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. 2024 Jan 11;10(1):6.
doi: 10.1186/s40798-023-00672-7.

Individual In-Situ GPS-Derived Acceleration-Speed Profiling: Toward Automatization and Refinement in Male Professional Rugby Union Players

Nathan Miguens  1   2 , Franck Brocherie  3 , Loïc Moulié  4 , Patrick Milhet  4 , Mathieu Bon  4 , Pierre Lassus  4 , Jean-François Toussaint  1   2   5 , Adrien Sedeaud  6   7
Affiliations

Individual In-Situ GPS-Derived Acceleration-Speed Profiling: Toward Automatization and Refinement in Male Professional Rugby Union Players

Nathan Miguens et al. Sports Med Open. .

Abstract

Background: Recently a proof-of-concept was proposed to derive the soccer players' individual in-situ acceleration-speed (AS) profile from global positioning system (GPS) data collected over several sessions and games. The present study aimed to propose an automatized method of individual GPS-derived in-situ AS profiling in a professional rugby union setting.

Method: AS profiles of forty-nine male professional rugby union players representing 61.5 million positions, from which acceleration was derived from speed during 51 training sessions and 11 official games, were analyzed. A density-based clustering algorithm was applied to identify outlier points. Multiple AS linear relationships were modeled for each player and session, generating numerous theoretical maximal acceleration (A0), theoretical maximal running speed (S0) and AS slope (ASslope, i.e., overall orientation of the AS profile). Each average provides information on the most relevant value while the standard deviation denotes the method accuracy. In order to assess the reliability of the AS profile within the data collection period, data were compared over two 2-week phases by the inter-class correlation coefficient. A0 and S0 between positions and type of sessions (trainings and games) were compared using ANOVA and post hoc tests when the significant threshold had been reached.

Results: All AS individual profiles show linear trends with high coefficient of determination (r2 > 0.81). Good reliability (Inter-class Correlation Coefficient ranging from 0.92 to 0.72) was observed between AS profiles, when determined 2 weeks apart for each player. AS profiles depend on players' positions, types of training and games. Training and games data highlight that highest A0 are obtained during games, while greatest S0 are attained during speed sessions.

Conclusions: This study provides individual in-situ GPS-derived AS profiles with automatization capability. The method calculates an error of measurement for A0 and S0, of paramount importance in order to improve their daily use. The AS profile differences between training, games and playing positions open several perspectives for performance testing, training monitoring, injury prevention and return-to-sport sequences in professional rugby union, with possible transferability to other sprint-based sports.

Key points: AS profiles computed from rugby union GPS data provide positional benchmarks during training and competition. This study provides automatic detection of atypical data and the computation of error measurement of theoretical maximal acceleration and speed components. This refinement constitutes a step forward for a daily use of ecological data by considering data collection and method reliabilities. This easy-to-implement approach may facilitate its use to the performance management process (talent identification, training monitoring and individualization, return-to-sport).

Keywords: Rugby union; Running; Sprint; Testing.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial, financial or any other kind of interest that could be construed as a potential competing interests.

Figures

Fig. 1
Fig. 1
Example of outliers’ identification in two individual GPS-induced AS relationships (computed from all training sessions and official games; one row for each player). From left to right: before, during and after outliers’ identification. The red dots are measurement errors corrected with density-based clustering algorithm (DBSCAN) whereas the black dots represent values corrected by 3σ-rule
Fig. 2
Fig. 2
Example of quantile regressions for selected dots, left and right panels represent a high variance in AO and in SO, respectively
Fig. 3
Fig. 3
Mean and error measurement (provided by the quantile regression method) of theoretical maximal acceleration (y-intercept of the AS linear relationship; A0) and theoretical maximal running speed (x-intercept of the AS relationship; S0) for each rugby union player. Positions are represented by color
Fig. 4
Fig. 4
Mean and error measurement (provided by the quantile regression method) of theoretical maximal acceleration (y-intercept of the AS linear relationship; A0) and theoretical maximal running speed (x-intercept of the AS relationship; S0) for each rugby player. Types of training and game are represented by different color
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