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Expert System for Ice Hockey Game Prediction: Data Mining with Human Judgment

Author

Listed:
  • Wei Gu

    (Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China)

  • Thomas L. Saaty

    (Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh 15206, PA, USA)

  • Rozann Whitaker

    (Creative Decisions Foundation, Pittsburgh 15213, PA, USA)

Abstract

This paper describes an expert system to predict National Hockey League (NHL) game outcome. A new method based on both data and judgments is used to estimate the hockey game performance. There are many facts and judgments that could influence an outcome. We employed the support vector machine to determine the importance of these factors before we incorporate them into the prediction system. Our system combines data and judgments and used them to predict the win–lose outcome of all the 89 post-season games before they took place. The accuracy of our prediction with the combined factors was 77.5%. This is to date the best accuracy reported of hockey games prediction.

Suggested Citation

  • Wei Gu & Thomas L. Saaty & Rozann Whitaker, 2016. "Expert System for Ice Hockey Game Prediction: Data Mining with Human Judgment," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 763-789, July.
    • Handle: RePEc:wsi:ijitdm:v:15:y:2016:i:04:n:s0219622016400022
    DOI: 10.1142/S0219622016400022
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    References listed on IDEAS

    as
    1. Thomas L. Saaty, 2005. "The Analytic Hierarchy and Analytic Network Processes for the Measurement of Intangible Criteria and for Decision-Making," International Series in Operations Research & Management Science, in: Multiple Criteria Decision Analysis: State of the Art Surveys, chapter 0, pages 345-405, Springer.
    2. Thomas L. Saaty, 2006. "The Analytic Network Process," International Series in Operations Research & Management Science, in: Decision Making with the Analytic Network Process, chapter 0, pages 1-26, Springer.
    3. Loeffelholz Bernard & Bednar Earl & Bauer Kenneth W, 2009. "Predicting NBA Games Using Neural Networks," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(1), pages 1-17, January.
    4. Gueorgui I. Kolev & Gonçalo Pina & Federico Todeschini, 2015. "Decision Making and Underperformance in Competitive Environments: Evidence from the National Hockey League," Kyklos, Wiley Blackwell, vol. 68(1), pages 65-80, February.
    5. Buttrey Samuel E & Washburn Alan R & Price Wilson L, 2011. "Estimating NHL Scoring Rates," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Roman Vavrek, 2021. "An Analysis of Usage of a Multi-Criteria Approach in an Athlete Evaluation: An Evidence of NHL Attackers," Mathematics, MDPI, vol. 9(12), pages 1-22, June.
    2. Manlio Migliorati & Marica Manisera & Paola Zuccolotto, 2023. "Integration of model-based recursive partitioning with bias reduction estimation: a case study assessing the impact of Oliver’s four factors on the probability of winning a basketball game," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 271-293, March.
    3. Ricardo Carreño & Verónica Aguilar & Daniel Pacheco & Marco Antonio Acevedo & Wen Yu & María Elena Acevedo, 2019. "An IoT Expert System Shell in Block-Chain Technology with ELM as Inference Engine," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 87-104, January.

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