init-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dfs = []\n",
    "fnames = ['Week 1 AU Football Forecasting Competition.csv',\n",
    "         'Week 2 AU Football Forecasting Competition.csv',\n",
    "         'Week 3 AU Football Forecasting Competition.csv',\n",
    "         'Week 4 AU Football Forecasting Competition.csv',\n",
    "         'Week 5 AU Football Forecasting Competition.csv',\n",
    "         'Week 6 AU Football Forecasting Competition.csv',\n",
    "         'Week 7 AU Football Forecasting Competition.csv',\n",
    "         'Week 8 AU Football Forecasting Competition.csv',\n",
    "         'Week 9 AU Football Forecasting Competition.csv',\n",
    "         'Week 10 AU Football Forecasting Competition.csv',\n",
    "         'Week 11 AU Football Forecasting Competition.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "for i in range(11):\n",
    "    dfs.append(pd.read_csv(fnames[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "group1 = []\n",
    "group2 = []\n",
    "group3 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "group1.append(dfs[0][dfs[0]['Collector ID'] == 246459632.0])\n",
    "group2.append(dfs[0][dfs[0]['Collector ID'] == 246459794.0])\n",
    "group3.append(dfs[0][dfs[0]['Collector ID'] == 246459113.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# The collectors used are different for each week. The only collectors that I know the labels for are the first week. The email addresses are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "emails1 = group1[0]['Email Address']\n",
    "emails2 = group2[0]['Email Address']\n",
    "emails3 = group3[0]['Email Address']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "for i in range(1,11):\n",
    "    group1.append(dfs[i][dfs[i]['Email Address'].isin(emails1)])\n",
    "    group2.append(dfs[i][dfs[i]['Email Address'].isin(emails2)])\n",
    "    group3.append(dfs[i][dfs[i]['Email Address'].isin(emails3)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "counts1 = {} \n",
    "for i in range(0,11):\n",
    "    emails = group1[i]\n",
    "    email_list = list(emails['Email Address'])\n",
    "    for j in range(len(email_list)):\n",
    "        try:\n",
    "            counts1[email_list[j]] += 1\n",
    "        except KeyError:\n",
    "            counts1[email_list[j]] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 2, 4, 5, 6, 6, 6, 6, 7, 9, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11]\n",
      "54\n"
     ]
    }
   ],
   "source": [
    "cts1 = list(counts1.values())\n",
    "cts1u = list(counts1.values())\n",
    "cts1.sort()\n",
    "print(cts1) \n",
    "print(len(cts1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "counts2 = {} \n",
    "for i in range(0,11):\n",
    "    emails = group2[i]\n",
    "    email_list = list(emails['Email Address'])\n",
    "    for j in range(len(email_list)):\n",
    "        try:\n",
    "            counts2[email_list[j]] += 1\n",
    "        except KeyError:\n",
    "            counts2[email_list[j]] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 8, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11]\n",
      "49\n"
     ]
    }
   ],
   "source": [
    "cts2 = list(counts2.values())\n",
    "cts2u = list(counts2.values())\n",
    "cts2.sort()\n",
    "print(cts2) \n",
    "print(len(cts2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "counts3 = {} \n",
    "for i in range(0,11):\n",
    "    emails = group3[i]\n",
    "    email_list = list(emails['Email Address'])\n",
    "    for j in range(len(email_list)):\n",
    "        try:\n",
    "            counts3[email_list[j]] += 1\n",
    "        except KeyError:\n",
    "            counts3[email_list[j]] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 5, 5, 6, 7, 8, 8, 8, 9, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11]\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "cts3 = list(counts3.values())\n",
    "cts3u = list(counts3.values())\n",
    "cts3.sort()\n",
    "print(cts3) \n",
    "print(len(cts3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Computing Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "group1tot = group1[0]\n",
    "group2tot = group2[0]\n",
    "\n",
    "group3tot = group3[0]\n",
    "for i in range(1,11):\n",
    "    group1tot = group1tot.merge(group1[i],on='Email Address')\n",
    "    group2tot = group2tot.merge(group2[i],on='Email Address')\n",
    "    group3tot = group3tot.merge(group3[i],on='Email Address')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 245)"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group1tot.shape #43 #5 #17 #21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 245)"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group2tot.shape #46 #5 #18 #23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 245)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group3tot.shape #42 #5 #15 #21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Group 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "group1tot = group1tot.iloc[:,3:]\n",
    "group1tot = pd.concat([group1tot.iloc[:,0], group1tot.iloc[:,2:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:2], group1tot.iloc[:,5:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:17], group1tot.iloc[:,19:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:19], group1tot.iloc[:,23:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:31], group1tot.iloc[:,33:35], group1tot.iloc[:,39:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:42], group1tot.iloc[:,44:46], group1tot.iloc[:,50:]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "group1tot = pd.concat([group1tot.iloc[:,:62], group1tot.iloc[:,64:66], group1tot.iloc[:,70:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:85], group1tot.iloc[:,87:89], group1tot.iloc[:,93:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:102], group1tot.iloc[:,104:106], group1tot.iloc[:,110:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:116], group1tot.iloc[:,118:120], group1tot.iloc[:,124:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:130], group1tot.iloc[:,132:134], group1tot.iloc[:,138:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:150], group1tot.iloc[:,152:154], group1tot.iloc[:,158:]], axis=1)\n",
    "group1tot = pd.concat([group1tot.iloc[:,:161], group1tot.iloc[:,163:165], group1tot.iloc[:,169:]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 178)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group1tot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Group 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "group2tot = group2tot.iloc[:,3:]\n",
    "group2tot = pd.concat([group2tot.iloc[:,0], group2tot.iloc[:,2:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:2], group2tot.iloc[:,5:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:17], group2tot.iloc[:,19:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:19], group2tot.iloc[:,23:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:31], group2tot.iloc[:,33:35], group2tot.iloc[:,39:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:42], group2tot.iloc[:,44:46], group2tot.iloc[:,50:]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "group2tot = pd.concat([group2tot.iloc[:,:62], group2tot.iloc[:,64:66], group2tot.iloc[:,70:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:85], group2tot.iloc[:,87:89], group2tot.iloc[:,93:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:102], group2tot.iloc[:,104:106], group2tot.iloc[:,110:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:116], group2tot.iloc[:,118:120], group2tot.iloc[:,124:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:130], group2tot.iloc[:,132:134], group2tot.iloc[:,138:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:150], group2tot.iloc[:,152:154], group2tot.iloc[:,158:]], axis=1)\n",
    "group2tot = pd.concat([group2tot.iloc[:,:161], group2tot.iloc[:,163:165], group2tot.iloc[:,169:]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 178)"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group2tot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Group 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "group3tot = group3tot.iloc[:,3:]\n",
    "group3tot = pd.concat([group3tot.iloc[:,0], group3tot.iloc[:,2:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:2], group3tot.iloc[:,5:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:17], group3tot.iloc[:,19:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:19], group3tot.iloc[:,23:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:31], group3tot.iloc[:,33:35], group3tot.iloc[:,39:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:42], group3tot.iloc[:,44:46], group3tot.iloc[:,50:]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "group3tot = pd.concat([group3tot.iloc[:,:62], group3tot.iloc[:,64:66], group3tot.iloc[:,70:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:85], group3tot.iloc[:,87:89], group3tot.iloc[:,93:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:102], group3tot.iloc[:,104:106], group3tot.iloc[:,110:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:116], group3tot.iloc[:,118:120], group3tot.iloc[:,124:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:130], group3tot.iloc[:,132:134], group3tot.iloc[:,138:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:150], group3tot.iloc[:,152:154], group3tot.iloc[:,158:]], axis=1)\n",
    "group3tot = pd.concat([group3tot.iloc[:,:161], group3tot.iloc[:,163:165], group3tot.iloc[:,169:]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 178)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group3tot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>What probability do you assign to this?.2</th>\n",
       "      <th>Auburn will beat Alabama?</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <th>What probability do you assign to this?.3</th>\n",
       "      <th>Tennessee will beat Vanderbilt?</th>\n",
       "      <th>Unnamed: 22_y</th>\n",
       "      <th>What probability do you assign to this?.4_y</th>\n",
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       "  </thead>\n",
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       "      <td>98</td>\n",
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       "      <td>99</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
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       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>24</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 16 What probability do you assign to this?.2  \\\n",
       "0         NaN                                        98   \n",
       "1         NaN                                       100   \n",
       "2         NaN                                       100   \n",
       "3         NaN                                       100   \n",
       "4         NaN                                        73   \n",
       "\n",
       "  Auburn will beat Alabama? Unnamed: 19  \\\n",
       "0                      True         NaN   \n",
       "1                      True         NaN   \n",
       "2                      True         NaN   \n",
       "3                      True         NaN   \n",
       "4                       NaN       False   \n",
       "\n",
       "  What probability do you assign to this?.3 Tennessee will beat Vanderbilt?  \\\n",
       "0                                        99                            True   \n",
       "1                                        48                            True   \n",
       "2                                       100                            True   \n",
       "3                                       100                            True   \n",
       "4                                        24                            True   \n",
       "\n",
       "  Unnamed: 22_y What probability do you assign to this?.4_y  \n",
       "0           NaN                                          96  \n",
       "1           NaN                                         100  \n",
       "2           NaN                                         100  \n",
       "3           NaN                                         100  \n",
       "4           NaN                                          41  "
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group1tot.iloc[:,170:].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Encoding the results of the games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "outcomes = []\n",
    "ignore = [2,2,0,None,1,1,2,None,0,None,3]\n",
    "# Vandy vs LSU, Florida vs TN, A&M vs AU, Miss St vs KY, Mizzou vs SC\n",
    "week1 = [False,True,False,True,True] #17\n",
    "outcomes.append(week1)\n",
    "# Arkansas vs A&M, Bama vs Miss, AU vs Miss St, SC vs KY\n",
    "week2 = [False,True,True,True] #14\n",
    "outcomes.append(week2)\n",
    "# Florida vs AU, TN vs UGA, Miss vs Vandy\n",
    "week3 = [True,False,True] #11\n",
    "outcomes.append(week3)\n",
    "# UGA vs SC, TN vs Miss St, A&M vs Bama, KY vs Arkansas, Mizzou vs Miss, LSU vs Florida \n",
    "week4 = [False,True,False,True,True,True] #20\n",
    "outcomes.append(week4)\n",
    "# SC vs Florida, Arkansas vs AU, Miss St vs LSU, Vandy vs Mizzou, UGA vs KY, Miss vs A&M, Bama vs TN\n",
    "week5 = [False,False,False,True,True,False,True] #23\n",
    "outcomes.append(week5)\n",
    "# A&M vs Miss St, LSU vs AU, TN vs SC, Bama vs Arkansas, KY vs Mizzou\n",
    "week6 = [True,True,True,True,True] #17\n",
    "outcomes.append(week6)\n",
    "# Florida vs UGA, Arkansas vs Miss St, AU vs Miss, SC vs Vandy \n",
    "week7 = [False,False,True,True] #14\n",
    "outcomes.append(week7)\n",
    "# Florida vs Vandy, Bama vs LSU, UGA vs Mizzou, KY vs TN\n",
    "week8 = [True,False,True,False] #14\n",
    "outcomes.append(week8)\n",
    "# AU vs UGA, Miss St vs Bama, Mizzou vs FL, Vandy vs KY, Miss vs LSU, A&M vs SC\n",
    "week9 = [False,False,False,False,False,True] #20\n",
    "outcomes.append(week9)\n",
    "# LSU vs Arkansas, UGA vs A&M, Mizzou vs TN\n",
    "week10 = [True,True,False] #11\n",
    "outcomes.append(week10)\n",
    "# Miss St vs Miss, Arkansas vs Mizzou, LSU vs A&M, AU vs Bama, TN vs Vandy\n",
    "week11 = [True,False,True,True,True] #17\n",
    "outcomes.append(week11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Encoding the indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "indicies = [18,32,43,63,86,103,117,131,151,162]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Check dates & times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "check = datetime.datetime(2019,9,28,12,0,0)\n",
    "g1ok = group1tot[pd.to_datetime(group1tot.iloc[:,0]) < check]\n",
    "g2ok = group2tot[pd.to_datetime(group2tot.iloc[:,0]) < check]\n",
    "g3ok = group3tot[pd.to_datetime(group3tot.iloc[:,0]) < check]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(36, 179)\n",
      "(38, 179)\n",
      "(39, 178)\n"
     ]
    }
   ],
   "source": [
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "check = datetime.datetime(2019,9,28,12,0,0)\n",
    "g1ok = g1ok[pd.to_datetime(g1ok.iloc[:,18]) < check]\n",
    "g2ok = g2ok[pd.to_datetime(g2ok.iloc[:,18]) < check]\n",
    "g3ok = g3ok[pd.to_datetime(g3ok.iloc[:,18]) < check]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# one person submitted late week 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 179)\n",
      "(38, 179)\n",
      "(39, 178)\n"
     ]
    }
   ],
   "source": [
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 179)\n",
      "(38, 179)\n",
      "(39, 178)\n"
     ]
    }
   ],
   "source": [
    "check = datetime.datetime(2019,10,5,12,0,0)\n",
    "g1ok = g1ok[pd.to_datetime(g1ok.iloc[:,32]) < check]\n",
    "g2ok = g2ok[pd.to_datetime(g2ok.iloc[:,32]) < check]\n",
    "g3ok = g3ok[pd.to_datetime(g3ok.iloc[:,32]) < check]\n",
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 179)\n",
      "(38, 179)\n",
      "(38, 178)\n"
     ]
    }
   ],
   "source": [
    "check = datetime.datetime(2019,10,12,12,0,0)\n",
    "g1ok = g1ok[pd.to_datetime(g1ok.iloc[:,43]) < check]\n",
    "g2ok = g2ok[pd.to_datetime(g2ok.iloc[:,43]) < check]\n",
    "g3ok = g3ok[pd.to_datetime(g3ok.iloc[:,43]) < check]\n",
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# one person was late on week 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 179)\n",
      "(38, 179)\n",
      "(38, 178)\n"
     ]
    }
   ],
   "source": [
    "check = datetime.datetime(2019,10,19,12,0,0)\n",
    "g1ok = g1ok[pd.to_datetime(g1ok.iloc[:,63]) < check]\n",
    "g2ok = g2ok[pd.to_datetime(g2ok.iloc[:,63]) < check]\n",
    "g3ok = g3ok[pd.to_datetime(g3ok.iloc[:,63]) < check]\n",
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# one person was late on week 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 179)\n",
      "(38, 179)\n",
      "(38, 178)\n"
     ]
    }
   ],
   "source": [
    "check = datetime.datetime(2019,10,26,12,0,0)\n",
    "g1ok = g1ok[pd.to_datetime(g1ok.iloc[:,86]) < check]\n",
    "g2ok = g2ok[pd.to_datetime(g2ok.iloc[:,86]) < check]\n",
    "g3ok = g3ok[pd.to_datetime(g3ok.iloc[:,86]) < check]\n",
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 179)\n",
      "(38, 179)\n",
      "(38, 178)\n"
     ]
    }
   ],
   "source": [
    "check = datetime.datetime(2019,11,2,12,0,0)\n",
    "g1ok = g1ok[pd.to_datetime(g1ok.iloc[:,103]) < check]\n",
    "g2ok = g2ok[pd.to_datetime(g2ok.iloc[:,103]) < check]\n",
    "g3ok = g3ok[pd.to_datetime(g3ok.iloc[:,103]) < check]\n",
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 179)\n",
      "(38, 179)\n",
      "(38, 178)\n"
     ]
    }
   ],
   "source": [
    "check = datetime.datetime(2019,11,9,12,0,0)\n",
    "g1ok = g1ok[pd.to_datetime(g1ok.iloc[:,117]) < check]\n",
    "g2ok = g2ok[pd.to_datetime(g2ok.iloc[:,117]) < check]\n",
    "g3ok = g3ok[pd.to_datetime(g3ok.iloc[:,117]) < check]\n",
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 179)\n",
      "(38, 179)\n",
      "(38, 178)\n"
     ]
    }
   ],
   "source": [
    "check = datetime.datetime(2019,11,16,12,0,0)\n",
    "g1ok = g1ok[pd.to_datetime(g1ok.iloc[:,131]) < check]\n",
    "g2ok = g2ok[pd.to_datetime(g2ok.iloc[:,131]) < check]\n",
    "g3ok = g3ok[pd.to_datetime(g3ok.iloc[:,131]) < check]\n",
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33, 179)\n",
      "(38, 179)\n",
      "(38, 178)\n"
     ]
    }
   ],
   "source": [
    "check = datetime.datetime(2019,11,23,12,0,0)\n",
    "g1ok = g1ok[pd.to_datetime(g1ok.iloc[:,151]) < check]\n",
    "g2ok = g2ok[pd.to_datetime(g2ok.iloc[:,151]) < check]\n",
    "g3ok = g3ok[pd.to_datetime(g3ok.iloc[:,151]) < check]\n",
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# one late participant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33, 179)\n",
      "(38, 179)\n",
      "(38, 178)\n"
     ]
    }
   ],
   "source": [
    "check = datetime.datetime(2019,11,30,12,0,0)\n",
    "g1ok = g1ok[pd.to_datetime(g1ok.iloc[:,162]) < check]\n",
    "g2ok = g2ok[pd.to_datetime(g2ok.iloc[:,162]) < check]\n",
    "g3ok = g3ok[pd.to_datetime(g3ok.iloc[:,162]) < check]\n",
    "print(g1ok.shape)\n",
    "print(g2ok.shape)\n",
    "print(g3ok.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1747,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "IGNORE = True\n",
    "ISOLATE = False\n",
    "weeks = 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1748,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#no1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1749,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tscores = None\n",
    "scores1 = None\n",
    "scorest1 = None\n",
    "probs = None\n",
    "probsl = None\n",
    "probsdf = None\n",
    "points1 = None\n",
    "log = None\n",
    "log_score = None\n",
    "emails1 = 'x'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1750,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ct = 0\n",
    "count = 2\n",
    "scores1 = np.zeros(38)\n",
    "emails = g1ok['Email Address']\n",
    "for it, outcome in enumerate(outcomes[:weeks]):\n",
    "    #print (outcome)\n",
    "    for ig, game in enumerate(outcome):\n",
    "        if ig == ignore[it] and IGNORE == True:\n",
    "            count += 3\n",
    "            ct += 1\n",
    "            continue\n",
    "        if ig != ignore[it] and ISOLATE == True:\n",
    "            count += 3\n",
    "            ct += 1\n",
    "            continue\n",
    "        #print (ig)\n",
    "        #print (game)\n",
    "        if game == True:\n",
    "            #add incrementing for scoring - add to count\n",
    "            #add each new df in place of old\n",
    "            #add tscore for each group\n",
    "            tscores = g1ok.iloc[:,count]*1\n",
    "            tscores = tscores.fillna(0)\n",
    "            #get prob\n",
    "            probs = g1ok.iloc[:,count+2]\n",
    "            #limit prob at 99\n",
    "            probs = pd.to_numeric(probs.iloc[:], downcast=\"float\")\n",
    "            probs[probs.iloc[:] > 99] = 99\n",
    "            probs[probs.iloc[:] < 1] = 1\n",
    "            #compute log score\n",
    "            probsl = probs.values.tolist()\n",
    "            log = np.zeros(len(probsl))\n",
    "            tscoresl = np.array(tscores)\n",
    "            #include email key\n",
    "            emailz = []\n",
    "            for i, prob in enumerate(probsl):\n",
    "                emailz.append(emails.iloc[i])\n",
    "                #print (prob)\n",
    "                x = math.log(prob/100.)\n",
    "                if tscoresl[i] == 1:\n",
    "                    x = x/2. + 0.5\n",
    "                else: \n",
    "                    x = x/2.\n",
    "                log[i] = ((10/(-0.00436+0.301))*(x+0.301))\n",
    "            log_score = pd.DataFrame(log)\n",
    "            try: \n",
    "                scores1 = pd.concat([scores1,tscores],axis=1)\n",
    "                probsdf = pd.concat([probsdf,probs],axis=1)\n",
    "                #scorest1 += tscores\n",
    "                #probsdf[alphabet[ct]] = probs\n",
    "                #points1 = points1 + log_score\n",
    "                #log_score = pd.DataFrame(log_score)\n",
    "                #emails1 = pd.concat([emails1,log_score],axis=1)\n",
    "                #print ('no error')\n",
    "            except:\n",
    "                scores1 = tscores\n",
    "                scorest1 = tscores\n",
    "                probsdf = probs\n",
    "                points1 = log_score\n",
    "                emails1 = pd.DataFrame(emailz)\n",
    "                #log_score = pd.DataFrame(log_score)\n",
    "                #emails1 = pd.concat([emails1,log_score],axis=1)\n",
    "                #print ('error')\n",
    "            try:\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails1 = pd.concat([emails1,log_score],axis=1)   \n",
    "                #print(\"EMAILS\")\n",
    "            except:\n",
    "                emails1 = pd.DataFrame(emailz)\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails1 = pd.concat([emails1,log_score],axis=1)\n",
    "                #print(\"ERROR\")\n",
    "            #add to count to balance\n",
    "            count += 3\n",
    "        if game == False:\n",
    "            #add incrementing for scoring - add to count\n",
    "            count += 1\n",
    "            #add new dfs instead of old\n",
    "            #add new tscore for each group\n",
    "            tscores = g1ok.iloc[:,count][g1ok.iloc[:,count].fillna(0)*1 == 0]+1\n",
    "            tscores = tscores.fillna(0)\n",
    "            #get prob\n",
    "            probs = g1ok.iloc[:,count+1]\n",
    "            probs = pd.to_numeric(probs.iloc[:], downcast=\"float\")\n",
    "            #limit prob at 99\n",
    "            probs[probs.iloc[:] > 99.] = 99.\n",
    "            probs[probs.iloc[:] < 1] = 1\n",
    "            #compute log score\n",
    "            probsl = probs.values.tolist()\n",
    "            log = np.zeros(len(probsl))\n",
    "            tscoresl = np.array(tscores)\n",
    "            #keep emails\n",
    "            emailz = []\n",
    "            for i, prob in enumerate(probsl):\n",
    "                #print (prob)\n",
    "                emailz.append(emails.iloc[i])\n",
    "                x = math.log(prob/100.)\n",
    "                if tscoresl[i] == 1:\n",
    "                    x = x/2. + 0.5\n",
    "                else: \n",
    "                    x = x/2.\n",
    "                log[i] = ((10/(-0.00436+0.301))*(x+0.301))\n",
    "            log_score = pd.DataFrame(log)\n",
    "            try: \n",
    "                scores1 = pd.concat([scores1,tscores],axis=1)\n",
    "                probsdf = pd.concat([probsdf,probs],axis=1)\n",
    "                points1 = points1 + log_score\n",
    "                #print ('no error')\n",
    "            except:\n",
    "                scores1 = tscores\n",
    "                probsdf = probs\n",
    "                points1 = log_score\n",
    "                #print ('error')\n",
    "            try:\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails1 = pd.concat([emails1,log_score],axis=1)                \n",
    "            except:\n",
    "                emails1 = pd.DataFrame(emailz)\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails1 = pd.concat([emails1,log_score],axis=1)\n",
    "                #print(\"ERROR\")\n",
    "            #add to count for balance\n",
    "            count += 2\n",
    "        ct += 1\n",
    "    #add to count for extra fields\n",
    "    count += 2\n",
    "    #print (scores1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1751,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#no2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1752,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tscores = None\n",
    "scores2 = None\n",
    "probs = None\n",
    "probsl = None\n",
    "probsdf = None\n",
    "points2 = None\n",
    "log = None\n",
    "log_score = None\n",
    "tscoresl = None\n",
    "emails2 = 'x'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1753,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR\n"
     ]
    }
   ],
   "source": [
    "ct = 0\n",
    "count = 2\n",
    "emails = g2ok['Email Address']\n",
    "for it, outcome in enumerate(outcomes[:weeks]):\n",
    "    #print (outcome)\n",
    "    for ig, game in enumerate(outcome):\n",
    "        if ig == ignore[it] and IGNORE == True:\n",
    "            count += 3\n",
    "            ct += 1\n",
    "            continue\n",
    "        if ig != ignore[it] and ISOLATE == True:\n",
    "            count += 3\n",
    "            ct += 1\n",
    "            continue\n",
    "        #print (game)\n",
    "        if game == True:\n",
    "            #add incrementing for scoring - add to count\n",
    "            #add each new df in place of old\n",
    "            #add tscore for each group\n",
    "            tscores = g2ok.iloc[:,count]*1\n",
    "            tscores = tscores.fillna(0)\n",
    "            #get prob\n",
    "            probs = g2ok.iloc[:,count+2]\n",
    "            #emails = g2ok['Email Address']\n",
    "            #limit prob at 99\n",
    "            probs = pd.to_numeric(probs.iloc[:], downcast=\"float\")\n",
    "            probs[probs.iloc[:] > 99] = 99\n",
    "            probs[probs.iloc[:] < 1] = 1\n",
    "            #compute log score\n",
    "            probsl = probs.values.tolist()\n",
    "            log = np.zeros(len(probsl))\n",
    "            tscoresl = np.array(tscores)\n",
    "            #keep emails\n",
    "            emailz = []\n",
    "            for i, prob in enumerate(probsl):\n",
    "                #print (prob)\n",
    "                emailz.append(emails.iloc[i])\n",
    "                x = math.log(prob/100.)\n",
    "                if tscoresl[i] == 1:\n",
    "                    x = x/2. + 0.5\n",
    "                else: \n",
    "                    x = x/2.\n",
    "                log[i] = ((10/(-0.00436+0.301))*(x+0.301))\n",
    "            log_score = pd.DataFrame(log)\n",
    "            try: \n",
    "                scores2 = pd.concat([scores2,tscores],axis=1)\n",
    "                probsdf = pd.concat([probsdf,probs],axis=1)\n",
    "                #probsdf[alphabet[ct]] = probs\n",
    "                points2 = points2 + log_score\n",
    "            except:\n",
    "                scores2 = tscores\n",
    "                probsdf = probs\n",
    "                points2 = log_score\n",
    "                #print ('error')\n",
    "            try:\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails2 = pd.concat([emails2,log_score],axis=1)   \n",
    "                #print(\"EMAILS\")\n",
    "            except:\n",
    "                emails2 = pd.DataFrame(emailz)\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails2 = pd.concat([emails2,log_score],axis=1)\n",
    "                print(\"ERROR\")\n",
    "            #add to count to balance\n",
    "            count += 3\n",
    "        if game == False:\n",
    "            #add incrementing for scoring - add to count\n",
    "            count += 1\n",
    "            #add new dfs instead of old\n",
    "            #add new tscore for each group\n",
    "            tscores = g2ok.iloc[:,count][g2ok.iloc[:,count].fillna(0)*1 == 0]+1\n",
    "            tscores = tscores.fillna(0)\n",
    "            #get prob\n",
    "            probs = g2ok.iloc[:,count+1]\n",
    "            probs = pd.to_numeric(probs, downcast=\"float\")\n",
    "            #limit prob at 99\n",
    "            probs[probs.iloc[:] > 99.] = 99.\n",
    "            probs[probs.iloc[:] < 1] = 1\n",
    "            #compute log score\n",
    "            probsl = probs.values.tolist()\n",
    "            log = np.zeros(len(probsl))\n",
    "            tscoresl = np.array(tscores)\n",
    "            #emails\n",
    "            emailz = []\n",
    "            for i, prob in enumerate(probsl):\n",
    "                #print (prob)\n",
    "                emailz.append(emails.iloc[i])\n",
    "                x = math.log(prob/100.)\n",
    "                if tscoresl[i] == 1:\n",
    "                    x = x/2. + 0.5\n",
    "                else: \n",
    "                    x = x/2.\n",
    "                log[i] = ((10/(-0.00436+0.301))*(x+0.301))\n",
    "            log_score = pd.DataFrame(log)\n",
    "            try: \n",
    "                scores2 = pd.concat([scores2,tscores],axis=1)\n",
    "                probsdf = pd.concat([probsdf,probs],axis=1)\n",
    "                points2 = points2 + log_score\n",
    "                #print ('no error')\n",
    "                #log_score = pd.DataFrame(log_score)\n",
    "                #emails2 = pd.concat([emails2,log_score])\n",
    "            except:\n",
    "                scores2 = tscores\n",
    "                probsdf = probs\n",
    "                points2 = log_score\n",
    "                #print ('error')\n",
    "                #log_score = pd.DataFrame(log_score)\n",
    "                #emails2 = pd.concat([emails2,log_score])\n",
    "            try:\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails2 = pd.concat([emails2,log_score],axis=1)   \n",
    "                #print(\"EMAILS\")\n",
    "            except:\n",
    "                emails2 = pd.DataFrame(emailz)\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails2 = pd.concat([emails2,log_score],axis=1)\n",
    "                print(\"ERROR\")\n",
    "            #add to count for balance\n",
    "            count += 2\n",
    "        ct += 1\n",
    "    #add to count for extra fields\n",
    "    count += 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1754,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#no3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1755,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tscores = None\n",
    "scores3 = None\n",
    "probs = None\n",
    "probsl = None\n",
    "probsdf = None\n",
    "points3 = None\n",
    "log = None\n",
    "log_score = None\n",
    "tscoresl = None\n",
    "emails3 = 'x'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1756,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "ct = 0\n",
    "count = 2\n",
    "emails = g3ok['Email Address']\n",
    "for it, outcome in enumerate(outcomes[:weeks]):\n",
    "    #print (outcome)\n",
    "    for ig, game in enumerate(outcome):\n",
    "        if ig == ignore[it] and IGNORE == True:\n",
    "            count += 3\n",
    "            ct += 1\n",
    "            continue\n",
    "        if ig != ignore[it] and ISOLATE == True:\n",
    "            count += 3\n",
    "            ct += 1\n",
    "            continue\n",
    "        #print (game)\n",
    "        if game == True:\n",
    "            #add incrementing for scoring - add to count\n",
    "            #add each new df in place of old\n",
    "            #add tscore for each group\n",
    "            tscores = g3ok.iloc[:,count]*1\n",
    "            tscores = tscores.fillna(0)\n",
    "            #get prob\n",
    "            probs = g3ok.iloc[:,count+2]\n",
    "            #limit prob at 99\n",
    "            probs = pd.to_numeric(probs.iloc[:], downcast=\"float\")\n",
    "            probs[probs.iloc[:] > 99] = 99\n",
    "            probs[probs.iloc[:] < 1] = 1\n",
    "            #compute log score\n",
    "            probsl = probs.values.tolist()\n",
    "            log = np.zeros(len(probsl))\n",
    "            tscoresl = np.array(tscores)\n",
    "            #emails\n",
    "            emailz = []\n",
    "            for i, prob in enumerate(probsl):\n",
    "                #print (prob)\n",
    "                emailz.append(emails.iloc[i])\n",
    "                x = math.log(prob/100.)\n",
    "                if tscoresl[i] == 1:\n",
    "                    x = x/2. + 0.5\n",
    "                else: \n",
    "                    x = x/2.\n",
    "                log[i] = ((10/(-0.00436+0.301))*(x+0.301))\n",
    "            log_score = pd.DataFrame(log)\n",
    "            try: \n",
    "                scores3 = pd.concat([scores3,tscores],axis=1)\n",
    "                #probsdf[alphabet[ct]] = probs\n",
    "                probsdf = pd.concat([probsdf,probs],axis=1)\n",
    "                points3 = points3 + log_score\n",
    "                #print ('no error')\n",
    "                #log_score = pd.DataFrame(log_score)\n",
    "                #emails3 = pd.concat([emails3,log_score])\n",
    "            except:\n",
    "                scores3 = tscores\n",
    "                probsdf = probs\n",
    "                points3 = log_score\n",
    "                #print ('error')\n",
    "                #log_score = pd.DataFrame(log_score)\n",
    "                #emails3 = pd.concat([emails3,log_score])\n",
    "            try:\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails3 = pd.concat([emails3,log_score],axis=1)   \n",
    "                #print(\"EMAILS\")\n",
    "            except:\n",
    "                emails3 = pd.DataFrame(emailz)\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails3 = pd.concat([emails3,log_score],axis=1)\n",
    "            #add to count to balance\n",
    "            count += 3\n",
    "        if game == False:\n",
    "            #add incrementing for scoring - add to count\n",
    "            count += 1\n",
    "            #add new dfs instead of old\n",
    "            #add new tscore for each group\n",
    "            tscores = g3ok.iloc[:,count][g3ok.iloc[:,count].fillna(0)*1 == 0]+1\n",
    "            tscores = tscores.fillna(0)\n",
    "            #get prob\n",
    "            probs = g3ok.iloc[:,count+1]\n",
    "            probs = pd.to_numeric(probs, downcast=\"float\")\n",
    "            #limit prob at 99\n",
    "            probs[probs.iloc[:] > 99.] = 99.\n",
    "            probs[probs.iloc[:] < 1] = 1\n",
    "            #compute log score\n",
    "            probsl = probs.values.tolist()\n",
    "            log = np.zeros(len(probsl))\n",
    "            tscoresl = np.array(tscores)\n",
    "            #emails\n",
    "            emailz = []\n",
    "            for i, prob in enumerate(probsl):\n",
    "                #print (prob)\n",
    "                emailz.append(emails.iloc[i])\n",
    "                x = math.log(prob/100.)\n",
    "                if tscoresl[i] == 1:\n",
    "                    x = x/2. + 0.5\n",
    "                else: \n",
    "                    x = x/2.\n",
    "                log[i] = ((10/(-0.00436+0.301))*(x+0.301))\n",
    "            log_score = pd.DataFrame(log)\n",
    "            try: \n",
    "                scores3 = pd.concat([scores3,tscores],axis=1)\n",
    "                probsdf = pd.concat([probsdf,probs],axis=1)\n",
    "                points3 = points3 + log_score\n",
    "                #print ('no error')\n",
    "            except:\n",
    "                scores3 = tscores\n",
    "                probsdf = probs\n",
    "                points3 = log_score\n",
    "                #print ('error')\n",
    "            try:\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails3 = pd.concat([emails3,log_score],axis=1)   \n",
    "                #print(\"EMAILS\")\n",
    "            except:\n",
    "                emails3 = pd.DataFrame(emailz)\n",
    "                log_score = pd.DataFrame(log_score)\n",
    "                emails3 = pd.concat([emails3,log_score],axis=1)\n",
    "            #add to count for balance\n",
    "            count += 2\n",
    "        ct += 1\n",
    "    #add to count for extra fields\n",
    "    count += 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Only AU Games "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NCt/otk7V8ApC0uqSvpkfZ31M0qOS7szD1ujPSiPiofx/AXAhqbXY+mmmRcTk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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=True)\n",
    "ax1.set_title('Historgrams of Automatable Tasks Questions\\' Responses')\n",
    "n, bins, patches = ax1.hist(points3.iloc[:,0],bins=20,range=(-200,400),color='red')#'powderblue')\n",
    "#(mu, sigma) = norm.fit(q5.Q5a)\n",
    "n, bins, patches = ax2.hist(points2.iloc[:,0],bins=20,range=(-200,400),color='green')#'skyblue')\n",
    "plt.ylabel('                                                '+\\\n",
    "            '10 years           5 years           Present')\n",
    "n, bins, patches = ax3.hist(points1.iloc[:,0],bins=20,range=(-200,400),color='blue')#'steelblue')\n",
    "plt.xlim(-200,400)\n",
    "plt.xlabel('Percentage of Human Tasks Possible to Automate')\n",
    "plt.savefig('PercentageTasksHistograms.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JEXFX7l4ITOxPaU9pVc8gLdNB3e4Y2+sJrgqSLgEmNRl0VET8MI9zFPAEcHYv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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=True)\n",
    "ax1.set_title('Box Plots of Automatable Tasks Questions\\' Responses')\n",
    "ax1.boxplot(points3.iloc[:,0],0,'rs',0,patch_artist=True)#,showfliers=False)\n",
    "ax2.boxplot(points2.iloc[:,0],0,'rs',0,patch_artist=True)\n",
    "ax3.boxplot(points1.iloc[:,0],0,'rs',0,patch_artist=True)\n",
    "plt.xlabel('Percentage of Human Tasks Possible to Automate')\n",
    "\n",
    "ax1.set_yticklabels([])\n",
    "ax2.set_yticklabels([])\n",
    "ax3.set_yticklabels([])\n",
    "plt.ylabel('                                                '+\\\n",
    "            '10 years           5 years           Present')\n",
    "plt.savefig('PercentageTasksBoxplots.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All Games but AU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XxAGwSX9W7niXzYiYBkzrdBzNJE2PiCmdjqMsx1stx1stxzt0qmzeuR/YqPB4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\nNfL/AYe0Ef78w04vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=True)\n",
    "ax1.set_title('Historgrams of Automatable Tasks Questions\\' Responses')\n",
    "n, bins, patches = ax1.hist(points3.iloc[:,0],bins=20,range=(-700,1200),color='red')#'powderblue')\n",
    "#(mu, sigma) = norm.fit(q5.Q5a)\n",
    "n, bins, patches = ax2.hist(points2.iloc[:,0],bins=20,range=(-700,1200),color='green')#'skyblue')\n",
    "plt.ylabel('                                                '+\\\n",
    "            '10 years           5 years           Present')\n",
    "n, bins, patches = ax3.hist(points1.iloc[:,0],bins=20,range=(-700,1200),color='blue')#'steelblue')\n",
    "plt.xlim(-700,1200)\n",
    "plt.xlabel('Percentage of Human Tasks Possible to Automate')\n",
    "plt.savefig('PercentageTasksHistograms.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J3ajVHYjFzMxGqMzdL0skvQwISesDHwJubaUQSYcChwLMmuWLJs1U7VTQDLxfd1OZpH4Y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\nZhXipG5mViFO6mZmFfL/AQ4br6n3PeHQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=True)\n",
    "ax1.set_title('Box Plots of Automatable Tasks Questions\\' Responses')\n",
    "ax1.boxplot(points3.iloc[:,0],0,'rs',0,patch_artist=True)#,showfliers=False)\n",
    "ax2.boxplot(points2.iloc[:,0],0,'rs',0,patch_artist=True)\n",
    "ax3.boxplot(points1.iloc[:,0],0,'rs',0,patch_artist=True)\n",
    "plt.xlabel('Percentage of Human Tasks Possible to Automate')\n",
    "\n",
    "ax1.set_yticklabels([])\n",
    "ax2.set_yticklabels([])\n",
    "ax3.set_yticklabels([])\n",
    "plt.ylabel('                                                '+\\\n",
    "            '10 years           5 years           Present')\n",
    "plt.savefig('PercentageTasksBoxplots.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ALL Games "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iSjezRcQcYFhEvBgRPwL2rzj+vSJiF1L10Uckvbk/QUqaLKlHUs/ChQv7Mwoz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\nf8ZDETEaxnRhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=True)\n",
    "ax1.set_title('Historgrams of Automatable Tasks Questions\\' Responses')\n",
    "n, bins, patches = ax1.hist(points3.iloc[:,0],bins=20,range=(-700,1200),color='red')#'powderblue')\n",
    "#(mu, sigma) = norm.fit(q5.Q5a)\n",
    "n, bins, patches = ax2.hist(points2.iloc[:,0],bins=20,range=(-700,1200),color='green')#'skyblue')\n",
    "plt.ylabel('                                                '+\\\n",
    "            '10 years           5 years           Present')\n",
    "n, bins, patches = ax3.hist(points1.iloc[:,0],bins=20,range=(-700,1200),color='blue')#'steelblue')\n",
    "plt.xlim(-700,1200)\n",
    "plt.xlabel('Percentage of Human Tasks Possible to Automate')\n",
    "plt.savefig('PercentageTasksHistograms.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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H+EFEPBMRfwPuAl6dX3dFxF8j4lngB3nejouISyPiufzvn4BJrebvcbl1rVya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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1, (ax1, ax2, ax3) = plt.subplots(3, sharex=True, sharey=True)\n",
    "ax1.set_title('Box Plots of Automatable Tasks Questions\\' Responses')\n",
    "ax1.boxplot(points3.iloc[:,0],0,'rs',0,patch_artist=True)#,showfliers=False)\n",
    "ax2.boxplot(points2.iloc[:,0],0,'rs',0,patch_artist=True)\n",
    "ax3.boxplot(points1.iloc[:,0],0,'rs',0,patch_artist=True)\n",
    "plt.xlabel('Percentage of Human Tasks Possible to Automate')\n",
    "\n",
    "ax1.set_yticklabels([])\n",
    "ax2.set_yticklabels([])\n",
    "ax3.set_yticklabels([])\n",
    "plt.ylabel('                                                '+\\\n",
    "            '10 years           5 years           Present')\n",
    "plt.savefig('PercentageTasksBoxplots.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STATS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.1257989919856393, 0.262635967375361, 113.0)"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.ttest_ind(points3.iloc[:,0], pd.concat([points2.iloc[:,0],points1.iloc[:,0]], axis=0), alternative='two-sided', usevar='pooled', weights=(None, None), value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1757,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t0 = pd.read_csv(\"AU Forecasting Control Group.csv\")\n",
    "Q1 = list(t0.iloc[:,-19])\n",
    "for i in range (len(Q1)):\n",
    "    if '5' in str(Q1[i]) and '2.15' not in str(Q1[i]) and '1.05' not in str(Q1[i]):\n",
    "        #print (Q1[i])\n",
    "        Q1[i] = 1\n",
    "    else:\n",
    "        #print (Q1[i])\n",
    "        Q1[i] = 0\n",
    "Q2 = list(t0.iloc[:,-17])\n",
    "for i in range (len(Q2)):\n",
    "    if '5' in str(Q2[i]) and '25' not in str(Q2[i]) and 'hours' not in str(Q2[i]):\n",
    "        #print (Q2[i])\n",
    "        Q2[i] = 1\n",
    "    else:\n",
    "        #print (Q2[i])\n",
    "        Q2[i] = 0\n",
    "Q3 = list(t0.iloc[:,-16])\n",
    "for i in range (len(Q3)):\n",
    "    if '47' in str(Q3[i]) and '25' not in str(Q3[i]) and 'hours' not in str(Q3[i]):\n",
    "        #print (Q3[i])\n",
    "        Q3[i] = 1\n",
    "    else:\n",
    "        #print (Q3[i])\n",
    "        Q3[i] = 0\n",
    "Q4 = list(t0.iloc[:,-6])\n",
    "for i in range (len(Q4)):\n",
    "    if 'Never' in str(Q4[i]): #and '25' not in str(Q3[i]) and 'hours' not in str(Q3[i]):\n",
    "        #print (Q3[i])\n",
    "        Q4[i] = 0\n",
    "    elif 'twice' in str(Q4[i]):\n",
    "        Q4[i] = 1\n",
    "    elif 'Once a' in str(Q4[i]):\n",
    "        Q4[i] = 2\n",
    "    elif '2-3' in str(Q4[i]):\n",
    "        Q4[i] = 3\n",
    "    else:\n",
    "        #print (Q3[i])\n",
    "        Q4[i] = 4\n",
    "Q4 = pd.DataFrame(Q4)\n",
    "t0['Qs'] = pd.DataFrame(np.sum([Q1,Q2,Q3],0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1758,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "t0['Female'] = t0.index\n",
    "t0.Female[t0.iloc[:,-14] == 'Female'] = 1\n",
    "t0.Female[t0.iloc[:,-14] == 'Male'] = 0\n",
    "t0 = pd.concat([t0.iloc[:,-4],t0.iloc[:,-1],t0[['Points', 'Minutes Spent','Qs']],t0.iloc[:,-12:-8],Q4,t0.iloc[:,-5]],axis=1)\n",
    "t0.columns = ['Email Address', 'Female', 'Points', 'Minutes Spent', 'Qs','Q-8','Q-7','Q-6','Q-5','Q-4','Q-3']\n",
    "t0 = t0.dropna()\n",
    "emails3t = pd.concat([emails3.iloc[:,0],emails3.iloc[:,1:].sum(axis=1)],axis=1)\n",
    "emails3t.columns = ['Email Address', 'Total Points']\n",
    "emails3t = emails3t.merge(t0,on='Email Address')\n",
    "points3t = emails3t.iloc[:,1:]\n",
    "points3t['log-time'] = np.log(points3t['Minutes Spent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1759,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "points3t.iloc[15,-6] = 8\n",
    "points3t.iloc[15,-5] = 4\n",
    "points3t.iloc[15,-4] = 2\n",
    "points3t.iloc[20,-6] = 8\n",
    "points3t.iloc[20,-5] = 4\n",
    "points3t.iloc[20,-4] = 8\n",
    "points3t.iloc[30,-6] = 5\n",
    "points3t.iloc[33,-6] = 8\n",
    "points3t.iloc[36,-6] = 8\n",
    "points3t.iloc[36,-5] = 10\n",
    "points3t.iloc[36,-4] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1760,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zeros = np.zeros(points3t.shape[0])\n",
    "neg = np.ones(points3t.shape[0])*-1000.\n",
    "#points3t['Points'] = neg\n",
    "points3t['Minutes Spent'] = zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1761,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#points3t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1762,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "t120 = pd.read_csv(\"AU Forecasting Treatment Group A.csv\")\n",
    "Q1 = list(t120.iloc[:,-23])\n",
    "for i in range (len(Q1)):\n",
    "    if '5' in str(Q1[i]) and '2.15' not in str(Q1[i]) and '1.05' not in str(Q1[i]):\n",
    "        #print (Q1[i])\n",
    "        Q1[i] = 1\n",
    "    else:\n",
    "        #print (Q1[i])\n",
    "        Q1[i] = 0\n",
    "Q2 = list(t120.iloc[:,-22])\n",
    "for i in range (len(Q2)):\n",
    "    if '5' in str(Q2[i]) and '25' not in str(Q2[i]) and 'hours' not in str(Q2[i]):\n",
    "        #print (Q2[i])\n",
    "        Q2[i] = 1\n",
    "    else:\n",
    "        #print (Q2[i])\n",
    "        Q2[i] = 0\n",
    "Q3 = list(t120.iloc[:,-21])\n",
    "for i in range (len(Q3)):\n",
    "    if '47' in str(Q3[i]) and '25' not in str(Q3[i]) and 'hours' not in str(Q3[i]):\n",
    "        #print (Q3[i])\n",
    "        Q3[i] = 1\n",
    "    else:\n",
    "        #print (Q3[i])\n",
    "        Q3[i] = 0\n",
    "Q4 = list(t120.iloc[:,-4])\n",
    "for i in range (len(Q4)):\n",
    "    if 'Never' in str(Q4[i]): #and '25' not in str(Q3[i]) and 'hours' not in str(Q3[i]):\n",
    "        #print (Q3[i])\n",
    "        Q4[i] = 0\n",
    "    elif 'twice' in str(Q4[i]):\n",
    "        Q4[i] = 1\n",
    "    elif 'Once a' in str(Q4[i]):\n",
    "        Q4[i] = 2\n",
    "    elif '2-3' in str(Q4[i]):\n",
    "        Q4[i] = 3\n",
    "    else:\n",
    "        #print (Q3[i])\n",
    "        Q4[i] = 4\n",
    "Q4 = pd.DataFrame(Q4)\n",
    "t120['Qs'] = pd.DataFrame(np.sum([Q1,Q2,Q3],0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1763,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "t120['Female'] = t120.index\n",
    "t120.Female[t120.iloc[:,-14] == 'Female'] = 1\n",
    "t120.Female[t120.iloc[:,-14] == 'Male'] = 0\n",
    "t120 = pd.concat([t120.iloc[:,-4],t120.iloc[:,-1],t120[['Points', 'Minutes Spent','Qs']],t120.iloc[:,-10:-6],Q4,t120.iloc[:,-5]],axis=1)\n",
    "t120.columns = ['Email Address', 'Female', 'Points', 'Minutes Spent', 'Qs','Q-8','Q-7','Q-6','Q-5','Q-4','Q-3']\n",
    "t120 = t120.dropna()\n",
    "emails1t = pd.concat([emails1.iloc[:,0],emails1.iloc[:,1:].sum(axis=1)],axis=1)\n",
    "emails1t.columns = ['Email Address', 'Total Points']\n",
    "emails1t = emails1t.merge(t120,on='Email Address')\n",
    "points1t = emails1t.iloc[:,1:]\n",
    "points1t['log-time'] = np.log(points1t['Minutes Spent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1764,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pos = np.ones(points1t.shape[0])*1000.\n",
    "#points1t.Points += pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1765,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#points1t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1766,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "t60 = pd.read_csv(\"AU Forecasting Treatment Group B.csv\")\n",
    "Q1 = list(t60.iloc[:,-23])\n",
    "for i in range (len(Q1)):\n",
    "    if '5' in str(Q1[i]) and '2.15' not in str(Q1[i]) and '1.05' not in str(Q1[i]):\n",
    "        #print (Q1[i])\n",
    "        Q1[i] = 1\n",
    "    else:\n",
    "        #print (Q1[i])\n",
    "        Q1[i] = 0\n",
    "Q2 = list(t60.iloc[:,-22])\n",
    "for i in range (len(Q2)):\n",
    "    if '5' in str(Q2[i]) and '25' not in str(Q2[i]) and 'hours' not in str(Q2[i]):\n",
    "        #print (Q2[i])\n",
    "        Q2[i] = 1\n",
    "    else:\n",
    "        #print (Q2[i])\n",
    "        Q2[i] = 0\n",
    "Q3 = list(t60.iloc[:,-21])\n",
    "for i in range (len(Q3)):\n",
    "    if '47' in str(Q3[i]) and '25' not in str(Q3[i]) and 'hours' not in str(Q3[i]):\n",
    "        #print (Q3[i])\n",
    "        Q3[i] = 1\n",
    "    else:\n",
    "        #print (Q3[i])\n",
    "        Q3[i] = 0\n",
    "Q4 = list(t60.iloc[:,-4])\n",
    "for i in range (len(Q4)):\n",
    "    if 'Never' in str(Q4[i]): #and '25' not in str(Q3[i]) and 'hours' not in str(Q3[i]):\n",
    "        #print (Q3[i])\n",
    "        Q4[i] = 0\n",
    "    elif 'twice' in str(Q4[i]):\n",
    "        Q4[i] = 1\n",
    "    elif 'Once a' in str(Q4[i]):\n",
    "        Q4[i] = 2\n",
    "    elif '2-3' in str(Q4[i]):\n",
    "        Q4[i] = 3\n",
    "    else:\n",
    "        #print (Q3[i])\n",
    "        Q4[i] = 4\n",
    "Q4 = pd.DataFrame(Q4)\n",
    "t60['Qs'] = pd.DataFrame(np.sum([Q1,Q2,Q3],0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1767,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/usr/local/Cellar/python3/3.6.0/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  app.launch_new_instance()\n"
     ]
    }
   ],
   "source": [
    "t60['Female'] = t60.index\n",
    "t60.Female[t60.iloc[:,-14] == 'Female'] = 1\n",
    "t60.Female[t60.iloc[:,-14] == 'Male'] = 0\n",
    "t60 = pd.concat([t60.iloc[:,-4],t60.iloc[:,-1],t60[['Points', 'Minutes Spent', 'Qs']],t60.iloc[:,-10:-6],Q4,t60.iloc[:,-5]],axis=1)\n",
    "t60.columns = ['Email Address', 'Female', 'Points', 'Minutes Spent', 'Qs','Q-8','Q-7','Q-6','Q-5','Q-4','Q-3']\n",
    "t60 = t60.dropna()\n",
    "#print(t60)\n",
    "emails2t = pd.concat([emails2.iloc[:,0],emails2.iloc[:,1:].sum(axis=1)],axis=1)\n",
    "emails2t.columns = ['Email Address', 'Total Points']\n",
    "emails2t = emails2t.merge(t60,on='Email Address')\n",
    "points2t = emails2t.iloc[:,1:]\n",
    "points2t['log-time'] = np.log(points2t['Minutes Spent'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1768,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# X1: D120, X2: D60, X3: Qs, X4: Points, X5: Female, X6: log(Completion Time), Games Attended,\n",
    "# X7: Games Attended, X8: SEC Games Watched, X9: Out-of-conference Games Watched, \n",
    "# X10: NFL Games Watched, X11: News Checking Frequency, X12: Self Reported Expertise\n",
    "# , "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1769,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = np.zeros((points1t.shape[0]+points2t.shape[0]+points3t.shape[0],12))\n",
    "#X = np.zeros((points1t.shape[0]+points2t.shape[0],12))#+points3.shape[0],3))\n",
    "for i in range (points1t.shape[0]):\n",
    "    X[i,0] = 1\n",
    "    X[i,2] = points1t.iloc[i,4]\n",
    "    X[i,3] = points1t.iloc[i,2]\n",
    "    X[i,4] = points1t.iloc[i,1]\n",
    "    X[i,5] = points1t.iloc[i,-1]\n",
    "    X[i,6] = points1t.iloc[i,5]\n",
    "    X[i,7] = points1t.iloc[i,6]\n",
    "    X[i,8] = points1t.iloc[i,7]\n",
    "    X[i,9] = points1t.iloc[i,8]\n",
    "    X[i,10] = points1t.iloc[i,9]\n",
    "    X[i,11] = points1t.iloc[i,10]\n",
    "    #X[i,12] = 1\n",
    "\n",
    "for i in range (points2t.shape[0]):\n",
    "    #X[i+points1t.shape[0],0] = 1\n",
    "    X[i+points1t.shape[0],1] = 1\n",
    "    X[i+points1t.shape[0],2] = points2t.iloc[i,4]\n",
    "    X[i+points1t.shape[0],3] = points2t.iloc[i,2]\n",
    "    X[i+points1t.shape[0],4] = points2t.iloc[i,1]\n",
    "    X[i+points1t.shape[0],5] = points2t.iloc[i,-1]\n",
    "    X[i+points1t.shape[0],6] = points2t.iloc[i,5]\n",
    "    X[i+points1t.shape[0],7] = points2t.iloc[i,6]\n",
    "    X[i+points1t.shape[0],8] = points2t.iloc[i,7]\n",
    "    X[i+points1t.shape[0],9] = points2t.iloc[i,8]\n",
    "    X[i+points1t.shape[0],10] = points2t.iloc[i,9]\n",
    "    X[i+points1t.shape[0],11] = points2t.iloc[i,10]\n",
    "    #X[i+points1t.shape[0],12] = 1\n",
    "    \n",
    "for i in range (points3t.shape[0]):\n",
    "    #print(i)\n",
    "    X[i+points1t.shape[0]+points2t.shape[0],2] = points3t.iloc[i,4]\n",
    "    X[i+points1t.shape[0]+points2t.shape[0],3] = points3t.iloc[i,2]\n",
    "    X[i+points1t.shape[0]+points2t.shape[0],4] = points3t.iloc[i,1]\n",
    "    X[i+points1t.shape[0]+points2t.shape[0],5] = points3t.iloc[i,-1]\n",
    "    X[i+points1t.shape[0]+points2t.shape[0],6] = points3t.iloc[i,5]\n",
    "    X[i+points1t.shape[0]+points2t.shape[0],7] = points3t.iloc[i,6]\n",
    "    X[i+points1t.shape[0]+points2t.shape[0],8] = points3t.iloc[i,7]\n",
    "    X[i+points1t.shape[0]+points2t.shape[0],9] = points3t.iloc[i,8]\n",
    "    X[i+points1t.shape[0]+points2t.shape[0],10] = points3t.iloc[i,9]\n",
    "    X[i+points1t.shape[0]+points2t.shape[0],11] = points3t.iloc[i,10]\n",
    "    \n",
    "#X = np.concatenate((X,x))\n",
    "X = np.append(X,np.ones([X.shape[0],1]),1)\n",
    "#print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1778,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 12)"
      ]
     },
     "execution_count": 1778,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points1t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1779,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y = np.asarray(pd.concat([points1t.iloc[:,0],points2t.iloc[:,0],points3t.iloc[:,0]], axis=0))\n",
    "#y = np.asarray(pd.concat([points1t.iloc[:,0],points2t.iloc[:,0]], axis=0))#,points3.iloc[:,0]], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1780,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.141\n",
      "Model:                            OLS   Adj. R-squared:                  0.045\n",
      "Method:                 Least Squares   F-statistic:                     1.462\n",
      "Date:                Sun, 01 Mar 2020   Prob (F-statistic):              0.150\n",
      "Time:                        20:44:49   Log-Likelihood:                -842.26\n",
      "No. Observations:                 120   AIC:                             1711.\n",
      "Df Residuals:                     107   BIC:                             1747.\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1           139.9672    107.561      1.301      0.196     -73.260     353.195\n",
      "x2           119.8918    113.911      1.053      0.295    -105.923     345.707\n",
      "x3            -3.2307     31.584     -0.102      0.919     -65.841      59.380\n",
      "x4            -0.2473      0.106     -2.322      0.022      -0.458      -0.036\n",
      "x5             1.0643      1.763      0.604      0.547      -2.431       4.559\n",
      "x6            -1.2688     20.113     -0.063      0.950     -41.140      38.603\n",
      "x7            11.9006      6.824      1.744      0.084      -1.627      25.428\n",
      "x8           -18.7505     16.028     -1.170      0.245     -50.523      13.022\n",
      "x9            -8.4506      8.317     -1.016      0.312     -24.938       8.037\n",
      "x10           13.6237     13.272      1.027      0.307     -12.686      39.933\n",
      "x11           16.8269     27.750      0.606      0.546     -38.185      71.839\n",
      "x12           17.1853     15.723      1.093      0.277     -13.983      48.354\n",
      "const        192.7598    135.288      1.425      0.157     -75.433     460.953\n",
      "==============================================================================\n",
      "Omnibus:                       17.316   Durbin-Watson:                   1.925\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               20.038\n",
      "Skew:                          -0.890   Prob(JB):                     4.46e-05\n",
      "Kurtosis:                       3.915   Cond. No.                     1.79e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.79e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(y, X)\n",
    "fit = model.fit()\n",
    "print(fit.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# X1: D120, X2: D60, -X3: Qs, X4: Points, X5: Female, X6: log(Completion Time), Games Attended,\n",
    "# X7: Games Attended, X8: SEC Games Watched, X9: Out-of-conference Games Watched, \n",
    "# X10: NFL Games Watched, X11: News Checking Frequency, X12: Self Reported Expertise\n",
    "# , "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1744,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X2 = np.concatenate([pd.DataFrame(X[:,:2]),pd.DataFrame(X[:,3:5]),pd.DataFrame(X[:,5:7]),\n",
    "                     pd.DataFrame(X[:,7:11]),pd.DataFrame(X[:,:0]),pd.DataFrame(X[:,-1:])],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1745,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = sm.OLS(y, X2)\n",
    "fit = model.fit()\n",
    "#print(fit.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Home team only predictions (i.e. assumed experts):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1717,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.255\n",
      "Model:                            OLS   Adj. R-squared:                  0.171\n",
      "Method:                 Least Squares   F-statistic:                     3.052\n",
      "Date:                Sun, 01 Mar 2020   Prob (F-statistic):           0.000998\n",
      "Time:                        17:51:19   Log-Likelihood:                -683.93\n",
      "No. Observations:                 120   AIC:                             1394.\n",
      "Df Residuals:                     107   BIC:                             1430.\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1            65.8594     28.748      2.291      0.024       8.869     122.850\n",
      "x2            43.7733     30.446      1.438      0.153     -16.582     104.128\n",
      "x3            -5.0272      8.442     -0.596      0.553     -21.762      11.707\n",
      "x4            -0.0514      0.028     -1.804      0.074      -0.108       0.005\n",
      "x5             0.5300      0.471      1.125      0.263      -0.404       1.464\n",
      "x6            -9.2479      5.376     -1.720      0.088     -19.905       1.409\n",
      "x7             6.4745      1.824      3.550      0.001       2.859      10.090\n",
      "x8            -4.1601      4.284     -0.971      0.334     -12.652       4.332\n",
      "x9            -4.2876      2.223     -1.929      0.056      -8.694       0.119\n",
      "x10            7.7712      3.547      2.191      0.031       0.739      14.803\n",
      "x11            9.4190      7.417      1.270      0.207      -5.284      24.122\n",
      "x12           -8.3852      4.202     -1.995      0.049     -16.716      -0.055\n",
      "const         29.0001     36.159      0.802      0.424     -42.681     100.681\n",
      "==============================================================================\n",
      "Omnibus:                       19.481   Durbin-Watson:                   1.709\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               23.924\n",
      "Skew:                          -0.930   Prob(JB):                     6.38e-06\n",
      "Kurtosis:                       4.152   Cond. No.                     1.79e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.79e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(y, X)\n",
    "fit = model.fit()\n",
    "print(fit.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1746,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.172\n",
      "Model:                            OLS   Adj. R-squared:                  0.079\n",
      "Method:                 Least Squares   F-statistic:                     1.847\n",
      "Date:                Sun, 01 Mar 2020   Prob (F-statistic):             0.0495\n",
      "Time:                        17:53:45   Log-Likelihood:                -859.46\n",
      "No. Observations:                 120   AIC:                             1745.\n",
      "Df Residuals:                     107   BIC:                             1781.\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1           205.8265    124.138      1.658      0.100     -40.262     451.915\n",
      "x2           163.6650    131.466      1.245      0.216     -96.951     424.281\n",
      "x3            -8.2579     36.451     -0.227      0.821     -80.518      64.002\n",
      "x4            -0.2986      0.123     -2.430      0.017      -0.542      -0.055\n",
      "x5             1.5943      2.035      0.784      0.435      -2.439       5.628\n",
      "x6           -10.5167     23.213     -0.453      0.651     -56.533      35.500\n",
      "x7            18.3750      7.876      2.333      0.022       2.763      33.987\n",
      "x8           -22.9106     18.498     -1.239      0.218     -59.580      13.759\n",
      "x9           -12.7382      9.599     -1.327      0.187     -31.767       6.290\n",
      "x10           21.3949     15.317      1.397      0.165      -8.969      51.759\n",
      "x11           26.2459     32.027      0.819      0.414     -37.244      89.736\n",
      "x12            8.8001     18.146      0.485      0.629     -27.172      44.772\n",
      "const        221.7599    156.138      1.420      0.158     -87.765     531.284\n",
      "==============================================================================\n",
      "Omnibus:                       11.686   Durbin-Watson:                   1.857\n",
      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               12.097\n",
      "Skew:                          -0.710   Prob(JB):                      0.00236\n",
      "Kurtosis:                       3.636   Cond. No.                     1.79e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.79e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(y, X)\n",
    "fit = model.fit()\n",
    "print(fit.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Non-home team predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1781,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.141\n",
      "Model:                            OLS   Adj. R-squared:                  0.045\n",
      "Method:                 Least Squares   F-statistic:                     1.462\n",
      "Date:                Sun, 01 Mar 2020   Prob (F-statistic):              0.150\n",
      "Time:                        20:45:04   Log-Likelihood:                -842.26\n",
      "No. Observations:                 120   AIC:                             1711.\n",
      "Df Residuals:                     107   BIC:                             1747.\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "x1           139.9672    107.561      1.301      0.196     -73.260     353.195\n",
      "x2           119.8918    113.911      1.053      0.295    -105.923     345.707\n",
      "x3            -3.2307     31.584     -0.102      0.919     -65.841      59.380\n",
      "x4            -0.2473      0.106     -2.322      0.022      -0.458      -0.036\n",
      "x5             1.0643      1.763      0.604      0.547      -2.431       4.559\n",
      "x6            -1.2688     20.113     -0.063      0.950     -41.140      38.603\n",
      "x7            11.9006      6.824      1.744      0.084      -1.627      25.428\n",
      "x8           -18.7505     16.028     -1.170      0.245     -50.523      13.022\n",
      "x9            -8.4506      8.317     -1.016      0.312     -24.938       8.037\n",
      "x10           13.6237     13.272      1.027      0.307     -12.686      39.933\n",
      "x11           16.8269     27.750      0.606      0.546     -38.185      71.839\n",
      "x12           17.1853     15.723      1.093      0.277     -13.983      48.354\n",
      "const        192.7598    135.288      1.425      0.157     -75.433     460.953\n",
      "==============================================================================\n",
      "Omnibus:                       17.316   Durbin-Watson:                   1.925\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               20.038\n",
      "Skew:                          -0.890   Prob(JB):                     4.46e-05\n",
      "Kurtosis:                       3.915   Cond. No.                     1.79e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.79e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(y, X)\n",
    "fit = model.fit()\n",
    "print(fit.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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