Preseason multiple biomechanics testing and dimension reduction for injury risk surveillance in elite female soccer athletes: short-communication

ABSTRACT

Background

Injury risk is regularly assessed during the preseason in susceptible populations like female soccer players. However, multiple outcomes (high-dimensional dataset) derived from multiple testing may make pattern recognition difficult. Thus, dimension reduction and clustering may be useful for improving injury surveillance when results of multiple assessment tools are available.

Aim

To determine the influence of dimension reduction for pattern recognition followed by clustering on multiple biomechanical injury markers in elite female soccer players during preseason.

Methdology

We introduced the use of dimension reduction through linear principal component analysis (PCA), non-linear kernel principal component analysis (k-PCA), t-distributed stochastic neighbor embedding (t-sne), and uniform manifold approximation and projection (umap) for injury markers via grid search. Muscle strength, muscle function, jump technique and power, balance, muscle stiffness, exercise tolerance, and running performance were assessed in an elite female soccer team (n = 21) prior to the competitive season.

Results

As a result, umap facilitated the injury pattern recognition compared to PCA, k-PCA, and t-sne. One of the three patterns was related to a team subgroup with acceptable muscle conditions. In contrast, the other two patterns showed higher injury risk profiles. For our dataset, umap improved injury surveillance through multiple testing characteristics.

Conclusion

Dimension reduction and clustering techniques present as useful strategies to analyze subgroups of female soccer players who have different risk profiles for injury.

KEYWORDS:

• Sports
• biomechanics
• machine learning
• football
• non-linear reduction

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available in its supplementary materials and online at https://www.researchgate.net/publication/360477020_Preseason_multiple_biomechanics_testing_and_dimension_reduction_for_injury_risk_surveillance_in_elite_female_soccer_athletes_short-communication_supplementary_data.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/24733938.2022.2075558

Additional information

Funding

No funding is associated with this study. Carlos De la Fuente was supported by Pontificia Universidad Catolica de Chile.