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Measuring Risk in NFL Playcalling

Author

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  • Alamar Benjamin C

    (Menlo College)

Abstract

Coaches in the NFL make approximately 1000 offensive play calls during the regular season. These calls are the result of countless hours of preparation and analysis and the coach's own personal experience and each coach has their own measures of success and biases regarding types of play calls. What has not been utilized previously is a systematic analytical approach to measure a play's outcome in relation to the drive, and an evaluation of whether coaches' are irrationally biased in their playcalling. Using play by play data from the 2005 through 2008 NFL regular season, an evaluation system is built around the concept of expected points. Expected points have been used in baseball for over 40 years and have been applied occasionally in football (Romer 2003; Carroll et al 1988). This framework allows for a true calculation of risk for different play types. Risk for passing plays is found to be lower than risk for running plays in certain situations, while still yielding a higher expected value. These results confirm previous analysis (Alamar 2008) that teams underutilize the pass.

Suggested Citation

  • Alamar Benjamin C, 2010. "Measuring Risk in NFL Playcalling," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-9, April.
    • Handle: RePEc:bpj:jqsprt:v:6:y:2010:i:2:n:11
    DOI: 10.2202/1559-0410.1235
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    References listed on IDEAS

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    1. David Romer, 2002. "It's Fourth Down and What Does the Bellman Equation Say? A Dynamic Programming Analysis of Football Strategy," NBER Working Papers 9024, National Bureau of Economic Research, Inc.
    2. Alamar Benjamin & Ma Jeff & Desjardins Gabriel M & Ruprecht Lucas, 2006. "Who Controls the Plate? Isolating the Pitcher/Batter Subgame," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(3), pages 1-10, July.
    3. Rockerbie Duane W., 2008. "The Passing Premium Puzzle Revisited," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(2), pages 1-13, April.
    4. Alamar Benjamin C, 2006. "The Passing Premium Puzzle," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(4), pages 1-10, October.
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    Cited by:

    1. Emara, Noha & Owens, David & Smith, John & Wilmer, Lisa, 2017. "Serial correlation in National Football League play calling and its effects on outcomes," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 69(C), pages 125-132.
    2. Snyder Kevin & Lopez Michael, 2015. "Consistency, accuracy, and fairness: a study of discretionary penalties in the NFL," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(4), pages 219-230, December.
    3. Jared Quenzel & Paul Shea, 2016. "Predicting the Winner of Tied National Football League Games," Journal of Sports Economics, , vol. 17(7), pages 661-671, October.
    4. Goldner Keith, 2012. "A Markov Model of Football: Using Stochastic Processes to Model a Football Drive," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-18, March.
    5. Emara, Noha & Owens, David & Smith, John & Wilmer, Lisa, 2014. "Minimax on the gridiron: Serial correlation and its effects on outcomes in the National Football League," MPRA Paper 58907, University Library of Munich, Germany.
    6. Heiny Erik L & Blevins David, 2011. "Predicting the Atlanta Falcons Play-Calling Using Discriminant Analysis," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-14, July.
    7. Urschel John D & Zhuang Jun, 2011. "Are NFL Coaches Risk and Loss Averse? Evidence from Their Use of Kickoff Strategies," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-17, July.
    8. 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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    Keywords

    football; risk; passing premium;
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