Year: 2016
Type: dissertation
Abstract: When a hockey game is being played, its data comes continuously. Therefore, it is possible \nto use the stream mining method to estimate the win probability (WP) of a team once the \ngame begins. Based on 8 seasons’ data of NHL from 2003-2014, we provide three methods \nto estimate the win probability in a hockey game. Win probability calculation method based \non statistics is the first model, which is built based on the summary of the historical data. \nWin probability calculation method based on data mining classification technique is the \nsecond model. In this model, we implemented some data classification algorithms on our \ndata and compared the results, then chose the best algorithm to build the win probability \nmodel. Naive Bayes, SVM, VFDT, and Random Tree data classification methods have been compared in this thesis on the hockey dataset. We used stream mining technique in \nour last model, which is a real time prediction model, which can be interpreted as a trainingupdate- \ntraining model. Every 20 events in a hockey game are split as a window. We use \nthe last window as the training data set to get decision tree rules used for classifying the \ncurrent window. Then a parameter can be calculated by the rules trained by these two \nwindows. This parameter can tell us which rule is better than another to train the next \nwindow. In our models the variables time, leadsize, number of shots, number of misses, \nnumber of penalties are combined to calculate the win probability. Our WP estimates can provide useful evaluations of plays, prediction of game result and in some cases, guidance \nfor coach decisions.
Author: Shudan Yang
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2593564570