Quantifying match time required for reliable in-situ profiling in rugby union players - Maviel, C.; Couderc, A.; Duché, P.; Morin, J. B.; Vercruyssen, F. (29th Annual Congress of the European College of Sport Science, 2-5 July 2024, Book of Abstracts, 2024)
INTRODUCTION: The force-velocity profile (P-FV) is commonly used to assess sprint-related physical qualities in sports, to orient training based on athletes performance. However, this method has limitations, particularly the inability to gather physical engagement`s information under actual match conditions in rugby. In this context, the use of Global Position System (GPS) technology has paved the way for collecting in-situ acceleration and speed data during rugby matches. Morin et al.[1] have introduced an in-situ method using GPS to generate an Acceleration-Speed (AS) profile conceptually close to the P-FV [2]. It is therefore crucial to understand players profiles in a match within a minimal time frame to avoid the confounding measures relative to the effects of training. This study aims to determine the saturation point of the AS profile in-match for rugby players, to provide a precise minimal time window for obtaining a meaningful AS profile in a match situation. METHODS: The playing time of 25 professional rugby players was recorded using GPS technology and segmented into four groups from 40 to 160 minutes of play. The analysis was conducted over 8 official matches during a period of 49 days. This segmentation enabled the gradual incorporation of data into the AS profile analysis. For each interval, the impact of integrating new match data on the AS profile outputs was assessed, aiming to identify the saturation point where additional data did not induce significant changes and thus, altered the profile. A repeated measures ANOVA was applied, and the significant differences were then explored using Bonferroni post-hoc tests, allowing for detailed comparisons between time windows. This methodology led to identifying the saturation point for theoretical maximum acceleration (A0) and maximum acceleration (S0), namely the threshold beyond which adding new gameplay data in the analysis does not result in statistically significant changes in these parameters. RESULTS: The ANOVA revealed a significant effect under all tested conditions with a loss of statistical significance from 120 minutes of play for acceleration (p = 0.12) and speed (p = 0.15), with an intra-subject variability of 3.29% for A0 and 1.99% for S0. Beyond 160 minutes, a lack of significant effect was observed for A0 (p = 1.00) and S0 (p = 0.99), with an intra-subject variability of 1.51% for A0 and 1.20% for S0. CONCLUSION: These findings indicate that the significant effect is not observed after 120 minutes of play, despite an important intra-subject variability. However, beyond 160 minutes, this variability is reduced, suggesting a clear saturation point. Therefore, it is recommended for practitioners to use 160 minutes of actual match play (equivalent to about 2 complete games), to derive a reliable AS profile.