xgboost_wpmodel_extended.ipynb

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    "# Binary Logistic Regressions with XGBoost\n",
    "\n",
    "I have always been fascinated with Data Science.  The first time I read a book about the power of data was when I read \"Flash Boys\" by Michael Lewis.  I was finally inspired to teach myself how to create statistical models when I wanted to better understand a betting advantage that I noticed during the 2017 Superbowl.   The Pats have a pattern of a 4th Quarter \"rally\" when they were down in the first half of the game.  I bet on the moneyline at half time, and won by a lot.  But I wanted to know if the strategy was reliable, so I committed to teaching myself how to validate that strategy.\n",
    "\n",
    "Long story short, there was data to back up the assertion.  Tom Brady is an effective game manager, and has pulled off some astounding feats in the 4th quarter against seemingly overwhelming odds.  But as a reliable betting strategy, I found that there's just not enough data to effectively predict the behavior.  I researched the topic, and found that betting is not about modeling the game.  In Nate Silver's book \"The Signal and the Noise\", he said that Sports books do not have perfect models of the games they cover.  Sports books do not have to model the game perfectly, but they do have to model their customers perfectly.  Sports books make their money from the House edge, and that current betting strategies attempt to beat that edge by creating model  high frequency events as their target metric.  Some examples are:\n",
    "\n",
    "* Expected Points Added\n",
    "* Expected Yards after Completion\n",
    "* Completion Percentage\n",
    "* In-game Win Probability\n",
    "\n",
    "I began toying with some example models using the `nflfastR` library.  I prefer Python by far, so I began fiddling around with what few examples I found in Python, but eventually I had to dive deep into the R toolset to better understand predictive sports analytics.  This notebook is a culmination of iterative attempts at recreating some of the models I have come across in blog posts that were written in R.\n",
    "\n",
    "## Data Prep\n",
    "\n",
    "First, let's load the data.  The `Data().__init__()` method imports play-by-play data, and then performs some cleanup tasks:"
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     "text": [
      "1999 done.\n",
      "2000 done.\n",
      "2001 done.\n",
      "2002 done.\n",
      "2003 done.\n",
      "2004 done.\n",
      "2005 done.\n",
      "2006 done.\n",
      "2007 done.\n",
      "2008 done.\n",
      "2009 done.\n",
      "2010 done.\n",
      "2011 done.\n",
      "2012 done.\n",
      "2013 done.\n",
      "2014 done.\n",
      "2015 done.\n",
      "2016 done.\n",
      "2017 done.\n",
      "2018 done.\n",
      "2019 done.\n",
      "2020 done.\n",
      "2021 done.\n",
      "2022 done.\n",
      "Downcasting floats.\n"
     ]
    }
   ],
   "source": [
    "import nfl_data_py as nfl\n",
    "\n",
    "from nfl_wp_model.data import Data\n",
    "\n",
    "# pbp_data = Data(import_csv='tests/test_data/test_pbp_df.csv', clean=True)\n",
    "pbp_data = Data(nfl.import_pbp_data, 1999, 2023, clean=True)"
   ],
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    "## Preprocessing\n",
    "\n",
    "Next, the `Preprocess().__init__()` method performs the following tasks:\n",
    "\n",
    "* Sets column types for categorical data\n",
    "* Adds a target column with a value of `1` if the home team won the game, or `0` if not.\n",
    "* Adds a one hot column to determine if the home team is in possession of the ball.\n",
    "* Add a column to determine if the home team will be receive possession in the second half of the game\n",
    "* Adds spread time, score differential, and other columns used in feature analysis\n",
    "* Drops NAs and Filters column selection"
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    {
     "data": {
      "text/plain": "   label          game_id home_team away_team  season  receive_2h_ko  \\\n1      0  1999_01_ARI_PHI       PHI       ARI    1999              0   \n2      0  1999_01_ARI_PHI       PHI       ARI    1999              0   \n3      0  1999_01_ARI_PHI       PHI       ARI    1999              0   \n4      0  1999_01_ARI_PHI       PHI       ARI    1999              0   \n5      0  1999_01_ARI_PHI       PHI       ARI    1999              0   \n\n   spread_time  home  half_seconds_remaining  game_seconds_remaining  \\\n1         -3.0     1                  1800.0                  3600.0   \n2         -3.0     1                  1800.0                  3600.0   \n3         -3.0     1                  1800.0                  3600.0   \n4         -3.0     1                  1800.0                  3600.0   \n5         -3.0     1                  1800.0                  3600.0   \n\n   diff_time_ratio  score_differential  down  ydstogo  yardline_100  \\\n1              0.0                 0.0   1.0     10.0          77.0   \n2              0.0                 0.0   2.0     10.0          77.0   \n3              0.0                 0.0   3.0      9.0          76.0   \n4              0.0                 0.0   3.0     14.0          81.0   \n5              0.0                 0.0   4.0      4.0          71.0   \n\n   posteam_timeouts_remaining  defteam_timeouts_remaining  \n1                         3.0                         3.0  \n2                         3.0                         3.0  \n3                         3.0                         3.0  \n4                         3.0                         3.0  \n5                         3.0                         3.0  ",
      "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>label</th>\n      <th>game_id</th>\n      <th>home_team</th>\n      <th>away_team</th>\n      <th>season</th>\n      <th>receive_2h_ko</th>\n      <th>spread_time</th>\n      <th>home</th>\n      <th>half_seconds_remaining</th>\n      <th>game_seconds_remaining</th>\n      <th>diff_time_ratio</th>\n      <th>score_differential</th>\n      <th>down</th>\n      <th>ydstogo</th>\n      <th>yardline_100</th>\n      <th>posteam_timeouts_remaining</th>\n      <th>defteam_timeouts_remaining</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1999_01_ARI_PHI</td>\n      <td>PHI</td>\n      <td>ARI</td>\n      <td>1999</td>\n      <td>0</td>\n      <td>-3.0</td>\n      <td>1</td>\n      <td>1800.0</td>\n      <td>3600.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>77.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1999_01_ARI_PHI</td>\n      <td>PHI</td>\n      <td>ARI</td>\n      <td>1999</td>\n      <td>0</td>\n      <td>-3.0</td>\n      <td>1</td>\n      <td>1800.0</td>\n      <td>3600.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>10.0</td>\n      <td>77.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1999_01_ARI_PHI</td>\n      <td>PHI</td>\n      <td>ARI</td>\n      <td>1999</td>\n      <td>0</td>\n      <td>-3.0</td>\n      <td>1</td>\n      <td>1800.0</td>\n      <td>3600.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>9.0</td>\n      <td>76.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1999_01_ARI_PHI</td>\n      <td>PHI</td>\n      <td>ARI</td>\n      <td>1999</td>\n      <td>0</td>\n      <td>-3.0</td>\n      <td>1</td>\n      <td>1800.0</td>\n      <td>3600.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>14.0</td>\n      <td>81.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0</td>\n      <td>1999_01_ARI_PHI</td>\n      <td>PHI</td>\n      <td>ARI</td>\n      <td>1999</td>\n      <td>0</td>\n      <td>-3.0</td>\n      <td>1</td>\n      <td>1800.0</td>\n      <td>3600.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>71.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
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   ],
   "source": [
    "from nfl_wp_model.data import Preprocess\n",
    "\n",
    "pbp_df = Preprocess(pbp_data.data)\n",
    "pbp_df.data.head()"
   ],
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    "## Cross Validation\n",
    "\n",
    "The `Model.__init__()` method performs the following:\n",
    "\n",
    "* Generates training data\n",
    "* Performs Hyperparameter tuning using Bayesian parameter optimization using a scoring function that performs cross validation\n",
    "* Generates test data\n",
    "* Trains the model"
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      "|   iter    |  target   | colsam... |    eta    |   gamma   | max_depth | min_ch... | subsample |\n",
      "-------------------------------------------------------------------------------------------------\n",
      "| \u001B[0m1        \u001B[0m | \u001B[0m-0.4802  \u001B[0m | \u001B[0m0.9172   \u001B[0m | \u001B[0m0.2165   \u001B[0m | \u001B[0m10.97    \u001B[0m | \u001B[0m12.07    \u001B[0m | \u001B[0m1.727    \u001B[0m | \u001B[0m0.8418   \u001B[0m |\n",
      "| \u001B[95m2        \u001B[0m | \u001B[95m-0.4527  \u001B[0m | \u001B[95m0.2619   \u001B[0m | \u001B[95m0.2309   \u001B[0m | \u001B[95m6.859    \u001B[0m | \u001B[95m14.29    \u001B[0m | \u001B[95m11.03    \u001B[0m | \u001B[95m0.5745   \u001B[0m |\n",
      "| \u001B[95m3        \u001B[0m | \u001B[95m-0.4495  \u001B[0m | \u001B[95m0.3113   \u001B[0m | \u001B[95m0.2629   \u001B[0m | \u001B[95m10.37    \u001B[0m | \u001B[95m3.657    \u001B[0m | \u001B[95m12.56    \u001B[0m | \u001B[95m0.1692   \u001B[0m |\n",
      "| \u001B[0m4        \u001B[0m | \u001B[0m-0.4513  \u001B[0m | \u001B[0m0.5139   \u001B[0m | \u001B[0m0.1627   \u001B[0m | \u001B[0m17.99    \u001B[0m | \u001B[0m7.451    \u001B[0m | \u001B[0m32.3     \u001B[0m | \u001B[0m0.9205   \u001B[0m |\n",
      "| \u001B[95m5        \u001B[0m | \u001B[95m-0.4494  \u001B[0m | \u001B[95m0.1554   \u001B[0m | \u001B[95m0.02769  \u001B[0m | \u001B[95m1.497    \u001B[0m | \u001B[95m2.828    \u001B[0m | \u001B[95m31.03    \u001B[0m | \u001B[95m0.19     \u001B[0m |\n",
      "| \u001B[0m6        \u001B[0m | \u001B[0m-0.4513  \u001B[0m | \u001B[0m0.7041   \u001B[0m | \u001B[0m0.279    \u001B[0m | \u001B[0m12.53    \u001B[0m | \u001B[0m4.727    \u001B[0m | \u001B[0m37.45    \u001B[0m | \u001B[0m0.3478   \u001B[0m |\n",
      "| \u001B[0m7        \u001B[0m | \u001B[0m-0.4499  \u001B[0m | \u001B[0m0.6333   \u001B[0m | \u001B[0m0.1985   \u001B[0m | \u001B[0m0.6858   \u001B[0m | \u001B[0m3.044    \u001B[0m | \u001B[0m29.06    \u001B[0m | \u001B[0m0.5552   \u001B[0m |\n",
      "| \u001B[0m8        \u001B[0m | \u001B[0m-0.4532  \u001B[0m | \u001B[0m0.1      \u001B[0m | \u001B[0m0.01     \u001B[0m | \u001B[0m8.659    \u001B[0m | \u001B[0m10.18    \u001B[0m | \u001B[0m22.01    \u001B[0m | \u001B[0m0.1      \u001B[0m |\n",
      "| \u001B[0m9        \u001B[0m | \u001B[0m-0.453   \u001B[0m | \u001B[0m0.1      \u001B[0m | \u001B[0m0.01     \u001B[0m | \u001B[0m0.3343   \u001B[0m | \u001B[0m1.0      \u001B[0m | \u001B[0m15.25    \u001B[0m | \u001B[0m0.1      \u001B[0m |\n",
      "| \u001B[0m10       \u001B[0m | \u001B[0m-0.4534  \u001B[0m | \u001B[0m0.1      \u001B[0m | \u001B[0m0.3      \u001B[0m | \u001B[0m17.5     \u001B[0m | \u001B[0m1.0      \u001B[0m | \u001B[0m20.25    \u001B[0m | \u001B[0m0.8682   \u001B[0m |\n",
      "| \u001B[0m11       \u001B[0m | \u001B[0m-0.4765  \u001B[0m | \u001B[0m1.0      \u001B[0m | \u001B[0m0.3      \u001B[0m | \u001B[0m0.885    \u001B[0m | \u001B[0m12.45    \u001B[0m | \u001B[0m36.49    \u001B[0m | \u001B[0m1.0      \u001B[0m |\n",
      "| \u001B[0m12       \u001B[0m | \u001B[0m-0.4529  \u001B[0m | \u001B[0m0.1      \u001B[0m | \u001B[0m0.01     \u001B[0m | \u001B[0m9.29     \u001B[0m | \u001B[0m1.0      \u001B[0m | \u001B[0m28.27    \u001B[0m | \u001B[0m0.1      \u001B[0m |\n",
      "=================================================================================================\n"
     ]
    }
   ],
   "source": [
    "from nfl_wp_model.model import Model\n",
    "\n",
    "cross_validation = Model(pbp_df.data, train=True, maximize=True, test=True, train_season_subset=2022, num_splits=5,\n",
    "                         init_points_factor=1, n_iter_factor=1, num_rounds=15000)"
   ],
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   "source": [
    "## Feature Importance\n",
    "\n",
    "Cross validation is done using a binary logistic regression objective.  Its scoring metric is a negative log-loss likelihood, where a lower score is better.  The Bayesian parameter Optimization function is then used to iteratively \"tune the dials\" of the scoring function's various parameters using Bayesian inference to quickly maximize the probability score.  XGBoost is used to perform the cross validation, which automatically stops boosting to provent overfitting.  Once the best parameters are determined, the model is trained with those parameters, we can see determine feature importance:"
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    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cross_validation.get_importance()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T01:22:40.242803300Z",
     "start_time": "2023-08-17T01:22:40.012113600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Scoring The Model\n",
    "\n",
    "Next, the model can be used to make predictions against test data, and binding the win probabilities to the test data frame:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "             wp  label          game_id home_team away_team  season  \\\n0      0.246801      0  2022_01_BAL_NYJ       NYJ       BAL    2022   \n1      0.271296      0  2022_01_BAL_NYJ       NYJ       BAL    2022   \n2      0.258428      0  2022_01_BAL_NYJ       NYJ       BAL    2022   \n3      0.244740      0  2022_01_BAL_NYJ       NYJ       BAL    2022   \n4      0.227738      0  2022_01_BAL_NYJ       NYJ       BAL    2022   \n...         ...    ...              ...       ...       ...     ...   \n40941  0.799330      1   2022_22_KC_PHI       PHI        KC    2022   \n40942  0.868566      1   2022_22_KC_PHI       PHI        KC    2022   \n40943  0.875195      1   2022_22_KC_PHI       PHI        KC    2022   \n40944  0.804326      1   2022_22_KC_PHI       PHI        KC    2022   \n40945  0.108434      0   2022_22_KC_PHI       PHI        KC    2022   \n\n       receive_2h_ko  spread_time  home  half_seconds_remaining  \\\n0                  0    -6.471175     1                  1796.0   \n1                  0    -6.279923     1                  1769.0   \n2                  0    -6.252074     1                  1765.0   \n3                  0    -6.087556     1                  1741.0   \n4                  0    -6.033684     1                  1733.0   \n...              ...          ...   ...                     ...   \n40941              0    -0.020651     0                   108.0   \n40942              0    -0.020377     0                    96.0   \n40943              0    -0.019448     0                    54.0   \n40944              0    -0.018541     0                    11.0   \n40945              0     0.018438     1                     6.0   \n\n       game_seconds_remaining  diff_time_ratio  score_differential  down  \\\n0                      3596.0         0.000000                 0.0   1.0   \n1                      3569.0         0.000000                 0.0   1.0   \n2                      3565.0         0.000000                 0.0   2.0   \n3                      3541.0         0.000000                 0.0   3.0   \n4                      3533.0         0.000000                 0.0   4.0   \n...                       ...              ...                 ...   ...   \n40941                   108.0         0.000000                 0.0   1.0   \n40942                    96.0         0.000000                 0.0   2.0   \n40943                    54.0         0.000000                 0.0   3.0   \n40944                    11.0         0.000000                 0.0   4.0   \n40945                     6.0      -162.706119                -3.0   1.0   \n\n       ydstogo  yardline_100  posteam_timeouts_remaining  \\\n0         10.0          78.0                         3.0   \n1         10.0          59.0                         3.0   \n2         10.0          59.0                         3.0   \n3          5.0          54.0                         3.0   \n4         15.0          64.0                         3.0   \n...        ...           ...                         ...   \n40941     10.0          11.0                         2.0   \n40942      1.0           2.0                         2.0   \n40943      6.0           7.0                         1.0   \n40944      8.0           9.0                         0.0   \n40945     10.0          64.0                         0.0   \n\n       defteam_timeouts_remaining  \n0                             3.0  \n1                             3.0  \n2                             3.0  \n3                             3.0  \n4                             3.0  \n...                           ...  \n40941                         1.0  \n40942                         0.0  \n40943                         0.0  \n40944                         0.0  \n40945                         0.0  \n\n[40946 rows x 18 columns]",
      "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>wp</th>\n      <th>label</th>\n      <th>game_id</th>\n      <th>home_team</th>\n      <th>away_team</th>\n      <th>season</th>\n      <th>receive_2h_ko</th>\n      <th>spread_time</th>\n      <th>home</th>\n      <th>half_seconds_remaining</th>\n      <th>game_seconds_remaining</th>\n      <th>diff_time_ratio</th>\n      <th>score_differential</th>\n      <th>down</th>\n      <th>ydstogo</th>\n      <th>yardline_100</th>\n      <th>posteam_timeouts_remaining</th>\n      <th>defteam_timeouts_remaining</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.246801</td>\n      <td>0</td>\n      <td>2022_01_BAL_NYJ</td>\n      <td>NYJ</td>\n      <td>BAL</td>\n      <td>2022</td>\n      <td>0</td>\n      <td>-6.471175</td>\n      <td>1</td>\n      <td>1796.0</td>\n      <td>3596.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>78.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.271296</td>\n      <td>0</td>\n      <td>2022_01_BAL_NYJ</td>\n      <td>NYJ</td>\n      <td>BAL</td>\n      <td>2022</td>\n      <td>0</td>\n      <td>-6.279923</td>\n      <td>1</td>\n      <td>1769.0</td>\n      <td>3569.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>59.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.258428</td>\n      <td>0</td>\n      <td>2022_01_BAL_NYJ</td>\n      <td>NYJ</td>\n      <td>BAL</td>\n      <td>2022</td>\n      <td>0</td>\n      <td>-6.252074</td>\n      <td>1</td>\n      <td>1765.0</td>\n      <td>3565.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>10.0</td>\n      <td>59.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.244740</td>\n      <td>0</td>\n      <td>2022_01_BAL_NYJ</td>\n      <td>NYJ</td>\n      <td>BAL</td>\n      <td>2022</td>\n      <td>0</td>\n      <td>-6.087556</td>\n      <td>1</td>\n      <td>1741.0</td>\n      <td>3541.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>54.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.227738</td>\n      <td>0</td>\n      <td>2022_01_BAL_NYJ</td>\n      <td>NYJ</td>\n      <td>BAL</td>\n      <td>2022</td>\n      <td>0</td>\n      <td>-6.033684</td>\n      <td>1</td>\n      <td>1733.0</td>\n      <td>3533.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>15.0</td>\n      <td>64.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>40941</th>\n      <td>0.799330</td>\n      <td>1</td>\n      <td>2022_22_KC_PHI</td>\n      <td>PHI</td>\n      <td>KC</td>\n      <td>2022</td>\n      <td>0</td>\n      <td>-0.020651</td>\n      <td>0</td>\n      <td>108.0</td>\n      <td>108.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>11.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>40942</th>\n      <td>0.868566</td>\n      <td>1</td>\n      <td>2022_22_KC_PHI</td>\n      <td>PHI</td>\n      <td>KC</td>\n      <td>2022</td>\n      <td>0</td>\n      <td>-0.020377</td>\n      <td>0</td>\n      <td>96.0</td>\n      <td>96.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>40943</th>\n      <td>0.875195</td>\n      <td>1</td>\n      <td>2022_22_KC_PHI</td>\n      <td>PHI</td>\n      <td>KC</td>\n      <td>2022</td>\n      <td>0</td>\n      <td>-0.019448</td>\n      <td>0</td>\n      <td>54.0</td>\n      <td>54.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>6.0</td>\n      <td>7.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>40944</th>\n      <td>0.804326</td>\n      <td>1</td>\n      <td>2022_22_KC_PHI</td>\n      <td>PHI</td>\n      <td>KC</td>\n      <td>2022</td>\n      <td>0</td>\n      <td>-0.018541</td>\n      <td>0</td>\n      <td>11.0</td>\n      <td>11.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>8.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>40945</th>\n      <td>0.108434</td>\n      <td>0</td>\n      <td>2022_22_KC_PHI</td>\n      <td>PHI</td>\n      <td>KC</td>\n      <td>2022</td>\n      <td>0</td>\n      <td>0.018438</td>\n      <td>1</td>\n      <td>6.0</td>\n      <td>6.0</td>\n      <td>-162.706119</td>\n      <td>-3.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n      <td>64.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>40946 rows × 18 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validation.get_scores()\n",
    "cross_validation.test_data_predictions"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T01:22:40.820922200Z",
     "start_time": "2023-08-17T01:22:40.243303400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Finally, we can view the Model's log-loss and accuracy scores:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Loss: 0.5203294471497274\n",
      "Accuracy: 0.7427831778439896\n"
     ]
    }
   ],
   "source": [
    "print(f\"Log Loss: {cross_validation.logloss_score}\")\n",
    "print(f\"Accuracy: {cross_validation.accuracy}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T01:22:40.821421800Z",
     "start_time": "2023-08-17T01:22:40.801915700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "And then plot the calibration data:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cross_validation.plot_calibration_score()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T01:22:41.077982600Z",
     "start_time": "2023-08-17T01:22:40.816921400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Predicting the NFL 2023 Preseason Games\n",
    "\n",
    "Now we are ready to begin predicting some upcoming games.  First, let's train the model with the entire data set:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|   iter    |  target   | colsam... |    eta    |   gamma   | max_depth | min_ch... | subsample |\n",
      "-------------------------------------------------------------------------------------------------\n",
      "| \u001B[0m1        \u001B[0m | \u001B[0m-0.452   \u001B[0m | \u001B[0m0.2666   \u001B[0m | \u001B[0m0.2437   \u001B[0m | \u001B[0m14.74    \u001B[0m | \u001B[0m6.729    \u001B[0m | \u001B[0m18.49    \u001B[0m | \u001B[0m0.7881   \u001B[0m |\n",
      "| \u001B[0m2        \u001B[0m | \u001B[0m-0.4563  \u001B[0m | \u001B[0m0.6777   \u001B[0m | \u001B[0m0.07681  \u001B[0m | \u001B[0m7.857    \u001B[0m | \u001B[0m1.812    \u001B[0m | \u001B[0m22.14    \u001B[0m | \u001B[0m0.1752   \u001B[0m |\n",
      "| \u001B[0m3        \u001B[0m | \u001B[0m-0.4631  \u001B[0m | \u001B[0m0.8244   \u001B[0m | \u001B[0m0.1336   \u001B[0m | \u001B[0m0.9554   \u001B[0m | \u001B[0m10.46    \u001B[0m | \u001B[0m47.29    \u001B[0m | \u001B[0m0.5615   \u001B[0m |\n",
      "| \u001B[0m4        \u001B[0m | \u001B[0m-0.472   \u001B[0m | \u001B[0m0.6262   \u001B[0m | \u001B[0m0.1514   \u001B[0m | \u001B[0m10.05    \u001B[0m | \u001B[0m13.68    \u001B[0m | \u001B[0m3.348    \u001B[0m | \u001B[0m0.5338   \u001B[0m |\n",
      "| \u001B[0m5        \u001B[0m | \u001B[0m-0.4552  \u001B[0m | \u001B[0m0.5253   \u001B[0m | \u001B[0m0.1913   \u001B[0m | \u001B[0m6.745    \u001B[0m | \u001B[0m6.107    \u001B[0m | \u001B[0m15.71    \u001B[0m | \u001B[0m0.1007   \u001B[0m |\n",
      "| \u001B[0m6        \u001B[0m | \u001B[0m-0.4569  \u001B[0m | \u001B[0m0.5498   \u001B[0m | \u001B[0m0.1006   \u001B[0m | \u001B[0m4.602    \u001B[0m | \u001B[0m9.891    \u001B[0m | \u001B[0m10.84    \u001B[0m | \u001B[0m0.7681   \u001B[0m |\n",
      "| \u001B[0m7        \u001B[0m | \u001B[0m-0.4524  \u001B[0m | \u001B[0m0.3098   \u001B[0m | \u001B[0m0.1843   \u001B[0m | \u001B[0m15.91    \u001B[0m | \u001B[0m6.157    \u001B[0m | \u001B[0m18.73    \u001B[0m | \u001B[0m0.9952   \u001B[0m |\n",
      "| \u001B[0m8        \u001B[0m | \u001B[0m-0.4564  \u001B[0m | \u001B[0m0.1      \u001B[0m | \u001B[0m0.3      \u001B[0m | \u001B[0m15.05    \u001B[0m | \u001B[0m15.07    \u001B[0m | \u001B[0m25.67    \u001B[0m | \u001B[0m1.0      \u001B[0m |\n",
      "| \u001B[0m9        \u001B[0m | \u001B[0m-0.457   \u001B[0m | \u001B[0m0.1      \u001B[0m | \u001B[0m0.3      \u001B[0m | \u001B[0m0.0      \u001B[0m | \u001B[0m16.0     \u001B[0m | \u001B[0m21.62    \u001B[0m | \u001B[0m1.0      \u001B[0m |\n",
      "| \u001B[0m10       \u001B[0m | \u001B[0m-0.4571  \u001B[0m | \u001B[0m0.8805   \u001B[0m | \u001B[0m0.3      \u001B[0m | \u001B[0m20.0     \u001B[0m | \u001B[0m1.0      \u001B[0m | \u001B[0m31.39    \u001B[0m | \u001B[0m0.1      \u001B[0m |\n",
      "| \u001B[0m11       \u001B[0m | \u001B[0m-0.4568  \u001B[0m | \u001B[0m0.1      \u001B[0m | \u001B[0m0.3      \u001B[0m | \u001B[0m20.0     \u001B[0m | \u001B[0m1.0      \u001B[0m | \u001B[0m48.0     \u001B[0m | \u001B[0m0.2008   \u001B[0m |\n",
      "| \u001B[0m12       \u001B[0m | \u001B[0m-0.4572  \u001B[0m | \u001B[0m0.8857   \u001B[0m | \u001B[0m0.3      \u001B[0m | \u001B[0m20.0     \u001B[0m | \u001B[0m1.0      \u001B[0m | \u001B[0m11.66    \u001B[0m | \u001B[0m0.1      \u001B[0m |\n",
      "=================================================================================================\n"
     ]
    }
   ],
   "source": [
    "wp_model = Model(pbp_df.data, train=True, maximize=True, test=False, train_season_subset=2023, num_splits=5,\n",
    "                 init_points_factor=12, n_iter_factor=10, num_rounds=15000)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T01:25:36.487632900Z",
     "start_time": "2023-08-17T01:22:41.065479800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wp_model.get_importance()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T01:25:36.704181400Z",
     "start_time": "2023-08-17T01:25:36.487132900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now let's run the predictions:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jonathan Bailey\\AppData\\Local\\Temp\\ipykernel_16336\\2734519129.py:45: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  results['correct_prediction'] = np.where(\n"
     ]
    },
    {
     "data": {
      "text/plain": "                game_id winner home_team  home_team_wp away_team  \\\n0   2023_PRE_01_NYJ_CLE    CLE       CLE      0.589396       NYJ   \n1    2023_PRE_02_HOU_NE    HOU        NE      0.431346       HOU   \n2   2023_PRE_02_MIN_SEA    SEA       SEA      0.590303       MIN   \n3   2023_PRE_02_ATL_MIA    ATL       MIA      0.416224       ATL   \n4    2023_PRE_02_PIT_TB    PIT        TB      0.441740       PIT   \n5    2023_PRE_02_GB_CIN     GB       CIN      0.397735        GB   \n6    2023_PRE_02_SF_DET    DET       DET      0.379108        SF   \n7   2023_PRE_02_WAS_CLE    WAS       CLE      0.589447       WAS   \n8   2023_PRE_02_DEN_ARI    ARI       ARI      0.344658       DEN   \n9   2023_PRE_02_TEN_CHI    CHI       CHI      0.695969       TEN   \n10  2023_PRE_02_IND_BUF    BUF       BUF      0.457887       IND   \n11  2023_PRE_02_NYJ_CAR    NYJ       CAR      0.449643       NYJ   \n12  2023_PRE_02_JAX_DAL    JAX       DAL      0.453334       JAX   \n13  2023_PRE_02_PHI_BAL    BAL       BAL      0.651284       PHI   \n14   2023_PRE_02_LAC_LA    LAC        LA      0.376983       LAC   \n15    2023_PRE_02_KC_NO     NO        NO      0.571304        KC   \n16    2023_PRE_02_SF_LV     LV        LV      0.340103        SF   \n\n    away_team_wp  correct_prediction  \n0       0.410604                   1  \n1       0.568654                   1  \n2       0.409697                   1  \n3       0.583776                   1  \n4       0.558260                   1  \n5       0.602265                   1  \n6       0.620892                   0  \n7       0.410553                   0  \n8       0.655342                   0  \n9       0.304031                   1  \n10      0.542113                   0  \n11      0.550357                   1  \n12      0.546666                   1  \n13      0.348716                   1  \n14      0.623017                   1  \n15      0.428696                   1  \n16      0.659897                   0  ",
      "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>game_id</th>\n      <th>winner</th>\n      <th>home_team</th>\n      <th>home_team_wp</th>\n      <th>away_team</th>\n      <th>away_team_wp</th>\n      <th>correct_prediction</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023_PRE_01_NYJ_CLE</td>\n      <td>CLE</td>\n      <td>CLE</td>\n      <td>0.589396</td>\n      <td>NYJ</td>\n      <td>0.410604</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023_PRE_02_HOU_NE</td>\n      <td>HOU</td>\n      <td>NE</td>\n      <td>0.431346</td>\n      <td>HOU</td>\n      <td>0.568654</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023_PRE_02_MIN_SEA</td>\n      <td>SEA</td>\n      <td>SEA</td>\n      <td>0.590303</td>\n      <td>MIN</td>\n      <td>0.409697</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023_PRE_02_ATL_MIA</td>\n      <td>ATL</td>\n      <td>MIA</td>\n      <td>0.416224</td>\n      <td>ATL</td>\n      <td>0.583776</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023_PRE_02_PIT_TB</td>\n      <td>PIT</td>\n      <td>TB</td>\n      <td>0.441740</td>\n      <td>PIT</td>\n      <td>0.558260</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2023_PRE_02_GB_CIN</td>\n      <td>GB</td>\n      <td>CIN</td>\n      <td>0.397735</td>\n      <td>GB</td>\n      <td>0.602265</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2023_PRE_02_SF_DET</td>\n      <td>DET</td>\n      <td>DET</td>\n      <td>0.379108</td>\n      <td>SF</td>\n      <td>0.620892</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2023_PRE_02_WAS_CLE</td>\n      <td>WAS</td>\n      <td>CLE</td>\n      <td>0.589447</td>\n      <td>WAS</td>\n      <td>0.410553</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2023_PRE_02_DEN_ARI</td>\n      <td>ARI</td>\n      <td>ARI</td>\n      <td>0.344658</td>\n      <td>DEN</td>\n      <td>0.655342</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2023_PRE_02_TEN_CHI</td>\n      <td>CHI</td>\n      <td>CHI</td>\n      <td>0.695969</td>\n      <td>TEN</td>\n      <td>0.304031</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2023_PRE_02_IND_BUF</td>\n      <td>BUF</td>\n      <td>BUF</td>\n      <td>0.457887</td>\n      <td>IND</td>\n      <td>0.542113</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2023_PRE_02_NYJ_CAR</td>\n      <td>NYJ</td>\n      <td>CAR</td>\n      <td>0.449643</td>\n      <td>NYJ</td>\n      <td>0.550357</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2023_PRE_02_JAX_DAL</td>\n      <td>JAX</td>\n      <td>DAL</td>\n      <td>0.453334</td>\n      <td>JAX</td>\n      <td>0.546666</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2023_PRE_02_PHI_BAL</td>\n      <td>BAL</td>\n      <td>BAL</td>\n      <td>0.651284</td>\n      <td>PHI</td>\n      <td>0.348716</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2023_PRE_02_LAC_LA</td>\n      <td>LAC</td>\n      <td>LA</td>\n      <td>0.376983</td>\n      <td>LAC</td>\n      <td>0.623017</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>2023_PRE_02_KC_NO</td>\n      <td>NO</td>\n      <td>NO</td>\n      <td>0.571304</td>\n      <td>KC</td>\n      <td>0.428696</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>2023_PRE_02_SF_LV</td>\n      <td>LV</td>\n      <td>LV</td>\n      <td>0.340103</td>\n      <td>SF</td>\n      <td>0.659897</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "games = [\"2023_PRE_01_NYJ_CLE\", \"2023_PRE_02_HOU_NE\", \"2023_PRE_02_MIN_SEA\", \"2023_PRE_02_ATL_MIA\",\n",
    "         \"2023_PRE_02_PIT_TB\",\n",
    "         \"2023_PRE_02_GB_CIN\", \"2023_PRE_02_SF_DET\", \"2023_PRE_02_WAS_CLE\", \"2023_PRE_02_DEN_ARI\",\n",
    "         \"2023_PRE_02_TEN_CHI\", \"2023_PRE_02_IND_BUF\", \"2023_PRE_02_NYJ_CAR\", \"2023_PRE_02_JAX_DAL\",\n",
    "         \"2023_PRE_02_PHI_BAL\", \"2023_PRE_02_LAC_LA\", \"2023_PRE_02_KC_NO\", \"2023_PRE_02_SF_LV\"]\n",
    "\n",
    "home_teams = [\"CLE\", \"NE\", \"SEA\", \"MIA\", \"TB\", \"CIN\", \"DET\", \"CLE\", \"ARI\", \"CHI\", \"BUF\", \"CAR\", \"DAL\", \"BAL\", \"LA\",\n",
    "              \"NO\", \"LV\"]\n",
    "away_teams = [\"NYJ\", \"HOU\", \"MIN\", \"ATL\", \"PIT\", \"GB\", \"SF\", \"WAS\", \"DEN\", \"TEN\", \"IND\", \"NYJ\", \"JAX\", \"PHI\", \"LAC\",\n",
    "              \"KC\", \"SF\"]\n",
    "\n",
    "preseason_games = {\n",
    "    \"game_id\": games,\n",
    "    \"home_team\": home_teams,\n",
    "    \"away_team\": away_teams,\n",
    "    \"game_seconds_remaining\": [3600] * len(games),\n",
    "    \"half_seconds_remaining\": [1800] * len(games),\n",
    "    \"yardline_100\": [75] * len(games),\n",
    "    \"defteam_timeouts_remaining\": [3] * len(games),\n",
    "    \"posteam_timeouts_remaining\": [3] * len(games),\n",
    "    \"down\": [1] * len(games),\n",
    "    \"ydstogo\": [10] * len(games),\n",
    "    \"result\": [0] * len(games),\n",
    "    \"posteam\": [\"BAMA\"] * len(games),\n",
    "    \"defteam\": [\"BAMA\"] * len(games),\n",
    "    \"qtr\": [1] * len(games),\n",
    "    \"spread_line\": [-2.5, 2.5, -2.5, 2.5, 1.5, 2.5, 3, 0, 3.5, -3.5, 2.5, 2.5, 2.5, -3.5, 1, -1.5, 3.5],\n",
    "    \"score_differential\": [0] * len(games),\n",
    "    \"season\": [2023] * len(games),\n",
    "}\n",
    "\n",
    "preseason_df = wp_model.predict(preseason_games)\n",
    "\n",
    "winners = [\"CLE\", \"HOU\", \"SEA\", \"ATL\", \"PIT\", \"GB\", \"DET\", \"WAS\", \"ARI\", \"CHI\", \"BUF\", \"NYJ\", \"JAX\", \"BAL\", \"LAC\",\n",
    "           \"NO\", \"LV\"]\n",
    "\n",
    "preds = pd.concat([pd.DataFrame({\"home_team_wp\": preseason_df, \"winner\": winners}),\n",
    "                   wp_model.test_data], axis=1)\n",
    "preds[\"away_team_wp\"] = 1 - preds[\"home_team_wp\"]\n",
    "results = preds[[\"game_id\", \"winner\", \"home_team\", \"home_team_wp\", \"away_team\", \"away_team_wp\"]]\n",
    "\n",
    "results['correct_prediction'] = np.where(\n",
    "    (results['winner'] == results['home_team']) & (results['home_team_wp'] > results['away_team_wp']) | (\n",
    "            results['winner'] == results['away_team']) & (results['away_team_wp'] > results['home_team_wp']), 1, 0)\n",
    "results"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T23:24:04.896864600Z",
     "start_time": "2023-08-17T23:24:04.608741200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "And the accuracy:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 70.59%\n"
     ]
    }
   ],
   "source": [
    "accuracy = results['correct_prediction'].mean()\n",
    "print(f\"Accuracy: {round(accuracy * 100, 2)}%\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T23:25:49.208015900Z",
     "start_time": "2023-08-17T23:25:48.965358200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "70% isn't bad for a small amount of boosting."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [],
   "source": [
    "def favorite_implied_probability(team, odds):\n",
    "    result = (-1 * odds) / ((-1 * odds) + 100)\n",
    "    print(f\"Favorite {team} likelihood of winning: {round(result * 100, 2)}%\")\n",
    "    return result\n",
    "\n",
    "\n",
    "def favorite_wp_odds(team, wp):\n",
    "    result = ((-1 * wp) / (1 - wp) * 100)\n",
    "    print(f\"Favorite {team} Expected Book Moneyline: {int(result)}\")\n",
    "    return result"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T23:26:57.637362900Z",
     "start_time": "2023-08-17T23:26:57.308361100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Favorite PHI likelihood of winning: 64.29%\n"
     ]
    },
    {
     "data": {
      "text/plain": "0.6428571428571429"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "favorite_implied_probability(\"PHI\", -180)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T23:09:12.109420800Z",
     "start_time": "2023-08-17T23:09:11.873565100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Favorite PHI Expected Book Moneyline: -145\n"
     ]
    },
    {
     "data": {
      "text/plain": "-145.7002457002457"
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "favorite_wp_odds(\"PHI\", .593)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-17T23:27:01.912924200Z",
     "start_time": "2023-08-17T23:27:01.890355200Z"
    }
   }
  }
 ],
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   "file_extension": ".py",
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