Abstract
ABSTRACTThe integration of statistical methods in sports sciences has become essential for decision‐making in performance analysis, injury prevention, and athlete outcomes. This work presents a systematic review following PRISMA guidelines to explore the application of ordinal regression models in the sports sciences field. A comprehensive search of articles published prior to March 4, 2023 identified 34 included studies. This search included widely recognized databases such as Web of Science, PubMed, and specialized journals of sports statistics, such as the Journal of Quantitative Analysis in Sports, and the Journal of Sports Analytics. The analysis reveals that 26.5% of these articles were published in statistics and sports statistics journals. Notably, R emerged as the primary software used for analysis in 38.8% of the studies. However, a significant majority (82.4%) of the studies did not provide data and code repositories. Among sports, soccer had the highest representation (28.6%), followed by basketball (17.1%). The most reported ordinal model was the proportional odds model (32.3%), followed by the mixed effects proportional odds model (11.8%), while a relevant proportion (29.4%) did not report the model used. Furthermore, 23.5% of articles proposed novel models. Validation tests for proportional odds models were not conducted in 53.3% of cases. This review underscores the importance of improved reporting practices, inclusivity in sport representation, and statistical education in advancing sports analytics.
Published Version
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