ML-Based Approach for NFL Defensive Pass Interference Prediction Using GPS Tracking Data

TL;DR

This study explores the use of NFL player GPS tracking data and machine learning models to predict Defensive Pass Interference, revealing limited success due to insufficient information in the data.

Contribution

First attempt to predict NFL DPI using GPS tracking data with various ML models, highlighting challenges in data sufficiency for accurate foul prediction.

Findings

ML models achieved high recall but low precision

GPS data alone is insufficient for reliable DPI prediction

Potential use as a filter in multi-step video analysis pipeline

Abstract

Defensive Pass Interference (DPI) is one of the most impactful penalties in the NFL. DPI is a spot foul, yielding an automatic first down to the team in possession. With such an influence on the game, referees have no room for a mistake. It is also a very rare event, which happens 1-2 times per 100 pass attempts. With technology improving and many IoT wearables being put on the athletes to collect valuable data, there is a solid ground for applying machine learning (ML) techniques to improve every aspect of the game. The work presented here is the first attempt in predicting DPI using player tracking GPS data. The data we used was collected by NFL's Next Gen Stats throughout the 2018 regular season. We present ML models for highly imbalanced time-series binary classification: LSTM, GRU, ANN, and Multivariate LSTM-FCN. Results showed that using GPS tracking data to predict DPI has limited success. The best performing models had high recall with low precision which resulted in the classification of many false positive examples. Looking closely at the data confirmed that there is just not enough information to determine whether a foul was committed. This study might serve as a filter for multi-step pipeline for video sequence classification which could be able to solve this problem.