Data‐driven Drafting: Applying Econometrics to Employ Quarterbacks

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)

Predicting the national football league potential of college quarterbacks

“…Section 3.1 revealed that good passing ability is effectively a prerequisite for NFL selection, so this outcome confirms the results in our preliminary analysis that a key indicator of the NFL performance of good college passers is their rushing ability. The finding that passing ability is not a significant determinant of the NFL performance of selected quarterbacks is consistent with the conclusions of Wolfson et al (2011) and Pitts and Evans (2018), and the result that college rushing ability is positively correlated with NFL success concurs with Rosen and Olbrecht (2020). Katz and Bradshaw (2015) postulate that good college rushers succeed in the NFL because good runners have the ability to extend drives.…”

“…Pitts and Evans (2018) show that quarterback Wonderlic scores -a test of cognitive ability -are positively correlated with NFL performance. Rosen and Olbrecht (2020) find that quarterbacks who demonstrated 'functional mobility' in college performed better than those who did not. The authors measure function mobility using rushing yards per attempt (positively correlated with NFL performance) and the log of the runpassing completion ratio (negatively correlated with NFL performance).…”

“…Our analysis is novel in at least four ways. To our knowledge, we present the first study to use Total Quarter Back Rating (QBR) to measure NFL performance, which Stuart (2014) shows is more strongly correlated with quarterback win percentages than other performance measures, such as adjusted net yards per attempt used by Rosen and Olbrecht (2020).…”

“…Fourth, in some specifications, we include scout scores (in addition to college statistics) as a predictor of NFL performance, which allows us to evaluate whether scouts use college statistics efficiently. Several studies include the order in which a player was drafted as an independent variable to explain NFL success (e.g., Rosen and Olbrecht, 2020), but this 'expert opinion' metric is unable to capture absolute differences in ability and can be distorted by the quality of draftees for other positions (e.g., a quarterback drafted in a year with an outstanding crop of running backs may have a worse draft order than a similar quarterback drafted in another year).…”

New contracts and dismissal threats from highly drafted rookies: What motivates NFL quarterbacks?

“…Nearly all previous studies that examined NFL quarterback performance attempted to project performance using only variables known to teams before quarterbacks were drafted (e.g., see Berri & Simmons, 2011; Cook et al, 2020; Craig & Winchester, 2021; Hendricks et al, 2003; Kitchens, 2015; Kuzmits & Adams, 2008; Mirabile, 2005; Pitts & Evans, 2018; Rosen & Olbrecht, 2020; and Wolfson et al, 2011). Thus, these studies were primarily interested in how NFL teams could identify talented players in the draft.…”