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Data‐Driven Drafting: Applying Econometrics To Employ Quarterbacks

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  • Jeremy Rosen
  • Alexandre Olbrecht

Abstract

We show that firms can employ data‐driven methods to improve their hiring decisions. Specifically, we use data available to National Football League (NFL) teams prior to the NFL draft to estimate econometric models that predict the future performance of drafted quarterbacks. As our methods are replicable, stakeholders can use them to improve the draft's efficiency and help it accomplish its mission to promote competitive balance. Furthermore, data‐driven methods such as ours can help firms avoid biases against employee characteristics that do not affect future job performance. (JEL L83)

Suggested Citation

  • Jeremy Rosen & Alexandre Olbrecht, 2020. "Data‐Driven Drafting: Applying Econometrics To Employ Quarterbacks," Contemporary Economic Policy, Western Economic Association International, vol. 38(2), pages 313-326, April.
    • Handle: RePEc:bla:coecpo:v:38:y:2020:i:2:p:313-326
    DOI: 10.1111/coep.12454
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    References listed on IDEAS

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    Cited by:

    1. Craig, J. Dean & Winchester, Niven, 2021. "Predicting the national football league potential of college quarterbacks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 733-743.
    2. Joshua D. Pitts & Brent A. Evans, 2023. "New contracts and dismissal threats from highly drafted rookies: What motivates NFL quarterbacks?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(1), pages 4-16, January.
    3. Brad R. Humphreys & Yulia Chikish & Peter von Allmen, 2024. "Should I Stay or Should I Go Pro? Early NFL Draft Entry by NCAA FBS Underclassmen," Journal of Sports Economics, , vol. 25(3), pages 346-368, April.

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    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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