Analytics for Decision-Making in Amateur Sport Organizations

In this thesis, we demonstrate the value that analytics can have in guiding decisions at amateur sport organizations. We ďŹ rst use machine learning methods to estimate how the demand for tennis is distributed across Canada on a geographical level; geographical prediction of demand for sport has not been seen in the sport management literature. We develop novel validation techniques to show that our estimates outperform the naive assumption that the same proportion of tennis players lives in each region in Canada. To present the estimated demand to decision-makers, we build interactive maps that display the supply and demand for tennis across Canada. Through a case study in facility location, we show that the predicted demand can be used to determine the optimal locations of new tennis programs. Lastly, we present interactive tools that we built for decision-makers to analyze their membership and make targeted decisions about recruitment and retention strategies.M.A.S