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Reseach Article

Predicting NHL Match Outcomes with ML Models

by Gianni Pischedda
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 9
Year of Publication: 2014
Authors: Gianni Pischedda
10.5120/17714-8249

Gianni Pischedda . Predicting NHL Match Outcomes with ML Models. International Journal of Computer Applications. 101, 9 ( September 2014), 15-22. DOI=10.5120/17714-8249

@article{ 10.5120/17714-8249,
author = { Gianni Pischedda },
title = { Predicting NHL Match Outcomes with ML Models },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 9 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 15-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number9/17714-8249/ },
doi = { 10.5120/17714-8249 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:31:13.469108+05:30
%A Gianni Pischedda
%T Predicting NHL Match Outcomes with ML Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 9
%P 15-22
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Finding ways of predicting the outcome of sports games from performance data has always been an attractive proposition for many statisticians and, lately, of data miners using machine learning (ML) techniques. A research paper [1] on ice hockey (NHL) by a University of Ottawa team (from now on referred simply as Ottawa), and their generous sharing of the data used for their research provided the main drive for this paper. In this research, the Ottawa data is used for a number of purpose, all involving the use of ML techniques to predict the outcome of NHL games. First, we repeat Ottawa's experiment, which looked ". . . at how effective traditional, advanced and mixed (both combined) statistics were for predicting success in the NHL". Then we split all the given attributes in the data into Categorical and Continuous, and build ML separate models, whose result we compare with those of the original model. The original data is also parsed to create a new dataset, and models built to compare with the results of the original one. Lastly, a framework for making use of this data in a practical application (betting) is proposed, and the accuracy of models built is evaluate and compared. Three ML techniques: Decision Trees (DT), Artificial Neural Networks (ANN), and ClusteR, a software developed by a betting company, were used for these experiments.

References
  1. Weissbock, J. , Viktor, H. , Inkpen, D. : Use of performance metrics to forecast success in the national hockey league. European Conference on Machine Learning: Sports Analytics and Machine Learning Workshop (2013)
  2. Chen, H. , Buntin Rinde, P. , She, L. , Sutjahjo, S. , Sommer, C. , Neely, D. : Expert prediction, symbolic learning, and neural networks. An experiment on greyhound racing. IEEE Expert 9(6) (1994) 21–27
  3. Huang, K. Y. , Chang, W. L. : A neural network method for prediction of 2006 World Cup football game. In: Neural Networks (IJCNN), IEEE (2010) 1–8
  4. Purucker, M. C. : Neural network quarterbacking. Potentials, IEEE 15(3) (1996) 9–15
  5. Pardee, M. : An artificial neural network approach to college football prediction and ranking. University of Wisconsin (1999)
  6. Loeffelholz, B. , Bednar, E. , Bauer, K. W. : Predicting NBA games using neural networks. Journal of Quantitative Analysis in Sports 5(1) (2009) 1–15
  7. Albrecht Zimmermann, Sruthi Moorthy, Zifan Shi: Predicting college basketball match outcomes using machine learning techniques: some results and lessons learned. European Conference on Machine Learning: Sports Analytics and Machine Learning Workshop (2013)
  8. Josh Weissbock, Diana Inkpen: Combining Textual Pre-game Reports and Statistical Data for Predicting Success in the National Hockey League. Canadian Conference on AI 2014: 251-262
  9. Kahn, Joshua, "Neural Network Prediction of NFL Games", University of Wisconsin – Electrical and Computer Engineering Department, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

NHL Decision Trees Bonferroni Neural Networks ClusteR ML techniques sabermetrics k-nearest neighbour