Multilevel Models Applied in Sport

Abstract

Since the inception of Moneyball, analytics in sport has evolved rapidly - often drawing interest from coaches, broadcasters and fans alike. Unlike anecdotal evidence, which are filled with visceral biases, numbers can provide an objective description of player performances or team tendencies. A common feature in collected sport data is repeated measurements on the same observational units. While typical regression models ignore this clustering paradigm, multilevel models explicitly account for cluster heterogeneity and can improve estimates or predictions. We present two multilevel model applications in sport - one comparing face-off skills in women’s hockey, and the other in predicting tennis serve decisions.