Predicting and understanding shooting performance in professional biathlon: A Bayesian approach

TL;DR

This paper introduces a Bayesian hierarchical model to predict and analyze shooting performance in professional biathlon, accounting for various factors and performance evolution over a season, with strong predictive validation.

Contribution

It presents a novel Bayesian approach that captures athlete-specific and contextual effects, providing personalized insights into biathlon shooting performance.

Findings

Significant athlete-specific differences in shooting accuracy.

Model accurately predicts performance at individual and overall levels.

Performance evolves over the season, influenced by multiple factors.

Abstract

Biathlon is a unique winter sport that combines precision rifle marksmanship with the endurance demands of cross-country skiing. We develop a Bayesian hierarchical model to predict and understand shooting performance using data from the 2021/22 Women's World Cup season. The model captures athlete-specific, position-specific, race-type, and stage-dependent effects, providing a comprehensive view of shooting accuracy variability. By incorporating dynamic components, we reveal how performance evolves over the season, with model validation showing strong predictive ability at both overall and individual levels. Our findings highlight substantial athlete-specific differences and underscore the value of personalized performance analysis for optimizing coaching strategies. This work demonstrates the potential of advanced Bayesian modeling in sports analytics, paving the way for future research in biathlon and similar sports requiring the integration of technical and endurance skills.