From Framework to Architecture: Operationalizing CoDI-SA Through Explainable AI and Multimodal Data Fusion

Abstract

Sports analytics platforms have come a long way in their ability to handle data themselves and their performance in visualization, but lags remain when it comes to human-centric decision support, explainability, and contextual reasoning. This study aims to map out the Contextual Decision Intelligence for Sports Analytics (CoDI-SA) framework to a feasible reference architecture, based on the Design Science Research (DSR) method. The proposed architecture includes multi-modal data fusion, contextual reasoning, explainable artificial intelligence (XAI), and human-in-the-loop decision intelligence, which will allow for intuitive, context-aware and actionable suggestions to coaches, analysts and performance teams while keeping transparency. The proposed architecture is adaptive in nature and can be implemented on elite level, semi-professional level or grassroots level, all while having a common architectural foundation and is an alternative to the existing commercial systems which emphasize descriptive analytics. The usefulness and trustworthiness of the architecture as well as the value of the architecture is assessed by comparing it to commercial sports analytics platforms and by surveying twelve sports-technology practitioners with a grand mean of 4.26/5 and SD of 0.69 for the three questions, where the mean represents the average of the practitioners' responses. This study offers a reference architecture that can be reused, and seven design principles and a tiered deployment model of next generation sports analytics systems.