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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"gbg_and_odds_this_year = pd.read_csv('Source/Data/gbg_and_odds_this_year.csv')\n",
"results = pd.read_csv('Source/Data/results.csv')\n",
"\n",
"from Source.Predict.predict import predict"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pickle as pkl\n",
"with open('Source/Pickles/team_abbreviation_to_name.pkl', 'rb') as f:\n",
" team_abbreviation_to_name = pkl.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 32/32 [00:04<00:00, 6.79it/s]\n"
]
}
],
"source": [
"from tqdm import tqdm\n",
"predictions = {}\n",
"for game_id,home,away,season,week,total in tqdm(gbg_and_odds_this_year[['game_id','home_team','away_team','Season','GP','Total Score Close']].values):\n",
" if week!=1:\n",
" predictions[game_id] = predict(home,away,season,week,total)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'winners_correct': '5', 'winners_incorrect': '11', 'over_unders_correct': '7', 'over_unders_incorrect': '9'}\n"
]
}
],
"source": [
"predictions_df = pd.DataFrame(predictions).T\n",
"predictions_df['predicted_winner'] = [i['Winner'][0] if type(i['Winner'])==list else None for i in predictions_df[1]]\n",
"predictions_df['predicted_winner'] = predictions_df['predicted_winner'].map(team_abbreviation_to_name)\n",
"predictions_df['predicted_over_under'] = [i['Over/Under'][0] if type(i['Over/Under'])==list else None for i in predictions_df[2]]\n",
"predictions_df = predictions_df.merge(results, left_index=True, right_on='game_id').merge(gbg_and_odds_this_year[['game_id','Total Score Close']]).dropna(subset=['predicted_winner'])\n",
"predictions_df['over_under'] = ['Over' if t>tsc else 'Under' if t<tsc else 'Push' for t,tsc in predictions_df[['total','Total Score Close']].values]\n",
"\n",
"predictions_df['winner_correct'] = (predictions_df['predicted_winner']==predictions_df['winner']).astype(int)\n",
"predictions_df['winner_incorrect'] = (predictions_df['predicted_winner']!=predictions_df['winner']).astype(int)\n",
"\n",
"predictions_df['over_under_correct'] = (predictions_df['predicted_over_under']==predictions_df['over_under']).astype(int)\n",
"predictions_df['over_under_incorrect'] = (predictions_df['predicted_over_under']!=predictions_df['over_under']).astype(int)\n",
"\n",
"winners_correct = predictions_df['winner_correct'].sum()\n",
"winners_incorrect = predictions_df['winner_incorrect'].sum()\n",
"\n",
"over_unders_correct = predictions_df['over_under_correct'].sum()\n",
"over_unders_incorrect = predictions_df['over_under_incorrect'].sum()\n",
"\n",
"record = {\"winners_correct\":str(winners_correct),\n",
" \"winners_incorrect\":str(winners_incorrect),\n",
" \"over_unders_correct\":str(over_unders_correct),\n",
" \"over_unders_incorrect\":str(over_unders_incorrect)}\n",
"\n",
"import json\n",
"with open('Static/record.json', 'w') as f:\n",
" json.dump(record,f)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.4"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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