Evidence map›Paper›PMID 41095074›Full record

ArticleSensors (Basel, Switzerland)2025

Physical Workload Patterns in U-18 Basketball Using LPS and MEMS Data: A Principal Component Analysis by Quarter and Playing Position.

Sergio J Ibáñez, Markel Rico-González, Carlos D Gómez-Carmona, José Pino-Ortega

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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.

Sergio J IbáñezResearch Group in Optimization of Training and Sports Performance (GOERD), Department of Didactics of Music, Plastic and Body Expression, Faculty of Sport Science, University of Extremadura, 10003 Caceres, Spain.ORCID 0000-0001-6009-4086
Markel Rico-GonzálezDepartment of Music, Plastic and Body Expression, University of the Basque Country, 48940 Leioa, Spain.ORCID 0000-0002-9849-0444
Carlos D Gómez-CarmonaResearch Group in Optimization of Training and Sports Performance (GOERD), Department of Didactics of Music, Plastic and Body Expression, Faculty of Sport Science, University of Extremadura, 10003 Caceres, Spain.ORCID 0000-0002-4084-8124
José Pino-OrtegaBioVetMed & SportSci Research Group, Department of Physical Activity and Sport, University of Murcia, 30100 Murcia, Spain.ORCID 0000-0002-9091-0897

Funding

Research Groups Aid Program from the Regional Government of Extremadura (Min-istry of Education, Science and Professional Training), co-financed by the European Union through the European Regional Development Fund GR24133Spanish National Research Agency via the project 'Scientific and Technological Support for Analyzing Basketball Training Workload Based on Gender, Player Level, and Competitive Period' MCIN/AEI/10.13039/501100011033Sports Superior Council of the Spanish Government via International Basketball Research Network IBRN 20-24Sports Superior Council of the Spanish Government via International Basketball Research Network IBRN 20-25
6 · The paper itself

Abstract

Basketball is a high-intensity, intermittent sport in which physical demands fluctuate depending on different contextual variables. Most studies addressed these demands in isolation without integrative approaches. Therefore, the present study aimed to identify key variables explaining players' physical workload across game quarters and playing positions through principal component analysis (PCA). Ninety-four elite U18 male basketball players were registered during the EuroLeague Basketball ANGT Finals using WIMU PRO™ multi-sensor wearable devices that integrate local positioning systems (LPS) and microelectromechanical systems (MEMS). From over 250 recorded variables, 31 were selected and analyzed by PCA for dimensionality reduction, analyzing the effects of game quarter and playing position. Five to eight principal components explained 61-73% of the variance per game quarter, while between four and seven components explained 64-69% per playing position. High-intensity variables showed strong component loadings in early quarters, with explosive distance (loading = 0.898 in total game, 0.645 in Q1) progressively declining to complete absence in Q4. Position-based analysis revealed specific workload profiles: guards required seven components to explain 69.25% of the variance, with complex movement patterns, forwards showed the highest explosive distance loading (0.810) among all positions, and centers demonstrated concentrated power demands, with PC1 explaining 34.12% of the variance, dominated by acceleration distance (loading = 0.887). These findings support situational and individualized training approaches, allowing coaches to design individual training programs, adjust rotation strategies during games, and replicate demanding scenarios in training while minimizing injury risk.

Indexed as

BasketballMicro-Electrical-Mechanical SystemsWorkloadAthletic PerformanceGeographic Information SystemsHumansMalePrincipal Component AnalysisWearable Electronic Devicesinertial measurement unitsmultivariate statisticsperformance analysisteam sportsultra-wideband positioningwearable sensorsworkload monitoring

Identifiers

PMID41095074
PMCPMC12527086

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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.