Applying Artificial Intelligence to Determine the Required Positions in Each Discipline for Overall Olympic Triathlon Success

Background: The aim of this study was to determine the optimal discipline position in the overall result of Olympic-distance triathlon. Methods: Data were extracted for free from the API (Application Programming Interface) service on the World Triathlon website and collected using a custom Python code. Statistical and machine learning analyses were employed within a Jupyter Notebook file. Linear and polynomial regressions were calculated between the overall race position and final positions in each discipline. Descriptive statistics and machine learning analyses were computed to identify the average position and most likely average position required in each discipline, respectively. A heatmap correlation analysis was conducted between the best overall triathletes and the best discipline triathletes. Differences between the two sub-databases were assessed using the student’s t-test. Results: Across all disciplines, the average position required in each segment remains consistently better than 13th place. The heat map shows a very small, negative correlation between the best time in each discipline and the overall best race time (p-values < 0.001). The student’s t-test establishes significant differences for all disciplines and overall race time (p-values < 0.001). Conclusions: Consistently high-level performance across all disciplines is essential for ensuring a podium finish or race victory in an Olympic triathlon. Achieving the best time in each discipline is not required to contend for victory, although running appears to be a strong predictor of overall race outcome.