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Quantifying the Contribution of NHL Player Types to Team Performance

Author

Listed:
  • Timothy C. Y. Chan

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • Justin A. Cho

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • David C. Novati

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

Abstract

In this paper, we use k -means clustering to define distinct player types for each of the three positions on a National Hockey League (NHL) team and then use regression to determine a quantitative relationship between team performance and the player types identified in the clustering. Using NHL regular-season data from 2005–2010, we identify four forward types, four defensemen types, and three goalie types. Goalies tend to contribute the most to team performance, followed by forwards and then defensemen. We also show that once we account for salary cap and playing-time information, the value of different player types may become similar. Lastly, we illustrate how to use the regression results to analyze trades and their impact on team performance.

Suggested Citation

  • Timothy C. Y. Chan & Justin A. Cho & David C. Novati, 2012. "Quantifying the Contribution of NHL Player Types to Team Performance," Interfaces, INFORMS, vol. 42(2), pages 131-145, April.
    • Handle: RePEc:inm:orinte:v:42:y:2012:i:2:p:131-145
    DOI: 10.1287/inte.1110.0612
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    References listed on IDEAS

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    Cited by:

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    2. Miquel Carreras-Simó & Jaume García, 2022. "Offensive/Defensive Talent and Sporting Success in Football: Evidence From the Big Five European Leagues," Journal of Sports Economics, , vol. 23(3), pages 251-276, April.
    3. Derek Lanoue, 2015. "Does it pay to win the Stanley Cup?," Working Papers 1502, University of Windsor, Department of Economics.
    4. Pantuso Giovanni, 2017. "The Football Team Composition Problem: a Stochastic Programming approach," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 13(3), pages 113-129, September.
    5. Michael J. Fry & Jeffrey W. Ohlmann, 2012. "Introduction to the Special Issue on Analytics in Sports, Part I: General Sports Applications," Interfaces, INFORMS, vol. 42(2), pages 105-108, April.
    6. Suzuki, Takahiro & Horita, Masahide, 2024. "Which set of agents plays a key role? An impossibility in transforming binary relations," Mathematical Social Sciences, Elsevier, vol. 129(C), pages 12-19.

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