model_test_2022.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "75890a77-c9ef-4722-bd7a-62ecd777f4cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates\n",
    "\n",
    "from sklearn.model_selection  import train_test_split\n",
    "from sklearn.metrics import f1_score, accuracy_score, confusion_matrix\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold \n",
    "from sklearn.svm import LinearSVC\n",
    "from lightgbm import LGBMClassifier\n",
    "import lightgbm\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "19493c9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-dark-palette')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dc27ca85-bd2c-4bec-a6cc-794d4527348f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(n_estimators=50)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('all_data.csv')\n",
    "y = df['beat_spread']\n",
    "X = df.copy()\n",
    "X.pop('beat_spread')\n",
    "X.pop('date')\n",
    "X.pop('Unnamed: 0')\n",
    "\n",
    "model = LGBMClassifier(n_estimators = 50)\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "aee73bc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightgbm.plot_importance(model)\n",
    "plt.savefig('figures/feature_importance.png', bbox_inches='tight', format = 'png', dpi=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e9cd1715-5f53-490c-8a70-dcff8291932c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>win_percentage_5</th>\n",
       "      <th>win_percentage</th>\n",
       "      <th>avg_margin_5</th>\n",
       "      <th>avg_margin</th>\n",
       "      <th>home_or_away</th>\n",
       "      <th>spread</th>\n",
       "      <th>opp_win_percentage_5</th>\n",
       "      <th>opp_win_percentage</th>\n",
       "      <th>opp_avg_margin_5</th>\n",
       "      <th>opp_avg_margin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     win_percentage_5  win_percentage  avg_margin_5  avg_margin  home_or_away  \\\n",
       "706               1.0             1.0           7.0         7.0             0   \n",
       "108               1.0             1.0          23.0        23.0             1   \n",
       "630               0.0             0.0           7.0         7.0             1   \n",
       "997               0.0             0.0          23.0        23.0             0   \n",
       "258               1.0             1.0          11.0        11.0             1   \n",
       "\n",
       "     spread  opp_win_percentage_5  opp_win_percentage  opp_avg_margin_5  \\\n",
       "706     3.5                   0.0                 0.0               7.0   \n",
       "108    -1.5                   0.0                 0.0              23.0   \n",
       "630    -3.5                   1.0                 1.0               7.0   \n",
       "997     1.5                   1.0                 1.0              23.0   \n",
       "258    -7.0                   0.0                 0.0              11.0   \n",
       "\n",
       "     opp_avg_margin  \n",
       "706             7.0  \n",
       "108            23.0  \n",
       "630             7.0  \n",
       "997            23.0  \n",
       "258            11.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2022 = pd.read_csv('2022_data.csv')\n",
    "dates_str = list(df_2022['date'])\n",
    "datetime_str = [datetime.strptime(date, '%Y-%m-%d %H:%M:%S') for date in dates_str]\n",
    "df_2022.pop('date')\n",
    "df_2022['date'] = datetime_str\n",
    "\n",
    "df_2022 = df_2022.sort_values(by=['date'])\n",
    "\n",
    "df_2022.pop('Unnamed: 0')\n",
    "\n",
    "y_2022 = df_2022['beat_spread']\n",
    "X_2022 = df_2022.copy()\n",
    "date_order = X_2022['date']\n",
    "X_2022.pop('date')\n",
    "X_2022.pop('beat_spread')\n",
    "X_2022.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a5de7cad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex([          '2021-10-19 00:00:00',\n",
      "               '2021-10-19 03:58:15.840867992',\n",
      "               '2021-10-19 07:56:31.681735985',\n",
      "               '2021-10-19 11:54:47.522603978',\n",
      "               '2021-10-19 15:53:03.363471971',\n",
      "               '2021-10-19 19:51:19.204339963',\n",
      "               '2021-10-19 23:49:35.045207956',\n",
      "               '2021-10-20 03:47:50.886075949',\n",
      "               '2021-10-20 07:46:06.726943942',\n",
      "               '2021-10-20 11:44:22.567811934',\n",
      "               ...\n",
      "               '2022-04-18 12:15:37.432188066',\n",
      "               '2022-04-18 16:13:53.273056058',\n",
      "               '2022-04-18 20:12:09.113924052',\n",
      "               '2022-04-19 00:10:24.954792044',\n",
      "               '2022-04-19 04:08:40.795660038',\n",
      "               '2022-04-19 08:06:56.636528030',\n",
      "               '2022-04-19 12:05:12.477396022',\n",
      "               '2022-04-19 16:03:28.318264016',\n",
      "               '2022-04-19 20:01:44.159132008',\n",
      "                         '2022-04-20 00:00:00'],\n",
      "              dtype='datetime64[ns]', length=1107, freq=None)\n",
      "DatetimeIndex([          '2021-10-19 00:00:00',\n",
      "               '2021-10-19 03:58:02.926829268',\n",
      "               '2021-10-19 07:56:05.853658536',\n",
      "               '2021-10-19 11:54:08.780487804',\n",
      "               '2021-10-19 15:52:11.707317073',\n",
      "               '2021-10-19 19:50:14.634146341',\n",
      "               '2021-10-19 23:48:17.560975609',\n",
      "               '2021-10-20 03:46:20.487804878',\n",
      "               '2021-10-20 07:44:23.414634146',\n",
      "               '2021-10-20 11:42:26.341463414',\n",
      "               ...\n",
      "               '2022-04-18 12:17:33.658536586',\n",
      "               '2022-04-18 16:15:36.585365854',\n",
      "               '2022-04-18 20:13:39.512195122',\n",
      "               '2022-04-19 00:11:42.439024390',\n",
      "               '2022-04-19 04:09:45.365853658',\n",
      "               '2022-04-19 08:07:48.292682928',\n",
      "               '2022-04-19 12:05:51.219512196',\n",
      "               '2022-04-19 16:03:54.146341464',\n",
      "               '2022-04-19 20:01:57.073170732',\n",
      "                         '2022-04-20 00:00:00'],\n",
      "              dtype='datetime64[ns]', length=1108, freq=None)\n"
     ]
    }
   ],
   "source": [
    "print(date_order)\n",
    "date_order = pd.date_range(start='10/19/2021', end='4/20/2022', periods = 1108)\n",
    "print(date_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fc32e082-d880-4db8-bf7f-7c6dc9bb565e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def season_simulator(bet_size, threshold, plot = True):\n",
    "    db_size = X_2022.shape[0]\n",
    "\n",
    "    if threshold<0.5:\n",
    "        threshold = 0.5\n",
    "    money = 100\n",
    "    num_failed_bets = 100/bet_size\n",
    "    money_series = []\n",
    "    num_bets_made = 0\n",
    "    correct_bets = 0\n",
    "    total_games = 0\n",
    "    for i in range(db_size):\n",
    "        if money>bet_size:\n",
    "            sample = np.array(X_2022.iloc[i])\n",
    "            prediction = model.predict(sample.reshape(1,-1))\n",
    "            model_certainty = model.predict_proba(sample.reshape(1,-1))\n",
    "            actual = y_2022.iloc[i]\n",
    "\n",
    "            if abs(model_certainty[0][0]-0.5)>abs(threshold-0.5):\n",
    "                num_bets_made+=1\n",
    "                if prediction == actual:\n",
    "                    money+=bet_size/1.1\n",
    "                    correct_bets+=1\n",
    "                else:\n",
    "                    money-=bet_size\n",
    "        total_games+=1\n",
    "\n",
    "        money_series.append(money)\n",
    "\n",
    "    if num_bets_made == 0:\n",
    "        accuracy = 0\n",
    "    else:\n",
    "        accuracy = correct_bets/num_bets_made\n",
    "    \n",
    "    description = 'accuracy of bets: ' + '{:.2f}'.format(100*accuracy)+'%\\npercent of games bet on: '+'{:.2f}'.format(100*num_bets_made/total_games)+'%'\n",
    "    money_made = '\\nmoney made: $'+'{:.2f}'.format(money)\n",
    "    legend_text = description+money_made\n",
    "    if plot:\n",
    "        dates_plt = matplotlib.dates.date2num(date_order)\n",
    "\n",
    "        fig, ax = plt.subplots()\n",
    "        plt.plot(date_order, money_series, label = legend_text)\n",
    "        plt.plot(date_order, [100]*len(date_order), label = 'starting money')\n",
    "        myFmt = matplotlib.dates.DateFormatter(\"%b\")\n",
    "        ax.xaxis.set_major_formatter(myFmt)\n",
    "        ax.set_title('Full Season Simulation\\nBet $90 on games with '+'{:.0f}'.format(100*threshold)+'% confidence')\n",
    "        #ax.suptitle('Bet on games with '+'{:.2f}'.format(100*threshold)+' confidence')\n",
    "        \n",
    "        ax.legend()\n",
    "        \n",
    "        fig.autofmt_xdate()\n",
    "        plt.savefig('figures/season_simulated.png', format=\"png\", dpi=1200)\n",
    "        plt.show()\n",
    "    \n",
    "    return money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b693e5e7-5363-4c56-83be-218e8f81d53e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bet Size: 10.0, Certainty Threshold 0.5, Money Made: 4197.272727272676\n",
      "Bet Size: 10.0, Certainty Threshold 0.55, Money Made: 4229.9999999999545\n",
      "Bet Size: 10.0, Certainty Threshold 0.6, Money Made: 3909.999999999961\n",
      "Bet Size: 10.0, Certainty Threshold 0.65, Money Made: 3639.999999999968\n",
      "Bet Size: 10.0, Certainty Threshold 0.7, Money Made: 3142.7272727272475\n",
      "Bet Size: 10.0, Certainty Threshold 0.75, Money Made: 2619.9999999999823\n",
      "Bet Size: 10.0, Certainty Threshold 0.8, Money Made: 1963.6363636363549\n",
      "Bet Size: 10.0, Certainty Threshold 0.85, Money Made: 1249.0909090909083\n",
      "Bet Size: 10.0, Certainty Threshold 0.8999999999999999, Money Made: 679.9999999999999\n",
      "Bet Size: 10.0, Certainty Threshold 0.95, Money Made: 472.7272727272722\n",
      "Bet Size: 20.0, Certainty Threshold 0.5, Money Made: 8294.545454545354\n",
      "Bet Size: 20.0, Certainty Threshold 0.55, Money Made: 8359.999999999909\n",
      "Bet Size: 20.0, Certainty Threshold 0.6, Money Made: 7719.999999999923\n",
      "Bet Size: 20.0, Certainty Threshold 0.65, Money Made: 7179.999999999937\n",
      "Bet Size: 20.0, Certainty Threshold 0.7, Money Made: 6185.454545454496\n",
      "Bet Size: 20.0, Certainty Threshold 0.75, Money Made: 5139.999999999965\n",
      "Bet Size: 20.0, Certainty Threshold 0.8, Money Made: 3827.272727272711\n",
      "Bet Size: 20.0, Certainty Threshold 0.85, Money Made: 2398.1818181818176\n",
      "Bet Size: 20.0, Certainty Threshold 0.8999999999999999, Money Made: 1259.9999999999993\n",
      "Bet Size: 20.0, Certainty Threshold 0.95, Money Made: 845.4545454545446\n",
      "Bet Size: 30.0, Certainty Threshold 0.5, Money Made: 12391.818181818326\n",
      "Bet Size: 30.0, Certainty Threshold 0.55, Money Made: 12490.000000000124\n",
      "Bet Size: 30.0, Certainty Threshold 0.6, Money Made: 11530.000000000078\n",
      "Bet Size: 30.0, Certainty Threshold 0.65, Money Made: 10720.00000000005\n",
      "Bet Size: 30.0, Certainty Threshold 0.7, Money Made: 9228.18181818182\n",
      "Bet Size: 30.0, Certainty Threshold 0.75, Money Made: 7659.999999999976\n",
      "Bet Size: 30.0, Certainty Threshold 0.8, Money Made: 5690.90909090909\n",
      "Bet Size: 30.0, Certainty Threshold 0.85, Money Made: 3547.2727272727384\n",
      "Bet Size: 30.0, Certainty Threshold 0.8999999999999999, Money Made: 1839.9999999999989\n",
      "Bet Size: 30.0, Certainty Threshold 0.95, Money Made: 1218.1818181818176\n",
      "Bet Size: 40.0, Certainty Threshold 0.5, Money Made: 16489.09090909071\n",
      "Bet Size: 40.0, Certainty Threshold 0.55, Money Made: 16619.999999999818\n",
      "Bet Size: 40.0, Certainty Threshold 0.6, Money Made: 15339.999999999845\n",
      "Bet Size: 40.0, Certainty Threshold 0.65, Money Made: 14259.999999999876\n",
      "Bet Size: 40.0, Certainty Threshold 0.7, Money Made: 12270.909090908992\n",
      "Bet Size: 40.0, Certainty Threshold 0.75, Money Made: 10179.999999999933\n",
      "Bet Size: 40.0, Certainty Threshold 0.8, Money Made: 7554.545454545423\n",
      "Bet Size: 40.0, Certainty Threshold 0.85, Money Made: 4696.363636363636\n",
      "Bet Size: 40.0, Certainty Threshold 0.8999999999999999, Money Made: 2419.9999999999986\n",
      "Bet Size: 40.0, Certainty Threshold 0.95, Money Made: 1590.9090909090896\n",
      "Bet Size: 50.0, Certainty Threshold 0.5, Money Made: 20586.363636363487\n",
      "Bet Size: 50.0, Certainty Threshold 0.55, Money Made: 20749.99999999987\n",
      "Bet Size: 50.0, Certainty Threshold 0.6, Money Made: 19149.999999999975\n",
      "Bet Size: 50.0, Certainty Threshold 0.65, Money Made: 17800.0\n",
      "Bet Size: 50.0, Certainty Threshold 0.7, Money Made: 15313.636363636419\n",
      "Bet Size: 50.0, Certainty Threshold 0.75, Money Made: 12700.000000000015\n",
      "Bet Size: 50.0, Certainty Threshold 0.8, Money Made: 9418.18181818179\n",
      "Bet Size: 50.0, Certainty Threshold 0.85, Money Made: 5845.454545454531\n",
      "Bet Size: 50.0, Certainty Threshold 0.8999999999999999, Money Made: 3000.000000000002\n",
      "Bet Size: 50.0, Certainty Threshold 0.95, Money Made: 1963.6363636363646\n",
      "Bet Size: 60.0, Certainty Threshold 0.5, Money Made: 24683.636363636644\n",
      "Bet Size: 60.0, Certainty Threshold 0.55, Money Made: 24880.000000000247\n",
      "Bet Size: 60.0, Certainty Threshold 0.6, Money Made: 22960.000000000153\n",
      "Bet Size: 60.0, Certainty Threshold 0.65, Money Made: 21340.000000000095\n",
      "Bet Size: 60.0, Certainty Threshold 0.7, Money Made: 18356.363636363636\n",
      "Bet Size: 60.0, Certainty Threshold 0.75, Money Made: 15219.999999999953\n",
      "Bet Size: 60.0, Certainty Threshold 0.8, Money Made: 11281.818181818182\n",
      "Bet Size: 60.0, Certainty Threshold 0.85, Money Made: 6994.545454545475\n",
      "Bet Size: 60.0, Certainty Threshold 0.8999999999999999, Money Made: 3579.9999999999977\n",
      "Bet Size: 60.0, Certainty Threshold 0.95, Money Made: 2336.363636363635\n",
      "Bet Size: 70.0, Certainty Threshold 0.5, Money Made: 28780.909090909314\n",
      "Bet Size: 70.0, Certainty Threshold 0.55, Money Made: 29010.000000000204\n",
      "Bet Size: 70.0, Certainty Threshold 0.6, Money Made: 26770.000000000175\n",
      "Bet Size: 70.0, Certainty Threshold 0.65, Money Made: 24880.00000000015\n",
      "Bet Size: 70.0, Certainty Threshold 0.7, Money Made: 21399.090909091028\n",
      "Bet Size: 70.0, Certainty Threshold 0.75, Money Made: 17740.000000000084\n",
      "Bet Size: 70.0, Certainty Threshold 0.8, Money Made: 13145.454545454599\n",
      "Bet Size: 70.0, Certainty Threshold 0.85, Money Made: 8143.636363636385\n",
      "Bet Size: 70.0, Certainty Threshold 0.8999999999999999, Money Made: 4159.999999999998\n",
      "Bet Size: 70.0, Certainty Threshold 0.95, Money Made: 2709.0909090909095\n",
      "Bet Size: 80.0, Certainty Threshold 0.5, Money Made: 32878.18181818143\n",
      "Bet Size: 80.0, Certainty Threshold 0.55, Money Made: 33139.999999999636\n",
      "Bet Size: 80.0, Certainty Threshold 0.6, Money Made: 30579.999999999694\n",
      "Bet Size: 80.0, Certainty Threshold 0.65, Money Made: 28419.999999999753\n",
      "Bet Size: 80.0, Certainty Threshold 0.7, Money Made: 24441.818181817987\n",
      "Bet Size: 80.0, Certainty Threshold 0.75, Money Made: 20259.999999999865\n",
      "Bet Size: 80.0, Certainty Threshold 0.8, Money Made: 15009.090909090846\n",
      "Bet Size: 80.0, Certainty Threshold 0.85, Money Made: 9292.727272727272\n",
      "Bet Size: 80.0, Certainty Threshold 0.8999999999999999, Money Made: 4739.999999999996\n",
      "Bet Size: 80.0, Certainty Threshold 0.95, Money Made: 3081.8181818181793\n",
      "Bet Size: 90.0, Certainty Threshold 0.5, Money Made: 36975.454545454224\n",
      "Bet Size: 90.0, Certainty Threshold 0.55, Money Made: 37269.99999999972\n",
      "Bet Size: 90.0, Certainty Threshold 0.6, Money Made: 34389.99999999963\n",
      "Bet Size: 90.0, Certainty Threshold 0.65, Money Made: 31959.99999999961\n",
      "Bet Size: 90.0, Certainty Threshold 0.7, Money Made: 27484.545454545198\n",
      "Bet Size: 90.0, Certainty Threshold 0.75, Money Made: 22779.999999999876\n",
      "Bet Size: 90.0, Certainty Threshold 0.8, Money Made: 16872.727272727298\n",
      "Bet Size: 90.0, Certainty Threshold 0.85, Money Made: 10441.818181818198\n",
      "Bet Size: 90.0, Certainty Threshold 0.8999999999999999, Money Made: 5320.0000000000055\n",
      "Bet Size: 90.0, Certainty Threshold 0.95, Money Made: 3454.5454545454563\n",
      "Bet Size: 100.0, Certainty Threshold 0.5, Money Made: 100\n",
      "Bet Size: 100.0, Certainty Threshold 0.55, Money Made: 100\n",
      "Bet Size: 100.0, Certainty Threshold 0.6, Money Made: 100\n",
      "Bet Size: 100.0, Certainty Threshold 0.65, Money Made: 100\n",
      "Bet Size: 100.0, Certainty Threshold 0.7, Money Made: 100\n",
      "Bet Size: 100.0, Certainty Threshold 0.75, Money Made: 100\n",
      "Bet Size: 100.0, Certainty Threshold 0.8, Money Made: 100\n",
      "Bet Size: 100.0, Certainty Threshold 0.85, Money Made: 100\n",
      "Bet Size: 100.0, Certainty Threshold 0.8999999999999999, Money Made: 100\n",
      "Bet Size: 100.0, Certainty Threshold 0.95, Money Made: 100\n"
     ]
    }
   ],
   "source": [
    "\n",
    "thresholds = np.linspace(0.5, 0.95, 10)\n",
    "money_made = {}\n",
    "\n",
    "betsizes = np.linspace(10,100,10)\n",
    "num_failed_bets = 100/betsizes\n",
    "\n",
    "all = []\n",
    "for num_failed_bet in num_failed_bets:\n",
    "    mini = []\n",
    "    for threshold in thresholds:\n",
    "        money = season_simulator(100/num_failed_bet,threshold, False)\n",
    "        print('Bet Size: {0}, Certainty Threshold {1}, Money Made: {2}'.format(100/num_failed_bet, threshold, money))\n",
    "        money_made[(num_failed_bet, threshold)] = money\n",
    "        mini.append(money)\n",
    "    all.append(mini)\n",
    "data = np.array(all)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(data, cmap = 'viridis')\n",
    "ax.set_xticks(np.arange(len(thresholds)), labels=[round(threshold, 2) for threshold in thresholds])\n",
    "ax.set_yticks(np.arange(len(betsizes)), labels=betsizes)\n",
    "ax.set_xlabel('Certainty Threshold Required to Bet')\n",
    "ax.set_ylabel('Bet Size')\n",
    "ax.set_title('End State\\nStart with $100')\n",
    "#ax = divider.append_axes('right', size='5%', pad=0.05)\n",
    "fig.colorbar(im)\n",
    "plt.savefig('figures/money_matrix.png', format = 'png', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7277a0c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4197.27272727  4230.          3910.          3640.\n",
      "   3142.72727273  2620.          1963.63636364  1249.09090909\n",
      "    680.           472.72727273]\n",
      " [ 8294.54545455  8360.          7720.          7180.\n",
      "   6185.45454545  5140.          3827.27272727  2398.18181818\n",
      "   1260.           845.45454545]\n",
      " [12391.81818182 12490.         11530.         10720.\n",
      "   9228.18181818  7660.          5690.90909091  3547.27272727\n",
      "   1840.          1218.18181818]\n",
      " [16489.09090909 16620.         15340.         14260.\n",
      "  12270.90909091 10180.          7554.54545455  4696.36363636\n",
      "   2420.          1590.90909091]\n",
      " [20586.36363636 20750.         19150.         17800.\n",
      "  15313.63636364 12700.          9418.18181818  5845.45454545\n",
      "   3000.          1963.63636364]\n",
      " [24683.63636364 24880.         22960.         21340.\n",
      "  18356.36363636 15220.         11281.81818182  6994.54545455\n",
      "   3580.          2336.36363636]\n",
      " [28780.90909091 29010.         26770.         24880.\n",
      "  21399.09090909 17740.         13145.45454545  8143.63636364\n",
      "   4160.          2709.09090909]\n",
      " [32878.18181818 33140.         30580.         28420.\n",
      "  24441.81818182 20260.         15009.09090909  9292.72727273\n",
      "   4740.          3081.81818182]\n",
      " [36975.45454545 37270.         34390.         31960.\n",
      "  27484.54545455 22780.         16872.72727273 10441.81818182\n",
      "   5320.          3454.54545455]\n",
      " [  100.           100.           100.           100.\n",
      "    100.           100.           100.           100.\n",
      "    100.           100.        ]]\n",
      "[0.5  0.55 0.6  0.65 0.7  0.75 0.8  0.85 0.9  0.95]\n",
      "[ 10.  20.  30.  40.  50.  60.  70.  80.  90. 100.]\n"
     ]
    }
   ],
   "source": [
    "print(data)\n",
    "print(thresholds)\n",
    "print(betsizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d4b53e7f-f4d2-40e6-9b56-919c980ba493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max amount of money to make is  37269.99999999972\n",
      "model confidence:  0.55\n",
      "max number of failed bets:  1.1111111111111112\n"
     ]
    }
   ],
   "source": [
    "most_money = max(money_made, key = money_made.get)\n",
    "maxed_money = money_made[most_money]\n",
    "print('max amount of money to make is ', maxed_money)\n",
    "print('model confidence: ', most_money[1])\n",
    "print('max number of failed bets: ', most_money[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2c2e25a2-02f3-4d5a-942a-e4444cedc208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "37269.99999999972"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season_simulator(90, 0.55)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcfb950-09f2-469d-9ba8-9c971aa364ed",
   "metadata": {},
   "source": [
    "# Final Results\n",
    "\n",
    "We have derived at optimal betting strategy for the 2022 Season.\n",
    "\n",
    "Our model is accurate enough to bet on 92.87% of all games, betting for the side it has 53% confidence for. The model has a 73.47% accuracy. If the model starts with 100 bucks and bets 50 bucks each time, it can reach profits of $20,813.64 without ever going negative. It's essentially a money printing algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37f41380-fb05-4269-b333-2bf53e8ebce0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}