National Institute of Physical Education of Catalonia (INEFC), Barcelona, Spain.
Neftchi Baku, Baku, Azerbaijan.
PeerJ. 2022 Apr 26;10:e13309. doi: 10.7717/peerj.13309. eCollection 2022.
The main objective of this study is to analyse sub-maximum intensity periods (SubMIP's) manifested by professional soccer players during official matches (number of events and time spent in each event), according to the player position, match halve and match, and also to group the players according to their SubMip values during the competition.
We collected a total of 247 individual records of 14 players using Global Positioning System (GPS) during 15 official league matches (Azerbaijan Premier League 2019-2020). We calculated both the number of SubMIPs events and the time each player spent in the SubMIPs zone (threshold of 85% MIP). We analysed the possible independence of the variables with the Kruskal-Wallis test and the possible specific relationships between the groups using a post-hoc analysis with Dunn's test. In order to explore the possible distribution of physical demands in homogeneous groups, a cluster analysis was performed.
The statistical analysis showed significant differences between the individual variables in the number of events and in the time spent by the player above the threshold in distance covered at speed >19.8 km/h (HSR), distance covered at speed >25.2 km/h (Sprint), acceleration density (AccDens), mean metabolic power (MetPow), metres per minute (Mmin) and high metabolic load distance >25.5 W/kg (HMLD). Differences were also found according to the playing position in MetPow, Mmin and between halves in AccDens, MetPow, Mmin. In the clustering based on the time spent by the player in SubMIPs, three main groups were described: (1) the centroid was located in lower values in each of the variables; (2) there were an accentuation of the AccDens variable; (3) all the variables, except AccDens, were accentuated.
The main differences with regard to SubMIPs were related to the player's individual physical performance and not to position. However, the player's position could act as an attractor and show significant differences during matches.
本研究的主要目的是分析职业足球运动员在正式比赛中表现出的次最大强度期(SubMIP),根据球员位置、比赛半场和比赛进行分析,并根据比赛期间的 SubMip 值对球员进行分组。
我们使用全球定位系统(GPS)收集了 14 名球员在 15 场正式联赛中的 247 个个人记录(2019-2020 年阿塞拜疆超级联赛)。我们计算了 SubMIP 事件的数量和每个球员在 SubMIP 区(MIP 的 85%阈值)花费的时间。我们使用 Kruskal-Wallis 检验分析了变量的独立性,并使用事后分析 Dunn 检验分析了组间可能的特定关系。为了探索同质组中可能的体力需求分布,进行了聚类分析。
统计分析显示,在速度大于 19.8km/h(HSR)、速度大于 25.2km/h(冲刺)、加速度密度(AccDens)、平均代谢功率(MetPow)、米/分钟(Mmin)和高代谢负荷距离 >25.5W/kg(HMLD)的距离上,每个球员的事件数量和超过阈值的时间之间的个体变量存在显著差异。在 MetPow、Mmin 和上下半场之间的 AccDens、MetPow、Mmin 中,根据球员位置也存在差异。在基于球员在 SubMIPs 中花费的时间进行聚类时,描述了三个主要组:(1)每个变量的质心都位于较低的值;(2)AccDens 变量突出;(3)除 AccDens 外,所有变量都被突出。
SubMIPs 的主要差异与球员的个人身体表现有关,而与位置无关。然而,球员的位置可能作为吸引子,在比赛中表现出显著差异。