Evidence map›Paper›PMID 40847588›Full record

ArticleEuropean journal of sport science2025

Machine Learning in Rugby Union: Predicting and Identifying Key Performance Indicators for Professional Rugby Union Players in Match Play Based Workload.

Xiangyu Ren, Simon Boisbluche, Kilian Philippe, Mathieu Demy, Sami Äyrämö, Ilkka Rautiainen, Shuzhe Ding, Jacques Prioux

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of sport science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Xiangyu RenSino-French Joint Research Center of Sport Science, Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, College of Physical Education and Health, East China Normal University, Shanghai, China.ORCID https://orcid.org/0000-0003-1549-8625
Simon BoisblucheRugby Club Vannes, French Rugby Federation, Vannes, France.
Kilian PhilippeLaboratory of Movement, Balance, Performance and Health (MEPS, EA-4445), University of Pau and Pays de l'Adour, Tarbes, France.ORCID https://orcid.org/0000-0001-6071-2868
Mathieu DemySino-French Joint Research Center of Sport Science, Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, College of Physical Education and Health, East China Normal University, Shanghai, China.
Sami ÄyrämöFaculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland.
Ilkka RautiainenFaculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland.
Shuzhe DingSino-French Joint Research Center of Sport Science, Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, College of Physical Education and Health, East China Normal University, Shanghai, China.
Jacques PriouxSino-French Joint Research Center of Sport Science, Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, College of Physical Education and Health, East China Normal University, Shanghai, China.

Funding

China Scholarship Council 202106140006
6 · The paper itself

Abstract

Rugby union is an intermittent high-intensity contact sport requiring the analysis of various training and match metrics. Time-motion analysis and video analysis have enhanced the understanding of the interplay between these two factors. However, limited studies have investigated the effect of workload on key performance indicators (KPIs) during matches. In this study, data collected from the global positioning system (GPS) were used to calculate cumulative workload values over 7, 14, and 21 days prior to each game. After dimensionality reduction through principal component analysis (PCA), these workload values were employed as features, with game KPIs as target variables. Modeling was conducted using linear regression (LR), support vector regression (SVR), random forest regression (RFR), and light gradient boosting machine (LightGBM) for regression tasks. The superiority of the model was assessed by coefficient of determination (

Indexed as

Athletic PerformanceFootballMachine LearningWorkloadGeographic Information SystemsHumansLinear ModelsMalePrincipal Component AnalysisTime and Motion StudiesYoung Adultperformance optimizationrugby trainingteam sportstime‐motion analysisvideo analysisworkload monitoring

Identifiers

PMID40847588
PMCPMC12373978

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.