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-1), total acceleration load, and the frequency of accelerations/decelerations (> 3 and > 4 m·s²). 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.
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