Football

The NFL draft is an opportunity for teams to draft new players, address positional needs, and strengthen their roster for the upcoming season. Teams often trade players or compensation packages to secure certain prospects, which can be critical for team success. Wide receivers were selected as the focus of this study because the position is heavily influenced by objective data — such as receiving statistics and combine metrics like speed, agility, and explosiveness — making it well-suited for predictive modeling using machine learning.…Read More

In the NFL, “quarterback pressure” refers to defensive actions that disrupt a passer’s timing, decisions, and positioning. Numerous quantitative measures have been proposed, with mixed effectiveness. We introduce the Pocket Reduction Speed Score (PRSS), a geometric, tracking-based metric that quantifies how quickly the quarterback’s pocket shrinks. We apply the metric to player-tracking data from the 2021 NFL regular season, compute PRSS for each play, and examine its association with yards gained. PRSS offers a continuous, interpretable measure that captures multi-defender effects and the moment of greatest pressure, complementing existing binary and closest-defender metrics while offering applications in pass-protection evaluation, scouting, and scheme design.…Read More

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.…Read More

Wharton Sports Analytics Journal Open Access · ISSN 3070-4065 FALL 2024 Expected Value Curves Don't Tell the Full Story: Exploring NFL Draft Position Trade Value Curves AUTHOR Blake Zilberman, University of Pennsylvania Ryan S. Brill, University of Pennsylvania ABSTRACT Football analysts traditionally value a future draft pick position by its…Read More

In the National Football League, after a team scores a touchdown, they can kick an extra point or attempt a two-point conversion. Historically, teams have opted for the former, as it had a much higher likelihood of success. In 2015, however, the NFL instituted a rule to move the extra point distance back 13 yards, making it a more difficult kick. This paper will analyze the effect of this rule on the extra point rate, statistically analyzing both strategies and offering recommendations to both NFL teams and the league itself.…Read More