Expected Value (#7)

* Math for expected value of bet. Need to implement

* Added research to notes

* Updated notes

* Working ev with updated readme

README.md

@@ -16,6 +16,8 @@ Use Python 3.8. In particular the packages/libraries used are...
 
 ## Usage
 
+<img src="https://github.com/kyleskom/NBA-Machine-Learning-Sports-Betting/blob/master/Screenshots/Expected_value.png" width="1010" height="292" />
+
 Make sure all packages above are installed.
 
 ```bash

Tests/Expected_Value_Test.py

@@ -0,0 +1,37 @@
+import unittest
+from src.Utils import Expected_Value
+
+
+class TestExpectedValue(unittest.TestCase):
+
+    def test_expected_value_1(self):
+        result = Expected_Value.expected_value(.76, -200)
+        self.assertEqual(result, 14)
+
+    def test_expected_value_2(self):
+        result = Expected_Value.expected_value(.3, -500)
+        self.assertEqual(result, -64)
+
+    def test_expected_value_3(self):
+        result = Expected_Value.expected_value(.6, 250)
+        self.assertEqual(result, 110)
+
+    def test_expected_value_4(self):
+        result = Expected_Value.expected_value(.2, -200)
+        self.assertEqual(result, -70)
+
+    def test_expected_value_5(self):
+        result = Expected_Value.expected_value(.8137, -200)
+        self.assertEqual(result, 22.05)
+
+    def test_expected_value_6(self):
+        result = Expected_Value.expected_value(.2175, -550)
+        self.assertEqual(result, -74.30)
+
+    def test_expected_value_7(self):
+        result = Expected_Value.expected_value(.5298, 1000)
+        self.assertEqual(result, 482.78)
+
+    def test_expected_value_8(self):
+        result = Expected_Value.expected_value(.638, 275)
+        self.assertEqual(result, 139.25)

main.py

@@ -19,11 +19,17 @@
 def createTodaysGames(games, df):
     match_data = []
     todays_games_uo = []
+    home_team_odds = []
+    away_team_odds = []
 
     for game in games:
         home_team = game[0]
         away_team = game[1]
         todays_games_uo.append(input(home_team + ' vs ' + away_team + ': '))
+
+        home_team_odds.append(input(home_team + ' odds: '))
+        away_team_odds.append(input(away_team + ' odds: '))
+
         home_team_series = df.iloc[team_index_current.get(home_team)]
         away_team_series = df.iloc[team_index_current.get(away_team)]
         stats = home_team_series.append(away_team_series)
@@ -36,31 +42,31 @@ def createTodaysGames(games, df):
     data = frame_ml.values
     data = data.astype(float)
 
-    return data, todays_games_uo, frame_ml
+    return data, todays_games_uo, frame_ml, home_team_odds, away_team_odds
 
 
 def main():
     data = get_todays_games_json(todays_games_url)
     games = create_todays_games(data)
     data = get_json_data(data_url)
     df = to_data_frame(data)
-    data, todays_games_uo, frame_ml = createTodaysGames(games, df)
+    data, todays_games_uo, frame_ml, home_team_odds, away_team_odds = createTodaysGames(games, df)
     if args.nn:
         print("------------Neural Network Model Predictions-----------")
         data = tf.keras.utils.normalize(data, axis=1)
-        NN_Runner.nn_runner(data, todays_games_uo, frame_ml, games)
+        NN_Runner.nn_runner(data, todays_games_uo, frame_ml, games, home_team_odds, away_team_odds)
         print("-------------------------------------------------------")
     if args.xgb:
         print("---------------XGBoost Model Predictions---------------")
-        XGBoost_Runner.xgb_runner(data, todays_games_uo, frame_ml, games)
+        XGBoost_Runner.xgb_runner(data, todays_games_uo, frame_ml, games, home_team_odds, away_team_odds)
         print("-------------------------------------------------------")
     if args.A:
         print("---------------XGBoost Model Predictions---------------")
-        XGBoost_Runner.xgb_runner(data, todays_games_uo, frame_ml, games)
+        XGBoost_Runner.xgb_runner(data, todays_games_uo, frame_ml, games, home_team_odds, away_team_odds)
         print("-------------------------------------------------------")
         data = tf.keras.utils.normalize(data, axis=1)
         print("------------Neural Network Model Predictions-----------")
-        NN_Runner.nn_runner(data, todays_games_uo, frame_ml, games)
+        NN_Runner.nn_runner(data, todays_games_uo, frame_ml, games, home_team_odds, away_team_odds)
         print("-------------------------------------------------------")
 
 

notes.txt

@@ -1,3 +1,4 @@
+https://arxiv.org/pdf/1710.02824.pdf
 https://www.sportsbookreviewsonline.com/scoresoddsarchives/nba/nbaoddsarchives.htm
 https://www.youtube.com/watch?v=wQ8BIBpya2k
 Random Forrest

src/Predict/NN_Runner.py

@@ -4,12 +4,13 @@
 import tensorflow as tf
 from colorama import Fore, Style
 from tensorflow.keras.models import load_model
+from src.Utils import Expected_Value
 
 model = load_model('Models/NN_Models/Trained-Model-ML')
 ou_model = load_model("Models/NN_Models/Trained-Model-OU")
 
 
-def nn_runner(data, todays_games_uo, frame_ml, games):
+def nn_runner(data, todays_games_uo, frame_ml, games, home_team_odds, away_team_odds):
     ml_predictions_array = []
 
     for row in data:
@@ -55,3 +56,21 @@ def nn_runner(data, todays_games_uo, frame_ml, games):
                 print(Fore.RED + home_team + Style.RESET_ALL + ' vs ' + Fore.GREEN + away_team + Style.RESET_ALL + Fore.CYAN + f" ({winner_confidence}%)" + Style.RESET_ALL + ': ' +
                       Fore.BLUE + 'OVER ' + Style.RESET_ALL + str(todays_games_uo[count]) + Style.RESET_ALL + Fore.CYAN + f" ({un_confidence}%)" + Style.RESET_ALL)
         count += 1
+
+    print("--------------------Expected Value---------------------")
+    count = 0
+    for game in games:
+        home_team = game[0]
+        away_team = game[1]
+        ev_home = float(Expected_Value.expected_value(ml_predictions_array[count][0][1], int(home_team_odds[count])))
+        ev_away = float(Expected_Value.expected_value(ml_predictions_array[count][0][0], int(away_team_odds[count])))
+        if ev_home > 0:
+            print(home_team + ' EV: ' + Fore.GREEN + str(ev_home) + Style.RESET_ALL)
+        else:
+            print(home_team + ' EV: ' + Fore.RED + str(ev_home) + Style.RESET_ALL)
+
+        if ev_away > 0:
+            print(away_team + ' EV: ' + Fore.GREEN + str(ev_away) + Style.RESET_ALL)
+        else:
+            print(away_team + ' EV: ' + Fore.RED + str(ev_away) + Style.RESET_ALL)
+        count += 1

src/Predict/XGBoost_Runner.py

@@ -4,6 +4,7 @@
 import pandas as pd
 import xgboost as xgb
 from colorama import Fore, Style
+from src.Utils import Expected_Value
 
 # from src.Utils.Dictionaries import team_index_current
 # from src.Utils.tools import get_json_data, to_data_frame, get_todays_games_json, create_todays_games
@@ -16,7 +17,7 @@
 xgb_uo.load_model('Models/XGBoost_Models/XGBoost_58.9%_UO-6.json')
 
 
-def xgb_runner(data, todays_games_uo, frame_ml, games):
+def xgb_runner(data, todays_games_uo, frame_ml, games, home_team_odds, away_team_odds):
     ml_predictions_array = []
 
     for row in data:
@@ -69,3 +70,20 @@ def xgb_runner(data, todays_games_uo, frame_ml, games):
                     Fore.BLUE + 'OVER ' + Style.RESET_ALL + str(
                         todays_games_uo[count]) + Style.RESET_ALL + Fore.CYAN + f" ({un_confidence}%)" + Style.RESET_ALL)
         count += 1
+    print("--------------------Expected Value---------------------")
+    count = 0
+    for game in games:
+        home_team = game[0]
+        away_team = game[1]
+        ev_home = float(Expected_Value.expected_value(ml_predictions_array[count][0][1], int(home_team_odds[count])))
+        ev_away = float(Expected_Value.expected_value(ml_predictions_array[count][0][0], int(away_team_odds[count])))
+        if ev_home > 0:
+            print(home_team + ' EV: ' + Fore.GREEN + str(ev_home) + Style.RESET_ALL)
+        else:
+            print(home_team + ' EV: ' + Fore.RED + str(ev_home) + Style.RESET_ALL)
+
+        if ev_away > 0:
+            print(away_team + ' EV: ' + Fore.GREEN + str(ev_away) + Style.RESET_ALL)
+        else:
+            print(away_team + ' EV: ' + Fore.RED + str(ev_away) + Style.RESET_ALL)
+        count += 1

src/Utils/Expected_Value.py

@@ -0,0 +1,11 @@
+def expected_value(Pwin, odds):
+    Ploss = 1 - Pwin
+    Mwin = payout(odds)
+    return round((Pwin * Mwin) - (Ploss * 100), 2)
+
+
+def payout(odds):
+    if odds > 0:
+        return odds
+    else:
+        return (100 / (-1 * odds)) * 100