International Journal of Sport Finance, 2013, 8, 263-278, © 2013 West Virginia University
On the Evaluation of Kickers in
the National Football League
David J. Berri1 and Martin B. Schmidt2
1
Southern Utah University
College of William and Mary
2
David J. Berri is a professor of economics in the Department of Economics and
Finance at Southern Utah University. His research primarily examines the economics of sports, with a specific focus on behavioral economics, worker productivity and
compensation, and competitive balance.
Martin B. Schmidt is a professor in the Department of Economics at the College of
William and Mary, where he specializes in macroeconomics and the economics of
sport.
Abstract
Kickers in the NFL have two jobs. The first is kicking off. The second is scoring via
field goal attempts and extra points. Of these two actions, the latter’s impact on outcomes is most easily observed. Decision-makers should be able to go beyond simple
visual observation and evaluate actions in terms of their impact on outcomes.
Consistent with past research in baseball and basketball, though, we find evidence that
decision-makers undervalue the factor that is hardest to visually connect to outcomes.
Keywords: National Football League, behavioral economics, specialization
Introduction
One of the main themes of Adam Smith’s seminal book The Wealth of Nations is the
central role specialization plays in determining an economy’s rate of economic
growth. For example, Smith describes the process of specialization within the production of a pin factory in the following way:
One man draws out the wire, another straights it, a third cuts it, a fourth
points it, a fifth grinds it at the top for receiving the head; to make the head
requires two or three distinct operations; to put it on is a particular business,
to whiten the pins is another ... and the important business of making a pin
is, in this manner, divided into about eighteen distinct operations, which in
some manufactories are all performed by distinct hands, though in others the
same man will sometime perform two or three of them. (Smith, 1776, p. 6)
Smith, recognizing the value of the division of labor, further argues that when the
“business of making a pin is” divided into smaller distinct operations, the returns to
productivity may be immense.1
This lesson was certainly not lost on many in the production sector. Henry Ford, for
example, used these principles in the production of the Model T. After incorporating
the Ford Motor Company in 1903, Ford went about revolutionizing the auto manu-
Berri, Schmidt
facturing process. By introducing the assembly line production to the process of producing automobiles, Ford was able to increase the production of his Model T from 1
car every 12 hours to 1 car every 93 minutes in 1914, a rate nearly 5 times the rate of
his competitors.2
In the world of sports, though, this message often appears lost. The best basketball
players are asked to dribble, shoot, pass, rebound, and defend. Baseball players are often
prized if they have the five tools, the ability to hit for average, hit for power, run, throw,
and field. In other words, in basketball and baseball, the nonspecialist is preferred.3
In American football, though, the gains from specialization have clearly been recognized. Whereas in other sports players are encouraged to be good at everything, football players focus on a very specific list of skills. On offense, one player throws the ball,
others run, still others catch, and others only block.4 This lesson also seems to have
been learned across time as, for example, the day of the two-way player is now a thing
of the past.
Perhaps for no position is this focus on specialization more important than for the
kicker. While other players on an NFL team are responsible for throwing the ball, carrying the ball, catching the ball, blocking, or tackling, a kicker has none of these
responsibilities.5 In sum, many of the activities generally associated with football are
not the responsibility of the kicker. Two jobs are generally assigned to the kicker. The
first is kicking off, with the objective being to kick the ball as far as possible, thus forcing the opposing team to travel more yards to score. The second task involves scoring,
either via field goals or extra points.
It is this latter task that generally earns a kicker fame or infamy. For example, Adam
Vinatieri’s 48-yard field goal as time expired proved to be the winning margin in the
New England Patriots triumph over the St. Louis Rams in Super Bowl XXXVI. And Jim
O’Brien—with five seconds left in Super Bowl V—kicked a 32-yard field goal to give
the Baltimore Colts a victory over the Dallas Cowboys. In contrast, Scott Norwood is
remembered for missing a 47-yard field goal with eight seconds left in Super Bowl
XXV against the New York Giants, the first of four consecutive Super Bowl losses for
the Buffalo Bills.
The ability—or inability—to convert field goal attempts (and extra point attempts)
into points certainly stands out when one thinks about NFL kickers. Aaron Schatz,
though, argued in the New York Times that this focus was misplaced:6
Game-winning field goals are what make kickers famous, but from season to
season it is impossible to tell which kickers will be the most trustworthy in the
closing seconds. Instead of wasting money on high-priced field-goal kickers,
teams would be better off signing kickers who can be counted on to help their
teams consistently by affecting field position with long kickoffs.
Schatz argument reminds one of the arguments advanced in Moneyball. In the
Michael Lewis classic it was argued that Major League Baseball teams focused too much
attention on slugging percentage and not enough on a player’s ability to get on base
(measured via on-base percentage). In other words, the ability to hit home runs captured
the attention of decision-makers while the ability to draw a walk was undervalued.
The research of Jahn Hakes and Raymond Sauer (2006, 2007) found support for the
story told by Lewis in Moneyball. Specifically, Hakes and Sauer found that prior to
2004, player salary was primarily determined by slugging percentage. On-base percent264 Volume 8 • Number 4 • 2013 • IJSF
On the Evaluation of Kickers in the National Football League
age—in many of the years the authors examined—failed to have a statistically significant impact on player compensation, this despite the fact that the on-base percentage
was a significant determinant of winning.
A similar story has been told about wages in professional basketball. Berri, Brook,
and Schmidt (2007) present evidence that a player’s scoring totals have the largest
impact on player salaries. Factors such as shooting efficiency, rebounds, and
turnovers—which play a larger role in determining team wins—are less important to
an NBA player’s compensation.
These stories suggest that decision-makers are drawn to the actions that are the most
dramatic during the course of the game. Factors, though, whose impact on outcomes
is harder to judge by just watching the game (i.e., walks in baseball and nonscoring
actions in basketball) would then be undervalued.7
Following this logic, Schatz is arguing that kickers are likely to be paid for their ability to score; therefore, the ability to excel with respect to kickoffs may be undervalued. In
order to test this hypothesis we examine the impact that these two factors (i.e., the ability to score and kicking off) have on a kicker’s salary. If the story of baseball and basketball also exists in football, then one would expect that kickers’ salaries will be overly
influenced by the ability to score, at least relative to each factors’ importance to winning.
In the end we find that a kicker’s performance with respect to scoring (i.e., field goals
and extra points) has a relatively larger impact on player compensation than a kicker’s
performance on kickoffs—this despite the fact that kickers’ performance with respect
to scoring is highly variable. In contrast, a kicker’s performance with respect to the
kicker’s other job, kickoffs, is much more consistent across time. In sum, what we see
in the NFL with respect to the evaluation of kickers is consistent with prior studies of
decision-making in basketball and baseball.
Methodological Approach and Data
Methodology
The present paper estimates the determinants of an NFL kicker’s salary. In order to
estimate these determinants we follow the literature and estimate the player’s marginal contribution to product quality—in most cases wins—and also include some measure of star quality as well as team-specific market demographics (Rosen & Sanderson,
2001). While the marginal contribution to product quality captures the impact that a
player may have on the firm’s bottom line through greater wins, the measures of star
quality capture the impact that the player may have to a team’s revenues through
increased revenues due to fans’ desire to see the player.
The first to directly estimate a player’s marginal product was Scully (1974). Scully
estimated a player’s marginal product through the player’s impact on a team’s probability of winning. This approach follows from the argument that a team’s revenue
stream is highly correlated with a team’s winning percentage. In which case, one need
only estimate the marginal impact of each of a player’s actions on the field on the
team’s likelihood of winning to capture the player’s marginal impact to the firm’s
product quality.
Such studies have become common in the literature for baseball and basketball markets.8 For example, while Hakes and Sauer (2006) estimate a positional player’s impact
Volume 8 • Number 4 • 2013 • IJSF 265
Berri, Schmidt
on winning through slugging and on-base percentage, Bradbury (2007) uses the DIPS
measures for pitchers’ values and finds a similar result. For basketball, Berri, Brook,
and Schmidt (2007) estimate individual player contribution through individual statistics such as shooting efficiency, turnovers, and rebounds. Each of these finds a disconnection, at least for periods, between player compensation and player contribution.
For the NFL, Ahlburg and Dworkin (1991) and Berri and Simmons (2009), among
others, examine individual player compensation. Ahlburg and Dworkin examine the
relative importance of seniority on player compensation. They incorporate individual
measures of player performance, both career and in the past season, as well as position,
draft round, and seniority. In the end, the authors find that an individual player’s
salary is significantly affected by the player’s seniority and original draft round.
Moreover, they find that a player’s career performance explains two to four times as
much of the variance as the player’s previous season’s performance. Berri and
Simmons (2009), for example, estimate individual quarterback productivity by incorporating a quarterback’s passing and rushing yards, as well as touchdowns, completions, and interceptions per attempt.
Krautmann (1999) critiques the Scully approach and argues that Scully’s estimates
of player productivity are biased upward as these productivity measures are only weakly correlated with free agent player salaries. Krautmann, in contrast, argues that the
competition for player services, which happens when a player reaches free agency,
should move player salaries closer to their unobservable productivities.
Bradbury (2013), however, argues that while Krautmann’s approach has some merit,
it also has several significant drawbacks. Perhaps the most significant is that the studies described above argue that private decision-makers in both professional baseball
and basketball do appear to price talent inefficiently. Using the Krautmann method,
which assumes that market prices are correct, would make it fundamentally difficult
to examine efficiency in a sport market.
In the end, our approach employs elements of both Scully’s and Krautmann’s
approach. Like Scully, we are interested in a kicker’s impact on outcomes. And like
Krautmann, we regress a player’s salary on performance—in this case, performance on
kickoffs and scoring actions—and a collection of control variables. In the case of a kicker, the contribution to team wins would likely come from their two distinct operations:
(1) kicking field goals and extra points and (2) kicking the ball off after scores and at
the beginning of both halves of football.9 If we assume that the NFL team’s goal is to
maximize its total number of wins, then we would expect that a kicker’s salary should
largely be determined by these two actions.10 This, then, requires that we first convert
both the ability to score (i.e., field goals and extra points) and kickoffs into their respective importance to winning. Once we have found a common metric for kickoffs and
scoring we can estimate how each factor drives the compensation of kickers in the NFL.
Measuring kicker performance
While valuing field goals and extra points would seem simple in that they may be
measured in terms of points scored, which is an essential component of winning, kickoffs are more difficult. What we require for kickoffs is ameasure that ties kickoff performance to points (which might then be tied to winning). Specifically, what is an
extra yard in a kick off worth to a team’s probability of winning?
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On the Evaluation of Kickers in the National Football League
Fortunately, for our purposes, empirical studies that tie field position to winning
probabilities have existed for over 40 years. Carter and Machol (1971, 1978), for example, estimate the expected point outcome given that the offensive team has a first down
at a particular yard-line. Their estimates however were averages across 10-yard segments rather than each possible yard outcome. Carroll, Palmer, and Thorn (1988)
extend Carter and Machol’s approach and provide expected point outcomes for each
yard-line on a football field.
The results of Carroll, Palmer, and Thorn are then exactly the type of data we need
to estimate the value of a kickoff. The data allows one to estimate the contribution of
a football play directly in terms of how many points it contributes (EP). For example,
Berri and Burke (2012) describe it this way:
EP is a tantalizing concept for valuing the performance of players because it
can measure the contribution of each play directly in terms of how many
points it contributes. For example, consider a situation where a QB snaps the
ball on a first down and 10 from his own 30-yard line, worth perhaps 1.1 EP.
If he completes a 15-yard pass, his team now has a first and 10 from its own
45-yard line, worth perhaps 1.9 EP. In this case, the QB’s play has added 0.8
EP to his team’s expected net point differential. If instead he threw an interception, this would give his opponents a first down at midfield. And this is
worth perhaps 2.0 EP for the opponent and −2.0 EP for the QB’s team. The
net value of the play would be negative: −2.0−1.1=−3.1. In this case, the interception was equivalent to a loss of 3.1 in net EP differential. (p. 146)
There are, however, several shortcomings of these studies. The first is that they both
use data throughout the game. This is problematic as the incentives are different late in
the game—teams ahead become more conservative, while teams behind are more
aggressive—where urgency becomes more of the issue than scoring optimization. The
second is that these studies assumed that the expected point outcome is a linear function.
Romer (2006) allows the expected points given field position to be nonlinear.
Specifically, Romer is examining whether NFL teams were optimizing with respect to
fourth-down decisions. His results indicated that teams were often kicking the ball (i.e.,
punting or kicking field goals) when the optimal choice was to go for the first down.
Similar to the studies mentioned above, Romer’s approach was to estimate, through
the use of actual game day data, the value of taking offensive possession at a particular point on a football field (i.e., the point value of having a first and 10 from any spot
on the field). Specifically, Romer was estimating not only the points that the offensive
team might score on the ensuing drive, but also the points the opponent’s might score
with the field position you’re likely to give them if you don’t score, and the points
you’re likely to score with the field position they give you after they do or don’t score,
and so on.
Figure 1 summarizes Romer’s estimates. The figure simply captures the net outcome
(on the game’s final score) of taking possession of the football at a particular point on
the field. For example, taking possession at your own 5-yard line would change a
team’s net point total by roughly -1.0.
The shape of curve is generally consistent with what one might expect. For example,
having a first and 10 on your own 20-yard line is worse, in terms of the final score, than
Volume 8 • Number 4 • 2013 • IJSF 267
Berri, Schmidt
Figure 1: Summarizing Romer’s Findings
having a first and 10 on your opponent’s 20-yard line—worse by about 3.3 points.
Also, the value of having a first and 10 on your own 15-yard line is zero, suggesting that
a team would be indifferent between having possession on their own 15-yard line or
the opposition gaining possession at their 15-yard line. In other words, both states
have the same impact on the chances of winning.
Measuring the value of kickers in the NFL
Given the data highlighted in Figure 1, we can estimate the value of a kickoff. First, we
recognize that over the period from 1994 to 2009 an offensive football team starts on
approximately their own 30-yard line following a kick off. There are, however, three
outcomes that can lead the opponent to start someplace else: a touchback, a kick out
of bounds, or a return of a kickoff to another point.
The kicker could kick the ball that enters the end zone and is not returned; this is
referred to as a touchback. The outcome of this play gives the opponent the ball on the
20-yard line. This outcome moves the opponent back 10 yards. So a kicker who can
kick the ball in such a way as to get a touchback rather than an average starting point
would force the opposing team to go 10 yards further to score. In terms of Romer’s
estimates, moving the football from the 30 back to the 20 is worth 0.556 points to a
kicker’s teams.
If the kicker kicks the ball out of bounds, this is against the rules and imparts a
penalty to the kicking team; the opponent is given the ball on the 40-yard line. Again
using Romer’s estimates of the cost of moving the opponent from the average outcome, (i.e., the 30-yard line) to the 40-yard line costs the kicker’s team 0.604 points.
Finally, if neither a touchback nor a kick out of bounds occurs, then the kickoff will
be returned. The average return over the period from 1994 to 2009 was 22 yards. In
which case if a kicker manages to kick the ball past the opponent’s eight-yard line (or
268 Volume 8 • Number 4 • 2013 • IJSF
On the Evaluation of Kickers in the National Football League
Table 1: The Best and Worst on Kickoffs
Top 10
kicking kickers
Kickoff
Value
Kickoff
Yards Touch- Out-of- per
points
Year Kickoffs ave.
backs Bounds kickoff above ave.
Mitch Berger
Pat McAfee
Morten Andersen
Thomas Morstead
Morten Andersen
Rhys Lloyd
Stephen Gostkowski
John Hall
Mitch Berger
Matt Prater
1998
2009
1998
2009
1995
2008
2009
1998
1996
2009
112
80
90
101
82
88
91
84
71
78
70.2
70.0
68.4
67.7
68.9
67.8
67.8
67.8
68.9
68.4
3
21
2
27
2
0
21
1
0
28
0
1
1
2
1
0
0
0
0
0
Bottom 10
kicking kickers
Chris Jacke
Steve Christie
Al Del Greco
Kris Brown
Mike Hollis
Al Del Greco
Neil Rackers
Craig Hentrich
Cary Blanchard
John Kasay
0.433
0.421
0.340
0.298
0.356
0.329
0.300
0.314
0.368
0.334
48.5
33.6
30.6
30.1
29.2
29.0
27.3
26.4
26.1
26.0
Kickoff points
below average
1994
2000
1995
1999
1995
1994
2002
2003
2000
1995
83
72
81
73
66
55
64
91
54
70
54.5
53.6
55.7
56.0
54.8
52.8
55.0
57.7
53.8
56.8
0
4
8
0
1
3
0
4
2
1
0
2
4
0
1
2
0
2
0
0
-0.549
-0.599
-0.428
-0.457
-0.492
-0.578
-0.491
-0.339
-0.568
-0.398
-45.6
-43.1
-34.7
-33.3
-32.5
-31.8
-31.4
-30.9
-30.7
-27.9
if he manages to kick the ball 62 yards), then he has—relative to an average kicker—
given his team a benefit. If the kick, though, is fielded past the 8-yard line then the kick
imposes a cost.
Given these three outcomes—and the corresponding values—we can now evaluate
each kicker. To illustrate, let’s consider the performance of Thomas Morstead of the
New Orleans Saints in 2009. Morstead kicked off 101 times that season. Of these kicks,
27 resulted in a touchback. Given that an average kicker would have only had 11.2
touchbacks, Morstead exceeded the performance of an average kicker by 15.8 touchbacks. As each touchback is worth 0.556 points, Morstead’s additional touchbacks generated 8.8 points for the Saints. Additionally, Morstead kicked 2 kickoffs out of
bounds, which was 0.7 about the average kicker who would have had only sent 1.3.
Given that each out-of-bounds kick costs the team 0.604 points, Morstead’s relatively
bad performance on out-of-bounds kicks cost the Saints -0.2 points. Finally 72 of his
were returned. Morstead’s returned kicks traveled 66.9 yards, or about five yards further than the kicks of an average kicker. Romer’s estimates suggest that saving five
yards on a kickoff is worth 0.289 points. So Morstead’s returned kicks generated 21.4
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Berri, Schmidt
points for the Saints. If we put all this together, we see that Morstead’s kickoffs were
worth 30.1 points beyond what an average kicker would produce.
Table 1, which reports the best and worst kickers from 1994 to 2009, places this result
in some perspective. As one can see, Morstead’s performance in 2009 ranks 4th among
the 568 kickers who have attempted at least 16 kickoffs in a regular season since 1994.
Next we can use Romer’s estimates to value the kicker’s other job of kicking field
goals and extra points. Whereas the latter is almost always kicked from the same spot
on the field, a field goal can be attempted anywhere. Data is tracked for how each kicker does from the 19 yards and closer, 20-29 yards, 30-39 yards, 40-49 yards, and beyond
50 yards. With such data in hand, we can determine how the average kicker did from
each distance. And with averages in hand, we can—as we did in our analysis of kickoffs—ascertain for each kicker how many points a team could have expected if an average kicker would have attempted a kicker’s field goal attempts from each distance. A
similar calculation was completed for extra points.
To illustrate, consider the performance of Neil Rackers in 2005. That season Rackers
made 40 of the 42 field goals he attempted. With each field goal worth three points,
Rackers generated 120 points from his field goals. An average kicker, kicking from the
same distances, would have only generated 96.8 points. So Rackers field goal kicking
generated 23.2 points more than what his team would have seen from an average kicker. Rackers was also perfect on extra points, a performance that was 0.3 points better
than an average kicker. In sum, Rackers scored 23.5 more points beyond what we
would see from just an average kicker.
This calculation, though, does not fully capture the impact of a kicker’s scoring.
When a kicker misses a field goal, the opposing team is able to take possession of the
ball at the spot where the field goal was missed.11Like the value of kickoffs, holding the
ball at certain points on the field is worth points to the opponent. And again, Romer’s
work tells us how many points each position on the field is worth.
To ascertain this value we need to note where the opponent gains possession of the ball
after each miss. Again, our data does not tell us the exact distance each kick was attempted. But we do know the ranges listed above. And from these ranges, we can estimate the
location of the ball and the value of this location to the opponent. For example, Rackers
in 2005 attempted 14 field goals from between 40 and 49 yards. Given that the goal posts
are 10 yards beyond the field of play and the field goal kicker tends to kick the ball from
seven yards behind the line of scrimmage, a missed field goal from this distance would
give the opponent the ball somewhere between the 37 and 46 yard-line. For the sake of
simplicity, we took the midpoint of this range and assumed a missed field goal from 40
to 49 yards would give the opponent the ball at about the 42 yard-line (or 41.5 rounded
up). From Romer’s work we know that the opponent can expect to score 1.52 points if
they have the ball at that point. Since Rackers, though, only missed one of these kicks, he
essentially saved his team 5.1 points (or 3.3*1.5) on his attempts from 40 to 49 yards.
Similar calculations from each distance indicate that Rackers saved his team 11.4 points
by kicking field goals at an above average rate in 2005.
In Table 2 we put both calculations with respect to field goals together. Specifically,
we see that from 1994 to 2009, what Rackers did in 2005 with respect to kicking field
goals and extra points was the best performance by any kicker. On the second half of
270 Volume 8 • Number 4 • 2013 • IJSF
Year
2003
1996
2001
2001
2000
1995
2001
2009
2002
1999
Seth Marler
Joe Nedney
Kris Brown
Wade Richey
Neil Rackers
Steve McLaughlin
Neil Rackers
Kris Brown
Todd Peterson
Doug Pelfrey
2005
1998
2003
2008
2009
1996
1997
2003
2005
1998
Neil Rackers
Gary Anderson
Mike Vanderjagt
Jason Hanson
Sebastian Janikowski
Cary Blanchard
Pete Stoyanovich
Jeff Wilkins
Joe Nedney
Al Del Greco
Bottom 10
scoring kickers
Year
Top 10
scoring kickers
42
35
37
22
29
40
27
42
28
39
90
89
124
89
57
41
74
106
61
81
33
29
44
32
21
16
28
32
21
27
Field
Total
goal
pts attempts
140
164
157
88
95
135
113
163
97
136
Field
Total
goal
pts attempts
60.6%
62.1%
68.2%
65.6%
57.1%
50.0%
60.7%
65.6%
57.1%
66.7%
Field
goal %
95.2%
100.0%
100.0%
95.5%
89.7%
90.0%
96.3%
92.9%
92.9%
92.3%
Field
goal %
30
35
34
26
21
17
23
43
25
27
Extra pt
attempts
20
59
46
25
17
27
35
46
19
28
Extra pt
attempts
Table 2: The Best and Worst on Kicking Field Goals and Extra Points
100.0%
97.2%
91.9%
100.0%
100.0%
100.0%
95.8%
97.7%
96.2%
100.0%
Extra
pt %
100.0%
100.0%
100.0%
96.2%
100.0%
100.0%
97.2%
100.0%
100.0%
100.0%
Extra
pt %
-19.3
-17.1
-17.5
-16.5
-15.4
-16.0
-16.5
-16.1
-16.1
-16.2
Pts
beyond
ave.
23.5
20.5
18.8
16.1
16.1
14.3
14.1
13.9
13.5
13.4
Pts
beyond
ave.
34.9
29.6
26.7
26.5
24.2
21.7
21.2
20.8
19.8
18.2
-7.8
-8.0
-6.3
-6.2
-6.6
-5.8
-5.3
-5.4
-5.4
-4.6
-27.2
-25.1
-23.9
-22.7
-22.0
-21.8
-21.8
-21.6
-21.4
-20.8
Value of
Total
missed
value of
field goals field goals
11.4
9.0
7.9
10.4
8.1
7.4
7.1
6.9
6.3
4.8
Value of
Total
missed
value of
field goals field goals
On the Evaluation of Kickers in the National Football League
Table 2, though, we see that Rackers, in 2001, offered one of the worst performances
by a kicker with respect to kicking field goals and extra points.
Such inconsistency highlights the point by Schatz. Kickers are simply not very consistent with respect to scoring. To further illustrate this observation, we examined the
correlation between a kickers performance on field goals in successive seasons. From
1994 to 2009, 375 kickers attempted at least 16 field goals in consecutive years. With
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Berri, Schmidt
respect to field goal percentage (field goals made divided by field goals attempted), we
only see a correlation of 0.07. And with respect to scoring points above or below average (including the impact of field position) we see a correlation of 0.04. In sum, kickers are quite inconsistent with respect to scoring.
In contrast, we see much more consistency on kickoffs. From 1994 to 2009 we see
404 kickers who kicked off at least 16 times in successive seasons. If we look at points
per kickoff in consecutive years we see a correlation coefficient of 0.54. As Schatz
argued, kickers are much more consistent with respect to kickoffs.
And when we look at the best and worst performances in Tables 1 and 2, we also see
that kickers are capable of producing more points via kickoffs. All of this suggests that
NFL teams should be primarily paying kickers for kickoffs.
Estimated equation and remaining data
To ascertain how kickoff and scoring impact salaries we employ the model detailed by
equation (1).
lnSAL= b0 + b1*KICKOFFVALUE + b2*SCORINGVALUE + b3*EXP + b4*EXPSQ +
b5*LNSMSA +b6*CHANGETEAM + b7*VETERAN + b8*DRAFTED +
b9*PROBOWL + b10*OSKR + et
(1)
The dependent variable is a kicker’s salary.12 This value is weighted by the size of the
NFL’s salary cap.13 Salaries were also logged. It is important to remember that NFL contracts are not guaranteed. So a kicker who does not perform to expectations can be
easily removed from the team.
Table 3 reports that the average kicker in our sample was paid about $750,000. Table
3 also reports descriptive statistics for the factors that might explain a kicker’s salary.
The two factors we primarily focus upon in our study of salaries is Total Kickoff Value
and Total Scoring Value.14 Each of these factors was calculated according to the above
descriptions. It is important to note that we are using lagged performance. In other
words, salary in 2009 is believed to be a function of how the kicker performed in 2008.
In addition to performance, we considered seven control variables. The first is experience, which we expect to have a positive impact on compensation early in a player’s
career. Eventually, though, we expect further increases in experience to diminish a
player’s wages. Additionally, the team’s market size is proxied using log population of
the local SMSA (LNSMSA).
Beyond experience and population, we consider four dummy variables. The first of
these is whether or not a kicker is a veteran player. Veteran players in the NFL are eligible for free agency.15 Consequently, these players should see higher salaries.
The next dummy variable is equal to one if a kicker is drafted. Unlike most positions
in the NFL, teams do not often spend draft picks on kickers. In fact, only 42% of kickers in our sample were actually drafted by an NFL team. Past research, though, has
shown that a player’s draft status can have lingering effects on player evaluation and
compensation.16 Consequently, it is possible that the kickers who were drafted are considered better players by decision-makers independent of actual performance.
Berri and Simmons (2009), in a study of NFL quarterbacks, argued that a football
player’s compensation might be affected by changing teams. Specifically, Berri and
Simmons found that quarterbacks who changed teams tended to see their salaries
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On the Evaluation of Kickers in the National Football League
Table 3: Descriptive Statistics for Factors Employed in Equation (1)
Variable
Observations
Real salary
316
Kickoffs
Kickoff yards
Touch backs
Out-of-bounds kicks
Total kickoff value
422
422
422
422
417
Average
Standard
Deviation
$750,241.90 $746,830.00
Minimum
Maximum
$31540.4
$4806760.0
65.92
4,126.37
7.31
1.08
-1.13
21.34
1,389.75
6.12
1.04
11.89
10.0
551.0
0.0
0.0
-43.1
112.0
7220.0
29.0
5.0
29.2
Field goals made
422
Field goals attempted 422
Field goal percentage 422
Extra points made
422
Extra points attempted 422
Total scoring value
422
22.24
27.64
0.80
31.78
32.20
0.38
7.05
8.00
0.09
11.22
11.28
6.62
1.0
1.0
0.3
2.0
2.0
-19.3
40.0
46.0
1.0
74.0
74.0
23.5
Experience
422
Population
422
(in millions)
Dummy variable for 422
veteran player
Dummy variable for 422
drafted player
Dummy variable for 422
changing team
Dummy variable for 422
Pro Bowl player
Onside kicks attempted 422
Onside kicks recovered 422
5.82
4.26
4.66
4.16
0.0
1.12
20.0
18.32
0.61
0.49
0.0
1.0
0.44
0.50
0.0
1.0
0.12
0.33
0.0
1.0
0.06
0.25
0.0
1.0
1.42
0.35
1.37
0.63
0.0
0.0
9.0
4.0
* - Salary data is taken from the website of the USA Today.
Performance data can be found at the website of NFL.com
decline. Because the NFL has a binding salary cap and extensive revenue sharing, it is
difficult for one team to out-bid another team for a player’s services. So players do not
often depart teams because they are getting better offers, but because their current
team had decided to let the player depart (primarily because the original team has lowered their estimation of a player’s value). To control for this effect, a dummy variable
was included that is equal to one if the kicker switched teams.
The final dummy variable also follows from the work of Berri and Simmons (2009).
These authors argued that it was possible that a Pro Bowl appearance in a player’s
career could have a lingering impact in a player’s salary. Consequently, a dummy variable is included that is equal to one if a kicker appeared in the Pro Bowl.
Volume 8 • Number 4 • 2013 • IJSF 273
Berri, Schmidt
Table 4: OLS Estimation of Equation (1)
Variable
OLS
Fixed Effects
Coefficient Standard Errors Coefficient Standard Errors
Kickoff value
Scoring value
Experience
Experience, squared
Population, logged
Veteran player
Drafted player
Changing team
Pro Bowl
On-side kicks recovered
Constant term
Observations
R-squared
0.008***
0.042*
0.217*
-0.011*
-0.066
-0.017
-0.057
0.016
-0.113
-0.052
12.911*
316
0.244
0.005
0.009
0.058
0.003
0.166
0.214
0.105
0.144
0.190
0.073
1.105
0.008
0.042*
0.214*
-0.010*
--0.012
-0.063
0.036
-0.056
-0.046
12.443*
0.005
0.009
0.061
0.003
-0.218
0.131
0.167
0.216
0.085
0.143
316
0.229
*-significant at the 1% level
**- significant at the 5% level
***-significant at the 10% level
The last factor included addresses the issue of on-side kicks. Once a kick has traveled 10 yards, the kicking team can take possession of the ball. To take advantage of this
rule, teams will intentionally kick the ball a bit beyond 10 yards in the hope of recovering the ball. The advantage of this strategy is the kicking team can retain possession.
The obvious downside is the receiving team can recover the on-side kick and have very
good field position.
Only about 25% of on-side kicks are recovered by the kicking team. And on average,
kickers attempt fewer than 1.5 on-side kicks per season. So these events are relatively
rare. Nevertheless, it is possible that a kicker who is perceived as proficient with respect
to on-side kicks might receive higher salary offers. Consequently, the number of onside kicks recovered was included as a factor in our salary equation.17
Which kicking gets a kicker paid?
Our first approach in estimating equation (1) is to employ Ordinary Least Squares.
Those results are reported in Table 4, which also presents what we see when we employ
a model with team-specific fixed effects.18
The OLS results indicate that a kicker’s salary is only statistically related to kickoff
value, scoring value, and experience. None of our control variables were found to have
any impact on player salary. In addition, kickoff value is only significant at the 10%
level. And when we turn to the fixed effects model, it is not even statistically significant
at that meager level.19
Simple OLS, though, is probably not the ideal approach to estimating this model.
Specifically, we next follow the example of Hamilton (1997), Leeds and Kowalewski
(2001), and Berri and Simmons (2009) and employ a quantile regression (Koenker,
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On the Evaluation of Kickers in the National Football League
Table 5: Quantile Regressions of the Log of Real Salary
Variables
0.1
Kickoff value
0.011
0.007
Scoring value
0.032**
0.013
Experience
0.305*
0.080
Experience, squared -0.018*
0.004
Population, logged
-0.134
0.238
Veteran player
0.074
0.270
Drafted player
-0.374**
0.159
Changing team
0.108
0.228
Pro bowl
0.371
0.304
On-side kicks recovered 0.059
0.101
Constant term
12.035*
1.568
Observations
Pseudo R-squared
316
0.202
0.25
Quantiles
0.5
0.75
0.9
-0.005
0.005
0.049*
0.010
0.200*
0.064
-0.010*
0.003
0.143
0.201
0.141
0.216
-0.141
0.118
-0.084
0.179
-0.164
0.227
-0.105
0.098
10.964*
1.336
0.008
0.007
0.043*
0.012
0.214**
0.087
-0.010**
0.004
0.073
0.251
-0.006
0.316
-0.153
0.151
-0.091
0.234
-0.227
0.303
-0.005
0.117
12.077*
1.663
0.013**
0.005
0.037*
0.010
0.267*
0.076
-0.014*
0.004
0.019
0.210
-0.108
0.286
-0.038
0.126
-0.037
0.194
-0.014
0.247
-0.103
0.095
12.827*
1.397
0.022*
0.007
0.040**
0.017
0.191**
0.093
-0.011**
0.005
-0.343
0.285
-0.344
0.392
-0.052
0.181
0.068
0.238
-0.340
0.355
-0.091
0.114
16.161*
1.869
316
0.161
316
0.141
316
0.129
316
0.099
*-significant at the 1% level
**- significant at the 5% level
***-significant at the 10% level
2005). Such a method is appropriate when your dependent variable—as is often the
case with respect to salary data in professional sports—fails to follow a normal distribution. This approach has a number of advantages over simple OLS. Specifically, it
allows us to ascertain the impact of our independent variables at different points in the
distribution. Furthermore, this approach is less sensitive to outliers and also the issue
of heteroskedasticity.
As one can see, across all the years considered in our study, a kicker’s salary is affected by experience and performance. The other nonperformance factors, though, are
generally insignificant at every point in the distribution. With respect to performance,
scoring matters at each point of the distribution while the value of kickoffs only matters for kickers at the ends of the distribution. So kicking off doesn’t appear to matter
in the evaluation of every kicker.
Volume 8 • Number 4 • 2013 • IJSF 275
Berri, Schmidt
Table 6: The Economic Value of Scoring and Kickoffs
Quantile
10%
25%
50%
75%
90%
Scoring coefficient Scoring value Kickoff coefficient Kickoff value
0.032
0.049
0.043
0.037
0.040
$2,892
$9,422
$23,430
$46,574
$97,880
not significant
not significant
not significant
0.013
0.022
$16,249
$52,842
When we turn to Table 6, which reports the economic value of scoring and kickoffs,
we can see, scoring generates a higher return.20 An additional scoring point consistently adds more to a kicker’s salary than an additional point from kickoffs. Such results
indicate that decision-makers in the NFL are focusing primarily on a kicker’s ability to
kick field goals and extra points. The ability to excel at kickoffs has some value, but this
skill is not considered as valuable at scoring.
Concluding Observations
In discussing a study it is important to note where the research might progress in the
future. With respect to this study, though, rules might slow that progression. Prior to
the 2011 season, the NFL changed where a kicker kicked off. The movement from the
30-yard line to the 35-yard line changed how often kicks were returned. To illustrate,
in 2010, 80.1% of all kickoffs were returned. After the rule change, only 53.5% were
returned in 2011 (and 53.2% in 2012).21 Consequently, it is possible that this current
study will be difficult to replicate in the future. Because so many kickoffs now result in
touchbacks,22 a study of kickers in the future will likely be quite different from the
study we present.
This study we present, though, does offer a result consistent with past studies of
compensation in sports. Specifically, past research in baseball and basketball has found
evidence that decision-makers tend to undervalue factors whose impact on outcomes
is not easily ascertained by simply watching a contest.
The past study that seems most relevant to our current inquiry is the work of Berri,
Brook, and Schmidt (2007) that indicated that scoring in the NBA dominates a basketball player’s compensation. We find a similar story with respect to kickers. A kicker’s scoring appears to dominate a kicker’s compensation in the NFL. But it is kicking
off that appears to have the largest impact on wins in football.
So why is scoring so important? The value of scoring appears most obvious to those
watching football. In contrast, ascertaining the value of kickoffs requires that someone
employ somewhat sophisticated statistical analysis to ascertain the impact an additional yard from kickoffs has on team wins. As the aforementioned study of the NBA indicated, decision-makers in sports tend to have trouble assessing the impact of actions
that require statistical analysis to measure.
A similar story can be told about kickers in the NFL. In the end, it appears that what
drives a kicker’s salary is performance on the field and experience. The other factors—
veteran status, changing team, draft status, pro bowl experience, and onside kicks—do
not impact salary. Decision-makers are only interested in whether or not a person can
do the job and how long that person has done the job. This suggests the market works
276 Volume 8 • Number 4 • 2013 • IJSF
On the Evaluation of Kickers in the National Football League
efficiently. The only problem is that decision-makers do not value scoring and kicking
off in a fashion consistent with how these factors impact wins in the NFL.
References
Ahlburg, D. A., & Dworkin, J. B. (1991). Player compensation in the National Football League.
In The Business of Professional Sports. P. D. Staudohar and J. A. Mangan (Eds.). Champaign,
IL: University of Illinois Press.
Berri, D. J., & Brook, S. (2010). On the evaluation of the “most important” position in professional sports. Journal of Sports Economics, 11, 157-171.
Berri, D. J., Brook, S. L., & Schmidt, M. B. (2007) Does one need to score to score? International
Journal of Sports Finance, 2, 190-205.
Berri, D. J., & Simmons, R. (2009). Race and the evaluation of signal callers in the National
Football League. Journal of Sports Economics, 10, 23-43.
Bradbury, J. C. (2013). What is right with Scully estimates of player’s marginal revenue product.
Journal of Sports Economics, 14, 87-96.
Bradbury, J. C. (2007). Does the baseball labor market properly value pitchers? Journal of Sports
Economics, 8, 616-632.
Carroll, B., Palmer. P., & Thorn, J. (1988). The hidden game of football. New York, NY: Grand
Central Publishing.
Carter, V., & Machol, R. E. (1971). Operations research on football. Operations Research, 19,
541–544.
Carter, V., & Machol, R. E. (1978). Optimal strategies on fourth down. Management Science, 24,
1758–1762.
Hakes, J. K., & Sauer, R. D.(2006). An economic evaluation of the moneyball hypothesis. Journal
of Economic Perspectives, 20, 173–186.
Hakes, J. K., & Sauer, R. D. (2007). The moneyball anomaly and payroll efficiency: A further
investigation. International Journal of Sports Finance, 2, 177-189.
Hamilton, B. H. (1997). Racial discrimination and professional basketball salaries in the 1990s.
Applied Economics, 29, 287-296.
Koenker, R. (2005). Quantile regression. Cambridge, England: Cambridge University Press.
Krautmann, A. C. (1999). What’s wrong with Scully-estimates of player’s marginal revenue
product. Economic inquiry, 37, 369-81.
Leeds, M. A., & Kowalewski, S. (2001). Winner take all in the NFL: The effect of the salary cap
and free agency on the compensation of skill position players. Journal of Sports Economics, 2,
244-256.
Romer, D. H.(2006) Do firms maximize: Evidence from professional football. Journal of Political
Economy, 114, 340-65
Rosen, S.,& Sanderson, A.(2001). Labour markets in professional sports. Economic Journal, Royal
Economic Society, 111, 47-68.
Schatz, A. (2006, November 12). Keeping score: N.F.L. kickers are judged on the wrong criteria.
New York Times.
Scully, G. W. (1974). Pay and performance in Major League Baseball. American Economic
Review. 65, 915-930.
Endnotes
In Smith’s example, he argues that one worker producing a pin in isolation would be lucky to
produce 1 pin a day. In contrast, separating out the tasks may result in the production of nearly 240 pins.
2
See www.econedlink.org.
1
Volume 8 • Number 4 • 2013 • IJSF 277
Berri, Schmidt
This is not to say that specialists do not exist, they certainly do; rather players who have multiple skills are generally held in greater regard than those with fewer skills.
4
Specialization is such an important part of the game that the rules prohibit those who block
from catching the ball.
5
This is not quite accurate as a kicker may have some slight responsibility tackling an opposing
player on kickoffs and blocked field goals.
6
Schatz, Aaron. (2006). “Keeping Score: N.F.L. Kickers Are Judged on the Wrong Criteria.”
(November 12). New York Times.
7
One should note that Berri and Brook (2010), in a study of hockey goalies, also reported evidence that decision-makers do not evaluate performance correctly in hockey.
8
For a review of many of these, see Kahn (2000).
9
Data on kickoffs and scoring can be found at NFL.com.
10
One could alternatively assume profit maximization as the team’s goal. The kicker’s salary
would still be determined by these two factors in the driving force behind revenues is a team’s
total number of wins.
11
We would like to thank Evan Osborne for making this observation.
12
Salary data can be found at the website of USA Today. USA Today does not report data for the
salaries of individual players after the 2009 season.
13
The value of the NFL’s salary cap can be found at Football 101, a site maintained by Mark
Lawrence. The specific website we looked at was the following: http://football.calsci.com/
SalaryCap3.html.
14
In order to be included in our sample a kicker had to kick field goals (and extra points) and
kickoff. So a kicker like Thomas Morstead—who only kicked the ball off in 2009—was not
included in our sample. In all, 422 observations were employed from 1995 to 2009. Of these, we
had salary data for 316 kickers.
15
As Berri and Simmons (2009) note, “NFL players are broadly eligible for free agency after four
seasons of experience. After three years, players have restricted free agent status in which teams
holding the player’s contract are allowed to make offers that at least match those available on the
free agent market. Experimentation revealed that the impact of veteran or free agent status does
not depend on whether we use three or four years as the qualifying period.”
16
Berri and Simmons (2009) found that draft position impacts a quarterback’s compensation
well into a quarterback’s career.
17
We also included dummy variables for each year considered in the study (i.e., 1996 to 2008).
18
With team-specific fixed effects, our market size variable was dropped.
19
We also considered whether performance from two years ago affected a player’s current salary.
The inclusion of performance from two years ago was not significant, and the model with performance from two years ago had a much lower r-squared. Although the model we report has
an unusually low r-squared for a salary model in sports, adding performance from previous
years does not increase explanatory power.
20
Since the model is semi-logged, the slope coefficient is found by multiplying the dependent
variable by the estimated coefficient. We employ the average value of the dependent variable
across the sample considered in making this calculation.
21
This data is reported at NFL.com.
22
According to NFL.com, there were 416 touchbacks in the regular season in 2010. In 2011, this
number rose to 1,120. And in 2012, the number was 1,156.
3
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