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Modified Drop Tower Impact Tests for American Football Helmets

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Partially constrained group variable selection to adjust for complementary unit performance in American college

¹New College of Florida, Sarasota, FL, USA.

Journal of Applied Statistics

|February 19, 2024

Summary

This study introduces "complementary football" metrics to improve American college football (CFB) rankings. By analyzing offensive and defensive unit interactions, new data-driven evaluations enhance team performance assessments.

Keywords:

Group penalty LASSO natural splines regularized estimation reverse causality sports statistics

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Area of Science:

Sports Analytics
Statistical Modeling
American College Football

Background:

Accurate American college football (CFB) team rankings are crucial for title and playoff implications.
Existing metrics often evaluate offensive and defensive units independently, neglecting their synergistic potential.
The concept of 'complementary football,' where units influence each other (e.g., defense creating scoring opportunities), remains largely unquantified.

Purpose of the Study:

To identify key features of complementary football through a data-driven approach.
To adjust offensive and defensive performance metrics based on the complementary unit's impact.
To enhance the accuracy of CFB team performance evaluations.

Main Methods:

Utilized data from 2009-2019 American college football seasons.
Incorporated natural splines with group penalty methods for statistical analysis.
Employed partially constrained optimization to adjust for strength of schedule and home-field advantage.

Main Results:

Identified consistently influential features of complementary football.
Developed a method to adjust team offensive and defensive performance scores.
Provided a more holistic evaluation of team strength beyond independent unit analysis.

Conclusions:

Complementary football is a significant, quantifiable factor in American college football performance.
The proposed methodology offers a more accurate and nuanced approach to ranking CFB teams.
Future evaluations should incorporate these complementary dynamics for improved predictive power and rankings.