ArticleScientific reports2024
Locomotor performance parameters as predictors of high-performing male soccer teams. A multiple-season study on professional soccer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Predicting movement intensity through anthro-fitness profiling and its association with match outcomes in youth badminton: a logistic regression-based machine approach.BMC sports science, medicine & rehabilitation · 2026Article
- Effects of Between-Sprint Running Intensity on Repeated-Sprint Performance in Professional Soccer Players.Sports (Basel, Switzerland) · 2026Article
- Influence of contextualized physical performance metrics on offensive and defensive outcomes in professional football players.Biology of sport · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to explore the interplay between locomotor demands and goal differentials to better understand their combined influence on overall success. Spanning three competitive seasons within the male Turkish Super League, this study analyzed all participating teams across 124 matches. Locomotor demands, including total distance (m) covered (TD), distances covered (m) at different speed thresholds (0.21-2.0 m/s; 2.01-4.0 m/s; 4.01-5.5 m/s; and 5.5-7.7 m/s), and the number of accelerations in range of 5.5-7.0 m/s (n), were quantified using an optical tracking system. Subsequently, regression models were employed to predict the total points earned by all teams over the three seasons. The logistic regression model, tailored to predict team categorization as high-points earners (HPE) or low-points earners (LPE) based on locomotor variables, exhibited a mean accuracy of 74%. Notably, total distance covered, running speed intervals between 4.4 and 5.5 m/s, and the number of accelerations in range of 5.5-7.0 m/s emerged as significant predictors of team success. Our findings highlight the pivotal role of running speed (4.01-5.5 m/s), number of accelerations, and total distance in predicting success for high-performing teams. Coaches can leverage these insights to refine training programs, thereby optimizing team performance, and fostering success in competitive environments.
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