Abstract
This study aimed to identify the situational and positional effects on the variation of players’ technical performance in the UEFA Champions League from a long-term perspective. The technical performance of full match observations from outfield players in the UEFA Champions League from season 2009/2010 to 2016/2017 was analysed. The coefficient of variation of each variable of each player in each season was calculated to evaluate the match-to-match variation of technical performance. The variation of technical performance between players was compared across five playing positions and five situational variables using the non-clinical magnitude-based inference. Results showed that variables related to goal scoring, passing and organising from five playing positions showed a relatively higher variation among five competing contexts (ES: −0.72 ± 0.38 – 0.82 ± 0.61). Quality of team, quality of opponent and match outcome showed relatively greater influences than competition stage and match location on the variation of a player’s technical performance (ES: −0.72 ± 0.38 – 0.57 ± 0.56). The technical performances of wide players (full backs and wide midfielders) were more variable between the group and knockout stage (ES: −0.37 ± 0.32 – 0.28 ± 0.19). This study provides an important understanding of the associations among the variation of technical indicators, playing positions and situational variables. These profiles of technical variation could be used by coaches and analysts for talent identification, player recruitment, pre-match preparation and post-match evaluation.
Highlights
Performance analysis in sports is a powerful communication and feedback tool to prepare or guide players during practice (Memmert and Rein, 2018)
Given the focus on the perspective of situational effects, the variation of technical variables of players from all five playing positions was compared with a focus on each situational variable
This study established performance profiles for players’ technical variation considering five playing positions and five situational variables to examine their interactions on players’ technical variation from a long-term perspective using one of the largest samples published to date
Summary
Performance analysis in sports is a powerful communication and feedback tool to prepare or guide players during practice (Memmert and Rein, 2018). The within-subject variation is best represented using the coefficient of variation (CV) (Hopkins, 2000), as was previously accounted for quantifying the performance variability of players or teams (Rampinini et al, 2007; Bush et al, 2015b; Liu et al, 2016). This approach allows one to identify relationships between the variation of performance indicators and the match performance of players or teams, and key performance indicators could be identified
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