Applying a Cluster-Analysis Approach to Monitor Training Load in Male Volleyball During the Preseason Period.

Abstract

Purpose: This study aimed to (1) classify the external-load measures carried out during the preseason period by male volleyball players via cluster technique identifying the most important external-load measures and (2) assess the differences between clusters in internal-load variables.

Methods: Twenty-two male Division 1 and 2 volleyball players (mean [SD] age 21.2 [3.0] y, stature 186.4 [6.0] cm, body mass 80.0[10.5 kg]) were recruited for this study. Players' external (jump, player load, acceleration, deceleration, and change of direction) and internal (percentage of peak heart rate, summated heart-rate zones, and session rating of perceived exertion) loads were monitored during 5 weeks of the preseason period for both Division 1 and Division 2 teams. External-load measures were classified via a 2-step cluster analysis followed by predicting importance analysis, while differences in internal-load measures between clusters were analyzed using linear mixed models.

Results: The 3 identified clusters classified the sessions in high (C1, 30.1%) moderate (C2, 31.8%), and low (C3, 38.1%) load. Predicting importance analysis found jump as the main cluster predictor (predicting value = 1), followed by player load (predicting value = 0.73). An effect of cluster was found on each internal-load measure (P < .001), with post hoc analyses showing lower values in C3 compared with C1 and C2 (P < .05, effect sizes ranges from small to moderate).

Conclusions: Volleyball coaches can adopt a monitoring system including cluster analysis to classify the preseason training sessions' load having a higher consideration for jump and player load as the main external-load measures.