The Best Defensive Player in the NFL is...Neil Rackers?

My previous look at place kickers considered field goal kicking. Accounting for attempt distances and home field environment (dome, warm, etc.), I estimated that one standard deviation in FG kicking accuracy results in about 2.3 more successful FGs per season (worth 6.7 points). In this article, I'll look at kick offs and how they affect the ability of the opposing team to score.

Starting field position is obviously an important factor in scoring. The closer an offense is to scoring position, the fewer consecutive first downs are required to get there. A kicker who can kick deep can help his team by giving his defense more opportunities to force a punt or a turnover before his opponent is able to move into scoring position. Further, if the opposing offense is stopped, his offense will receive possession of the ball that much closer to scoring position on its own.

Kick off depth is easily measured, but the resulting field position is dependent on kick return and kick coverage performance. To isolate the performance of the kicker from the rest of the kick off squad, I ran a quick linear regression that estimates the expected resulting starting field position based on the depth of the kick. Average kick distances and return yards for each kicker (with >20 kick offs in a season) in the 2004-2006 seasons are plotted below. (Kicks that result in touchbacks are excluded at this point because there is no return. They will be factored back in later.)


We see that the deeper the kick, the longer the return. The longer a kick travels the more time and space the returner has to run before being met by the coverage team. Not every yard of extra kick distance translates into field position. About half of each marginal yard of kick distance is given back up in the return. If one kicker kicks 60 yd kicks, and another kicker kicks 70 yd kicks, the second kicker will only benefit by 5 yds of resulting field position after the average return.

Now we can calculate the expected resulting field position for each kicker in the NFL. Touchbacks are now factored back in. The expected starting field position of each kicker over the previous three seasons ranges from the 23.7 yd line to the 31.4 yd line. The average is the 27.4 yd line with a standard deviation of 1.49 yds.

Click on the table headers to sort.























































KickerSeasonsKOsAvg DistTB PctExp Fld Pos
Neil Rackers320667.930.623.7
Michael Koenen214764.218.424.8
Paul Ernster17567.925.325.0
Olindo Mare318465.727.625.0
Josh Scobee322565.422.725.6
Kris Brown320164.414.925.8
Stephen Gostkowski18165.514.825.9
Todd Sauerbrun210060.011.326.2
Joe Nedney213260.28.626.3
Sebastian Janikowski319363.615.426.3
Jason Hanson320464.115.226.4
Micah Knorr16164.424.626.4
Jeff Wilkins323363.48.426.5
Wade Richey15363.113.226.5
Rob Bironas214163.214.926.5
Jay Feely324963.915.126.5
Mitch Berger28965.15.226.8
Phil Dawson319161.410.026.9
Aaron Elling27063.52.926.9
John Carney29561.64.727.0
Shaun Suisham12664.10.027.1
Billy Cundiff312263.16.227.1
Jose Cortez13863.910.527.3
Paul Edinger212859.10.027.4
Jason Baker12463.04.227.4
Adam Vinatieri324763.510.927.4
David Akers320464.312.027.5
Rian Lindell322159.74.627.5
Matt Bryant211962.57.627.6
Craig Hentrich17357.64.127.6
Josh Brown324663.18.927.6
John Kasay319462.15.428.1
Jeff Reed324461.66.528.2
Dave Rayner215463.38.328.2
Nate Kaeding327062.65.628.3
Lawrence Tynes326061.25.428.4
Robbie Gould214963.36.828.5
Matt Stover312861.36.728.6
Shayne Graham325761.56.728.6
Ryan Longwell321859.63.728.7
Toby Gowin17663.09.228.8
Todd Peterson16156.41.629.0
Mike Nugent213760.02.229.1
John Hall28160.72.729.3
Martin Gramatica24660.94.429.3
Jay Taylor12157.00.029.4
Steve Christie17157.72.829.7
Mike Vanderjagt27859.03.330.0
Nick Novak24458.82.131.4
Average2.214462.39.727.4



The Importance of Field Position

I think the best way to examine the football-significance of kickoff field position is to look at two contrasting cases. Let's compare the league's best kicker, Neil Rackers, with one of its recent "worst," Mike Vanderjagt, who was cut by the Cowboys last year. Racker's average expected field position was the 23.7 yd line and Vanderjagt's was the 30.0 yd line, a difference of 6.3 yds.

How important are those 6 yards? The field is 100 yds long, so are they 6% important? It's difficult to quantify, but here is one way to think of field position: In almost all cases, to move into scoring position, either for a TD or FG, an offense needs a consecutive number of first downs. The closer an offense is to scoring position, the fewer first downs it needs. In other words, lets think of a typical drive as a sequence of 1st downs instead of a sequence of plays.

In baseball, the game rests on the outcomes of at bats, not necessarily every ball and strike. Think of a series of downs as the equivalent of an at bat, and individual plays as pitches. We're not as interested in each pitch as we are with the outcome of the at bat. The analogy continues because to score in baseball, several consecutive successes (hits or walks) are usually required. In football, several first downs are usually needed to score. (And in both sports there is always the slim possibility of a home run/long pass or breakaway run.)

In the NFL, a successful first down yields 15 yards on average. So those 6 yards of field position mean that in 6 out of 15 cases, an opposing offense needs one additional first down more than they otherwise would need to score. That additional first down gives a defense one more opportunity to force a kick or a turnover. That additional first down could turn what would be a TD drive into a FG drive, or turn a FG drive into a scoreless one.

In any given series of 4 downs in the NFL, an offense succeeds in gaining another first down (or touchdown) 65% of the time. So defenses are able to interrupt the consecutive series of first downs 35% of the time. So 6 yds of deeper field position per kickoff reduces the probability the the offense will score.

(6 / 15) * 0.35 = 0.14 --> 6 yds of field position adds a 0.14 probability of interrupting a scoring drive.

(It was pointed out to me that series in the red zone are often more difficult because of the compression of the field, so not every series has an equal probability of success. That's true, however the red zone series happen on a TD drive whether or not the drive started on the 20 or the 40 yd line. The "extra series" that is sometimes required by a deep kickoff does not cause an additional red zone series. It would be on the front end of a drive and not in the red zone.)

Over the previous five years, NFL teams scored an average of 23.4 FGs and 34.6 offensive TDs per season, resulting in 312 pts per season. A 14% reduction equates to 43.7 point difference between Neil Rackers and Mike Vanderjagt.

312 * 0.14 = 43.7 pts per season

I know my favorite team sure could use an extra 43.7 points this season. Depending on how they are distributed, that would usually mean an extra win or two. However, only 37% of drives begin immediately following a kickoff. That would mean that the difference isn't really 43.7 pts per season, but:

0.37 * 43.7 = 16.1 pts per season

But, in a way, all drives originate from a kickoff. All drives' starting field position subsequent to a kickoff are biased by the kickoff result. For example, a poor kick that results in a starting field position at the 40 yd line will result in all subsequent drives being closer to the kicking team's end zone than otherwise, until another kickoff. For that reason, I think the real answer is a lot closer to 43.7 points per season than 16.1 points per season.

Additionally, an extra series on a drive means there are more plays that could result in a turnover. About 10% of all series result in either a fumble or interception. That further strengthens the effect of field position and the importance of deep kickoffs.

We began the analysis by comparing Rackers and Vanderjagt, who were extreme examples--about 4 standard deviations apart. If we repeat the above analysis by looking just a single standard deviation in kickoff distance (1.49 yds), the effect is 10.9 pts per season per SD.

My previous look at FG kicking estimated that every additional SD in accuracy resulted in 6.7 more pts per season. When compared to 10.9 pts per kickoff SD, it appears that kickoff performance is the more important aspect of place kicking. Interestingly, when Vanderjagt was cut in 2006 by the Cowboys, the news articles didn't bother to mention his short kickoffs, only that he didn't like to do it.

An Alternate Analysis of Field Position

Previous studies have examined the point value of various field positions. Perhaps the best has been David Romer's study regarding 4th down situations. To evaluate when coaches should "go for it" on 4th down, he evaluated the point value of a 1st and 10 from each position on the field. The figure below is from his study.


Between the 15 yard lines, the value change is linear--0.04 points per yard. A 6 yard difference, therefore, equates to 0.24 pts per drive. With an average of 182.6 drives per season, that's a difference of 43.8 pts per season--almost exactly the same result yielded by the "one additional first down" analysis.

Season Win Projections Week 14

Season win totals and division standing projections are listed below. As before, projections are based on each team's opponent-adjusted generic win probability (GWP). The projections account for future opponent strength (Fut Opp), and projected wins (proj W) is a total of current and estimated future wins. The methodology is described more fully here. Precise playoff predictions based on individual game probabilities can be found at NFL Forecast.
















































TeamRankProj GWPFut. OppCurrentProj W
AFC E
NE10.920.421315.7
BUF160.480.5278.4
NYJ190.360.5934.1
MIA280.240.5600.7
AFC N
PIT80.710.45911.1
CLE170.610.3689.8
CIN200.640.3056.9
BAL220.320.5545.0
AFC S
IND20.900.421113.7
JAX50.750.46911.2
TEN130.510.5578.5
HOU180.250.7266.7
AFC W
SD90.750.38810.2
DEN120.510.5667.5
KC250.380.4445.1
OAK300.110.7444.3
TeamRankProj GWPFut. OppCurrentProj W
NFC E
DAL30.840.471214.5
NYG140.390.64910.2
WAS150.370.6467.1
PHI100.500.5856.5
NFC N
GB60.860.281113.6
MIN110.630.4478.9
DET260.270.5566.8
CHI310.200.5655.6
NFC S
TB40.900.26810.7
NO210.520.4067.6
CAR270.130.7655.4
ATL240.250.6033.8
NFC W
SEA70.810.34911.4
ARI230.520.3567.6
STL290.220.5833.7
SF320.120.5633.4

The Bills, Redskins, and Unsportsmanlike Conduct

On December 2nd, Joe Gibbs may have cost his team a playoff spot. His 15 yd unsportsmanlike conduct penalty for consecutive time outs to ice Buffalo kicker Ryan Lindell backfired. It gave Lindell a 36 yd FG attempt instead of a 51 yd attempt to win the game as time expired. The cost of Gibbs’ mistake is only now becoming clear as the playoffs picture comes into focus.

Lindell is a decent kicker and has kicked 5 out of 7 lifetime FGs from beyond 50 yds. But 7 attempts is not terribly significant statistically. Chances are he’s not too different than most starting NFL kickers. Below is a graph of NFL-wide FG probability of success vs. attempt distance. A best-fit equation yields how that 15 yd penalty changed Lindell’s chance to win the game with only seconds remaining.


Those 15 yards changed the FG probability from 0.58 to 0.88, a difference of 0.30. And although Lindell proved (as Gibbs called the first timeout) that he had the leg to make the longer kick, that is not an indication that Lindell is immune from the laws of probability.

Today the Redskins find themselves at 6-7, one game back in the race for the last wildcard spot in the NFC. Had Lindell missed from 51 yds, the Redskins would have won and they’d be tied with Minnesota at 7-6, who they will face in week 16.

The Bills, on the other hand, are now at 7-6 partially thanks to their win over Washington. They are now one game behind Cleveland for the 2nd wildcard spot in the AFC with about a 20% chance of sneaking into the playoffs.

What a difference 15 yards and one kick can make /could have made.

Playoff Probabilities

Updated playoff probabilities based on updated Week 14 data are now available at NFL-Forecast.com.

Game Predictions Week 15

Game probabilities for week 15 NFL games are listed below. The probabilities are based on an efficiency win model explained here and here. The model considers offensive and defensive efficiency stats including running, passing, sacks, turnover rates, and penalty rates. Team stats are adjusted for previous opponent strength.





















VprobVistorHomeHprob
0.52DENHOU0.48
0.76CINSF0.24
0.36ARINO0.64
0.10ATLTB0.90
0.50BALMIA0.50
0.45BUFCLE0.55
0.80GBSTL0.20
0.47JAXPIT0.53
0.07NYJNE0.93
0.77SEACAR0.23
0.65TENKC0.35
0.93INDOAK0.07
0.15DETSD0.85
0.17PHIDAL0.83
0.40WASNYG0.60
0.15CHIMIN0.85

Week 14 Efficiency Rankings

NFL team efficiency rankings are listed below in terms of generic winning probability. The GWP is the probability a team would beat the league average team at a neutral site. Each team's opponent's average GWP is also listed, which can be considered to-date strength of schedule. GWP modifies the generic win probability to reflect the strength of past opponents. Offensive ranking (O Rank) is based on each team's offensive GWP, i.e. it's the team's GWP assuming it had a league-average defense. D Rank is vice-versa. Rankings are based on a logistic regression model applied to data through week 14. A full explanation of the methodology can be found here.





































RankTeamLast WkGWPOpp GWPO RankD Rank
1NE10.890.5515
2IND30.870.5441
3DAL20.820.4926
4TB40.760.4867
5JAX60.720.55313
6GB70.700.48512
7SEA80.680.40118
8PIT50.670.46143
9SD90.640.52154
10PHI100.580.54821
11MIN110.570.451015
12DEN150.560.49723
13TEN140.550.53242
14NYG120.520.491910
15WAS130.510.54209
16BUF170.500.561614
17CLE160.470.47929
18HOU180.460.501726
19NYJ190.440.532122
20CIN200.430.511230
21NO240.410.501332
22BAL210.360.512516
23ARI220.360.442219
24ATL250.340.491828
25KC230.320.533111
26DET300.310.522318
27CAR270.310.493017
28MIA260.290.542725
29STL280.280.462920
30OAK290.260.462627
31CHI310.250.522831
32SF320.140.453224

Team Efficiency Stats

I've received a few requests to see the data used by the efficiency model, so here it is a table of all 32 teams. The NFL average is listed at the bottom. O Pass, D Run, etc. are in "yards per attempt." Turnover rates are also per attempt--interceptions per pass attempt and fumbles per play. Pen rate is penalty yards per play for both offense and defense.

The weights of each variable can be found in the post here.






































TeamO PassO RunO Int
O FumD PassD RunD Int
Pen Rate
ARI6.543.610.0380.0375.773.950.0320.46
ATL5.193.880.0260.0226.484.250.0380.47
BAL5.104.020.0230.0306.562.830.0350.32
BUF5.863.810.0330.0186.684.220.0290.45
CAR5.104.070.0360.0296.143.620.0310.41
CHI5.493.230.0390.0336.464.420.0230.37
CIN6.593.520.0320.0216.944.230.0360.47
CLE6.884.070.0340.0276.464.630.0280.43
DAL8.134.350.0370.0155.483.790.0400.32
DEN6.844.410.0340.0346.654.610.0350.36
DET6.103.960.0340.0496.493.930.0350.55
GB7.273.790.0210.0276.073.970.0320.34
HOU7.003.660.0390.0376.334.500.0280.21
IND7.043.970.0320.0145.124.000.0430.30
JAX6.544.330.0180.0186.643.960.0350.33
KC5.303.320.0400.0255.964.160.0350.38
MIA5.144.240.0360.0266.854.290.0340.37
MIN5.845.630.0290.0276.362.990.0280.37
NE8.044.090.0140.0125.734.130.0450.38
NO6.323.530.0310.0277.253.890.0260.27
NYG5.644.400.0410.0215.703.810.0300.35
NYJ5.483.710.0380.0146.804.250.0340.24
OAK5.624.240.0420.0386.474.810.0460.41
PHI6.114.530.0330.0156.153.750.0180.33
PIT6.444.100.0370.0204.393.620.0210.34
SD6.394.080.0370.0215.924.150.0480.40
SF3.994.210.0380.0436.163.780.0250.36
SS6.293.690.0230.0165.454.010.0340.19
STL5.273.550.0460.0236.174.140.0300.38
TB6.684.300.0140.0175.353.730.0310.32
TEN5.694.060.0390.0315.473.970.0360.42
WAS5.933.910.0270.0385.713.940.0210.37
Avg6.124.010.0320.0266.134.010.0330.37

Another Plug for NFL Forecast

One cool feature of the web-based playoff prediction engine found at NFL-Forecast.com is the "predicted wins" tab. Each teams' final win total is plotted as a probability distribution according to the game probabilities calculated here at NFL Stats. The neat part to me is the "probability of making the playoffs with x wins." It gives the probability of making the playoffs for each win total.

For example, Cleveland's playoff probabilities for each win total look like this:

8 wins--5%
9 wins--30%
10 wins--80%
11 wins--100%

It takes a minute or two to play with the app to realize how neat it really is. You can move the slider bars for any game, setting your own game probabilities--you're not stuck with mine. (To account for today's results, I moved the sliders to 100% for each winner.) Then click on Forecast/Forecast Remainder of Season. You can then click on the tabs at the bottom of the app to see playoff probabilities, expected win totals, division standings, and (for the MIA, SF, STL, and ATL fans out there) the 2008 draft order probabilities.

If you're a fan of a team on the bubble, you can move the sliders to see your team's playoff odds for "what if" scenarios. Let's say the Redskins win out--they'd have a 97% chance for a playoff spot. (Three straight wins would include a victory over MIN in week 16, and determine the tie-breaker. )

And if Detroit wins out, they'd have a 53% chance of making the playoffs.

Pretty cool.