Reporting of clustering techniques in sports sciences: a scoping review

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About The Authors

Daniel Fernández
Department of Statistics and Operations Research (DEIO). Universitat Politècnica de Catalunya · BarcelonaTech (UPC), 08028 Barcelona, Spain.
Spain

He is a Serra-Húnter Associate Professor fellow in the Department of Statistics and Operations Research at the Universitat Politècnica de Catalunya-BarcelonaTech.

From 2017 to 2020 I worked as a Senior Research Biostatistician in the Research, Teaching, and Innovation Unit at Sant Joan de Déu Sanitary Park and as a adjunct professor at the Geneva Business School, the European University Business School, the University of Barcelona, and the Open University of Catalonia.

In 2017, I was a lecturer in the Department of Epidemiology and Biostatistics, School of Public Health, at the SUNY: University at Albany.

In 2016, I was a Moore-Sloan postdoctoral associate in the Center for Data Science at NYU working on the project "Computational statistic algorithms to evaluate rater quality in crowdsourced speech rating tasks”.

In 2015, I was involved in the project “Cluster analysis for ordinal categorical data”, a research programme at the interface between Statistics and Computer Sciences to develop methods of classification and dimension reduction focused on ordinal data.

I have a the Tenure-track lecturer certification (professor lector) and the Accreditation of research (professor agregat) issued by the Agencia per la Qualitat del Sistema Universitari de Catalunya (AQU). I am a member of the CIBERSAM group.

Key interests: Biostatistics, categorical data analysis, clustering and classification, computational statistics, mixture models, multilevel/hierarchical models, goodness-of-fit, and statistical modeling.

Montse Plensa
Universitat Politècnica de Catalunya · BarcelonaTech (UPC), Barcelona, Spain
Spain

Graduated in statistics from the universities UB and UPC in Barcelona and with a master's degree in sports Big Data, I seek to be able to use my skills and knowledge in data processing and analysis and my experience in the world of sports.

Martí Oliver
Universitat Politècnica de Catalunya · BarcelonaTech (UPC), Barcelona, Spain
Spain

He has a degree in statistics from the universities UB and UPC in Barcelona, and he is a soccer analyst.

Marica Marica Manisera
BODaI-Lab, Department of Economics and Management, University of Brescia, Brescia, Italy
Italy

She is an Associate Professor in Statistics – University of Brescia, Italy.

She has published more than 50 peer-reviewed papers on national and international journals and books, one book and more than 35 working papers and conference proceedings (Google Scholar profile: https://scholar.google.com/citations?user=DBQRGsYAAAAJ&hl=it&oi=ao).

Martí Casals
National Institute of Physical Education of Catalonia (INEFC), University of Barcelona, Barcelona, Spain
Spain

Martí Casals holds a PhD, an MSc in Biostatistics and a BSc in Statistics. Martí works as a full professor of Statistics, Research Methodology and Sport Analytics at the National Institute of Physical Education of Catalonia (INEFC) at University of Barcelona (UB). In addition, he is a senior researcher at the Center for the Study of Physical Activity and Sport (CEEAF), belonging to the consolidated research group Sport, Exercise, and Human Movement (SEaHM) of the University of Vic - Central University of Catalonia (UVic - UCC) and he is a part-time professor of biostatistics at the Faculty of Medicine of the UVic-UCC. He has collaborated as an external researcher at the Australian Centre for Research Injury in Sport and its Prevention (ACRISP). In the sports industry, He has collaborated as a sports statistician at FC Barcelona (2017- 2021) in the Sports Science Department and in the Barça Innovation Hub, a research, development, training and innovation platform of FC. Barcelona, and also as an external biostatistical consultant and Basketball analyst at Memphis Grizzlies of the NBA (2016-2018).

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Reporting of clustering techniques in sports sciences: a scoping review

Daniel Fernández, Montse Plensa, Martí Oliver, Marica Marica Manisera, Martí Casals

Abstract

Multivariate statistical methods are among the most used ones in sportssciences with clustering methods emerging as prominent unsupervised learningtechniques. This study presents a scoping review of original articlesutilizing clustering techniques in sports sciences, following the PRISMASCRguidelines. A comprehensive search across various databases using theboolean “AND” combination of “clustering” and “sport” yielded 278 articles.Notably, 86.7% of these articles were published within the last 14 years,with a predominant focus (66.2%) on sports performance analysis. The majorityof studies included professional athletes (56.4%), with football/soccer,basketball, and tennis being the most commonly studied sports, representing12.2%, 7.5%, and 2.2% of the selected articles, respectively. Hierarchical clusteringwas the most frequently used method (31.6%), followed by the k-meansalgorithm for partitional clustering. However, the clustering method was notreported in 26.6% of the articles, and 55.0% did not specify the criterion usedfor determining the optimal number of clusters. Moreover, more than 85%of the articles lacked computational details related to data reproducibility.These findings underscore the urgent need for substantial improvement in reportingpractices regarding the methodology, algorithms, criteria for clusteridentification, and software usage in sports science literature.

Keywords: Cluster analysis, unsupervised learning, scoping review, sports sciences.

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