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How the West will be won: using Monte Carlo simulations to estimate the effects of NHL realignment

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  • Pettigrew Stephen

    (Harvard University – Department of Government, 1737 Cambridge Street, Cambridge, MA 02138, USA)

Abstract

The NHL has realigned its conferences and divisions, and starting with the 2013–2014 season the Eastern Conference features 16 teams and the Western Conference features 14. Yet because there are eight playoff spots available in both conferences, teams in the West have a 57% probability of making the playoffs, compared to just 50% for teams in the East. As a result we should expect that, on average, the last team to make the playoffs in the West will have a worse record than the last playoff team in the East. We call the difference in points earned by the 8th seed in each conference the “conference gap.” The purpose of this paper is to estimate the expected size of the conference gap under the new alignment. Using tens of thousands of simulated seasons, we demonstrate that the conference gap will be, on average, 2.74 points, meaning that Eastern Conference teams hoping to make the playoffs will have to win 1–2 games more than Western Conference playoff-hopefuls. We also show the 9th place team in the Eastern Conference has a better record than the 8th place Western team twice as often as the 9th best Western team has a better record than the East’s 8th best. Our findings inform questions about competitive balance and equity in the NHL.

Suggested Citation

  • Pettigrew Stephen, 2014. "How the West will be won: using Monte Carlo simulations to estimate the effects of NHL realignment," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(3), pages 1-11, September.
    • Handle: RePEc:bpj:jqsprt:v:10:y:2014:i:3:p:11:n:3
    DOI: 10.1515/jqas-2013-0125
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    References listed on IDEAS

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    1. Quinn Kevin G. & Bursik Paul B., 2007. "Growing and Moving the Game: Effects of MLB Expansion and Team Relocation 1950-2004," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(2), pages 1-30, April.
    2. Newton Paul K & Aslam Kamran, 2009. "Monte Carlo Tennis: A Stochastic Markov Chain Model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(3), pages 1-44, July.
    3. J. P. Royston, 1982. "Expected Normal Order Statistics (Exact and Approximate)," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 161-165, June.
    4. Rudelius Thomas W., 2012. "Did the Best Team Win? Analysis of the 2010 Major League Baseball Postseason Using Monte Carlo Simulation," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-13, March.
    5. Rosenfeld Jason W. & Fisher Jake I & Adler Daniel & Morris Carl, 2010. "Predicting Overtime with the Pythagorean Formula," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-19, April.
    6. Pasteur R. Drew & Janning Michael C., 2011. "Monte Carlo Simulation for High School Football Playoff Seed Projection," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-10, May.
    7. Rump Christopher M, 2008. "Data Clustering for Fitting Parameters of a Markov Chain Model of Multi-Game Playoff Series," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(1), pages 1-19, January.
    8. Beaudoin David, 2013. "Various applications to a more realistic baseball simulator," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(3), pages 271-283, September.
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    Cited by:

    1. Michal Friesl & Liam J. A. Lenten & Jan Libich & Petr Stehlík, 2017. "In search of goals: increasing ice hockey’s attractiveness by a sides swap," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1006-1018, September.
    2. Michal Friesl & Jan Libich & Petr Stehlík, 2020. "Fixing ice hockey’s low scoring flip side? Just flip the sides," Annals of Operations Research, Springer, vol. 292(1), pages 27-45, September.

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