Evidence map›Paper›PMID 35497181›Full record

ArticlePeerJ2022

Analysis of professional soccer players in competitive match play based on submaximum intensity periods.

Eduardo Caro, Miguel Ángel Campos-Vázquez, Manuel Lapuente-Sagarra, Toni Caparrós

Abstract read
In one paragraph

Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Eduardo CaroNational Institute of Physical Education of Catalonia (INEFC), Barcelona, Spain.
Miguel Ángel Campos-VázquezWatford Football Club, Watford, England.
Manuel Lapuente-SagarraFC Barcelona, Barcelona, Spain.
Toni CaparrósNational Institute of Physical Education of Catalonia (INEFC), Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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. Methods: 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. Results: 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. Conclusions: 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.

Indexed as

Athletic PerformanceRunningSoccerAccelerationGeographic Information SystemsHumansCluster analysisExternal loadGlobal positioning systemMaximum intensity periodPerformanceSoccer

Identifiers

PMID35497181
PMCPMC9053299

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.