Castellano Julen, Bellmunt Sergi, Resta Ricardo, Lopez Del Campo Roberto, Casamichana David
GIKAFIT research group, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain.
HUDL Company.
Biol Sport. 2025 Feb 4;42(3):169-176. doi: 10.5114/biolsport.2025.146784. eCollection 2025 Jul.
The main aims of this study were to describe the physical performance of the teams during official matches using a multivariate approach, considering their rivals and the final competition standings. The study analysed the professional teams that competed in the first division of Spanish football during the 2023-2024 season. A total of 756 physical performances of teams were analysed across 378 matches. Data for nine external match load variables were collected using the TRACAB optical tracking system: total distance and distance covered at high speeds (> 21, > 24 and > 28 km·h), total acceleration load, and the frequency of accelerations/decelerations (> 3 and > 4 m·s). Principal component analysis (PCA) and clustering analysis were used to reduce multidimensionality and facilitate grouping. 1) PCA grouped the external load variables into three components: intensity (characterized by a high number of accelerations and decelerations), velocity (characterized by high values in high-speed distance), and volume (characterized by a high total distance). 2) Teams' physical performances were primarily grouped into four clusters: cluster 1 (high intensity), cluster 2 (highest values across all physical variables), cluster 3 (lowest values across all physical variables), and cluster 4 (high velocity). 3) No significant differences were found in the distribution of physical performances within each cluster based on the teams' final rankings. 4) Teams' physical performances showed a tendency to play most of their matches against opponents from the same cluster. The clustering analysis revealed differences in physical demands across teams during the season, which can guide training and match preparation. Teams can use this knowledge to improve injury prevention and recovery management by aligning physical preparation with match external loads.
本研究的主要目的是采用多变量方法描述球队在正式比赛中的体能表现，同时考虑其对手和最终比赛排名。该研究分析了参加2023 - 2024赛季西班牙足球甲级联赛的职业球队。在378场比赛中，共分析了756次球队的体能表现。使用TRACAB光学跟踪系统收集了九个外部比赛负荷变量的数据：总距离和高速（>21、>24和>28公里·小时）下的跑动距离、总加速负荷以及加速/减速频率（>3和>4米·秒）。主成分分析（PCA）和聚类分析用于降维和便于分组。1）主成分分析将外部负荷变量分为三个成分：强度（以大量的加速和减速为特征）、速度（以高速距离的高值为特征）和运动量（以总距离高为特征）。2）球队的体能表现主要分为四类：第1类（高强度）、第2类（所有体能变量值最高）、第3类（所有体能变量值最低）和第4类（高速度）。3）根据球队的最终排名，在每个类别内的体能表现分布上未发现显著差异。4）球队的体能表现显示出一种趋势，即他们的大多数比赛是与同一类别的对手进行的。聚类分析揭示了赛季中各球队在体能需求上的差异，这可以指导训练和比赛准备。球队可以利用这些知识，通过使体能准备与比赛外部负荷相匹配，来改善伤病预防和恢复管理。
