More Data Retrieval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Retrieval for Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we retrieve MLB data from 1996-2017 that we will use to fit a machine learning model to. We decided that we wanted to see if we could find a model that would predict team wins even better than the Pythagorean Expectation does. After researching and examining baseball statistics, we decided to not only focus on team statistics as a whole, but pitcher statistics as well. The performance of the pitcher has a huge impact on the game results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import plotly.offline as py\n",
    "import matplotlib.pyplot as plt\n",
    "from plotly.graph_objs import *\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.cluster import KMeans\n",
    "from bs4 import BeautifulSoup\n",
    "%matplotlib inline\n",
    "pd.set_option(\"max_r\", 15)\n",
    "py.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For unknown reasons, the seventh and eighth tables that we need from the above URLs do not appear in the html returned from BeautifulSoup (it skips over them). Thus, instead of scraping from them like we did for the other datasets we need in the previous notebook, we are taking CSV's. It is also worth noting that this is the only website that has the advanced metrics we need for our calculations, which makes this situation even more of a shame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read in all the csvs\n",
    "dfs = []\n",
    "for i in range(1996, 2017):\n",
    "    fname = \"mlb%d.csv\" % i\n",
    "    df = pd.read_csv(fname)\n",
    "    df[\"year\"] = i\n",
    "    dfs.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": 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>Rk</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Lg</th>\n",
       "      <th>G</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>W-L%</th>\n",
       "      <th>R</th>\n",
       "      <th>RA</th>\n",
       "      <th>Rdiff</th>\n",
       "      <th>...</th>\n",
       "      <th>Inter</th>\n",
       "      <th>Home</th>\n",
       "      <th>Road</th>\n",
       "      <th>ExInn</th>\n",
       "      <th>1Run</th>\n",
       "      <th>vRHP</th>\n",
       "      <th>vLHP</th>\n",
       "      <th>≥.500</th>\n",
       "      <th>&lt;.500</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>162.0</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>0.518</td>\n",
       "      <td>5.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>4-12</td>\n",
       "      <td>46-36</td>\n",
       "      <td>38-42</td>\n",
       "      <td>5-8</td>\n",
       "      <td>27-25</td>\n",
       "      <td>66-55</td>\n",
       "      <td>18-23</td>\n",
       "      <td>23-34</td>\n",
       "      <td>61-44</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>162.0</td>\n",
       "      <td>85</td>\n",
       "      <td>77</td>\n",
       "      <td>0.525</td>\n",
       "      <td>4.9</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10-6</td>\n",
       "      <td>42-39</td>\n",
       "      <td>43-38</td>\n",
       "      <td>6-6</td>\n",
       "      <td>24-19</td>\n",
       "      <td>62-59</td>\n",
       "      <td>23-18</td>\n",
       "      <td>30-36</td>\n",
       "      <td>55-41</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.0</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>162.0</td>\n",
       "      <td>70</td>\n",
       "      <td>92</td>\n",
       "      <td>0.432</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>6-12</td>\n",
       "      <td>37-44</td>\n",
       "      <td>33-48</td>\n",
       "      <td>4-8</td>\n",
       "      <td>23-25</td>\n",
       "      <td>51-73</td>\n",
       "      <td>19-19</td>\n",
       "      <td>27-45</td>\n",
       "      <td>43-47</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>162.0</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "      <td>0.506</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12-6</td>\n",
       "      <td>46-35</td>\n",
       "      <td>36-45</td>\n",
       "      <td>9-7</td>\n",
       "      <td>32-23</td>\n",
       "      <td>61-55</td>\n",
       "      <td>21-25</td>\n",
       "      <td>42-49</td>\n",
       "      <td>40-31</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>162.0</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>0.463</td>\n",
       "      <td>4.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>10-8</td>\n",
       "      <td>39-42</td>\n",
       "      <td>36-45</td>\n",
       "      <td>7-8</td>\n",
       "      <td>24-24</td>\n",
       "      <td>53-60</td>\n",
       "      <td>22-27</td>\n",
       "      <td>37-55</td>\n",
       "      <td>38-32</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rk   Tm  Lg      G   W   L   W-L%    R   RA  Rdiff  ...   Inter   Home  \\\n",
       "10  11.0  ANA  AL  162.0  84  78  0.518  5.1  4.9    0.2  ...    4-12  46-36   \n",
       "11  12.0  ANA  AL  162.0  85  77  0.525  4.9  4.8    0.0  ...    10-6  42-39   \n",
       "22  23.0  ANA  AL  162.0  70  92  0.432  4.4  5.1   -0.7  ...    6-12  37-44   \n",
       "14  15.0  ANA  AL  162.0  82  80  0.506  5.3  5.4    0.0  ...    12-6  46-35   \n",
       "19  20.0  ANA  AL  162.0  75  87  0.463  4.3  4.5   -0.2  ...    10-8  39-42   \n",
       "\n",
       "     Road  ExInn   1Run   vRHP   vLHP  ≥.500  <.500  year  \n",
       "10  38-42    5-8  27-25  66-55  18-23  23-34  61-44  1997  \n",
       "11  43-38    6-6  24-19  62-59  23-18  30-36  55-41  1998  \n",
       "22  33-48    4-8  23-25  51-73  19-19  27-45  43-47  1999  \n",
       "14  36-45    9-7  32-23  61-55  21-25  42-49  40-31  2000  \n",
       "19  36-45    7-8  24-24  53-60  22-27  37-55  38-32  2001  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat all the team data into one\n",
    "mlb = pd.concat(dfs).dropna()\n",
    "mlb = mlb.sort_values(by=[\"Tm\",\"year\"])\n",
    "mlb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read in pitching data\n",
    "dfs = []\n",
    "for i in range(1996, 2017):\n",
    "    fname = \"pitch%d.csv\" % i\n",
    "    df = pd.read_csv(fname)\n",
    "    df[\"year\"] = i\n",
    "    dfs.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": 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>Tm</th>\n",
       "      <th>#P</th>\n",
       "      <th>PAge</th>\n",
       "      <th>RA/G</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>W-L%</th>\n",
       "      <th>ERA</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>...</th>\n",
       "      <th>ERA+</th>\n",
       "      <th>FIP</th>\n",
       "      <th>WHIP</th>\n",
       "      <th>H9</th>\n",
       "      <th>HR9</th>\n",
       "      <th>BB9</th>\n",
       "      <th>SO9</th>\n",
       "      <th>SO/W</th>\n",
       "      <th>LOB</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANA</td>\n",
       "      <td>21</td>\n",
       "      <td>28.3</td>\n",
       "      <td>4.90</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>0.519</td>\n",
       "      <td>4.52</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>...</td>\n",
       "      <td>104</td>\n",
       "      <td>4.83</td>\n",
       "      <td>1.451</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1207</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANA</td>\n",
       "      <td>19</td>\n",
       "      <td>29.8</td>\n",
       "      <td>4.83</td>\n",
       "      <td>85</td>\n",
       "      <td>77</td>\n",
       "      <td>0.525</td>\n",
       "      <td>4.49</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>...</td>\n",
       "      <td>106</td>\n",
       "      <td>4.51</td>\n",
       "      <td>1.462</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1211</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANA</td>\n",
       "      <td>20</td>\n",
       "      <td>31.5</td>\n",
       "      <td>5.10</td>\n",
       "      <td>70</td>\n",
       "      <td>92</td>\n",
       "      <td>0.432</td>\n",
       "      <td>4.79</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>...</td>\n",
       "      <td>101</td>\n",
       "      <td>4.94</td>\n",
       "      <td>1.464</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1138</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANA</td>\n",
       "      <td>25</td>\n",
       "      <td>28.9</td>\n",
       "      <td>5.36</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "      <td>0.506</td>\n",
       "      <td>5.00</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>...</td>\n",
       "      <td>101</td>\n",
       "      <td>5.46</td>\n",
       "      <td>1.517</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1188</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANA</td>\n",
       "      <td>18</td>\n",
       "      <td>28.9</td>\n",
       "      <td>4.51</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>0.463</td>\n",
       "      <td>4.20</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>...</td>\n",
       "      <td>107</td>\n",
       "      <td>4.48</td>\n",
       "      <td>1.375</td>\n",
       "      <td>9.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1152</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tm  #P  PAge  RA/G   W   L   W-L%   ERA    G   GS  ...   ERA+   FIP  \\\n",
       "0  ANA  21  28.3  4.90  84  78  0.519  4.52  162  162  ...    104  4.83   \n",
       "0  ANA  19  29.8  4.83  85  77  0.525  4.49  162  162  ...    106  4.51   \n",
       "0  ANA  20  31.5  5.10  70  92  0.432  4.79  162  162  ...    101  4.94   \n",
       "0  ANA  25  28.9  5.36  82  80  0.506  5.00  162  162  ...    101  5.46   \n",
       "0  ANA  18  28.9  4.51  75  87  0.463  4.20  162  162  ...    107  4.48   \n",
       "\n",
       "    WHIP   H9  HR9  BB9  SO9  SO/W   LOB  year  \n",
       "0  1.451  9.3  1.2  3.7  6.5  1.74  1207  1997  \n",
       "0  1.462  9.2  1.0  3.9  6.8  1.73  1211  1998  \n",
       "0  1.464  9.3  1.1  3.9  5.5  1.41  1138  1999  \n",
       "0  1.517  9.5  1.4  4.1  5.3  1.28  1188  2000  \n",
       "0  1.375  9.1  1.1  3.3  5.9  1.80  1152  2001  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine pitching data \n",
    "pitch = pd.concat(dfs).dropna()\n",
    "pitch = pitch.sort_values(by=[\"Tm\",\"year\"])\n",
    "pitch.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mlb = mlb.reset_index().drop(\"index\",axis=1)\n",
    "pitch = pitch.reset_index().drop(\"index\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tm      True\n",
       "W       True\n",
       "L       True\n",
       "G       True\n",
       "year    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensure all overlapping columns have the same values.\n",
    "overlapping_cols = ['Tm', 'W', 'L', 'G', 'year']\n",
    "df = (mlb[overlapping_cols] == pitch[overlapping_cols])\n",
    "df.all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Convert runs and runs allowed to yearly numbers from averages per game.\n",
    "mlb['R'] = mlb['R'] * 162\n",
    "mlb['RA'] = mlb['RA'] * 162"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": 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>Rk</th>\n",
       "      <th>Tm</th>\n",
       "      <th>Lg</th>\n",
       "      <th>G</th>\n",
       "      <th>W</th>\n",
       "      <th>L</th>\n",
       "      <th>W-L%</th>\n",
       "      <th>R</th>\n",
       "      <th>RA</th>\n",
       "      <th>Rdiff</th>\n",
       "      <th>...</th>\n",
       "      <th>Inter</th>\n",
       "      <th>Home</th>\n",
       "      <th>Road</th>\n",
       "      <th>ExInn</th>\n",
       "      <th>1Run</th>\n",
       "      <th>vRHP</th>\n",
       "      <th>vLHP</th>\n",
       "      <th>≥.500</th>\n",
       "      <th>&lt;.500</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.0</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>162.0</td>\n",
       "      <td>84</td>\n",
       "      <td>78</td>\n",
       "      <td>0.518</td>\n",
       "      <td>826.2</td>\n",
       "      <td>793.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>4-12</td>\n",
       "      <td>46-36</td>\n",
       "      <td>38-42</td>\n",
       "      <td>5-8</td>\n",
       "      <td>27-25</td>\n",
       "      <td>66-55</td>\n",
       "      <td>18-23</td>\n",
       "      <td>23-34</td>\n",
       "      <td>61-44</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.0</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>162.0</td>\n",
       "      <td>85</td>\n",
       "      <td>77</td>\n",
       "      <td>0.525</td>\n",
       "      <td>793.8</td>\n",
       "      <td>777.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10-6</td>\n",
       "      <td>42-39</td>\n",
       "      <td>43-38</td>\n",
       "      <td>6-6</td>\n",
       "      <td>24-19</td>\n",
       "      <td>62-59</td>\n",
       "      <td>23-18</td>\n",
       "      <td>30-36</td>\n",
       "      <td>55-41</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.0</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>162.0</td>\n",
       "      <td>70</td>\n",
       "      <td>92</td>\n",
       "      <td>0.432</td>\n",
       "      <td>712.8</td>\n",
       "      <td>826.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>6-12</td>\n",
       "      <td>37-44</td>\n",
       "      <td>33-48</td>\n",
       "      <td>4-8</td>\n",
       "      <td>23-25</td>\n",
       "      <td>51-73</td>\n",
       "      <td>19-19</td>\n",
       "      <td>27-45</td>\n",
       "      <td>43-47</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.0</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>162.0</td>\n",
       "      <td>82</td>\n",
       "      <td>80</td>\n",
       "      <td>0.506</td>\n",
       "      <td>858.6</td>\n",
       "      <td>874.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12-6</td>\n",
       "      <td>46-35</td>\n",
       "      <td>36-45</td>\n",
       "      <td>9-7</td>\n",
       "      <td>32-23</td>\n",
       "      <td>61-55</td>\n",
       "      <td>21-25</td>\n",
       "      <td>42-49</td>\n",
       "      <td>40-31</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>ANA</td>\n",
       "      <td>AL</td>\n",
       "      <td>162.0</td>\n",
       "      <td>75</td>\n",
       "      <td>87</td>\n",
       "      <td>0.463</td>\n",
       "      <td>696.6</td>\n",
       "      <td>729.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>10-8</td>\n",
       "      <td>39-42</td>\n",
       "      <td>36-45</td>\n",
       "      <td>7-8</td>\n",
       "      <td>24-24</td>\n",
       "      <td>53-60</td>\n",
       "      <td>22-27</td>\n",
       "      <td>37-55</td>\n",
       "      <td>38-32</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rk   Tm  Lg      G   W   L   W-L%      R     RA  Rdiff  ...   Inter  \\\n",
       "0  11.0  ANA  AL  162.0  84  78  0.518  826.2  793.8    0.2  ...    4-12   \n",
       "1  12.0  ANA  AL  162.0  85  77  0.525  793.8  777.6    0.0  ...    10-6   \n",
       "2  23.0  ANA  AL  162.0  70  92  0.432  712.8  826.2   -0.7  ...    6-12   \n",
       "3  15.0  ANA  AL  162.0  82  80  0.506  858.6  874.8    0.0  ...    12-6   \n",
       "4  20.0  ANA  AL  162.0  75  87  0.463  696.6  729.0   -0.2  ...    10-8   \n",
       "\n",
       "    Home   Road  ExInn   1Run   vRHP   vLHP  ≥.500  <.500  year  \n",
       "0  46-36  38-42    5-8  27-25  66-55  18-23  23-34  61-44  1997  \n",
       "1  42-39  43-38    6-6  24-19  62-59  23-18  30-36  55-41  1998  \n",
       "2  37-44  33-48    4-8  23-25  51-73  19-19  27-45  43-47  1999  \n",
       "3  46-35  36-45    9-7  32-23  61-55  21-25  42-49  40-31  2000  \n",
       "4  39-42  36-45    7-8  24-24  53-60  22-27  37-55  38-32  2001  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add all of the columns we need into one dataframe.\n",
    "mlb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variables that we felt would be most usuful for predicting wins with a machine learning model were:\n",
    "+ Runs: the total number of runs the team scored during the season\n",
    "+ Runs Allowed: the total number of runs that were scored against the team during the season\n",
    "+ Strength of Schedule: the difficulty or ease of a team's opponent as compared to other teams.\n",
    "+ ERA: the mean of the earned runs given up by a pitcher per nine innings pitched\n",
    "+ WHIP: walks hits per inning pitched\n",
    "+ FIP: field independent pitching\n",
    "+ SO: strike outs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create a dataframe for our training data.\n",
    "machine_data = pd.DataFrame(mlb[['R',\"RA\",\"SOS\",'W',\"year\"]])\n",
    "machine_data['ERA'] = pitch['ERA']\n",
    "machine_data['WHIP'] = pitch['WHIP']\n",
    "machine_data['FIP'] = pitch['FIP']\n",
    "machine_data['SO'] = pitch['SO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R       float64\n",
       "RA      float64\n",
       "SOS     float64\n",
       "W         int64\n",
       "year      int64\n",
       "ERA     float64\n",
       "WHIP    float64\n",
       "FIP     float64\n",
       "SO        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "machine_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "machine_data.to_csv(\"training.csv\", index=False)"
   ]
  }
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