Predicting golf scores at the shot level - Drappi, C.; Co Ting Keh, L. (Journal of Sports Analytics, 2019)
We present the only model to date that predicts the discrete probability distribution of a golfer`s score for each hole of a tournament on a shot-by-shot basis. We first generalized Broadie`s technique of score-based skill estimation to allow a golfer`s skill (e.g. scoring average, driving spray, iron play, putting) to vary continuously by time-weighting data with exponential decay. Training a single-layer 50-node neural network to predict probabilities of scoring by hole resulted in an out-of-sample cross-entropy error of 0.974. We then added features of each hole (e.g. par, green size, sand area) onto the model, representing golfers and holes in an N-by-M dimensional space and achieved an error of 0.953. Adding in course features provided by ShotLink (e.g. fairway height, firmness, wind speed) dropped error to 0.9374. Finally, generalizing the model to update probabilities per shot further reduced error to 0.891.