Methodological Quality and Reporting of Regression Models for Ordinal Responses in Sports Field: A Scoping Review

Abstract

The integration of statistical methods in sports science has become essential for decision-making in performance analysis, injury prevention, and athlete outcomes. This work presents a scoping review following PRISMA guidelines to explore the application of ordinal regression models in the sports field. A comprehensive search of articles published, until 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 Journal of Quantitative Analysis in Sports, and Journal of Sports Analytics. The analysis reveals that 26.5% of these articles were published in statistics and sports statistics journals. However, a significant majority (82.4%) of the studies did not provide data and code repositories. Notably, R emerged as the primary software used for analysis in 38.8% of the studies. Football had the highest representation (28.6%), followed by basketball (17.1%). The most commonly 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 test for proportional odds model 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. In addition to the scoping review, a real case example to demonstrate the application of the proportional odds model and the mixed effects proportional odds model in the sports field is provided. This case example aims to showcase the practical implementation of these statistical methods and their potential impact on decision-making in sports analytics.