  \n",
       "3          8.9       RB  \n",
       "4         10.3       RB  \n",
       "60        -1.4       RB  \n",
       "2         -1.4       RB  \n",
       "2         19.4       RB  \n",
       "0         15.0       RB  \n",
       "2         -4.8       RB  \n",
       "1         12.2       RB  \n",
       "4          8.5       RB  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "position_q = 'RB'\n",
    "name = 'LeSean McCoy'\n",
    "holdout_results[(holdout_results.position == position_q) &\n",
    "                (holdout_results.full_name == name)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:57.894691Z",
     "start_time": "2017-12-14T17:12:57.875688Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "holdout_results['abs_error'] = holdout_results.ERROR.apply(lambda x: np.abs(x))\n",
    "holdout_results['spread'] = np.abs(holdout_results['ERROR'] - holdout_results['ESPN_ERROR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T17:12:58.883464Z",
     "start_time": "2017-12-14T17:12:58.823445Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_name</th>\n",
       "      <th>target_week</th>\n",
       "      <th>target</th>\n",
       "      <th>proj_pts</th>\n",
       "      <th>PREDICTION</th>\n",
       "      <th>ERROR</th>\n",
       "      <th>ESPN_ERROR</th>\n",
       "      <th>position</th>\n",
       "      <th>abs_error</th>\n",
       "      <th>spread</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Tyrod Taylor</td>\n",
       "      <td>11</td>\n",
       "      <td>18.12</td>\n",
       "      <td>0.1</td>\n",
       "      <td>17.810070</td>\n",
       "      <td>-0.309930</td>\n",
       "      <td>-18.02</td>\n",
       "      <td>QB</td>\n",
       "      <td>0.309930</td>\n",
       "      <td>17.710070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Case Keenum</td>\n",
       "      <td>5</td>\n",
       "      <td>11.80</td>\n",
       "      <td>0.1</td>\n",
       "      <td>14.832260</td>\n",
       "      <td>3.032260</td>\n",
       "      <td>-11.70</td>\n",
       "      <td>QB</td>\n",
       "      <td>3.032260</td>\n",
       "      <td>14.732260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Trevor Siemian</td>\n",
       "      <td>12</td>\n",
       "      <td>14.86</td>\n",
       "      <td>0.1</td>\n",
       "      <td>14.174078</td>\n",
       "      <td>-0.685922</td>\n",
       "      <td>-14.76</td>\n",
       "      <td>QB</td>\n",
       "      <td>0.685922</td>\n",
       "      <td>14.074078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Ryan Fitzpatrick</td>\n",
       "      <td>9</td>\n",
       "      <td>9.72</td>\n",
       "      <td>0.1</td>\n",
       "      <td>12.989316</td>\n",
       "      <td>3.269316</td>\n",
       "      <td>-9.62</td>\n",
       "      <td>QB</td>\n",
       "      <td>3.269316</td>\n",
       "      <td>12.889316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Matt Moore</td>\n",
       "      <td>11</td>\n",
       "      <td>15.28</td>\n",
       "      <td>0.1</td>\n",
       "      <td>12.451871</td>\n",
       "      <td>-2.828129</td>\n",
       "      <td>-15.18</td>\n",
       "      <td>QB</td>\n",
       "      <td>2.828129</td>\n",
       "      <td>12.351871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rob Gronkowski</td>\n",
       "      <td>2</td>\n",
       "      <td>17.60</td>\n",
       "      <td>19.2</td>\n",
       "      <td>7.227893</td>\n",
       "      <td>-10.372107</td>\n",
       "      <td>1.60</td>\n",
       "      <td>TE</td>\n",
       "      <td>10.372107</td>\n",
       "      <td>11.972107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Le'Veon Bell</td>\n",
       "      <td>2</td>\n",
       "      <td>9.10</td>\n",
       "      <td>20.5</td>\n",
       "      <td>8.656121</td>\n",
       "      <td>-0.443879</td>\n",
       "      <td>11.40</td>\n",
       "      <td>RB</td>\n",
       "      <td>0.443879</td>\n",
       "      <td>11.843879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Antonio Brown</td>\n",
       "      <td>10</td>\n",
       "      <td>4.70</td>\n",
       "      <td>22.1</td>\n",
       "      <td>10.602481</td>\n",
       "      <td>5.902481</td>\n",
       "      <td>17.40</td>\n",
       "      <td>WR</td>\n",
       "      <td>5.902481</td>\n",
       "      <td>11.497519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Odell Beckham</td>\n",
       "      <td>3</td>\n",
       "      <td>19.90</td>\n",
       "      <td>16.8</td>\n",
       "      <td>5.593743</td>\n",
       "      <td>-14.306257</td>\n",
       "      <td>-3.10</td>\n",
       "      <td>WR</td>\n",
       "      <td>14.306257</td>\n",
       "      <td>11.206257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Cody Kessler</td>\n",
       "      <td>10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.1</td>\n",
       "      <td>11.098470</td>\n",
       "      <td>10.898470</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>QB</td>\n",
       "      <td>10.898470</td>\n",
       "      <td>10.998470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Kareem Hunt</td>\n",
       "      <td>11</td>\n",
       "      <td>7.70</td>\n",
       "      <td>21.1</td>\n",
       "      <td>10.262414</td>\n",
       "      <td>2.562414</td>\n",
       "      <td>13.40</td>\n",
       "      <td>RB</td>\n",
       "      <td>2.562414</td>\n",
       "      <td>10.837586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kevin Hogan</td>\n",
       "      <td>5</td>\n",
       "      <td>16.76</td>\n",
       "      <td>0.1</td>\n",
       "      <td>10.138477</td>\n",
       "      <td>-6.621523</td>\n",
       "      <td>-16.66</td>\n",
       "      <td>QB</td>\n",
       "      <td>6.621523</td>\n",
       "      <td>10.038477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LeSean McCoy</td>\n",
       "      <td>11</td>\n",
       "      <td>24.60</td>\n",
       "      <td>19.8</td>\n",
       "      <td>9.777387</td>\n",
       "      <td>-14.822613</td>\n",
       "      <td>-4.80</td>\n",
       "      <td>RB</td>\n",
       "      <td>14.822613</td>\n",
       "      <td>10.022613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kareem Hunt</td>\n",
       "      <td>7</td>\n",
       "      <td>11.70</td>\n",
       "      <td>19.8</td>\n",
       "      <td>9.956462</td>\n",
       "      <td>-1.743538</td>\n",
       "      <td>8.10</td>\n",
       "      <td>RB</td>\n",
       "      <td>1.743538</td>\n",
       "      <td>9.843538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Emmanuel Sanders</td>\n",
       "      <td>2</td>\n",
       "      <td>18.20</td>\n",
       "      <td>16.3</td>\n",
       "      <td>6.459301</td>\n",
       "      <td>-11.740699</td>\n",
       "      <td>-1.90</td>\n",
       "      <td>WR</td>\n",
       "      <td>11.740699</td>\n",
       "      <td>9.840699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Nathan Peterman</td>\n",
       "      <td>13</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.1</td>\n",
       "      <td>9.907294</td>\n",
       "      <td>7.007294</td>\n",
       "      <td>-2.80</td>\n",
       "      <td>QB</td>\n",
       "      <td>7.007294</td>\n",
       "      <td>9.807294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Le'Veon Bell</td>\n",
       "      <td>3</td>\n",
       "      <td>15.80</td>\n",
       "      <td>22.3</td>\n",
       "      <td>12.518290</td>\n",
       "      <td>-3.281710</td>\n",
       "      <td>6.50</td>\n",
       "      <td>RB</td>\n",
       "      <td>3.281710</td>\n",
       "      <td>9.781710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Tevin Coleman</td>\n",
       "      <td>12</td>\n",
       "      <td>21.70</td>\n",
       "      <td>17.2</td>\n",
       "      <td>7.544769</td>\n",
       "      <td>-14.155231</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>RB</td>\n",
       "      <td>14.155231</td>\n",
       "      <td>9.655231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kareem Hunt</td>\n",
       "      <td>6</td>\n",
       "      <td>11.00</td>\n",
       "      <td>18.5</td>\n",
       "      <td>8.982259</td>\n",
       "      <td>-2.017741</td>\n",
       "      <td>7.50</td>\n",
       "      <td>RB</td>\n",
       "      <td>2.017741</td>\n",
       "      <td>9.517741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ezekiel Elliott</td>\n",
       "      <td>4</td>\n",
       "      <td>25.90</td>\n",
       "      <td>21.6</td>\n",
       "      <td>12.145029</td>\n",
       "      <td>-13.754971</td>\n",
       "      <td>-4.30</td>\n",
       "      <td>RB</td>\n",
       "      <td>13.754971</td>\n",
       "      <td>9.454971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           full_name target_week  target  proj_pts  PREDICTION      ERROR  \\\n",
       "21      Tyrod Taylor          11   18.12       0.1   17.810070  -0.309930   \n",
       "26       Case Keenum           5   11.80       0.1   14.832260   3.032260   \n",
       "31    Trevor Siemian          12   14.86       0.1   14.174078  -0.685922   \n",
       "24  Ryan Fitzpatrick           9    9.72       0.1   12.989316   3.269316   \n",
       "26        Matt Moore          11   15.28       0.1   12.451871  -2.828129   \n",
       "0     Rob Gronkowski           2   17.60      19.2    7.227893 -10.372107   \n",
       "0       Le'Veon Bell           2    9.10      20.5    8.656121  -0.443879   \n",
       "75     Antonio Brown          10    4.70      22.1   10.602481   5.902481   \n",
       "8      Odell Beckham           3   19.90      16.8    5.593743 -14.306257   \n",
       "28      Cody Kessler          10    0.20       0.1   11.098470  10.898470   \n",
       "54       Kareem Hunt          11    7.70      21.1   10.262414   2.562414   \n",
       "14       Kevin Hogan           5   16.76       0.1   10.138477  -6.621523   \n",
       "2       LeSean McCoy          11   24.60      19.8    9.777387 -14.822613   \n",
       "3        Kareem Hunt           7   11.70      19.8    9.956462  -1.743538   \n",
       "24  Emmanuel Sanders           2   18.20      16.3    6.459301 -11.740699   \n",
       "29   Nathan Peterman          13    2.90       0.1    9.907294   7.007294   \n",
       "0       Le'Veon Bell           3   15.80      22.3   12.518290  -3.281710   \n",
       "15     Tevin Coleman          12   21.70      17.2    7.544769 -14.155231   \n",
       "3        Kareem Hunt           6   11.00      18.5    8.982259  -2.017741   \n",
       "0    Ezekiel Elliott           4   25.90      21.6   12.145029 -13.754971   \n",
       "\n",
       "    ESPN_ERROR position  abs_error     spread  \n",
       "21      -18.02       QB   0.309930  17.710070  \n",
       "26      -11.70       QB   3.032260  14.732260  \n",
       "31      -14.76       QB   0.685922  14.074078  \n",
       "24       -9.62       QB   3.269316  12.889316  \n",
       "26      -15.18       QB   2.828129  12.351871  \n",
       "0         1.60       TE  10.372107  11.972107  \n",
       "0        11.40       RB   0.443879  11.843879  \n",
       "75       17.40       WR   5.902481  11.497519  \n",
       "8        -3.10       WR  14.306257  11.206257  \n",
       "28       -0.10       QB  10.898470  10.998470  \n",
       "54       13.40       RB   2.562414  10.837586  \n",
       "14      -16.66       QB   6.621523  10.038477  \n",
       "2        -4.80       RB  14.822613  10.022613  \n",
       "3         8.10       RB   1.743538   9.843538  \n",
       "24       -1.90       WR  11.740699   9.840699  \n",
       "29       -2.80       QB   7.007294   9.807294  \n",
       "0         6.50       RB   3.281710   9.781710  \n",
       "15       -4.50       RB  14.155231   9.655231  \n",
       "3         7.50       RB   2.017741   9.517741  \n",
       "0        -4.30       RB  13.754971   9.454971  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holdout_results.sort_values('spread', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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