A study on the key predictors of 100m sprint performance: identification and ranking

Abstract

Background and Study Aim. The sprint is one of the most prestigious events in athletics, requiring a combination of explosive power and acceleration. However, talent identification remains a challenge, as early sprint performance does not always predict long-term success. The aim of this study is to identify and rank the most relevant predictors of 100m sprint performance among young athletes. Material and Methods. This study involved 11 subjects (6 boys and 5 girls) born in 2008, who were in their first year of the U18 category in 2024. They participated in the 100m event both in 2023 and 2024. Speed, strength, coordination, and mobility were assessed using tests, including 30m sprint from a standing start, 60m sprint from a standing start, 30m sprint with a flying start, bounding strides over 30m, standing long jump, triple jump, countermovement jump, medicine ball throw, and Sit and Reach test. Specific agility was evaluated using the Witty SEM system. Balance parameters and lower limb strength were assessed with the SensaBalance platform and the OptoJump system, respectively. Statistical analysis was conducted using Pearson’s correlation, Spearman’s rank correlation, and Bootstrapped Pearson’s correlation to identify the most relevant predictors. The bootstrapping technique was applied to enhance the reliability of the correlation estimates. Statistical significance was set at p < 0.05. Results. The analysis revealed that not all of the 12 assessed tests had significant predictive value for sprint performance. Parameters such as specific agility, static and dynamic balance, squat jump, and Sit and Reach mobility test did not show strong correlations with 100m sprint outcomes. These findings support the use of selected physical and anthropometric variables in a secondary selection model for young sprinters. Conclusions. This study confirms that specific physical, psychomotor, and anthropometric variables significantly influence 100m sprint performance among young athletes. It also proposes a secondary selection model that incorporates the most relevant predictors to support talent identification and training optimization.