Establishing the links between running technique and economy remains elusive due to high inter-individual variability. Clustering runners by technique may enable tailored training recommendations, yet it is unclear if different techniques are equally economical and whether clusters are speed-dependent. This study aimed to identify clusters of runners based on technique and to compare cluster kinematics and running economy. Additionally, we examined the agreement of clustering partitions of the same runners at different speeds. Trunk and lower-body kinematics were captured from 84 trained runners at different speeds on a treadmill. We used Principal Component Analysis for dimensionality reduction and agglomerative hierarchical clustering to identify groups of runners with a similar technique, and we evaluated cluster agreement across speeds. Clustering runners at different speeds independently produced different partitions, suggesting single speed clustering can fail to capture the full speed profile of a runner. The two clusters identified using data from the whole range of speeds showed differences in pelvis tilt and duty factor. In agreement with self-optimisation theories, there were no differences in running economy, and no differences in participants characteristics between clusters. Considering inter-individual technique variability may enhance the efficacy of training designs as opposed to “one size fits all” approaches.
Original languageEnglish
JournalSports Biomechanics
Publication statusPublished - 11 Jul 2024


  • running performance
  • energetics
  • kinematics
  • unsupervised learning
  • machine learning

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