ArticleJournal of human kinetics2021
Prediction of Match Outcomes with Multivariate Statistical Methods for the Group Stage in the UEFA Champions League.
Article in Journal of human kinetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Can different scores in first and second halves influence running and explosive-based measures?Biology of sport · 2025Article
- Physical performance discriminating winning and losing in UEFA Champions League: a full-season study.Biology of sport · 2025Article
- Home Advantage and Away Disadvantage of Teams in Champions League: Is It Valid for All Teams and Against Every Opponent?Journal of human kinetics · 2024Article
- Exploring the recurrent states of football teams' tactical organization on the pitch during Brazilian official matches.PloS one · 2024Article
- The seven phases of match status differentiate the running performance of soccer players in UEFA Champions League.Scientific reports · 2023Article
- Application of Deep Learning Technology in Strength Training of Football Players and Field Line Detection of Football Robots.Frontiers in neurorobotics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to analyse the win, draw, and loss outcomes of soccer matches with situational variables and performance indicators. Data from group stage matches spanning the ten years between the 2010/2011 and 2019/2020 seasons in the European Champions League, were used. One-way analysis of variance (ANOVA) and Tukey HSD (honestly significant difference) tests indicated performance indicators which affected the outcome of matches. K-mean clustering, with statistically significant variables, categorized the quality of the opposition into three clusters: weak, balanced, and strong. Multidimensional scaling (MDS) and decision tree analysis were applied to each of these clusters, highlighting that performance indicators of the teams differed according to the quality of their opponent. Furthermore, according to the decision tree analysis, certain performance indicators, including scoring first and shots on target, increased the chances of winning regardless of the quality of the opposition. Finally, particular performance indicators increased the chance of winning, while others decreased this, in accordance with the quality of the opposition. These findings can help coaches develop different strategies, before or during the match, based on the quality of opponents, situational variables, and performance indicators.
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Registered trials
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