ArticlePloS one2023
A deep dive into the use of local positioning system in professional handball: Automatic detection of players' orientation, position and game phases to analyse specific physical demands.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- How different defensive formations affect physical match demands in the German Handball-Bundesliga: a comparison of the attacking and defending team.Frontiers in sports and active living · 2026Article
- Evaluating external load responses to cumulative playing time and position in the European Handball Federation Women's Euro 2022 through an IoT and Big Data architecture approach.Biology of sport · 2025Article
- Differences in game-based performance by playing position in young elite male team handball players.Frontiers in sports and active living · 2025Article
- Inter-individual differences in weekly training load in international-level handball: the effect of congested match schedules.Frontiers in sports and active living · 2025Article
- Neuromuscular Load in Professional Women's Handball: Segmentation of the Player Load and the Impacts at Group and Individual Level.Sensors (Basel, Switzerland) · 2024Article
- A framework for player movement analysis in team sports.Frontiers in sports and active living · 2024Review
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this study is to automate and analyse the quantification of external load during an elite men's handball match. This study was carried out using data from a local positioning system and inertial measurement units. The literature review leads us to assume that physical demands are different depending on position, player specialty and phases of the game. In order to do this analysis, raw data was used from professional competitors of a Spanish club during National and European competition matches. First, a game phase algorithm was designed to automate phase recognition. Then, a descriptive evaluation of the means and standard deviation was performed with the following variables: total distance, total time, total Accel'Rate, the percentages of distance and time per speed and displacement direction. A Kruskal Wallis test was applied to normalized distance and normalized Accel'Rate. Defensive play showed the highest values on covered distance (930.6 ± 395.0 m). However, normalized distance showed significant differences (p<0.05) across all phases with defensive play (558.8 ± 53.9 m/10min) lower than offensive play (870.3 ± 145.7 m/10min), offensive transition (1671.3 ± 242.0 m/10min) or defensive transition (1604.5 ± 242.0 m/10min). Regarding position, wing players covered the most distance (2925.8 ± 998.8 m) at the second highest intensity (911.4 ± 63.3 m/10min) after offensive back players (1105.0 ± 84.9 m/10min). Significant difference in normalized requirements were found between each playing position: goalkeepers, wings, versatile backs, versatile line players, offensive backs and defensive backs (p<0.05), so a separation between offensive or defensive specialists is plausible and necessary. In conclusion, as physical demands differ for each game phase, activity profile among players is modulated by their playing position and their specialty (offense, defense or none). This study may help to create individual training programs according to precise on-court demands.
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