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Machine Learning and Data Mining in Exercise, Sports and Health...
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Machine Learning and Data Mining in Exercise, Sports and Health Research

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 7989

Special Issue Editor

Dr. Daniel Rojas-Valverde

E-Mail Website
Guest Editor
1. Clínica de Lesiones Deportivas (Rehab&Readapt), Escuela de Ciencias del Movimiento Humano y Calidad de Vida, Universidad Nacional, Heredia 86-3000, Costa Rica
2. Centro de Investigación y Diagnóstico en Salud y Deporte (CIDISAD), Escuela de Ciencias del Movimiento Humano y Calidad de Vida, Universidad Nacional, Heredia 86-3000, Costa Rica
Interests: sports injuries; athletic injuries; return to play; trauma; sport medicine; sport rehabilitation; physical therapy; rehabilitation; readaptation; injury prevention; injury epidemiology; disability; recovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

ML and data mining have completely revamped the fields of exercise, sports, and health research by providing sophisticated tools to exploit massive datasets for important inferences. In exercise science, ML algorithms can handle complicated patterns in physiological responses, which then assist in developing customized training regimes. Predictive modelling contributes a lot to sports research, as coaches use such models to guide their decisions and prevent many injuries while assessing individual game performance. In addition, ML enables the investigation of health data in order to find risk factors and apply individualized interventions aimed at those particulars. The use of ML and data mining in these areas gives researchers the ability to unearth hidden connections that help improve performance optimization, injury prevention, and health care. With the increasing number of wearable technology and sensor devices, an enormous amount of data are produced, which offers many new possibilities for improving the models and developing in-depth knowledge about human physiology. This interdisciplinary approach holds great potential to influence the shape of exercise, sports, and health research in the future, which will lead to precision, efficiency, and evidence-based decision making.

Dr. Daniel Rojas-Valverde
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data science
  • sensor data
  • health informatics
  • sport analytics
  • health data
  • performance analysis
  • optimization

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Published Papers (2 papers)

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Research

9 pages, 878 KiB  
Article
An Expected Goals on Target (xGOT) Metric as a New Metric for Analyzing Elite Soccer Player Performance
by Anselmo Ruiz-de-Alarcón-Quintero and Blanca De-la-Cruz-Torres
Data 2024, 9(9), 102; https://doi.org/10.3390/data9090102 - 28 Aug 2024
Cited by 3 | Viewed by 3773
Abstract
Introduction: Football analysis is an applied research area that has seen a huge upsurge in recent years. More complex analysis to understand the soccer players’ or teams’ performances during matches is required. The objective of this study was to prove the usefulness of [...] Read more.
Introduction: Football analysis is an applied research area that has seen a huge upsurge in recent years. More complex analysis to understand the soccer players’ or teams’ performances during matches is required. The objective of this study was to prove the usefulness of the expected goals on target (xGOT) metric, as a good indicator of a soccer team’s performance in professional Spanish football leagues, both in the women’s and men’s categories. Method: The data for the Spanish teams were collected from the statistical website Football Reference. The 2023/24 season was analyzed for Spanish leagues, both in the women’s and men’s categories (LigaF and LaLiga, respectively). For all teams, the following variables were calculated: goals, possession value (PV), expected goals (xG) and xGOT. All data obtained for each variable were normalized by match (90 min). A descriptive and correlational statistical analysis was carried out. Results: In the men’s league, this study found a high correlation between goals per match and xGOT (R2 = 0.9248) while in the women’s league, there was a high correlation between goals per match (R2 = 0.9820) and xG and between goals per match and xGOT (R2 = 0.9574). Conclusions: In the LaLiga, the xGOT was the best metric that represented the match result while in the LigaF, the xG and the xGOT were the best metrics that represented the match score. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining in Exercise, Sports and Health Research)
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20 pages, 995 KiB  
Article
Leveraging Sports Analytics and Association Rule Mining to Uncover Recovery and Economic Impacts in NBA Basketball
by Vangelis Sarlis, George Papageorgiou and Christos Tjortjis
Data 2024, 9(7), 83; https://doi.org/10.3390/data9070083 - 24 Jun 2024
Cited by 3 | Viewed by 3142
Abstract
This study examines the multifaceted field of injuries and their impacts on performance in the National Basketball Association (NBA), leveraging a blend of Data Science, Data Mining, and Sports Analytics. Our research is driven by three pivotal questions: Firstly, we explore how Association [...] Read more.
This study examines the multifaceted field of injuries and their impacts on performance in the National Basketball Association (NBA), leveraging a blend of Data Science, Data Mining, and Sports Analytics. Our research is driven by three pivotal questions: Firstly, we explore how Association Rule Mining can elucidate the complex interplay between players’ salaries, physical attributes, and health conditions and their influence on team performance, including team losses and recovery times. Secondly, we investigate the relationship between players’ recovery times and their teams’ financial performance, probing interdependencies with players’ salaries and career trajectories. Lastly, we examine how insights gleaned from Data Mining and Sports Analytics on player recovery times and financial influence can inform strategic financial management and salary negotiations in basketball. Harnessing extensive datasets detailing player demographics, injuries, and contracts, we employ advanced analytic techniques to categorize injuries and transform contract data into a format conducive to deep analytical scrutiny. Our anomaly detection methodologies, an ensemble combination of DBSCAN, isolation forest, and Z-score algorithms, spotlight patterns and outliers in recovery times, unveiling the intricate dance between player health, performance, and financial outcomes. This nuanced understanding emphasizes the economic stakes of sports injuries. The findings of this study provide a rich, data-driven foundation for teams and stakeholders, advocating for more effective injury management and strategic planning. By addressing these research questions, our work not only contributes to the academic discourse in Sports Analytics but also offers practical frameworks for enhancing player welfare and team financial health, thereby shaping the future of strategic decisions in professional sports. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining in Exercise, Sports and Health Research)
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