Coming into this project, the prediction of the total result using just pregame statistics and information was inherently challenging. The results from the decision tree model very much reflect that. The cross validation average accuracy for the GridSearchCV tuned model was 0.53 or 53%. This is slightly above the the necessary percentage of 52.4% to remain profitable and as a result the decision tree model can be used. However, it barely outperforms the necessary percentage, which means that the only way to profit a considerable amount of money is to also place a considerable amount of money on each bet. This is not ideal, but at least the model would be profitable.

However, only predicting with accuracy ~0.6% higher than necessary leaves a very small margin for error. Also, it will only generate meaningful profits if a sports bettor is placing somewhat large wagers every time. For example, if a sports bettor placed $100 bets on every game of the upcoming NFL season (272 games), the sports bettor would profit $334.56. However, if a sports bettor placed $5 bets on every game of the upcoming NFL season (272 games), the sports bettor would only profit $16.73.

Amount Bet: 100

Decimal Odds for -110: 1 – (100 / Sports Odds) = 1 – (100 / -110) = 1.91

Total Amount Won Per Bet: Amount Bet * Decimal Odds = 100 * (1.91) = 191

Profit Per Bet: Total Amount Won Per Bet – Amount Bet = 191 – 100 = 91

Profit Over 272 Games Betting $100: (91 * (272 * 0.53) – (100 * (272 * 0.47) = 334.56

Amount Bet: 5

Decimal Odds for -110: 1 – (100 / Sports Odds) = 1 – (100 / -110) = 1.91

Total Amount Won Per Bet: Amount Bet * Decimal Odds = 5 * (1.91) = 9.55

Profit Per Bet: Total Amount Won Per Bet – Amount Bet = 9.55 – 5 = 4.55

Profit Over 272 Games Betting $5: (4.55 * (272 * 0.53) – (5 * (272 * 0.47) = 16.73