Article Title



Greg A. Ryan1, Hannah Ramirez2, Cameron Horsfall2, Drew S. DeJohn3, Lucas Haaren2, Stephen J. Rossi2. 1Piedmont University, Demorest, GA. 2Georgia Southern University, Statesboro, GA. 3South Georgia Tormenta FC, Statesboro, GA.

BACKGROUND: In-game performance elicits maximal effort from players in order to maximize performance and increase the likelihood of success. As the season progresses, and weekly training loads accumulate, players may not fully recover, which could impact player performance during matches. PURPOSE: To determine the variation between in-game performance metrics during a competitive season in professional soccer players. METHODS: Data from 26 third division professional male soccer players were monitored with an individual GPS bioharness during 19 games were included for analyses. Bioharness metrics of total distance (TD), maximum speed (MS), sprint distance (SD), number of sprints (#S), and explosive distance (ED) were used for analyses. Matches were combined into three groups for all analyses. Each group consists of matches during two-month periods (April-May [6 matches], June-July [7], August-September [6]). Due to violations of normality, a Kruskal-Wallis analysis of variance (ANOVA) was used to analyze main effect differences. A Fisherโ€™s least significant difference post hoc pairwise analysis determined intervariable differences. Alpha was set at 0.05 for all significant main effect findings. RESULTS: Significant main effect differences were noted for TD (๐œ’;2(2) = 9.502, p < 0.01), SD (๐œ’;2(2) = 7.004, p = 0.03), #S (๐œ’;2(2) = 8.893, p = 0.01), and ED (๐œ’;2(2) = 11.694, p < 0.01). Post-hoc analyses revealed that players had increased TD (p = 0.02; p = 0.03), SD (p = 0.05;p = 0.05), #S (p = 0.03; p = 0.03), and ED (p < 0.01; p = 0.03) in games played in April-May compared to June-July and August-September, respectively. No differences were noted between June-July and August-September matches in any variables of interest. MS was not impacted as the season progressed (p = 0.38). CONCLUSIONS: In-game performance variables decreased ~9-15% as the season progressed. While game and tactical decisions may somewhat influence in-game performance variables, it appears that player performance is negatively impacted as the season progresses. It is possible that players are not adequately recovered during the week prior to the next competition.

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