Predicting competitive alpine skiing performance by multivariable statistics-the need for individual profiling.

Area of Science:

• Sports Science
• Physiological Performance Analysis
• Alpine Skiing Biomechanics

Background:

• Conventional statistical methods struggle to predict competitive alpine skiing performance.
• Existing studies often use questionable statistical validity and unreliable performance metrics like Fédération Internationale de Ski (FIS) points.
• This limits the selection of appropriate tests and accurate prediction of skiing outcomes.

Purpose of the Study:

• To evaluate the predictive capability of a physiological test battery for alpine skiing performance.
• To measure performance using Fédération Internationale de Ski (FIS) points.
• To utilize multivariable data analysis (MVDA) for prediction modeling.

Main Methods:

• Collected physiological test data from twelve world-class female alpine skiers.
• Applied multivariable data analysis (MVDA), specifically Orthogonal Projection to Latent Structures (OPLS).
• Assessed predictive power using goodness of regression (R²) and goodness of prediction (Q²).

Main Results:

• Orthogonal Projection to Latent Structures (OPLS) models showed limited predictive power at a group level for Slalom and Giant Slalom (low Q²).
• However, high predictive power for competitive performance was achieved at an individual level (R² = 0.88–0.99, Q² = 0.64–0.96).
• This indicates physiological parameters have athlete-dependent predictive value.

Conclusions:

• The selected physiological tests have limited generalizability for assessing elite alpine skiers.
• Predictive value of physiological parameters on competitive performance is highly athlete-dependent.
• Individualized assessment strategies are crucial for accurately predicting elite alpine skiing success.