Generalised additive models for location, scale, and shape in sports science: a systematic review
Main Article Content
Abstract
Sport has increasingly evolved into an interdisciplinary research field, where statistical modelling plays a central role in performance analysis, injury prediction, tactical optimisation, and audience behaviour. Although traditional methods like linear regression and generalised linear models are frequently employed, their inability to handle complex data structures has led to the expanding usage of more flexible approaches. In this context, generalised additive models for location, scale and shape (GAMLSS) are one of the most flexible statistical frameworks currently available, enabling the simultaneous modelling of multiple distributional parameters. Hence, in this paper, we perform a detailed systematic review exploring the application of GAMLSS in sports science, drawing on peer-reviewed articles. The results show a variety of applications, such as the development of reference growth curves, athlete performance modelling, match-fixing detection, and forecasting of sport-related consumer behaviour. GAMLSS have proven especially useful in contexts where traditional models are inadequate, offering enhanced flexibility in capturing distributional nuances. Nonetheless, opportunities remain to integrate GAMLSS with machine learning techniques and to extend their use across underexplored domains in sport. This review contributes to the field by outlining current trends, highlighting methodological strengths, and identifying promising directions for future research.
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