Quantifying future Olympic sport selection
A data-driven framework for SDE evaluation and selection
Bibliographic Data
| ID | 13130842 |
|---|---|
| Authors | Yong Song (0000-0002-1067-7598), Rui Dai (0009-0008-5082-0652, Yangtze University), Q Zhang (0000-0003-3439-336X), Yang Sun (0009-0001-1587-6633, Southern University of Science and Technology, corresponding author) |
| Year | 2025 |
| Volume | 7 |
| Pages | 1596196-1596196 |
| Publication date | 2025-07-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Sports and Active Living (JOURNAL) |
| Journal identifiers | ISSN: 2624-9367 • E-ISSN: 2624-9367 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fspor.2025.1596196 |
| PMID | 40799343 |
| OpenAlex | W4412732737 |
| Language | EN |
| References cited | 25 |
The Olympic Games are the world’s foremost sporting event, with over 200 countries participating. As the Games evolve, sports, disciplines, and events (SDEs) are periodically added or removed. The selection process for Olympic sports is inherently subjective, as seen with breakdancing’s inclusion in the 2024 Paris Olympics and exclusion from the 2028 Los Angeles Olympics. Thus, developing a quantitative decision-making model is crucial for the International Olympic Committee (IOC). This study evaluates IOC criteria for new sports by considering factors such as social media engagement, TV viewership across demographics, affordability, gender equity, youth appeal, cultural diversity, and global involvement. Our model employs a scoring and labelling system based on the Analytic Hierarchy Process (AHP), which calculates the relative importance of each factor. Using Principal Component Analysis (PCA) for feature extraction, we apply a k -nearest neighbour (KNN) classifier for further evaluation. We apply this model to assess potential SDEs for the 2032 Brisbane Olympics, considering their popularity in Australia and alignment with Olympic criteria. Our findings suggest that Esports, Australian rules football, and pickleball are the top candidates for inclusion, while tug of war, bowling, and chess are also recommended based on their historical relevance and global popularity
Data science · Selection (genetic algorithm · Computer Science · Sports Performance and Training · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |