Through price discovery, markets produce incredibly accurate predictions about the future. Even the best models can benefit from regressing their predictions to those of the market. Using these methods, models like nfelo can predict NFL margins better than the market by itself.
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WEPA is a framework that weights EPA to improve its predictive power
The goal of WEPA is to use publicly available play-by-play data to create an open source model that is free, reproducible, and more predictive than proprietary alternatives
The 2018 WEPA proof-of-concept suffered from overfitting and used forward looking data to train, resulting in an unreliable model
This post addresses these issues while improving the model through an expanded dataset and feature group
The new WEPA model is substantially more reliable and predicts future point margin better than current proprietary alternatives
By weighting certain play types, Expected Points Added (EPA) can be made more accurate than Point Margin in predicting out of sample Point Margin. Even without controlling for strength of schedule, homefield, or any pre-season priors, a weighted EPA can even be more accurate than DVOA, which includes all of the aforementioned data points.
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