Work

Multi-Output Regression for the Prediction of World-Class Performances in Women’s Handball

Year: 2025

Type: article

Abstract: The purpose of this study was to investigate the relationship between workload and in-game technical and athletic performance. To achieve this, multi-output regression models were used to predict 7 performance indicators based on previous training and game athletic workloads measured by Inertial Measurement Units (IMU) indicators, previous in-game actions annotated by staff members and game contextual factors. We compared 4 single-output models (kNN, regression tree, random forest and neural networks), their multi-output counterparts and a dummy baseline (predicting the average performance of each player over the last month) in terms of average RMSE (aRMSE) during a chronological evaluation where previous trainings and games data are used to train models to predict the next game performances. Overall, the use of multi-output regression models enabled a decrease of the average predictive error (aRMSE = 4.23) in regards to their single-output counterparts (aRMSE = 4.35) while providing a significant decrease of average computation times (4.75 to 0.82 seconds). Among the 4 multi-output models, only the kNN (aRMSE = 3.852) and random forest (aRMSE = 3.888) performed better than thedummyregressor (aRMSE = 3.944). These results point towards that physical training may have a limited impact on game performance.

Source: IEEE Access

DOI: 10.1109/access.2025.3560838

Authors: Rayane Elimam, Nicolas Sutton-Charani, Jacques Prioux, Jacky Montmain, Stéphane Perrey

Institutions: Université de Montpellier, IMT Mines Alès, EuroMov Digital Health in Motion, Université Rennes 2, Laboratoire Mouvement Sport Santé

Language: English

Topic: Sports Performance and Training

Subfield: Orthopedics and Sports Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075