FALL 2025

AUTHOR

Ruben Chung, Brown University

ABSTRACT

Forecasting touchdowns for NFL wide receivers is a challenging but valuable problem in football analytics and player evaluation. Touchdowns are notoriously volatile, influenced by red zone usage, quarterback play, and situational variance, making year-to-year outcomes difficult to predict. This study develops a temporal linear regression model to project wide receiver touchdown totals using a feature-rich dataset spanning 1990–2024. The dataset incorporates lagged statistics, two-year rolling averages, player age and experience, team offensive strength, and efficiency metrics such as catch rate and touchdowns per target. The model was trained on 1990–2010 player-seasons and tested on 2011–2024 data, with strict time-bounded feature engineering to prevent data leakage.

Results show strong predictive accuracy (R² = 0.803, MAE = 0.82 TDs), demonstrating that systematic patterns can be identified even within a highly volatile statistic. Feature importance analysis indicates that efficiency and usage metrics are more reliable predictors than raw prior year touchdown totals, aligning with football intuition and highlighting regression-to-the-mean 3 effects. The model generates 2025 projections that identify both elite scorers and likely regression candidates, providing insight into the stability of touchdown production. This work demonstrates that with careful feature engineering, a transparent and interpretable linear model can yield valuable insights in sports analytics. Beyond forecasting, the results underscore the importance of efficiency and opportunity metrics in understanding touchdown outcomes, offering a framework that can inform research on statistical predictability in professional sports.