Predicting performance is a core focus in the field of talent identification and -selection. Most research has attempted to define distinct (e.g., psychological, physiological) skills and characteristics that distinguish elite from non-elite athletes. However, no specific skills and characteristics have been identified that uniformly predict elite sports performance . This may be due to the fact that sports performance comes about through a complex interaction between a multitude of skills and characteristics . Therefore, researchers have proposed to implement “sample-based” tests, in which a constellation of skills and characteristics of athletes is measured in a representative context [2,3]. In soccer, for instance, a typical sample is a small-sided game (SSG), in which the relevant (interactions between) personal, environmental, and task constraints remain intact. Accordingly, the focus of the current study was twofold. First, we examined whether SSGs can be considered as representative for 11 vs 11 soccer games. Second, we tested whether, in contrast with distinct skills, soccer players’ performance in SSGs is predictive of their performance in 11 vs 11 games.
Sixty-three players of the U15, U17, U19, and U23 teams of a professional Dutch soccer club were included. These teams played between 11 and 17 SSGs during the season, which we recorded. To assess performance in the SSGs, we measured 9 offensive and defensive indicators (e.g., passes forward, offensive and defensive duels) using a coding scheme. Moreover, physiological and motor skills of the players (i.e., sprinting, interval endurance, agility) were measured with separate tests in the season. The criterion was the performance of players across six 11 vs 11 matches.
Results and Discussion
The distribution of actions performed in SSGs was comparable to the distribution in the matches (rs = .78), which suggests that SSGs provide a representative selection context. Furthermore, individual performance in the SSGs and 11-vs-11 matches was moderately-to-largely correlated for 6 of the 9 performance indicators. (rs = .35 - .53). In contrast, the physiological and motor tests showed trivial to small correlations with the offensive and defensive performances demonstrated by players in the matches (rs = -.20 - .15). Together, this suggests a moderate-to-large predictive validity of individual SSG performance, but a small predictive validity of the physiological and motor tests.
This study provides first insights into the usefulness of SSGs in predicting soccer performance. More generally, the results support the idea that sample-based tests provide better predictions of sports performance than distinct skills and characteristics measured in isolation [2,3]. Future research may employ novel technologies to measure performance of talented athletes more comprehensively.
 Johnston, K., Wattie, N., Schorer, J., & Baker, J. (2018). Talent identification in sport: a systematic review. Sports Medicine, 48(1), 97-109.
 Den Hartigh, R. J. R., Niessen, A. S. M., Frencken, W. G. P., & Meijer, R. R. (2018). Selection procedures in sports: Improving predictions of athletes’ future performance. European journal of sport science, 18(9), 1191-1198.
 Bergkamp, T. L. G., Niessen, A. S. M., Den Hartigh, R. J. R., Frencken, W. G. P., & Meijer, R. R. (2019). Methodological issues in soccer talent identification research. Sports Medicine, 49(9), 1317-1335.
|Event title||19th ACAPS international congress|
|Degree of Recognition||International|
- talent identification
- talent development
- selection psychology