Intensity Thresholds for External Workload Demands in Basketball: Is Individualization Based on Playing Positions Necessary?

Author:

Ibáñez Sergio J.1ORCID,Gómez-Carmona Carlos D.12ORCID,López-Sierra Pablo1ORCID,Feu Sebastián1ORCID

Affiliation:

1. Research Group in Optimization of Training and Sports Performance (GOERD), Department of Didactics of Music Plastic and Body Expression, Faculty of Sport Science, University of Extremadura, 10003 Caceres, Extremadura, Spain

2. BioVetMed & SportSci Research Group, International Excellence Campus “Mare Nostrum”, Faculty of Sport Sciences, University of Murcia, 30720 San Javier, Murcia, Spain

Abstract

Currently, basketball teams use inertial devices for monitoring external and internal workload demands during training and competitions. However, the intensity thresholds preset by device manufacturers are generic and not adapted for specific sports (e.g., basketball) and players’ positions (e.g., guards, forwards, and centers). Using universal intensity thresholds may lead to failure in accurately capturing the true external load faced by players in different positions. Therefore, the present study aimed to identify external load demands based on playing positions and establish different intensity thresholds based on match demands in order to have specific reference values for teams belonging to the highest competitive level of Spanish basketball. Professional male players (n = 68) from the Spanish ACB league were monitored during preseason official games. Three specific positions were used to group the players: guards, forwards, and centers. Speed, accelerations, decelerations, impacts/min, and player load/min were collected via inertial devices. Two-step clustering and k-means clustering categorized load metrics into intensity zones for guards, forwards, and centers. Guards covered more distance at high speeds (12.72–17.50 km/h) than forwards and centers (p < 0.001). Centers experienced the most impacts/min (p < 0.001). Guards exhibited greater accelerations/decelerations, albeit mostly low magnitude (p < 0.001). K-means clustering allowed the setting of five zones revealing additional thresholds. All positions showed differences in threshold values (p < 0.001). The findings provide insights into potential disparities in the external load during competition and help establish position-specific intensity thresholds for optimal monitoring in basketball. These data are highly applicable to the design of training tasks at the highest competitive level.

Funder

Spanish National Agency of Investigation

Publisher

MDPI AG

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