IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v67y2021i1p72-92.html

How to Play Fantasy Sports Strategically (and Win)

Author

Listed:
  • Martin B. Haugh

    (Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom;)

  • Raghav Singal

    (Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027)

Abstract

Daily fantasy sports (DFS) is a multibillion-dollar industry with millions of annual users and widespread appeal among sports fans across a broad range of popular sports. Building on recent work, we provide a coherent framework for constructing DFS portfolios where we explicitly model the behavior of other DFS players. We formulate an optimization problem that accurately describes the DFS problem for a risk-neutral decision maker in both double-up and top-heavy payoff settings. Our formulation maximizes the expected reward subject to feasibility constraints, and we relate this formulation to mean-variance optimization and the outperformance of stochastic benchmarks. Using this connection, we show how the problem can be reduced to the problem of solving a series of binary quadratic programs. We also propose an algorithm for solving the problem where the decision maker can submit multiple entries to the DFS contest. This algorithm is motivated by submodularity properties of the objective function and by some new results on parimutuel betting. One of the contributions of our work is the introduction of a Dirichlet-multinomial data-generating process for modeling opponents’ team selections, and we estimate the parameters of this model via Dirichlet regressions. A further benefit to modeling opponents’ team selections is that it enables us to estimate the value, in a DFS setting, of both insider trading and collusion. We demonstrate the value of our framework by applying it to DFS contests during the 2017 National Football League season.

Suggested Citation

  • Martin B. Haugh & Raghav Singal, 2021. "How to Play Fantasy Sports Strategically (and Win)," Management Science, INFORMS, vol. 67(1), pages 72-92, January.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:1:p:72-92
    DOI: 10.1287/mnsc.2019.3528
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mnsc.2019.3528
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.2019.3528?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. 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.
    3. repec:czx:journl:v:10:y:2003:i:18:id:117 is not listed on IDEAS
    4. Bryan Clair & David Letscher, 2007. "Optimal Strategies for Sports Betting Pools," Operations Research, INFORMS, vol. 55(6), pages 1163-1177, December.
    5. Erhan Bayraktar & Alexander Munk, 2016. "High-Roller Impact: A Large Generalized Game Model of Parimutuel Wagering," Papers 1605.03653, arXiv.org, revised Mar 2017.
    6. Thaler, Richard H & Ziemba, William T, 1988. "Parimutuel Betting Markets: Racetracks and Lotteries," Journal of Economic Perspectives, American Economic Association, vol. 2(2), pages 161-174, Spring.
    7. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Charles R. Plott & Jorgen Wit & Winston C. Yang, 2003. "Parimutuel betting markets as information aggregation devices: experimental results," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 22(2), pages 311-351, September.
    9. Terrell, Dek & Farmer, Amy, 1996. "Optimal Betting and Efficiency in Parimutuel Betting Markets with Information Costs," Economic Journal, Royal Economic Society, vol. 106(437), pages 846-868, July.
    10. Edward H. Kaplan & Stanley J. Garstka, 2001. "March Madness and the Office Pool," Management Science, INFORMS, vol. 47(3), pages 369-382, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jörg Döpke & Tim Köhler & Lars Tegtmeier, 2024. "Are they worth it? – An evaluation of predictions for NBA ‘Fantasy Sports’," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 48(1), pages 142-165, March.
    2. Martin B. Haugh & Chun Wang, 2022. "Play Like the Pros? Solving the Game of Darts as a Dynamic Zero-Sum Game," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2540-2551, September.
    3. Ju Liu & Changchun Liu & Chung Piaw Teo, 2023. "Picking winners: Diversification through portfolio optimization," Production and Operations Management, Production and Operations Management Society, vol. 32(9), pages 2864-2884, September.
    4. David Bergman & Carlos Cardonha & Jason Imbrogno & Leonardo Lozano, 2023. "Optimizing the Expected Maximum of Two Linear Functions Defined on a Multivariate Gaussian Distribution," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 304-317, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ju Liu & Changchun Liu & Chung Piaw Teo, 2023. "Picking winners: Diversification through portfolio optimization," Production and Operations Management, Production and Operations Management Society, vol. 32(9), pages 2864-2884, September.
    2. Hyoshin (John) Park & Ali Haghani & Song Gao & Michael A. Knodler & Siby Samuel, 2018. "Anticipatory Dynamic Traffic Sensor Location Problems with Connected Vehicle Technologies," Service Science, INFORMS, vol. 52(6), pages 1299-1326, December.
    3. Steven Gjerstad, 2004. "Risk Aversion, Beliefs, and Prediction Market Equilibrium," Microeconomics 0411002, University Library of Munich, Germany.
    4. Shaojie Tang & Jing Yuan, 2023. "Beyond submodularity: a unified framework of randomized set selection with group fairness constraints," Journal of Combinatorial Optimization, Springer, vol. 45(4), pages 1-22, May.
    5. Frederic Koessler & Ch. Noussair & A. Ziegelmeyer, 2005. "Individual Behavior and Beliefs in Experimental Parimutuel Betting Markets," Thema Working Papers 2005-08, THEMA (Théorie Economique, Modélisation et Applications), CY Cergy-Paris University, ESSEC and CNRS.
    6. David Bergman & Carlos Cardonha & Jason Imbrogno & Leonardo Lozano, 2023. "Optimizing the Expected Maximum of Two Linear Functions Defined on a Multivariate Gaussian Distribution," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 304-317, March.
    7. Lu Han & Dachuan Xu & Donglei Du & Dongmei Zhang, 2018. "A local search approximation algorithm for the uniform capacitated k-facility location problem," Journal of Combinatorial Optimization, Springer, vol. 35(2), pages 409-423, February.
    8. Mohit Singh & Weijun Xie, 2020. "Approximation Algorithms for D -optimal Design," Mathematics of Operations Research, INFORMS, vol. 45(4), pages 1512-1534, November.
    9. Ortiz-Astorquiza, Camilo & Contreras, Ivan & Laporte, Gilbert, 2018. "Multi-level facility location problems," European Journal of Operational Research, Elsevier, vol. 267(3), pages 791-805.
    10. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2022. "Submodularity and local search approaches for maximum capture problems under generalized extreme value models," European Journal of Operational Research, Elsevier, vol. 300(3), pages 953-965.
    11. Eszter Julianna Csókás & Tamás Vinkó, 2023. "An exact method for influence maximization based on deterministic linear threshold model," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 269-286, March.
    12. Goldengorin, Boris, 2009. "Maximization of submodular functions: Theory and enumeration algorithms," European Journal of Operational Research, Elsevier, vol. 198(1), pages 102-112, October.
    13. Lisa Fleischer & Michel X. Goemans & Vahab S. Mirrokni & Maxim Sviridenko, 2011. "Tight Approximation Algorithms for Maximum Separable Assignment Problems," Mathematics of Operations Research, INFORMS, vol. 36(3), pages 416-431, August.
    14. Yang, Yue & Umboh, Seeun William & Ramezani, Mohsen, 2024. "Freelance drivers with a decline choice: Dispatch menus in on-demand mobility services for assortment optimization," Transportation Research Part B: Methodological, Elsevier, vol. 190(C).
    15. Qi Luo & Viswanath Nagarajan & Alexander Sundt & Yafeng Yin & John Vincent & Mehrdad Shahabi, 2023. "Efficient Algorithms for Stochastic Ride-Pooling Assignment with Mixed Fleets," Transportation Science, INFORMS, vol. 57(4), pages 908-936, July.
    16. Beck, Yasmine & Ljubić, Ivana & Schmidt, Martin, 2023. "A survey on bilevel optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 311(2), pages 401-426.
    17. Valente, Matteo & Reniers, Genserik & Cozzani, Valerio, 2026. "Techno-Economic Analysis of Protection Barriers Against Fire-Induced Domino Effects in the Chemical and Process Industry," Reliability Engineering and System Safety, Elsevier, vol. 266(PB).
    18. Guanyi Wang, 2024. "Robust Network Targeting with Multiple Nash Equilibria," Papers 2410.20860, arXiv.org, revised Nov 2024.
    19. Goldengorin, Boris & Tijssen, Gert A. & Tso, Michael, 1999. "The maximization of submodular functions : old and new proofs for the correctness of the dichotomy algorithm," Research Report 99A17, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    20. Majun Shi & Zishen Yang & Wei Wang, 2023. "Greedy Guarantees for Non-submodular Function Maximization Under Independent System Constraint with Applications," Journal of Optimization Theory and Applications, Springer, vol. 196(2), pages 516-543, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:67:y:2021:i:1:p:72-92. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.