Determining the Most Predictive Discipline in Olympic Triathlon: A Machine Learning Approach

Background: The aim of the present study was to identify the discipline with the greatest predictive value for overall performance in Olympic-distance triathlon. Methods: Data were extracted from the API (Application Programming Interface) service on the World Triathlon website by signing up for the free service. A custom Python code was written to perform different data collection operations. General statistical analyses and machine learning analyses were performed by creating a Jupyter Notebook file. TensorFlow and PyTorch libraries were used for machine learning analysis. Results: Fifty percent of the employed models identified cycling as the most predictive discipline for race success for both sexes, whereas 33% selected running as the determining discipline. To achieve a podium finish, approximately 78% of the models classified running as the most predictive discipline for males, and approximately 56% of the models did so for females. For finishes between fourth and tenth place, approximately 78% of the models proposed running as the most predictive discipline for both sexes. Swimming was never identified as the most predictive discipline by the majority of models for any group or sex. Conclusion: The most predictive discipline in Olympic triathlon depends on the athlete’s sex and competitive level. Nonetheless, running remains the most consistently predictive discipline, whereas swimming rarely acts as a performance differentiator.