SPRING 2026

AUTHOR

Vishnu Datta Jayanti, Allen High School, Allen, Texas

ABSTRACT

While traditional fantasy football projections have long relied on expert intuition, the rise of data-driven forecasting has shifted the analytical landscape. This analysis evaluates four XGBoost models designed for quarterbacks, running backs, wide receivers, and tight ends. The models were trained on data from the 2013 through 2023 NFL seasons and validated against the 2024 season.

Custom features—such as career-maximum performance indicators and lagged inputs—were incorporated to help the models identify trends and better estimate a player’s performance ceiling. This research examines whether “pure” algorithmic models can mitigate the cognitive biases present in the “hybrid” approaches used by industry leaders like ESPN.

Performance was assessed using Mean Absolute Error and Spearman’s Rank Correlation Coefficient, benchmarking the models against ESPN’s preseason projections for the 2025 NFL season. The results show that while industry projections still perform better in minimizing absolute error, the algorithmic models deliver highly competitive ranking performance.

These findings suggest that “pure” machine learning models can serve as a valuable complement to publicly available fantasy football rankings.