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

Area of Science:

• Sports Science
• Biomechanics
• Performance Analysis

Background:

• Team defensive organization significantly influences player activity and match demands in handball.
• Understanding these demands is crucial for optimizing player performance and recovery.

Purpose of the Study:

• To develop an automated tool for detecting and classifying team defensive organizations using local positioning system (LPS) data.
• To quantify the physical demands (match demands) associated with specific defensive organizations in handball.

Main Methods:

• Analysis of LPS positional data (X and Y coordinates) from 25 Spanish League handball games.
• Development of an algorithm to automatically detect defensive phases and classify formations (e.g., 1-5, 0-6, 2-4, 3-3).
• Quantification of physical exertion (distance covered at various speeds) per player role and defensive strategy.

Main Results:

• High accuracy in automatically detecting game phases and classifying defensive formations.
• Identified discrepancies between intended and actual defensive organizations, impacting physical demands.
• Quantified the physical cost (demands) for each player role within different defensive setups.

Conclusions:

• Automated detection and classification of defensive strategies are feasible and accurate.
• Actual defensive organization significantly influences player physical demands, often differing from initial tactical plans.
• Quantifying the physical cost of defensive strategies is essential for optimizing player load management and tactical decision-making.