examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sportsref.baseball import Player, Franchise, Season, League, Frivolities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.rc_context at 0x7feb031dee10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.xkcd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the strike outs from 1918"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "season1918 = Season(1918)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['batting_pages',\n",
       " 'fielding_pages',\n",
       " 'league',\n",
       " 'managers',\n",
       " 'minor_leagues',\n",
       " 'other_pages',\n",
       " 'overview',\n",
       " 'pitching_pages',\n",
       " 'standings',\n",
       " 'year']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[ x for x in dir(season1918) if '__' not in x ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available tables: ['teams_standard_pitching', 'players_standard_pitching']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "< Page, web_url : 'https://www.baseball-reference.com/leagues/MLB/1918-standard-pitching.shtml' >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season1918.pitching_pages('standard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available tables: ['teams_standard_pitching', 'players_standard_pitching']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feb020d1890>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "season1918.pitching_pages('standard').get_df('teams_standard_pitching').set_index(\"Tm\").SO.plot(figsize=(10,10), kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wade Boggs 1987 rolling 10 game batting average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm 90% sure that you want Wade Boggs\n"
     ]
    }
   ],
   "source": [
    "boggs = Player(\"boggs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available tables: ['batting_gamelogs']\n"
     ]
    }
   ],
   "source": [
    "boggs_gamelog_page = boggs.game_logs_pages(1987, 'b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "boggs_87_logs = boggs_gamelog_page.get_df('batting_gamelogs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Gcar</th>\n",
       "      <th>Gtm</th>\n",
       "      <th>Date</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Opp</th>\n",
       "      <th>Rslt</th>\n",
       "      <th>Inngs</th>\n",
       "      <th>PA</th>\n",
       "      <th>...</th>\n",
       "      <th>OBP</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OPS</th>\n",
       "      <th>BOP</th>\n",
       "      <th>aLI</th>\n",
       "      <th>WPA</th>\n",
       "      <th>acLI</th>\n",
       "      <th>cWPA</th>\n",
       "      <th>RE24</th>\n",
       "      <th>Pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>726</td>\n",
       "      <td>1</td>\n",
       "      <td>Apr 6</td>\n",
       "      <td>BOS</td>\n",
       "      <td>@</td>\n",
       "      <td>MIL</td>\n",
       "      <td>L,1-5</td>\n",
       "      <td>CG</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1</td>\n",
       "      <td>0.63</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.91</td>\n",
       "      <td>-0.01%</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>727</td>\n",
       "      <td>2</td>\n",
       "      <td>Apr 8</td>\n",
       "      <td>BOS</td>\n",
       "      <td>@</td>\n",
       "      <td>MIL</td>\n",
       "      <td>L,2-3</td>\n",
       "      <td>CG</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.393</td>\n",
       "      <td>1</td>\n",
       "      <td>1.27</td>\n",
       "      <td>-0.090</td>\n",
       "      <td>1.64</td>\n",
       "      <td>-0.07%</td>\n",
       "      <td>-0.59</td>\n",
       "      <td>3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>728</td>\n",
       "      <td>3</td>\n",
       "      <td>Apr 9</td>\n",
       "      <td>BOS</td>\n",
       "      <td>@</td>\n",
       "      <td>MIL</td>\n",
       "      <td>L,11-12</td>\n",
       "      <td>CG</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.600</td>\n",
       "      <td>1.062</td>\n",
       "      <td>1</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.148</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.11%</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>729</td>\n",
       "      <td>4</td>\n",
       "      <td>Apr 10</td>\n",
       "      <td>BOS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOR</td>\n",
       "      <td>W,3-0</td>\n",
       "      <td>CG</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.983</td>\n",
       "      <td>1</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.51</td>\n",
       "      <td>-0.02%</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>730</td>\n",
       "      <td>5</td>\n",
       "      <td>Apr 11</td>\n",
       "      <td>BOS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOR</td>\n",
       "      <td>L,1-11</td>\n",
       "      <td>CG</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.381</td>\n",
       "      <td>0.556</td>\n",
       "      <td>0.937</td>\n",
       "      <td>1</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-0.079</td>\n",
       "      <td>0.72</td>\n",
       "      <td>-0.05%</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>3B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rk  Gcar Gtm    Date   Tm Unnamed: 5  Opp     Rslt Inngs  PA  ...    OBP  \\\n",
       "0   1   726   1   Apr 6  BOS          @  MIL    L,1-5    CG   4  ...  0.250   \n",
       "1   2   727   2   Apr 8  BOS          @  MIL    L,2-3    CG   4  ...  0.250   \n",
       "2   3   728   3   Apr 9  BOS          @  MIL  L,11-12    CG   5  ...  0.462   \n",
       "3   4   729   4  Apr 10  BOS        NaN  TOR    W,3-0    CG   4  ...  0.412   \n",
       "4   5   730   5  Apr 11  BOS        NaN  TOR   L,1-11    CG   4  ...  0.381   \n",
       "\n",
       "     SLG    OPS  BOP   aLI    WPA  acLI    cWPA  RE24  Pos  \n",
       "0  0.250  0.500    1  0.63 -0.009  0.91  -0.01% -0.17   3B  \n",
       "1  0.143  0.393    1  1.27 -0.090  1.64  -0.07% -0.59   3B  \n",
       "2  0.600  1.062    1  1.15  0.148  1.40   0.11%  2.20   3B  \n",
       "3  0.571  0.983    1  0.46 -0.023  0.51  -0.02% -0.33   3B  \n",
       "4  0.556  0.937    1  0.65 -0.079  0.72  -0.05% -0.42   3B  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boggs_87_logs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7feaffccd090>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boggs_87_logs.set_index(\"Rk\").BA.rolling(10).mean().plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2017 batting ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "szn17 = Season(2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available tables: ['teams_ratio_batting', 'players_ratio_batting']\n"
     ]
    }
   ],
   "source": [
    "ratio = szn17.batting_pages(\"ratio\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tm</th>\n",
       "      <th>R/G</th>\n",
       "      <th>HR%</th>\n",
       "      <th>SO%</th>\n",
       "      <th>BB%</th>\n",
       "      <th>XBH%</th>\n",
       "      <th>X/H%</th>\n",
       "      <th>SO/W</th>\n",
       "      <th>AB/SO</th>\n",
       "      <th>AB/HR</th>\n",
       "      <th>AB/RBI</th>\n",
       "      <th>GB/FB</th>\n",
       "      <th>GO/AO</th>\n",
       "      <th>IP%</th>\n",
       "      <th>LD%</th>\n",
       "      <th>HR/FB</th>\n",
       "      <th>IF/FB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARI</td>\n",
       "      <td>5.01</td>\n",
       "      <td>3.5%</td>\n",
       "      <td>23.4%</td>\n",
       "      <td>9.3%</td>\n",
       "      <td>9.2%</td>\n",
       "      <td>41%</td>\n",
       "      <td>2.52</td>\n",
       "      <td>3.8</td>\n",
       "      <td>25.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.15</td>\n",
       "      <td>62%</td>\n",
       "      <td>26%</td>\n",
       "      <td>11.1%</td>\n",
       "      <td>11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ATL</td>\n",
       "      <td>4.52</td>\n",
       "      <td>2.7%</td>\n",
       "      <td>19.1%</td>\n",
       "      <td>7.6%</td>\n",
       "      <td>7.7%</td>\n",
       "      <td>33%</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.7</td>\n",
       "      <td>33.8</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.31</td>\n",
       "      <td>69%</td>\n",
       "      <td>25%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BAL</td>\n",
       "      <td>4.59</td>\n",
       "      <td>3.8%</td>\n",
       "      <td>23.0%</td>\n",
       "      <td>6.4%</td>\n",
       "      <td>8.4%</td>\n",
       "      <td>35%</td>\n",
       "      <td>3.60</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.05</td>\n",
       "      <td>66%</td>\n",
       "      <td>26%</td>\n",
       "      <td>11.2%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BOS</td>\n",
       "      <td>4.85</td>\n",
       "      <td>2.7%</td>\n",
       "      <td>19.3%</td>\n",
       "      <td>9.0%</td>\n",
       "      <td>7.7%</td>\n",
       "      <td>34%</td>\n",
       "      <td>2.14</td>\n",
       "      <td>4.6</td>\n",
       "      <td>33.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.09</td>\n",
       "      <td>68%</td>\n",
       "      <td>25%</td>\n",
       "      <td>8.0%</td>\n",
       "      <td>13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHC</td>\n",
       "      <td>5.07</td>\n",
       "      <td>3.6%</td>\n",
       "      <td>22.3%</td>\n",
       "      <td>9.9%</td>\n",
       "      <td>8.4%</td>\n",
       "      <td>38%</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3.9</td>\n",
       "      <td>24.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.84</td>\n",
       "      <td>1.23</td>\n",
       "      <td>62%</td>\n",
       "      <td>25%</td>\n",
       "      <td>11.3%</td>\n",
       "      <td>11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHW</td>\n",
       "      <td>4.36</td>\n",
       "      <td>3.1%</td>\n",
       "      <td>23.1%</td>\n",
       "      <td>6.6%</td>\n",
       "      <td>7.9%</td>\n",
       "      <td>34%</td>\n",
       "      <td>3.48</td>\n",
       "      <td>3.9</td>\n",
       "      <td>29.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.18</td>\n",
       "      <td>65%</td>\n",
       "      <td>24%</td>\n",
       "      <td>9.8%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CIN</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.5%</td>\n",
       "      <td>21.4%</td>\n",
       "      <td>9.1%</td>\n",
       "      <td>8.1%</td>\n",
       "      <td>36%</td>\n",
       "      <td>2.35</td>\n",
       "      <td>4.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.10</td>\n",
       "      <td>64%</td>\n",
       "      <td>25%</td>\n",
       "      <td>10.6%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CLE</td>\n",
       "      <td>5.05</td>\n",
       "      <td>3.4%</td>\n",
       "      <td>18.5%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>9.2%</td>\n",
       "      <td>40%</td>\n",
       "      <td>1.91</td>\n",
       "      <td>4.8</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.04</td>\n",
       "      <td>67%</td>\n",
       "      <td>25%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COL</td>\n",
       "      <td>5.09</td>\n",
       "      <td>3.1%</td>\n",
       "      <td>22.7%</td>\n",
       "      <td>8.4%</td>\n",
       "      <td>8.4%</td>\n",
       "      <td>35%</td>\n",
       "      <td>2.71</td>\n",
       "      <td>3.9</td>\n",
       "      <td>28.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.29</td>\n",
       "      <td>64%</td>\n",
       "      <td>26%</td>\n",
       "      <td>9.6%</td>\n",
       "      <td>10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DET</td>\n",
       "      <td>4.54</td>\n",
       "      <td>3.0%</td>\n",
       "      <td>21.4%</td>\n",
       "      <td>8.2%</td>\n",
       "      <td>8.3%</td>\n",
       "      <td>36%</td>\n",
       "      <td>2.61</td>\n",
       "      <td>4.2</td>\n",
       "      <td>29.7</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>66%</td>\n",
       "      <td>28%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HOU</td>\n",
       "      <td>5.53</td>\n",
       "      <td>3.8%</td>\n",
       "      <td>17.3%</td>\n",
       "      <td>8.1%</td>\n",
       "      <td>9.6%</td>\n",
       "      <td>38%</td>\n",
       "      <td>2.14</td>\n",
       "      <td>5.2</td>\n",
       "      <td>23.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.02</td>\n",
       "      <td>69%</td>\n",
       "      <td>25%</td>\n",
       "      <td>10.7%</td>\n",
       "      <td>13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KCR</td>\n",
       "      <td>4.33</td>\n",
       "      <td>3.2%</td>\n",
       "      <td>19.4%</td>\n",
       "      <td>6.5%</td>\n",
       "      <td>7.9%</td>\n",
       "      <td>33%</td>\n",
       "      <td>2.99</td>\n",
       "      <td>4.7</td>\n",
       "      <td>28.7</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.98</td>\n",
       "      <td>70%</td>\n",
       "      <td>28%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LAA</td>\n",
       "      <td>4.38</td>\n",
       "      <td>3.1%</td>\n",
       "      <td>19.7%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>7.4%</td>\n",
       "      <td>34%</td>\n",
       "      <td>2.29</td>\n",
       "      <td>4.5</td>\n",
       "      <td>29.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.18</td>\n",
       "      <td>67%</td>\n",
       "      <td>23%</td>\n",
       "      <td>9.4%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LAD</td>\n",
       "      <td>4.75</td>\n",
       "      <td>3.6%</td>\n",
       "      <td>22.3%</td>\n",
       "      <td>10.5%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>41%</td>\n",
       "      <td>2.13</td>\n",
       "      <td>3.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.05</td>\n",
       "      <td>62%</td>\n",
       "      <td>25%</td>\n",
       "      <td>10.7%</td>\n",
       "      <td>10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MIA</td>\n",
       "      <td>4.80</td>\n",
       "      <td>3.1%</td>\n",
       "      <td>20.5%</td>\n",
       "      <td>7.8%</td>\n",
       "      <td>7.9%</td>\n",
       "      <td>33%</td>\n",
       "      <td>2.64</td>\n",
       "      <td>4.4</td>\n",
       "      <td>28.9</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.39</td>\n",
       "      <td>67%</td>\n",
       "      <td>24%</td>\n",
       "      <td>10.1%</td>\n",
       "      <td>11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MIL</td>\n",
       "      <td>4.52</td>\n",
       "      <td>3.7%</td>\n",
       "      <td>25.6%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>8.4%</td>\n",
       "      <td>38%</td>\n",
       "      <td>2.87</td>\n",
       "      <td>3.5</td>\n",
       "      <td>24.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.23</td>\n",
       "      <td>60%</td>\n",
       "      <td>25%</td>\n",
       "      <td>11.9%</td>\n",
       "      <td>10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>MIN</td>\n",
       "      <td>5.03</td>\n",
       "      <td>3.3%</td>\n",
       "      <td>21.4%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>8.4%</td>\n",
       "      <td>36%</td>\n",
       "      <td>2.26</td>\n",
       "      <td>4.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.98</td>\n",
       "      <td>65%</td>\n",
       "      <td>26%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NYM</td>\n",
       "      <td>4.54</td>\n",
       "      <td>3.6%</td>\n",
       "      <td>20.9%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>39%</td>\n",
       "      <td>2.44</td>\n",
       "      <td>4.3</td>\n",
       "      <td>24.6</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.91</td>\n",
       "      <td>65%</td>\n",
       "      <td>25%</td>\n",
       "      <td>10.3%</td>\n",
       "      <td>13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NYY</td>\n",
       "      <td>5.30</td>\n",
       "      <td>3.8%</td>\n",
       "      <td>21.8%</td>\n",
       "      <td>9.7%</td>\n",
       "      <td>8.3%</td>\n",
       "      <td>36%</td>\n",
       "      <td>2.25</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.01</td>\n",
       "      <td>63%</td>\n",
       "      <td>25%</td>\n",
       "      <td>11.5%</td>\n",
       "      <td>13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OAK</td>\n",
       "      <td>4.56</td>\n",
       "      <td>3.8%</td>\n",
       "      <td>24.3%</td>\n",
       "      <td>9.2%</td>\n",
       "      <td>9.0%</td>\n",
       "      <td>41%</td>\n",
       "      <td>2.64</td>\n",
       "      <td>3.7</td>\n",
       "      <td>23.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.84</td>\n",
       "      <td>62%</td>\n",
       "      <td>27%</td>\n",
       "      <td>10.8%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PHI</td>\n",
       "      <td>4.26</td>\n",
       "      <td>2.8%</td>\n",
       "      <td>23.1%</td>\n",
       "      <td>8.1%</td>\n",
       "      <td>8.1%</td>\n",
       "      <td>36%</td>\n",
       "      <td>2.87</td>\n",
       "      <td>3.9</td>\n",
       "      <td>31.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.86</td>\n",
       "      <td>1.16</td>\n",
       "      <td>65%</td>\n",
       "      <td>25%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PIT</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.5%</td>\n",
       "      <td>19.8%</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>7.1%</td>\n",
       "      <td>33%</td>\n",
       "      <td>2.34</td>\n",
       "      <td>4.5</td>\n",
       "      <td>36.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.23</td>\n",
       "      <td>67%</td>\n",
       "      <td>25%</td>\n",
       "      <td>7.6%</td>\n",
       "      <td>10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SDP</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.2%</td>\n",
       "      <td>25.2%</td>\n",
       "      <td>7.7%</td>\n",
       "      <td>7.5%</td>\n",
       "      <td>36%</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.6</td>\n",
       "      <td>28.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.00</td>\n",
       "      <td>62%</td>\n",
       "      <td>24%</td>\n",
       "      <td>10.3%</td>\n",
       "      <td>15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SEA</td>\n",
       "      <td>4.63</td>\n",
       "      <td>3.2%</td>\n",
       "      <td>20.6%</td>\n",
       "      <td>7.9%</td>\n",
       "      <td>8.1%</td>\n",
       "      <td>35%</td>\n",
       "      <td>2.60</td>\n",
       "      <td>4.4</td>\n",
       "      <td>27.8</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.03</td>\n",
       "      <td>67%</td>\n",
       "      <td>25%</td>\n",
       "      <td>9.6%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SFG</td>\n",
       "      <td>3.94</td>\n",
       "      <td>2.1%</td>\n",
       "      <td>19.6%</td>\n",
       "      <td>7.6%</td>\n",
       "      <td>7.3%</td>\n",
       "      <td>32%</td>\n",
       "      <td>2.58</td>\n",
       "      <td>4.6</td>\n",
       "      <td>43.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.17</td>\n",
       "      <td>70%</td>\n",
       "      <td>24%</td>\n",
       "      <td>6.3%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>STL</td>\n",
       "      <td>4.70</td>\n",
       "      <td>3.2%</td>\n",
       "      <td>21.7%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>8.2%</td>\n",
       "      <td>36%</td>\n",
       "      <td>2.27</td>\n",
       "      <td>4.1</td>\n",
       "      <td>27.9</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.08</td>\n",
       "      <td>64%</td>\n",
       "      <td>26%</td>\n",
       "      <td>9.5%</td>\n",
       "      <td>11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>TBR</td>\n",
       "      <td>4.28</td>\n",
       "      <td>3.7%</td>\n",
       "      <td>25.0%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>7.9%</td>\n",
       "      <td>36%</td>\n",
       "      <td>2.82</td>\n",
       "      <td>3.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.02</td>\n",
       "      <td>61%</td>\n",
       "      <td>24%</td>\n",
       "      <td>11.8%</td>\n",
       "      <td>13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>TEX</td>\n",
       "      <td>4.93</td>\n",
       "      <td>3.9%</td>\n",
       "      <td>24.4%</td>\n",
       "      <td>8.9%</td>\n",
       "      <td>8.4%</td>\n",
       "      <td>39%</td>\n",
       "      <td>2.74</td>\n",
       "      <td>3.6</td>\n",
       "      <td>22.9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.04</td>\n",
       "      <td>61%</td>\n",
       "      <td>24%</td>\n",
       "      <td>12.2%</td>\n",
       "      <td>13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>TOR</td>\n",
       "      <td>4.28</td>\n",
       "      <td>3.6%</td>\n",
       "      <td>21.6%</td>\n",
       "      <td>8.8%</td>\n",
       "      <td>8.1%</td>\n",
       "      <td>38%</td>\n",
       "      <td>2.45</td>\n",
       "      <td>4.1</td>\n",
       "      <td>24.8</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.09</td>\n",
       "      <td>65%</td>\n",
       "      <td>24%</td>\n",
       "      <td>11.0%</td>\n",
       "      <td>13%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>WSN</td>\n",
       "      <td>5.06</td>\n",
       "      <td>3.5%</td>\n",
       "      <td>21.4%</td>\n",
       "      <td>8.7%</td>\n",
       "      <td>9.0%</td>\n",
       "      <td>38%</td>\n",
       "      <td>2.45</td>\n",
       "      <td>4.2</td>\n",
       "      <td>25.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.05</td>\n",
       "      <td>65%</td>\n",
       "      <td>25%</td>\n",
       "      <td>10.1%</td>\n",
       "      <td>10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>LgAvg</td>\n",
       "      <td>4.65</td>\n",
       "      <td>3.3%</td>\n",
       "      <td>21.6%</td>\n",
       "      <td>8.5%</td>\n",
       "      <td>8.3%</td>\n",
       "      <td>36%</td>\n",
       "      <td>2.53</td>\n",
       "      <td>4.1</td>\n",
       "      <td>27.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.09</td>\n",
       "      <td>65%</td>\n",
       "      <td>25%</td>\n",
       "      <td>9.9%</td>\n",
       "      <td>12%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tm   R/G   HR%    SO%    BB%  XBH% X/H%  SO/W  AB/SO  AB/HR  AB/RBI  \\\n",
       "0     ARI  5.01  3.5%  23.4%   9.3%  9.2%  41%  2.52    3.8   25.1     7.1   \n",
       "1     ATL  4.52  2.7%  19.1%   7.6%  7.7%  33%  2.50    4.7   33.8     7.9   \n",
       "2     BAL  4.59  3.8%  23.0%   6.4%  8.4%  35%  3.60    4.0   24.4     7.9   \n",
       "3     BOS  4.85  2.7%  19.3%   9.0%  7.7%  34%  2.14    4.6   33.7     7.7   \n",
       "4     CHC  5.07  3.6%  22.3%   9.9%  8.4%  38%  2.25    3.9   24.6     7.0   \n",
       "5     CHW  4.36  3.1%  23.1%   6.6%  7.9%  34%  3.48    3.9   29.6     8.2   \n",
       "6     CIN  4.65  3.5%  21.4%   9.1%  8.1%  36%  2.35    4.1   25.0     7.7   \n",
       "7     CLE  5.05  3.4%  18.5%   9.7%  9.2%  40%  1.91    4.8   26.0     7.1   \n",
       "8     COL  5.09  3.1%  22.7%   8.4%  8.4%  35%  2.71    3.9   28.8     7.0   \n",
       "9     DET  4.54  3.0%  21.4%   8.2%  8.3%  36%  2.61    4.2   29.7     7.9   \n",
       "10    HOU  5.53  3.8%  17.3%   8.1%  9.6%  38%  2.14    5.2   23.6     6.6   \n",
       "11    KCR  4.33  3.2%  19.4%   6.5%  7.9%  33%  2.99    4.7   28.7     8.4   \n",
       "12    LAA  4.38  3.1%  19.7%   8.6%  7.4%  34%  2.29    4.5   29.1     8.0   \n",
       "13    LAD  4.75  3.6%  22.3%  10.5%  8.9%  41%  2.13    3.9   24.5     7.4   \n",
       "14    MIA  4.80  3.1%  20.5%   7.8%  7.9%  33%  2.64    4.4   28.9     7.5   \n",
       "15    MIL  4.52  3.7%  25.6%   8.9%  8.4%  38%  2.87    3.5   24.4     7.9   \n",
       "16    MIN  5.03  3.3%  21.4%   9.5%  8.4%  36%  2.26    4.1   27.0     7.1   \n",
       "17    NYM  4.54  3.6%  20.9%   8.6%  8.7%  39%  2.44    4.3   24.6     7.7   \n",
       "18    NYY  5.30  3.8%  21.8%   9.7%  8.3%  36%  2.25    4.0   23.2     6.8   \n",
       "19    OAK  4.56  3.8%  24.3%   9.2%  9.0%  41%  2.64    3.7   23.4     7.7   \n",
       "20    PHI  4.26  2.8%  23.1%   8.1%  8.1%  36%  2.87    3.9   31.8     8.5   \n",
       "21    PIT  4.12  2.5%  19.8%   8.5%  7.1%  33%  2.34    4.5   36.1     8.6   \n",
       "22    SDP  3.73  3.2%  25.2%   7.7%  7.5%  36%  3.26    3.6   28.3     9.3   \n",
       "23    SEA  4.63  3.2%  20.6%   7.9%  8.1%  35%  2.60    4.4   27.8     7.8   \n",
       "24    SFG  3.94  2.1%  19.6%   7.6%  7.3%  32%  2.58    4.6   43.4     9.1   \n",
       "25    STL  4.70  3.2%  21.7%   9.5%  8.2%  36%  2.27    4.1   27.9     7.5   \n",
       "26    TBR  4.28  3.7%  25.0%   8.9%  7.9%  36%  2.82    3.6   24.0     8.2   \n",
       "27    TEX  4.93  3.9%  24.4%   8.9%  8.4%  39%  2.74    3.6   22.9     7.2   \n",
       "28    TOR  4.28  3.6%  21.6%   8.8%  8.1%  38%  2.45    4.1   24.8     8.3   \n",
       "29    WSN  5.06  3.5%  21.4%   8.7%  9.0%  38%  2.45    4.2   25.8     7.0   \n",
       "30  LgAvg  4.65  3.3%  21.6%   8.5%  8.3%  36%  2.53    4.1   27.1     7.7   \n",
       "\n",
       "    GB/FB  GO/AO  IP%  LD%  HR/FB IF/FB  \n",
       "0    0.84   1.15  62%  26%  11.1%   11%  \n",
       "1    0.90   1.31  69%  25%   7.8%    9%  \n",
       "2    0.79   1.05  66%  26%  11.2%   12%  \n",
       "3    0.84   1.09  68%  25%   8.0%   13%  \n",
       "4    0.84   1.23  62%  25%  11.3%   11%  \n",
       "5    0.91   1.18  65%  24%   9.8%   12%  \n",
       "6    0.76   1.10  64%  25%  10.6%   12%  \n",
       "7    0.77   1.04  67%  25%   9.7%   12%  \n",
       "8    0.87   1.29  64%  26%   9.6%   10%  \n",
       "9    0.75   1.00  66%  28%   8.6%   10%  \n",
       "10   0.79   1.02  69%  25%  10.7%   13%  \n",
       "11   0.71   0.98  70%  28%   8.6%   12%  \n",
       "12   0.88   1.18  67%  23%   9.4%   12%  \n",
       "13   0.77   1.05  62%  25%  10.7%   10%  \n",
       "14   1.00   1.39  67%  24%  10.1%   11%  \n",
       "15   0.86   1.23  60%  25%  11.9%   10%  \n",
       "16   0.73   0.98  65%  26%   9.7%   12%  \n",
       "17   0.69   0.91  65%  25%  10.3%   13%  \n",
       "18   0.77   1.01  63%  25%  11.5%   13%  \n",
       "19   0.63   0.84  62%  27%  10.8%   12%  \n",
       "20   0.86   1.16  65%  25%   8.9%   11%  \n",
       "21   0.90   1.23  67%  25%   7.6%   10%  \n",
       "22   0.77   1.00  62%  24%  10.3%   15%  \n",
       "23   0.80   1.03  67%  25%   9.6%   12%  \n",
       "24   0.90   1.17  70%  24%   6.3%   12%  \n",
       "25   0.77   1.08  64%  26%   9.5%   11%  \n",
       "26   0.78   1.02  61%  24%  11.8%   13%  \n",
       "27   0.78   1.04  61%  24%  12.2%   13%  \n",
       "28   0.79   1.09  65%  24%  11.0%   13%  \n",
       "29   0.79   1.05  65%  25%  10.1%   10%  \n",
       "30   0.81   1.09  65%  25%   9.9%   12%  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio.get_df('teams_ratio_batting')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2018 Arizona Diamondbacks walk off losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "azn2018 = Franchise('dbacks').season(2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available tables: ['results', 'team_schedule']\n"
     ]
    }
   ],
   "source": [
    "game_logs = azn2018.schedule_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gm#</th>\n",
       "      <th>Date</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Opp</th>\n",
       "      <th>WL</th>\n",
       "      <th>R</th>\n",
       "      <th>RA</th>\n",
       "      <th>Inn</th>\n",
       "      <th>...</th>\n",
       "      <th>GB</th>\n",
       "      <th>Win</th>\n",
       "      <th>Loss</th>\n",
       "      <th>Save</th>\n",
       "      <th>Time</th>\n",
       "      <th>D/N</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>cLI</th>\n",
       "      <th>Streak</th>\n",
       "      <th>Orig. Scheduled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Tuesday, Apr 10</td>\n",
       "      <td>boxscore</td>\n",
       "      <td>ARI</td>\n",
       "      <td>@</td>\n",
       "      <td>SFG</td>\n",
       "      <td>L-wo</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>up 2.5</td>\n",
       "      <td>Strickland</td>\n",
       "      <td>De La Rosa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3:24</td>\n",
       "      <td>N</td>\n",
       "      <td>37869</td>\n",
       "      <td>1.40</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>Saturday, May 19</td>\n",
       "      <td>boxscore</td>\n",
       "      <td>ARI</td>\n",
       "      <td>@</td>\n",
       "      <td>NYM</td>\n",
       "      <td>L-wo</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>up 0.5</td>\n",
       "      <td>Familia</td>\n",
       "      <td>Chafin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3:04</td>\n",
       "      <td>N</td>\n",
       "      <td>39515</td>\n",
       "      <td>1.27</td>\n",
       "      <td>---</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>Wednesday, Jun 6</td>\n",
       "      <td>boxscore</td>\n",
       "      <td>ARI</td>\n",
       "      <td>@</td>\n",
       "      <td>SFG</td>\n",
       "      <td>L-wo</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Tied</td>\n",
       "      <td>Strickland</td>\n",
       "      <td>Chafin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3:38</td>\n",
       "      <td>D</td>\n",
       "      <td>41042</td>\n",
       "      <td>1.53</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>104</td>\n",
       "      <td>Thursday, Jul 26</td>\n",
       "      <td>boxscore</td>\n",
       "      <td>ARI</td>\n",
       "      <td>@</td>\n",
       "      <td>CHC</td>\n",
       "      <td>L-wo</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Butler</td>\n",
       "      <td>Boxberger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3:22</td>\n",
       "      <td>D</td>\n",
       "      <td>38979</td>\n",
       "      <td>1.74</td>\n",
       "      <td>--</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>124</td>\n",
       "      <td>Saturday, Aug 18</td>\n",
       "      <td>boxscore</td>\n",
       "      <td>ARI</td>\n",
       "      <td>@</td>\n",
       "      <td>SDP</td>\n",
       "      <td>L-wo</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>up 0.5</td>\n",
       "      <td>Stammen</td>\n",
       "      <td>Chafin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2:54</td>\n",
       "      <td>N</td>\n",
       "      <td>24440</td>\n",
       "      <td>2.03</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>Tuesday, Aug 28</td>\n",
       "      <td>boxscore</td>\n",
       "      <td>ARI</td>\n",
       "      <td>@</td>\n",
       "      <td>SFG</td>\n",
       "      <td>L-wo</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Tied</td>\n",
       "      <td>Smith</td>\n",
       "      <td>Ziegler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3:03</td>\n",
       "      <td>N</td>\n",
       "      <td>37276</td>\n",
       "      <td>2.28</td>\n",
       "      <td>--</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>137</td>\n",
       "      <td>Sunday, Sep 2</td>\n",
       "      <td>boxscore</td>\n",
       "      <td>ARI</td>\n",
       "      <td>@</td>\n",
       "      <td>LAD</td>\n",
       "      <td>L-wo</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Jansen</td>\n",
       "      <td>Boxberger</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3:35</td>\n",
       "      <td>D</td>\n",
       "      <td>48517</td>\n",
       "      <td>3.38</td>\n",
       "      <td>---</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>Wednesday, Sep 12</td>\n",
       "      <td>boxscore</td>\n",
       "      <td>ARI</td>\n",
       "      <td>@</td>\n",
       "      <td>COL</td>\n",
       "      <td>L-wo</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Davis</td>\n",
       "      <td>Hirano</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3:07</td>\n",
       "      <td>N</td>\n",
       "      <td>31687</td>\n",
       "      <td>2.84</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>160</td>\n",
       "      <td>Friday, Sep 28</td>\n",
       "      <td>boxscore</td>\n",
       "      <td>ARI</td>\n",
       "      <td>@</td>\n",
       "      <td>SDP</td>\n",
       "      <td>L-wo</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Brewer</td>\n",
       "      <td>Andriese</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5:02</td>\n",
       "      <td>N</td>\n",
       "      <td>28055</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>162</td>\n",
       "      <td>Sunday, Sep 30</td>\n",
       "      <td>boxscore</td>\n",
       "      <td>ARI</td>\n",
       "      <td>@</td>\n",
       "      <td>SDP</td>\n",
       "      <td>L-wo</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Castillo</td>\n",
       "      <td>Barrett</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3:29</td>\n",
       "      <td>D</td>\n",
       "      <td>31243</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gm#               Date Unnamed: 2   Tm Unnamed: 4  Opp    WL  R  RA  \\\n",
       "10    11    Tuesday, Apr 10   boxscore  ARI          @  SFG  L-wo  4   5   \n",
       "44    45   Saturday, May 19   boxscore  ARI          @  NYM  L-wo  4   5   \n",
       "60    61   Wednesday, Jun 6   boxscore  ARI          @  SFG  L-wo  4   5   \n",
       "103  104   Thursday, Jul 26   boxscore  ARI          @  CHC  L-wo  6   7   \n",
       "123  124   Saturday, Aug 18   boxscore  ARI          @  SDP  L-wo  6   7   \n",
       "131  132    Tuesday, Aug 28   boxscore  ARI          @  SFG  L-wo  0   1   \n",
       "136  137      Sunday, Sep 2   boxscore  ARI          @  LAD  L-wo  2   3   \n",
       "145  146  Wednesday, Sep 12   boxscore  ARI          @  COL  L-wo  4   5   \n",
       "159  160     Friday, Sep 28   boxscore  ARI          @  SDP  L-wo  2   3   \n",
       "161  162     Sunday, Sep 30   boxscore  ARI          @  SDP  L-wo  3   4   \n",
       "\n",
       "      Inn  ...      GB         Win        Loss Save  Time D/N Attendance  \\\n",
       "10    NaN  ...  up 2.5  Strickland  De La Rosa  NaN  3:24   N      37869   \n",
       "44    NaN  ...  up 0.5     Familia      Chafin  NaN  3:04   N      39515   \n",
       "60   10.0  ...    Tied  Strickland      Chafin  NaN  3:38   D      41042   \n",
       "103   NaN  ...     1.5      Butler   Boxberger  NaN  3:22   D      38979   \n",
       "123   NaN  ...  up 0.5     Stammen      Chafin  NaN  2:54   N      24440   \n",
       "131   NaN  ...    Tied       Smith     Ziegler  NaN  3:03   N      37276   \n",
       "136   NaN  ...     1.0      Jansen   Boxberger  NaN  3:35   D      48517   \n",
       "145   NaN  ...     3.5       Davis      Hirano  NaN  3:07   N      31687   \n",
       "159  15.0  ...     9.0      Brewer    Andriese  NaN  5:02   N      28055   \n",
       "161  10.0  ...     9.0    Castillo     Barrett  NaN  3:29   D      31243   \n",
       "\n",
       "      cLI  Streak  Orig. Scheduled  \n",
       "10   1.40       -              NaN  \n",
       "44   1.27     ---              NaN  \n",
       "60   1.53       -              NaN  \n",
       "103  1.74      --              NaN  \n",
       "123  2.03       -              NaN  \n",
       "131  2.28      --              NaN  \n",
       "136  3.38     ---              NaN  \n",
       "145  2.84       -              NaN  \n",
       "159  0.00       -              NaN  \n",
       "161  0.00       -              NaN  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_logs.get_df('team_schedule').rename({\"W/L\" : 'WL'}, axis=1).query(\"WL == 'L-wo'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# an All Tampa-born team "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sportsref.baseball import Frivolities, Player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available tables: ['bio_batting', 'bio_pitching', 'bio_manage']\n"
     ]
    }
   ],
   "source": [
    "bio_state = Frivolities().bio_state(\"FL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "bats = bio_state.get_df(\"bio_batting\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_bats = bats.query(\"To == 2019\").sort_values(\"OPS\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Name</th>\n",
       "      <th>Yrs</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>ASG</th>\n",
       "      <th>G</th>\n",
       "      <th>PA</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>...</th>\n",
       "      <th>SO</th>\n",
       "      <th>BA</th>\n",
       "      <th>OBP</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OPS</th>\n",
       "      <th>WAR</th>\n",
       "      <th>Birthdate</th>\n",
       "      <th>Debut</th>\n",
       "      <th>Birthplace</th>\n",
       "      <th>Pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>126</td>\n",
       "      <td>Taylor Davis</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.256</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.59</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>Nov 28, 1989</td>\n",
       "      <td>Sep 8, 2017</td>\n",
       "      <td>Tampa</td>\n",
       "      <td>/2H351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>Sam Dyson</td>\n",
       "      <td>8</td>\n",
       "      <td>2012</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>238</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>May 7, 1988</td>\n",
       "      <td>Jul 5, 2012</td>\n",
       "      <td>Tampa</td>\n",
       "      <td>*1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk          Name  Yrs  From    To  ASG    G  PA  AB    R  ...   SO  \\\n",
       "125  126  Taylor Davis    3  2017  2019    0   20  39  36  3.0  ...  9.0   \n",
       "147  148     Sam Dyson    8  2012  2019    0  238   2   2  0.0  ...  0.0   \n",
       "\n",
       "        BA    OBP    SLG   OPS   WAR     Birthdate        Debut  Birthplace  \\\n",
       "125  0.222  0.256  0.333  0.59 -0.18  Nov 28, 1989  Sep 8, 2017       Tampa   \n",
       "147  0.000  0.000  0.000  0.00  5.54   May 7, 1988  Jul 5, 2012       Tampa   \n",
       "\n",
       "        Pos  \n",
       "125  /2H351  \n",
       "147      *1  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_bats[current_bats.Birthplace == \"Tampa\"].head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pro Football Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eli Manning Touchdown passes by game in Career"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sportsref.football import Player as football_player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm 100% sure that you want Eli Manning\n"
     ]
    }
   ],
   "source": [
    "eli = football_player(\"Eli manning\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available tables: ['stats', 'stats_playoffs']\n"
     ]
    }
   ],
   "source": [
    "eli2013 = eli.gamelogs_pages('career')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: \"\\\\'30\\\\'\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-f7142f5b92fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0meli2013\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'stats'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Passing\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"TD\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"bar\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m250\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/juplab/llwin/baseball_util/sportsref/sportsref/page.py\u001b[0m in \u001b[0;36mget_df\u001b[0;34m(self, table)\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0mtbl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cached_tables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtbl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    297\u001b[0m                 )\n\u001b[1;32m    298\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 299\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36mread_html\u001b[0;34m(io, match, flavor, header, index_col, skiprows, attrs, parse_dates, thousands, encoding, decimal, converters, na_values, keep_default_na, displayed_only)\u001b[0m\n\u001b[1;32m   1098\u001b[0m         \u001b[0mna_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1099\u001b[0m         \u001b[0mkeep_default_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_default_na\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m         \u001b[0mdisplayed_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdisplayed_only\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m     )\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_parse\u001b[0;34m(flavor, io, match, attrs, encoding, displayed_only, **kwargs)\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m             \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_data_to_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    212\u001b[0m         \"\"\"\n\u001b[1;32m    213\u001b[0m         \u001b[0mtables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_tables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_doc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_thead_tbody_tfoot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtable\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_attr_getter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_parse_thead_tbody_tfoot\u001b[0;34m(self, table_html)\u001b[0m\n\u001b[1;32m    411\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    412\u001b[0m         \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expand_colspan_rowspan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheader_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 413\u001b[0;31m         \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expand_colspan_rowspan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    414\u001b[0m         \u001b[0mfooter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expand_colspan_rowspan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfooter_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.7/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_expand_colspan_rowspan\u001b[0;34m(self, rows)\u001b[0m\n\u001b[1;32m    457\u001b[0m                 \u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_remove_whitespace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text_getter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    458\u001b[0m                 \u001b[0mrowspan\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_attr_getter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rowspan\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 459\u001b[0;31m                 \u001b[0mcolspan\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_attr_getter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"colspan\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    461\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolspan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: \"\\\\'30\\\\'\""
     ]
    }
   ],
   "source": [
    "eli2013.get_df('stats')[\"Passing\"][\"TD\"].plot(figsize=(20,10), kind=\"bar\", xticks=range(0,250, 30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keyshawn Johnson Yards per reception on the road from 2001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "keyshawn = football_player(\"keyshawn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_2001_splits = keyshawn.splits_pages(2001).get_df('stats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_2001_splits.loc[:,key_2001_splits.columns.get_level_values(1)==\"Split\"] = key_2001_splits.loc[:,key_2001_splits.columns.get_level_values(1)==\"Split\"].ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "road_split = key_2001_splits.loc[key_2001_splits[\"Unnamed: 1_level_0\"][\"Value\"] == 'Road']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "road_split[\"Receiving\"][\"Y/R\"].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Derrick Henry's longest run form 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = football_player(\"derrk henry\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_plays = d.plays_pages(2018, 'rushing').get_df('all_plays')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_plays.loc[d_plays.Yds.idxmax(), \"Detail\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Suh's Penalites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "suh = football_player(\"suh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "suh_penalites = suh.more_pages(\"penalties\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "suh_penalites.get_df(\"penalties\")\\\n",
    "    .assign(dt=lambda x: pd.to_datetime(x.Date).dt.year)\\\n",
    "    .groupby([\"dt\", \"Penalty\"]).Yds.sum().unstack()\\\n",
    "    .fillna(0)\\\n",
    "    .plot(kind='bar', stacked=True, figsize=(20,10), cmap=\"tab20\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sportsref.football import Franchise as football_fran "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nwe = football_fran(\"patriots\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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