Evidence-based policy-making in sports funding using a data-driven optimization approach

Regular physical activity is essential for the healthy development of children, and sports clubs are one of the main drivers of regular exercise. Previous studies have demonstrated that public subsidies can increase participation rates in sports clubs. The effectiveness of funding in increasing participation rates depends on multiple factors, such as geographic location, the size of the sports club, and the socio-economic conditions of the population. Here, we show how an optimal allocation of government funds to sports facilitators (e.g., sports clubs) can be achieved using a data-driven simulation model that maximizes children's access to sports facilities. We compile a dataset for all 1,854 football clubs in Austria, including estimates for their budgets, geolocations, tallies, and the age profiles of their members. We find a characteristic sublinear relationship between the number of active club members and the budget, which depends on the socio-economic conditions of the club's municipality. In the model, where we assume this relationship to be causal, we evaluate different funding strategies. We show that an optimization strategy, where funds are distributed based on regional socio-economic characteristics and club budgets, outperforms a naive approach by up to 117% in attracting children to sports clubs with 5 million euros of additional funding. Our results suggest that the impact of public funding strategies can be substantially increased by tailoring them to regional socio-economic characteristics in an evidence-based and individualized way.