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Mitigation of Perverse Incentives in Professional Sports Leagues with Reverse-Order Drafts

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  • Liam J. A. Lenten

    (La Trobe University)

Abstract

Efficacy of industry policy implementation is enhanced when governments expedite resolution of private sector uncertainty. This study reinforces the mechanism through which production outcomes improve, via program evaluation analysis from professional sports. An alternative determination rule for allocating picks in reverse-order drafts—fewest games played when eliminated from playoffs—is considered in comparison with the current standard (fewest wins at end-of-season). Elimination timing is estimated via Monte Carlo simulation. Using MLB and NBA data from 2005–2013, results from a quasi-natural experiment show that when a team’s perverse incentive to underperform is effectively removed prior to its final game of the season, its subsequent performance improves significantly.

Suggested Citation

  • Liam J. A. Lenten, 2016. "Mitigation of Perverse Incentives in Professional Sports Leagues with Reverse-Order Drafts," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 49(1), pages 25-41, August.
    • Handle: RePEc:kap:revind:v:49:y:2016:i:1:d:10.1007_s11151-015-9494-8
    DOI: 10.1007/s11151-015-9494-8
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    References listed on IDEAS

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    Cited by:

    1. Lenten, Liam J.A. & Smith, Aaron C.T. & Boys, Noel, 2018. "Evaluating an alternative draft pick allocation policy to reduce ‘tanking’ in the Australian Football League," European Journal of Operational Research, Elsevier, vol. 267(1), pages 315-320.
    2. Kendall, Graham & Lenten, Liam J.A., 2017. "When sports rules go awry," European Journal of Operational Research, Elsevier, vol. 257(2), pages 377-394.
    3. László Csató, 2025. "Mitigating the risk of tanking in multi-stage tournaments," Annals of Operations Research, Springer, vol. 344(1), pages 135-151, January.
    4. Arlegi, Ritxar & Dimitrov, Dinko, 2020. "Fair elimination-type competitions," European Journal of Operational Research, Elsevier, vol. 287(2), pages 528-535.
    5. Sylvain Béal & Eric Rémila & Philippe Solal & Sylvain Ferrières, 2016. "An axiomatization of the iterated h-index and applications to sport rankings," Working Papers hal-01394818, HAL.
    6. Bastian Kordyaka & Mario Lackner & Hendrik Sonnabend, 2022. "Can too many cooks spoil the broth? Coordination costs, fatigue, and performance in high‐intensity tasks," Southern Economic Journal, John Wiley & Sons, vol. 88(3), pages 1065-1085, January.
    7. Eduardo G. Minuci & Alexander Cardazzi & Candon Johnson, 2026. "Should more teams “trust the process” of tanking?," Contemporary Economic Policy, Western Economic Association International, vol. 44(2), pages 408-425, April.
    8. László Csató, 2020. "The UEFA Champions League seeding is not strategy-proof since the 2015/16 season," Annals of Operations Research, Springer, vol. 292(1), pages 161-169, September.
    9. Csató, László, 2022. "Quantifying incentive (in)compatibility: A case study from sports," European Journal of Operational Research, Elsevier, vol. 302(2), pages 717-726.

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    Keywords

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • Z28 - Other Special Topics - - Sports Economics - - - Policy

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