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

Predicting competitive alpine skiing performance using conventional statistical methods has proven challenging. Many studies assessing the relationship between physiological performance and skiing outcomes have employed statistical methods of questionable validity. Furthermore, the reliance on Fédération Internationale de Ski (FIS) points as a performance outcome variable presents additional limitations due to its potential unreliability in reflecting short-term, sport-specific performance. These factors complicate the selection of appropriate tests and the accurate prediction of competitive outcomes.

This study aimed to evaluate the predictive power of a generalized physiological test battery for alpine skiing performance, as measured by FIS points, utilizing multivariable data analysis (MVDA). Physiological test results from a total of twelve (n = 12) world-class female skiers were included in the analysis.

The result on goodness of regression (R²) and goodness of prediction (Q²) in this study indicate that valid Orthogonal Projection to Latent Structures (OPLS) models for both Slalom and Giant Slalom can be generated (R² = 0.39 to 0.40, Q² = 0.21 to 0.15), but also that competition performance still cannot be predicted at a group level (low Q2). In contrast, higher predictive power of competitive performance was achieved on an individual level using the same data (R² = 0.88 to 0.99 and Q² = 0.64 to 0.96).

The findings of this investigation indicate that the selected tests employed in this study exhibit limited generalizability for the assessment of elite alpine skiers, as the predictive value of specific physiological parameters on competitive performance appears to be highly athlete-dependent.