A Multi-Domain Collaborative Framework for Practical Application of Causal Knowledge Discovery from Public Data in Elite Sports
Year: 2026
Type: article
Abstract: In elite sports, discovering interdisciplinary causal relationships from public data is critical for gaining a competitive edge. However, the causal knowledge required for these practices is difficult to obtain through either existing intervention-based sports science methods or computational techniques focused on statistical association. This paper formalizes a multi-domain collaborative framework, which involves three roles: (1) the elite sports team; (2) the sport science expert; and (3) the causal inference expert. Our nine-step workflow, which processes three core elements of problem, data, and computing, guides these experts through a cycle that systematically transforms practical problems into computational models and, crucially, translates complex analytical outputs back into actionable strategies. The framework also introduces a dual-dimensional “field evaluation” method, encompassing both process and outcome, to quantify the trustworthiness of knowledge in practical settings where a “gold standard” is absent. This framework was applied in an illustrative case study prior to the Paris 2024 Olympics, providing one additional evidence-informed input for the national team. The success was observed and interpreted as contextual consistency rather than causal validation. This framework ensures the practical application of causal discovery in elite sports, offering a repeatable and explainable pathway for generating credible, evidence-based insights from public data for elite sports decision-making.
Source: Applied System Innovation
DOI: 10.3390/asi9020043
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,689
OpenAlex ID: W7128960377