exploratory-analysis-Evan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Exploratory analysis notebook\n",
    "\n",
    "Use this notebook for adding new report requirements into scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### NOTE: All endzone heatmap content moved to \"Endzone Heatmap.ipynb\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data exploration\n",
    "\n",
    "Making notes on the Yewwah-SUIS game I just watched/ did data entry for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"YW-Dogs/Passes vs. Suis 2017-03-24_16-10.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Thrower</th>\n",
       "      <th>Receiver</th>\n",
       "      <th>Start X (0 -&gt; 1 = left sideline -&gt; right sideline)</th>\n",
       "      <th>Start Y (0 -&gt; 1 = back of opponent endzone -&gt; back of own endzone)</th>\n",
       "      <th>End X (0 -&gt; 1 = left sideline -&gt; right sideline)</th>\n",
       "      <th>End Y (0 -&gt; 1 = back of opponent endzone -&gt; back of own endzone)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Julia Zhang</td>\n",
       "      <td>Simon Li</td>\n",
       "      <td>0.669355</td>\n",
       "      <td>0.505964</td>\n",
       "      <td>0.526882</td>\n",
       "      <td>0.083499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Julia Zhang</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>0.072581</td>\n",
       "      <td>0.415507</td>\n",
       "      <td>0.494624</td>\n",
       "      <td>0.108350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>#17 Kevin Yang</td>\n",
       "      <td>Bobby</td>\n",
       "      <td>0.266129</td>\n",
       "      <td>0.305169</td>\n",
       "      <td>0.529570</td>\n",
       "      <td>0.105368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>#17 Kevin Yang</td>\n",
       "      <td>#18 Danielle Xu</td>\n",
       "      <td>0.198925</td>\n",
       "      <td>0.241551</td>\n",
       "      <td>0.456989</td>\n",
       "      <td>0.129225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Thrower         Receiver  \\\n",
       "39      Julia Zhang         Simon Li   \n",
       "121     Julia Zhang            Bobby   \n",
       "141  #17 Kevin Yang            Bobby   \n",
       "143  #17 Kevin Yang  #18 Danielle Xu   \n",
       "\n",
       "     Start X (0 -> 1 = left sideline -> right sideline)  \\\n",
       "39                                            0.669355    \n",
       "121                                           0.072581    \n",
       "141                                           0.266129    \n",
       "143                                           0.198925    \n",
       "\n",
       "     Start Y (0 -> 1 = back of opponent endzone -> back of own endzone)  \\\n",
       "39                                            0.505964                    \n",
       "121                                           0.415507                    \n",
       "141                                           0.305169                    \n",
       "143                                           0.241551                    \n",
       "\n",
       "     End X (0 -> 1 = left sideline -> right sideline)  \\\n",
       "39                                           0.526882   \n",
       "121                                          0.494624   \n",
       "141                                          0.529570   \n",
       "143                                          0.456989   \n",
       "\n",
       "     End Y (0 -> 1 = back of opponent endzone -> back of own endzone)  \n",
       "39                                            0.083499                 \n",
       "121                                           0.108350                 \n",
       "141                                           0.105368                 \n",
       "143                                           0.129225                 "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.iloc[:,9]==1].iloc[:,(3,4,19,20,21,22)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: I accidentally used \"Julia Zhang\" for \"James Zhang\" the entire game... will need some standardized way of referring to each player.  It is possible to edit player names simply using pandas, but annoying and manual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df.loc[df.Thrower==\"Julia Zhang\",\"Thrower\"] = \"#89 James Zhu\"\n",
    "#df.loc[df.Thrower==\"#89 QB (James Zhu)\",\"Thrower\"] = \"Julia Zhang\"\n",
    "#df.loc[df.Receiver==\"Julia Zhang\",\"Receiver\"] = \"#89 James Zhu\"\n",
    "#df.loc[df.Receiver==\"#89 QB (James Zhu)\",\"Receiver\"] = \"Julia Zhang\"\n",
    "#df.to_csv(\"YW-Dogs/Passes vs. Suis 2017-03-24_16-10.csv\")  ### Do this once, to fix wrongly input names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Huck completion percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Huck completion percentage:\n",
      "Thrower\n",
      "#17 Kevin Yang     0.0\n",
      "#89 James Zhu     25.0\n",
      "Name: Turnover?, dtype: float64\n",
      "\n",
      "\n",
      "Huck attempts:\n",
      "Thrower\n",
      "#17 Kevin Yang    3\n",
      "#89 James Zhu     4\n",
      "Name: Turnover?, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"YW-Dogs/Passes vs. Suis 2017-03-24_16-10.csv\")\n",
    "huckthrows = df[df[\"Huck? (> 40m)\"]==1]\n",
    "print \"Huck completion percentage:\\n\",(1 - huckthrows.groupby(by=\"Thrower\")[\"Turnover?\"].mean())*100\n",
    "print \"\\n\\nHuck attempts:\\n\",huckthrows.groupby(by=\"Thrower\")[\"Turnover?\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dump vs upfield percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Throw attempts</th>\n",
       "      <th>Dump %</th>\n",
       "      <th>Dump throws attempted</th>\n",
       "      <th>Dump error % (throwing)</th>\n",
       "      <th>Dump receptions attempted</th>\n",
       "      <th>Dump error % (receiving)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>#89 James Zhu</th>\n",
       "      <td>63</td>\n",
       "      <td>22.2</td>\n",
       "      <td>14</td>\n",
       "      <td>28.6</td>\n",
       "      <td>12</td>\n",
       "      <td>41.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#17 Kevin Yang</th>\n",
       "      <td>41</td>\n",
       "      <td>17.1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#0 John Yan</th>\n",
       "      <td>22</td>\n",
       "      <td>40.9</td>\n",
       "      <td>9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>11</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#18 Danielle Xu</th>\n",
       "      <td>4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#23 GayGay (Lancelot Li)</th>\n",
       "      <td>4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>3</td>\n",
       "      <td>33.3</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michelle Ma</th>\n",
       "      <td>3</td>\n",
       "      <td>33.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bobby</th>\n",
       "      <td>2</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soap</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simon Li</th>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Throw attempts  Dump %  Dump throws attempted  \\\n",
       "#89 James Zhu                         63    22.2                     14   \n",
       "#17 Kevin Yang                        41    17.1                      7   \n",
       "#0 John Yan                           22    40.9                      9   \n",
       "#18 Danielle Xu                        4    50.0                      2   \n",
       "#23 GayGay (Lancelot Li)               4    50.0                      2   \n",
       "Max                                    3    33.3                      1   \n",
       "Michelle Ma                            3    33.3                      1   \n",
       "Bobby                                  2    50.0                      1   \n",
       "Soap                                   2     0.0                      0   \n",
       "Simon Li                               2   100.0                      2   \n",
       "\n",
       "                          Dump error % (throwing)  Dump receptions attempted  \\\n",
       "#89 James Zhu                                28.6                         12   \n",
       "#17 Kevin Yang                                0.0                         10   \n",
       "#0 John Yan                                  11.1                         11   \n",
       "#18 Danielle Xu                               0.0                          0   \n",
       "#23 GayGay (Lancelot Li)                     50.0                          0   \n",
       "Max                                         100.0                          0   \n",
       "Michelle Ma                                   0.0                          4   \n",
       "Bobby                                         0.0                          1   \n",
       "Soap                                          NaN                          0   \n",
       "Simon Li                                      0.0                          0   \n",
       "\n",
       "                          Dump error % (receiving)  \n",
       "#89 James Zhu                                 41.7  \n",
       "#17 Kevin Yang                                 0.0  \n",
       "#0 John Yan                                    9.1  \n",
       "#18 Danielle Xu                                NaN  \n",
       "#23 GayGay (Lancelot Li)                       NaN  \n",
       "Max                                            NaN  \n",
       "Michelle Ma                                    0.0  \n",
       "Bobby                                          0.0  \n",
       "Soap                                           NaN  \n",
       "Simon Li                                       NaN  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"YW-Dogs/Passes vs. Suis 2017-03-24_16-10.csv\")\n",
    "total_throws = df.Thrower.value_counts().sort_values(ascending=False)\n",
    "players = total_throws.index\n",
    "total_dumps_thrown = df[df[\"Dump?\"]==1].groupby(by=\"Thrower\")[\"Thrower error?\"].count().reindex(players,fill_value=0)\n",
    "dump_errors_thrown = df[df[\"Dump?\"]==1].groupby(by=\"Thrower\")[\"Thrower error?\"].sum().reindex(players,fill_value=0)\n",
    "total_dumps_rec    = df[df[\"Dump?\"]==1].groupby(by=\"Receiver\")[\"Receiver error?\"].count().reindex(players,fill_value=0)\n",
    "dump_errors_rec    = df[df[\"Dump?\"]==1].groupby(by=\"Receiver\")[\"Receiver error?\"].sum().reindex(players,fill_value=0)\n",
    "\n",
    "#print \"\\n\\nThrow attempts:\\n\", df.groupby(by=\"Thrower\")[\"Dump?\"].count().to_string()\n",
    "#print \"\\n\\nDump throws attempted:\\n\", total_dumps_thrown.to_string()\n",
    "#print \"\\n\\nDump error pct (throwing)\\n\", np.round(100*(dump_errors_thrown / total_dumps_thrown),1).to_string()\n",
    "#print \"\\n\\nDump receptions attempted:\\n\", total_dumps_rec.to_string()\n",
    "#print \"\\n\\nDump error pct (receiving):\\n\",np.round(100*dump_errors_rec / total_dumps_rec,1).to_string()\n",
    "#print \"Turnover rate from dumping (throws):\\n\",df.groupby(by=\"Thrower\")[\"Dump?\"].count()\n",
    "\n",
    "df_dumps   = pd.DataFrame(index=players)\n",
    "df_dumps[\"Throw attempts\"]             = total_throws\n",
    "df_dumps[\"Dump %\"]                     = np.round(100*total_dumps_thrown / total_throws,1)\n",
    "df_dumps[\"Dump throws attempted\"]      = total_dumps_thrown\n",
    "df_dumps[\"Dump error % (throwing)\"]    = np.round(100*(dump_errors_thrown / total_dumps_thrown),1)\n",
    "df_dumps[\"Dump receptions attempted\"]  = total_dumps_rec\n",
    "df_dumps[\"Dump error % (receiving)\"]   = np.round(100*dump_errors_rec / total_dumps_rec,1)\n",
    "df_dumps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xa4924e0>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9346588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter(df_dumps[\"Dump throws attempted\"],dump_errors_thrown)\n",
    "xlim(0,df_dumps[\"Dump throws attempted\"].max()); ylim(0,df_dumps[\"Dump throws attempted\"].max())\n",
    "xlabel(\"Dump attempts\");ylabel(\"Dump errors\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Goal: \n",
    "\n",
    "Plot all possessions, extra graphics for turnovers, stalls, receiver errors, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u9+HfM7bD2EvXPwoKYlirLcxYojndunLddbBunfay5A0ZFaU5/SdjY3k4IKDK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8e60630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"YW-Dogs/Passes vs. Suis 2017-03-24_16-10.csv\")\n",
    "\n",
    "point = np.unique(df.Point)\n",
    "\n",
    "i=1\n",
    "for pt in point:\n",
    "    plt.subplot(3,7,i)\n",
    "    possessions = np.unique(df.loc[df.Point==pt,\"Possession\"])\n",
    "    for poss in possessions:\n",
    "        Xs = df.loc[(df.Point==pt)&(df.Possession==poss),\"Start X (0 -> 1 = left sideline -> right sideline)\"].tolist()\n",
    "        Ys = df.loc[(df.Point==pt)&(df.Possession==poss),\"Start Y (0 -> 1 = back of opponent endzone -> back of own endzone)\"].tolist()\n",
    "        turn_or_goal_x = df.loc[(df.Point==pt)&(df.Possession==poss),\"End X (0 -> 1 = left sideline -> right sideline)\"].tolist()[-1]\n",
    "        turn_or_goal_y = df.loc[(df.Point==pt)&(df.Possession==poss),\"End Y (0 -> 1 = back of opponent endzone -> back of own endzone)\"].tolist()[-1]\n",
    "        Xs += [turn_or_goal_x]\n",
    "        Ys += [turn_or_goal_y]        \n",
    "        plt.plot(Xs,Ys)\n",
    "    plt.xticks([]);plt.yticks([])\n",
    "    plt.xlim(0,1);plt.ylim(0,1)\n",
    "    \n",
    "    ## endzones\n",
    "    normalized_line = 25. / (25+70+25)  ## normalized between 0 and 1, like the X-Y coordinates\n",
    "    plt.plot([0,1],[normalized_line,normalized_line],'r')\n",
    "    plt.plot([0,1],[1-normalized_line,1-normalized_line],'r')\n",
    "    plt.title(pt)\n",
    "    i+=1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}