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Many European countries face rising obesity rates among children, compoundedby decreased opportunities for sports activities during the COVID-19 pandemic.Access to sports facilities depends on multiple factors, such as geographiclocation, proximity to population centers, budgetary constraints, and othersocio-economic covariates. Here we show how an optimal allocation of governmentfunds towards sports facilitators (e.g. sports clubs) can be achieved in adata-driven simulation model that maximizes children's access to sportsfacilities. We compile a dataset for all 1,854 football clubs in Austria,including estimates for their budget, geolocation, tally, and the age profileof their members. We find a characteristic sub-linear relationship between thenumber of active club members and the budget, which depends on thesocio-economic conditions of the clubs' municipality. In the model, where weassume this relationship to be causal, we evaluate different fundingstrategies. We show that an optimization strategy where funds are distributedbased on regional socio-economic characteristics and club budgets outperforms anaive approach by up to 117\% in attracting children to sports clubs for 5million Euros of additional funding. Our results suggest that the impact ofpublic funding strategies can be substantially increased by tailoring them toregional socio-economic characteristics in an evidence-based and individualizedway.

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