FALL 2025

AUTHOR

Vaasvi Kuthiala, Strawberry Fields High School

ABSTRACT

This study develops a predictive model for elite badminton match outcomes to identify the key performance drivers in the sport. Using a comprehensive dataset of 3,761 men’s singles matches from the BWF World Tour (2018-2021), features have been engineered to capture player skill, via custom Elo rating system, experience, recent form and match context. The Elo was then benchmarked against logistic regression and an optimized XGBoost classifier, with evaluations tested on a held-out test set. The XGBoost model achieved superior prediction accuracy of 76.49%, statistically improving upon traditional methods.