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The Future of the NFL: Predicting Plays Using Artificial Neural Networks
- Using neural networks, a team of researchers sought to accurately predict NFL plays, which could help defensive coaches in selecting an optimal defense strategy that could thwart the opponents, as well as bring a premature end to dominant teams like the Patriots.
- The team based their model on NFL data sourced from the nflfastR, which contains over 350 variables and play-by-play data stretching back to 1999.
- The model, which used Long Short-Term Memory (LSTM) algorithms, managed 69.5% accuracy when applied only to the Patriots between 2012 and 2020.
- However, the team found that the correlation between certain features and play-type was insufficient, with several correlated parameters occurring only after the play.
- The team improved the model's accuracy by 4% by adding more data from all NFL teams from all available years and utilizing more features.
- The model could not guarantee perfect accuracy in predicting play-calling, but could be leveraged as a helpful tool in decision-making by coaches.
- Using more data was found to be more important than targeting specific coaches, as highly unpredictable coaches like Belichick could hinder model accuracy.
- Models like these could bring new rule considerations to the game, forcing the NFL to decide on how to handle extreme advantages that accurate models could procure for teams.
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