Contextualizing Physical Data in Professional Handball: Using Local Positioning Systems to Automatically Define Defensive Organizations - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Sensors Année : 2022

Contextualizing Physical Data in Professional Handball: Using Local Positioning Systems to Automatically Define Defensive Organizations

Claude Karcher
  • Fonction : Auteur
Xavier Reche
  • Fonction : Auteur
Roger Font
  • Fonction : Auteur
John Komar

Résumé

In handball, the way the team organizes itself in defense can greatly impact the player’s activity and displacement during the play, therefore impacting the match demands. This paper aims (1) to develop an automatic tool to detect and classify the defensive organization of the team based on the local positioning system data and check its classification quality, and (2) to quantify the match demands per defensive organization, i.e., defining a somehow cost of specific defensive organizations. For this study, LPS positional data (X and Y location) of players from a team in the Spanish League were analyzed during 25 games. The algorithm quantified the physical demands of the game (distance stand, walk, jog, run and sprint) broken down by player role and by specific defensive organizations, which were automatically detected from the raw data. Results show that the different attacking and defending phases of a game can be automatically detected with high accuracy, the defensive organization can be classified between 1–5, 0–6, 2–4, and 3–3. Interestingly, due to the highly adaptive nature of handball, differences were found between what was the intended defensive organization at a start of a phase and the actual organization that can be observed during the full defensive phase, which consequently impacts the physical demands of the game. From there, quantifying for each player role the cost of each specific defensive organization is the first step into optimizing the use of the players in the team and their recovery time, but also at the team level, it allows to balance the cost (i.e., physical demand) and the benefit (i.e., the outcome of the defensive phase) of each type of defensive organization.
Fichier principal
Vignette du fichier
sensors-22-05692-v2.pdf (1.67 Mo) Télécharger le fichier
Origine Publication financée par une institution

Dates et versions

hal-04330575 , version 1 (07-02-2024)

Identifiants

Citer

Brice Guignard, Claude Karcher, Xavier Reche, Roger Font, John Komar. Contextualizing Physical Data in Professional Handball: Using Local Positioning Systems to Automatically Define Defensive Organizations. Sensors, 2022, 22 (15), pp.5692. ⟨10.3390/s22155692⟩. ⟨hal-04330575⟩

Collections

UNIV-LYON1 UDL
7 Consultations
9 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More