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A Player Selection Heuristic for a Sports League Draft

Author

Listed:
  • Fry Michael J

    (University of Cincinnati)

  • Lundberg Andrew W

    (University of Cincinnati)

  • Ohlmann Jeffrey W

    (University of Iowa)

Abstract

Sports leagues conduct new player entry drafts in which franchises select, in a pre-determined order, players to complement their existing rosters. We model the decision-making process of a single sports franchise during a player selection draft. The basic premise of our model is that a team selects a particular player based on a combination of the player's estimated value, the value of the other players currently available, and the team's need at each position. We first conceptualize a sports league draft using a stochastic dynamic program. However, this formulation is not directly solvable for practical-sized problems due to the overwhelming computational complexity. Therefore, we introduce additional assumptions and restrictions that result in a tractable deterministic dynamic program. We implement the model within a spreadsheet-based decision support system that allows the user to compute solutions under a variety of conditions. To benchmark our approach, we perform computational comparisons against several competing draft strategies in a series of simulated fantasy football drafts for the 2005 season. With perfect information regarding opposing teams' selections, our drafting strategy dominates these competing strategies. With imperfect information, there are draft instances in which our method is not guaranteed to dominate an alternate strategy; however, our drafting strategy outperforms the competing strategies on average and is more robust on the instances tested. Furthermore, we demonstrate that the decision-maker can incorporate information regarding the drafting behavior of opposing teams to improve the performance of our method.

Suggested Citation

  • Fry Michael J & Lundberg Andrew W & Ohlmann Jeffrey W, 2007. "A Player Selection Heuristic for a Sports League Draft," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(2), pages 1-35, April.
    • Handle: RePEc:bpj:jqsprt:v:3:y:2007:i:2:n:5
    DOI: 10.2202/1559-0410.1050
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    Citations

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    Cited by:

    1. Becker Adrian & Sun Xu Andy, 2016. "An analytical approach for fantasy football draft and lineup management," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(1), pages 17-30, March.
    2. repec:jdm:journl:v:17:y:2022:i:4:p:691-719 is not listed on IDEAS
    3. Vojtěch Kotrba, 2020. "Heuristics in fantasy sports: is it profitable to strategize based on favourite of the match?," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 19(1), pages 195-206, June.
    4. repec:cup:judgdm:v:17:y:2022:i:4:p:691-719 is not listed on IDEAS
    5. Young William A & Holland William S & Weckman Gary R, 2008. "Determining Hall of Fame Status for Major League Baseball Using an Artificial Neural Network," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(4), pages 1-46, October.
    6. Martin B. Haugh & Raghav Singal, 2021. "How to Play Fantasy Sports Strategically (and Win)," Management Science, INFORMS, vol. 67(1), pages 72-92, January.
    7. Miguel Ángel Pérez-Toledano & Francisco J Rodriguez & Javier García-Rubio & Sergio José Ibañez, 2019. "Players’ selection for basketball teams, through Performance Index Rating, using multiobjective evolutionary algorithms," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-20, September.
    8. 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.
    9. Timothy C. Y. Chan & Craig Fernandes & Martin L. Puterman, 2021. "Points Gained in Football: Using Markov Process-Based Value Functions to Assess Team Performance," Operations Research, INFORMS, vol. 69(3), pages 877-894, May.

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