A mathematical model of speedskating performance improvement for goal setting and program evaluation

Area of Science:

• Sports Science
• Biomechanical Analysis
• Performance Analytics

Background:

• World-class performance in speedskating has historically shown continuous improvement over time.
• Understanding the trajectory of performance improvement is crucial for elite athlete development and training program design.

Purpose of the Study:

• To develop mathematical performance curves for speedskating events based on historical data.
• To identify trends and patterns in performance improvement, including the principle of diminishing returns.
• To establish criteria for validating performance models in sports analytics.

Main Methods:

• Utilized an unconstrained non-linear least squares iterative curve-fitting technique to analyze chronological performance data.
• Applied a non-linear model to represent the principle of diminishing returns in human performance.
• Set minimum acceptance criteria for model validation, including coefficient of determination and future performance predictions.

Main Results:

• Successfully generated validated mathematical performance curves for seven out of eight male and female speedskating events.
• Observed a distinct four-year Olympic cycle influencing performance trends.
• Identified a significant shift in performance trends occurring around the mid-1960s.

Conclusions:

• The developed mathematical curves accurately model speedskating performance improvement, incorporating the concept of diminishing returns.
• These models offer practical applications for setting objective performance goals and evaluating training programs.
• The findings highlight a notable change in performance trajectories starting in the mid-20th century, impacting sports science research.