University of Pennsylvania ScholarlyCommons Statistics Papers Wharton Faculty Research 10-2014 Predicting the Draft and Career Success of Tight Ends in the National Football League Jason Mulholland University of Pennsylvania Shane T. Jensen University of Pennsylvania Follow this and additional works at: https://repository.upenn.edu/statistics_papers Part of the Physical Sciences and Mathematics Commons Recommended Citation Mulholland, J., & Jensen, S. T. (2014). Predicting the Draft and Career Success of Tight Ends in the National Football League. Journal of Quantitative Analysis in Sports, 10 (4), 381-396. http://dx.doi.org/ 10.1515/jqas-2013-0134 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/statistics_papers/54 For more information, please contact [email protected]. Predicting the Draft and Career Success of Tight Ends in the National Football League 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. Keywords football, prediction, regression Disciplines Physical Sciences and Mathematics This journal article is available at ScholarlyCommons: https://repository.upenn.edu/statistics_papers/54 JQAS 2014; 10(4): 381–396 Jason Mulholland and Shane T. Jensen* Predicting the draft and career success of tight ends in the National Football League Abstract: National Football League teams have complex of effectively drafting tight ends are the motivation for our drafting strategies based on college and combine perfor- focus on the tight end position in this paper. mance that are intended to predict success in the NFL. In the past, tight ends were primarily blockers for In this paper, we focus on the tight end position, which running plays and only occasionally targets for passing is seeing growing importance as the NFL moves towards plays. Now, as many NFL teams have turned to a more a more passing-oriented league. We create separate pre- aggressive, passing-oriented offensive scheme, the tight diction models for 1. the NFL Draft and 2. NFL career per- end has become of the main playmakers on offense and formance based on data available prior to the NFL Draft: often a quarterback’s primary receiver. One illustration college performance, the NFL combine, and physical of the larger emphasis on passing is that in 2003, only 3 measures. We use linear regression and recursive parti- of the 32 NFL teams called pass plays more than 60% of tioning decision trees to predict both NFL draft order and the time, whereas in 2012, 11 of those 32 teams called pass NFL career success based on this pre-draft data. With both plays more than 60% of the time. modeling approaches, we find that the measures that are One of the leading teams in the tight end evolution most predictive of NFL draft order are not necessarily the is the New England Patriots, who have featured tight most predictive measures of NFL career success. This find- ends prominently in recent years. In the 2010 NFL Draft, ing suggests that we can improve upon current drafting the Patriots selected Rob Gronkowski with the 42nd pick strategies for tight ends. After factoring the salary cost of and Aaron Hernandez with the 113th pick. Over the next drafted players into our analysis in order to predict tight three seasons, Gronkowski and Hernandez established ends with the highest value, we find that size measures themselves as two of the premier talents at tight end in the (BMI, weight, height) are over-emphasized in the NFL NFL, though both have had off-field struggles that we will draft. discuss later. This paper will use data available to teams before the Keywords: football; prediction; regression. NFL draft, specifically size, college and combine perfor- mance measures, to address two important questions: 1. What are the best quantitative predictors for NFL draft DOI 10.1515/jqas-2013-0134 order of tight ends? 2. What are the best quantitative predictors of NFL 1 Introduction career success at the tight end position? We will address both questions by constructing prediction Tight end is a very unique position in football due to the models with either NFL draft order or NFL career success fact that tight ends have multiple roles that require a varied as the outcome variable. The available predictor variables skill set. Tight ends not only need to be able to block defen- are any quantitative measures available before the NFL sive ends and outside linebackers, but also need to be able draft, namely, measures from player’s college careers and to run routes and catch passes. As the National Football the NFL combine, as well as physical measures. League (NFL) continues to evolve, tight end has emerged Our analysis differs in several aspects from past studies as an extremely important position. Their changing role that have focused on valuing prospective professional ath- in NFL offensive schemes and the increasing importance letes in several sports. Burger and Walters (2003) analyzed the impact of market size and marginal revenue on how *Corresponding author: Shane T. Jensen, Associate Professor of teams value players in Major League Baseball. Massey and Statistics, Department of Statistics, The Wharton School, University Thaler (2005) researched when teams find the most value of Pennsylvania, Philadelphia, PA 19104, USA, e-mail: [email protected] in the NFL draft and found that teams often overvalue the Jason Mulholland: Undergraduate student, The Wharton School, draft’s earliest picks. Berri, Brook, and Fenn (2010) ana- University of Pennsylvania, Philadelphia, PA 19104, USA lyzed which factors contribute most to the selection of Unauthenticated Download Date | 8/23/17 6:28 PM 382 J. Mulholland and S.T. Jensen: Predicting the draft and career success of tight ends in the National Football League college basketball players in the NBA draft as well as NBA 1. Forty Yard Dash, 2. Bench Press, 3. Vertical, 4. Broad performance. Our own analysis follows a similar process Jump, 5. Shuttle, and 6. Three Cone Drill. From the height of identifying what factors best predict NFL draft results and weight measures we calculated one additional physi- as well as future NFL performance of college players. cal measure, body mass index (BMI), which is often used Our analysis indicates that the best predictors of NFL to measure the muscle mass of athletes. draft order are not the best predictors of NFL career per- College football data was collected from two public formance, which suggests that tight ends are not currently websites: www.sports-reference.com/cfb and www.ncaa. being drafted in an optimal way with respect to predicted org. The college measures for tight ends that we collected career success. We further explore this result by evaluat- were each player’s receptions, receiving yards, and receiv- ing NFL performance in the context of the salary cost of ing touchdowns over their entire college career as well each player, so that we can isolate the best predictors of as these same totals specifically in their final season of tight end “value” (performance per unit cost). Our overall college football. approach emulates the Dhar (2011) study of NFL wide From these measures, we created several additional receivers, though we examine a greater set of NFL perfor- variables to reflect the impact of the player’s final year in mance measures as well as undertaking a cost analysis of college: 1. final year college receptions percentage, 2. final performance per salary cost. year college yards percentage, and 3. final year college Our paper is organized as follows: We first describe the touchdowns percentage. We also created an overall college data used in our study in Section 2 and then outline our pre- yards per reception measure for each player as well as an diction models in Section 3. We explore the results of our indicator variable, BCS, for whether or not they played quantitative modeling of the NFL draft order of tight ends at a Bowl Championship Series school. BCS schools tend in Section 4 and then the results of our quantitative model- to receive larger amounts of media and scouting atten- ing of NFL career performance of tight ends in Section 5. In tion and tend to play against more talented competition, Section 6, we examine the difference between the selected which may be predictive of either the NFL Draft or NFL predictors of NFL draft order and NFL career performance. career performance. Finally, we incorporate salary cost into our measures of NFL NFL Draft data was collected from the public website: career performance in Section 7, explore the use of a differ- www.nfl.com/draft. We collected the draft order of the 223 ent measure of NFL performance in Section 8, and then tight ends (out of 315 total) that were drafted in the 1999– conclude with a summary and discussion in Section 9.