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Predicting the draft and career success of tight ends in the National Football League

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  • Mulholland Jason

    (Undergraduate student, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA)

  • Jensen Shane T.

    (Associate Professor of Statistics, Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA)

Abstract

National Football League teams have complex drafting strategies based on college and combine performance that are intended to predict success in the NFL. In this paper, we focus on the tight end position, which is seeing growing importance as the NFL moves towards a more passing-oriented league. We create separate prediction models for 1. the NFL Draft and 2. NFL career performance based on data available prior to the NFL Draft: college performance, the NFL combine, and physical measures. We use linear regression and recursive partitioning decision trees to predict both NFL draft order and NFL career success based on this pre-draft data. With both modeling approaches, we find that the measures that are most predictive of NFL draft order are not necessarily the most predictive measures of NFL career success. This finding suggests that we can improve upon current drafting strategies for tight ends. After factoring the salary cost of drafted players into our analysis in order to predict tight ends with the highest value, we find that size measures (BMI, weight, height) are over-emphasized in the NFL draft.

Suggested Citation

  • Mulholland Jason & Jensen Shane T., 2014. "Predicting the draft and career success of tight ends in the National Football League," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(4), pages 1-16, December.
    • Handle: RePEc:bpj:jqsprt:v:10:y:2014:i:4:p:16:n:2
    DOI: 10.1515/jqas-2013-0134
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    References listed on IDEAS

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    1. Cade Massey & Richard Thaler, 2005. "Overconfidence vs. Market Efficiency in the National Football League," NBER Working Papers 11270, National Bureau of Economic Research, Inc.
    2. John D. Burger & Stephen J. K. Walters, 2003. "Market Size, Pay, and Performance," Journal of Sports Economics, , vol. 4(2), pages 108-125, May.
    3. David Berri & Stacey Brook & Aju Fenn, 2011. "From college to the pros: predicting the NBA amateur player draft," Journal of Productivity Analysis, Springer, vol. 35(1), pages 25-35, February.
    4. David Berri & Rob Simmons, 2011. "Catching a draft: on the process of selecting quarterbacks in the National Football League amateur draft," Journal of Productivity Analysis, Springer, vol. 35(1), pages 37-49, February.
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    Cited by:

    1. Joshua D. Pitts & Brent Evans, 2019. "Drafting for Success: How Good Are NFL Teams at Identifying Future Productivity at Offensive-Skill Positions in the Draft?," The American Economist, Sage Publications, vol. 64(1), pages 102-122, March.
    2. 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.
    3. Russell Brook T. & Hogan Paul, 2018. "Analyzing dependence matrices to investigate relationships between national football league combine event performances," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(4), pages 201-212, December.
    4. 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.
    5. Yurko Ronald & Ventura Samuel & Horowitz Maksim, 2019. "nflWAR: a reproducible method for offensive player evaluation in football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(3), pages 163-183, September.

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