Browsing by Subject "ice hockey"

Estimating the effect of random chance (’luck’) has long been a question of particular interest in various team sports. In this thesis, we aim to determine the role of luck in a single icehockey game by building a model to predict the outcome based on the course of events in a game. The obtained prediction accuracy should also to some extent reveal the effect of random chance. Using the course of events from over 10,000 games, we train feedforward and convolutional neural networks to predict the outcome and final goal differential, which has been proposed as a more informative proxy for outcome. Interestingly, we are not able to obtain distinctively higher accuracy than previous studies, which have focused on predicting the outcome with infomation available before the game. The results suggest that there might exist an upper bound for prediction accuracy even if we knew ’everything’ that went on in a game. This further implies that random chance could affect the outcome of a game, although assessing this is difficult, as we do not have a good quantitative metric for luck in the case of single ice hockey game prediction.