Univ Rouen Normandie, Normandie Univ, CETAPS UR 3832, Rouen, France.
Faculty of Medicine, Mitochondria, Oxidative Stress and Muscular Protection Laboratory (EA 3072), University of Strasbourg, Strasbourg, France.
PLoS One. 2023 Aug 16;18(8):e0289752. doi: 10.1371/journal.pone.0289752. eCollection 2023.
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.
本研究旨在对手球比赛中外部负荷进行量化分析和自动化处理。该研究使用了来自局部定位系统和惯性测量单元的数据。文献综述表明，球员的位置、特长和比赛阶段等因素会影响身体需求。为了进行分析，我们使用了一家西班牙俱乐部的职业运动员在全国和欧洲比赛中的原始数据。首先，设计了一个比赛阶段算法来自动识别阶段。然后，对总距离、总时间、总加速度率、各速度和位移方向的距离和时间百分比、平均速度和加速度率进行了均值和标准差的描述性评估。对归一化距离和归一化加速度率进行了 Kruskal Wallis 检验。防守阶段的覆盖距离最高（930.6 ± 395.0 m）。然而，防守阶段的归一化距离与进攻阶段（558.8 ± 53.9 m/10min）、进攻转换阶段（1671.3 ± 242.0 m/10min）和防守转换阶段（1604.5 ± 242.0 m/10min）相比有显著差异（p<0.05）。就位置而言，边锋球员的跑动距离最长（2925.8 ± 998.8 m），强度第二高（911.4 ± 63.3 m/10min），仅次于进攻后位（1105.0 ± 84.9 m/10min）。不同位置的球员在归一化需求方面存在显著差异（p<0.05）：守门员、边锋、全能后卫、全能中场球员、进攻后卫和防守后卫。因此，进攻或防守专家之间的分工是合理且必要的。综上所述，由于各比赛阶段的身体需求不同，球员的活动模式会根据他们的位置和特长（进攻、防守或无）进行调整。本研究有助于根据特定的比赛需求制定个性化训练计划。
