Methods for tracking athletes' competitive performance in skeleton.

Area of Science:

• Sports Science
• Biomechanics
• Performance Analytics

Background:

• Performance fluctuations in sports like skeleton are often greater than changes in an athlete's inherent ability.
• External factors such as venue and weather significantly impact race times.
• Accurate athlete performance tracking is crucial for training and competition strategies.

Purpose of the Study:

• To compare three distinct methods for tracking individual athlete performance in skeleton.
• To evaluate the efficacy of these methods in predicting Olympic performance.
• To identify the most reliable performance tracking metric for skeleton athletes.

Main Methods:

• Developed and applied three performance tracking methods using World Cup race data from 33 male and 34 female skeleton athletes.
• Fitted quadratic trajectories to race placing, percent behind winner, and race time.
• Utilized a mixed-effects model for race time analysis to account for mean race time variations.

Main Results:

• Initial model development showed similar predictive correlations (approx. 0.7) across all three methods.
• Olympic performance prediction varied by gender: race placing was superior for women (0.78 correlation).
• For men, race time (0.46 correlation) and percent behind the winner (0.30 correlation) outperformed race placing (0.14 correlation).

Conclusions:

• All three developed methods show potential utility for tracking skeleton athlete performance.
• The optimal performance metric differs between male and female athletes.
• Further investigation into these methods is warranted for skeleton and other sports.