Inspired by Can You Beat FiveThirtyEight’s NFL Forecasts?, I wanted to use machine learning with publicly available data to make a probabilistic forecast for each NFL game.
More specifically, these forecast can be used to bet on NFL games and make a consistent profit. We will be focusing on the line bet by simply selecting the winners (different from betting on the spreads).
It is important that the models give us a confidence probability of the winners so that we can decide whether to use the prediction or not. For example, if the model predicts for a particular game that the Minnesota Vikings have a 52% chance of winning, we might not want to place a bet on this. On the other hand, if the model is 90% confident that the Minnesota Vikings will win, we should consider placing a bet (given that the overall performance of the model is "good").
- Elo Data - https://github.com/fivethirtyeight/data/tree/master/nfl-elo
- NFL historic game and betting info - https://www.kaggle.com/tobycrabtree/nfl-scores-and-betting-data/home
- NFL Scores and Betting data: https://www.kaggle.com/tobycrabtree/nfl-scores-and-betting-data/home
- NFL Elo data: https://github.com/fivethirtyeight/data/tree/master/nfl-elo
- NFL Betting codes: https://www.kaggle.com/twalters20/nfl-betting-model
- Using Machine Learning for Predicting NFL Games: https://www.youtube.com/watch?v=8emUyzczThY&t=896s
- Predicting Point Spread in NFL Games: http://cs229.stanford.edu/proj2016/report/WadsworthVera-PredictingPointSpreadinNFLGames-report.pdf