Kickers Are People Too – Part 1: Modeling Kicker Quality

Kickers

Let’s face it. You play in at least one league that still uses kickers.1 And you know what? That’s totally fine! While kicking is still the hardest part of the game to model, it doesn’t mean we shouldn’t at least get better at it. So that’s what I’m here to do in this three part series.

In this article — Part 1 — I’ll introduce my kicking quality model, which takes into account all sorts of factors including kick distance, weather, altitude, field surface, era,2 and more. From there, I’ll derive a power ranking for each kicker based off of how well he performed relative to the situation, so we can get a retrospective look at how well each kicker performed in 2015 relative to his peers.3

In Part 2, I’ll derive a model to  predict the number of attempts per game each kicker gets. Finally, in Part 3 we will put the first two parts together to create kicker rankings for 2016.

Let’s jump into Part 1, the kicker quality model.

Why We’re Doing This

Let’s start with a hypothetical. Let’s say I kick 10 field goals and make nine of them, and you kick 10 field goals and make eight of them. Who was better?

If we went by raw percentages, I made 90 percent of my attempts while you made 80 percent of your attempts. But what if all of your kicks came from 60 yards out, in snowy conditions, on grass, while mine came from 20 yards out, in perfectly calm weather, on AstroTurf? Suddenly your 80 percent from 60 yards out in a blizzard looks a whole lot better than my 90 percent on chip shots during a beautiful day.

That’s the basis of my kicker model that I’ve developed using the Armchair Analysis data set, which goes all the way back to 2000. I have to say, the idea for this model wasn’t originally mine. Torin Clark, Aaron Johnson, and Alexander Stimpson presented their kicker model at the MIT Sloan Sports Analytics Conference back in 2013.

I’ve simply taken their work, and updated it with a few additional features that I believe extend their work to make the model as accurate as possible. I’ve also changed their kicker evaluation from what they call “added points” or “added points per attempt” to a power ranking. I’ll explain why down below. If you want the details of their work, I highly suggest you check out their paper on the topic.

Model Factors

Clark, Johnson, and Stimpson used the following factors in their model:

  • Field goal distance (dist)
  • Temperature (temp, a binary variable where temperature is either above or below 50 degrees)
  • Wind speed (wspd, also a binary variable above or below 10 mph)
  • Field surface (surface, artificial turf yes or no)
  • Altitude (alt, above 4000 feet, yes or no)
  • Precipitation (precip, yes or no)

I’ve changed the model in the following ways:

  • The quadratic term for distance is also significant, giving the model more accuracy on the rare, but extremely long kicks.4
  • Temperature is now a continuous variable
  • As is wind speed
  • I forced the temperature to room temperature and wind speed to zero for indoor games5 and nested those two variables within another variable for indoor/outdoor (InOut)
  • Field surface now has three categories (AstroTurf, Synthetic turf,6 and grass)
  • I added a variable for the era (seas)7

Additionally, experience matters. Kickers tend to get better with experience, up until a point after which they start to decline, presumably due to losing strength in their older age. I left experience out of the model, because we want to see how well a kicker performed, independent of his experience. However, when trying to project 2016, I will add the experience terms8 back in, to see how much we can expect an individual kicker to improve or decline.

Basic Model Results

Here’s the table with the standard error, Wald statistic, and p-value for each term in the model, where I’m modeling the probability of a make:9

Term Estimate Std Error Wald Statistic P-value
dist -0.1108 0.0033 1149.1 <.0001*
seas  0.0500 0.0050 97.19 <.0001*
Intercept -94.3338 10.1693 86.05 <.0001*
surface(Grass) -0.21577 0.03867 31.14 <.0001*
InOut[temp]  0.0084 0.0016 28.20 <.0001*
exp  0.0237 0.0052 21.15 <.0001*
alt  0.2803 0.0691 16.46 <.0001*
surface(AstroTurf)  0.2107 0.0536 15.47 <.0001*
InOut[wspd] -0.01817 0.0048 14.52 0.0001*
precip  0.1237 0.0471 6.90 0.0086*
exp*exp  -0.00166 0.0007 5.74 0.0166*
dist*dist  0.0005 0.0003 4.31 0.0378*
InOut  -0.01878 0.0341 0.30 0.5823

(Note: when looking back, we want to find out how well a kicker did independent of his experience, so I remove the experience terms from the model when looking backward. But predicting forward, we want to account for the fact that the kicker’s experience changed, which is statistically significant, so we do include the experience terms. Removing the experience terms changes the estimates slightly for all the other parameters. I’ve included that as a footnote at the end so replication is still possible.)10

So what the hell is going on here? Well, if we look at AstroTurf, the estimate is 0.21, meaning we expect kicks on AstroTurf to have a higher success rate compared to synthetic turf (which has its estimate set to zero, so the baseline surface). Grass has the highest miss probability with its estimate of -0.22. Here’s what the probability of a make looks like at each distance, for each surface.11

DistSurf

You can see, in this case our kicker has a 40 percent chance of making a 60 yarder on grass, but put him on a super flat AstroTurf surface and the probability jumps to just over 50 percent. We could show similar with the other attributes.

Modeling Kicker Quality

Now that we have a model that lets us determine the rate at which NFL kickers make field goals under each possible condition, we can determine who was the best kicker, say, in 2015. First, let’s go back to the example where you and I faced off. Remember, your kicks were from 60 yards out in blizzard conditions, while mine were from 20 yards out on a beautiful day. We’ll give each of us zero years experience because, unless you’re Nick Novak,12 you’re probably not an experienced NFL kicker.

So what does the model give the chances of an NFL player in your situation making these kicks? About 25 percent. My kicks? 99 percent. So, in other words, for each of your kicks, you are expected to make

0.25 * 3 = 0.75

points for your team. So your Expected Points (EP) for each attempt is 0.75. If you make a kick, you score 3 points for your team, so you get

3 – 0.75 = 2.25

Points Added (PA). If you miss, you lost out on those 0.75 EP, so we say you get -0.75 PA. Since you made 8 of 10, you get 16.5 PA on your 10 kicks. Me? Well I went 9 of 10, but I actually scored -2.7 PA, because I only made 90 percent of my attempts despite NFL average being 99 percent in my situation. We can then divide by the number of attempts (10 for each of us) to get a Points Added Per Attempt (PAPA). Your PAPA was 1.65, my PAPA was -0.27. Extreme example, but it lets you see where I’m going with this.

So PAPA Knows Best?

If only it were as simple as Points Added Per Attempt. The issues with PAPA is that, on your attempts there were 2.25 Available Points Added (APA, which is just 3 – EP. This is also the degree of difficulty of a kick.), whereas on mine there were only 0.03 APA per attempt. We shouldn’t penalize a kicker for making kicks, but that’s exactly what PAPA does to the guy making the chip shots — it doesn’t allow him to score as many PAPA. Instead, what I’ve done is I’ve scaled everything to the total APA. So, your kicks summed up to 22.5 APA (10 kicks at 2.25 APA available each). Mine summed up to 0.3 APA. If we each made 100 percent of our kicks, we’d each have gained all the APA, meaning we’d tie. So that’s exactly how I’m going to calculate things.

If a kicker has a positive PA then the formula becomes:

FGRate = Total PA / Total APA

And if a kicker has a negative PA then the formula becomes:

FGRate = Total PA / EP

So the FGRate on your kicks was 16.5 / 22.5 = 0.733 while I achieved -2.7 / 29.7 = -0.091 for my FGRate. In other words, if you make every kick, your FGRate becomes 1 (you grabbed every APA), and if you miss every kick your FGRate becomes -1 (you lost out on 100 percent of the EPs). This allows us to derive a POWER ranking for kickers with the following formula:

POWER = (FGRate + 1) / 2 * 100

which scales everything to a 0 to 100 scale where making every kick gives a kicker a 100, missing every kick a POWER of 0, and kicking league average for the conditions faced a POWER of 50.

In our example, you received a POWER rating of 86.67, while I had a below average 45.45 rating.

So Who Was 2015’s Best Kicker?

Here’s how 2015’s kickers fared. I’ve included the number of attempts, the average EP and APA (remember, APA is also the difficulty of the kick. More APA means a tougher kick.), the POWER rating, and their raw percentages, to show you how raw percentages can be misleading.

KickerYearMakeAttPctEPAPAPOWER
M.Prater2015222491.7%2.350.6580.78
D.Bailey2015303293.8%2.610.3976.12
S.Gostkowski2015374092.5%2.540.4675.55
J.Brown2015303293.8%2.630.3774.90
A.Vinatieri2015252792.6%2.570.4374.07
S.Hauschka2015313491.2%2.530.4772.12
C.Boswell2015363992.3%2.590.4171.71
R.Succop2015141687.5%2.440.5666.68
B.Walsh2015374386.0%2.370.6366.56
D.Hopkins2015262989.7%2.590.4162.41
M.Crosby2015283287.5%2.540.4659.52
C.Catanzaro2015303390.9%2.670.3358.28
B.McManus2015404588.9%2.610.3957.11
R.Gould2015333984.6%2.480.5255.91
P.Dawson2015242788.9%2.630.3754.60
N.Novak2015182185.7%2.540.4653.60
S.Graham2015111384.6%2.510.4952.63
D.Carpenter2015232785.2%2.530.4752.28
C.Santos2015354283.3%2.490.5151.16
T.Coons2015283287.5%2.620.3850.73
J.Tucker2015334082.5%2.480.5249.81
J.Lambo2015263281.3%2.460.5449.57
J.Myers2015263086.7%2.630.3749.42
G.Gano2015344182.9%2.540.4648.92
M.Nugent2015242982.8%2.560.4448.54
S.Janikowski2015212680.8%2.510.4948.36
A.Franks2015131681.3%2.530.4748.25
N.Folk2015131681.3%2.530.4748.24
C.Barth2015232882.1%2.590.4147.48
R.Bullock2015192382.6%2.620.3847.33
C.Sturgis2015182281.8%2.590.4147.30
M.Bryant2015141877.8%2.680.3243.55
G.Zuerlein2015203066.7%2.350.6542.54
C.Parkey20153475.0%2.660.3442.31
Z.Hocker2015101471.4%2.650.3540.48
K.Forbath2015101566.7%2.520.4839.61
J.Scobee201561060.0%2.560.4435.10
K.Brindza201561250.0%2.390.6131.40

You can see, while Matt Prater didn’t have the highest field goal percentage, he did have one of the best percentages while also facing the most difficult kicks. He tied for the league lead in degree of difficulty with Greg Zuerlein, but “Greg The Leg” only converted at a 66.7 percent clip compared to Prater’s 91.7 percent rate.

I’ve included all the POWER ratings for all kickers since 2000 below. In Part 2, I will look at predicting kicking attempts per game.

KickerYearMakeAttPctEPAPAPOWER
A.Del Greco2000283775.7%2.460.5446.13
A.Vinatieri2000273381.8%2.330.6759.12
B.Conway200066100.0%1.781.22100.00
B.Daluiso2000212875.0%2.450.5545.85
C.Blanchard2000162369.6%2.250.7546.43
C.Hentrich2000010.0%1.011.990.00
D.Akers2000303683.3%2.450.5554.57
D.Brien2000253180.6%2.370.6353.76
E.Murray200081266.7%1.991.0150.57
G.Anderson2000242596.0%2.270.7391.75
J.Carney2000182572.0%2.210.7948.86
J.Elam2000192576.0%2.420.5847.05
J.Hall2000213265.6%2.170.8344.37
J.Hall2000213265.6%2.220.7844.37
J.Hanson2000243080.0%2.470.5348.54
J.Holmes200022100.0%2.750.25100.00
J.Nedney2000343889.5%2.430.5772.51
J.Wilkins20001717100.0%2.530.47100.00
K.Brown2000253083.3%1.701.3080.75
K.Heppner2000101566.7%2.420.5841.35
M.Andersen2000253180.6%2.330.6756.96
M.Gramatica2000293582.9%2.220.7867.07
M.Hollis2000242692.3%2.280.7283.88
M.Husted20004850.0%2.650.3528.33
M.Stover2000414787.2%2.320.6871.90
M.Vanderjagt2000283190.3%2.390.6176.14
N.Rackers2000122157.1%2.290.7137.37
O.Mare2000293387.9%2.360.6471.70
P.Dawson2000141782.4%2.330.6760.45
P.Edinger2000212777.8%2.210.7957.77
P.Stoyanovich20005955.6%2.250.7537.09
R.Cunningham20005771.4%1.901.1061.15
R.Lindell2000151788.2%2.120.8879.89
R.Longwell2000333886.8%2.200.8075.42
S.Bentley200011100.0%1.261.74100.00
S.Christie2000263574.3%2.450.5545.40
S.Janikowski2000253669.4%2.190.8147.56
S.Lechler2000010.0%2.570.430.00
S.Lindsey20005771.4%2.400.6044.66
T.Peterson2000152075.0%2.420.5846.46
T.Seder2000253375.8%2.080.9260.68
W.Richey2000152268.2%2.360.6443.42
A.Vinatieri2001303781.1%2.210.7964.05
B.Conway2001263378.8%2.310.6953.86
B.Daluiso20013475.0%2.490.5145.20
B.Gramatica2001162080.0%2.360.6452.91
C.Oglesby20015683.3%2.580.4248.53
D.Akers2001323786.5%2.350.6568.76
D.Brien20015683.3%2.400.6058.02
G.Anderson2001151883.3%2.180.8269.64
J.Arians2001122157.1%2.270.7337.80
J.Carney2001273187.1%2.410.5966.92
J.Cortez2001182669.2%2.390.6143.45
J.Elam2001313686.1%2.350.6567.85
J.Feely2001293778.4%2.430.5748.39
J.Hall2001253473.5%2.210.7949.95
J.Hanson2001213070.0%2.080.9251.18
J.Hilbert2001111668.8%2.390.6143.10
J.Kasay2001232882.1%2.230.7765.22
J.Nedney2001202871.4%2.150.8549.80
J.Wilkins2001283677.8%2.400.6048.65
K.Brown2001334967.3%2.130.8747.51
M.Andersen2001232882.1%2.270.7363.08
M.Gramatica2001263281.3%2.360.6456.17
M.Hollis2001182864.3%2.220.7843.36
M.Stover2001333984.6%2.460.5457.05
M.Vanderjagt2001283482.4%2.310.6961.45
N.Rackers2001172860.7%2.260.7440.23
O.Mare2001202290.9%2.510.4972.37
O.Pochman2001020.0%0.982.020.00
P.Dawson2001222588.0%2.410.5969.52
P.Edinger2001273284.4%2.230.7769.42
R.Lindell2001203262.5%2.190.8142.86
R.Longwell2001233467.6%2.140.8647.50
S.Christie200191181.8%2.550.4548.06
S.Graham20016875.0%2.370.6347.43
S.Janikowski2001283384.8%2.350.6564.78
T.Peterson2001273577.1%2.320.6849.82
T.Seder2001111764.7%2.420.5840.03
W.Richey2001213265.6%2.400.6041.07
A.Vinatieri2002273090.0%2.270.7379.34
B.Conway200211100.0%2.580.42100.00
B.Cundiff2002121963.2%2.150.8544.01
B.Gramatica2002152171.4%2.230.7748.05
D.Akers2002333886.8%2.380.6268.01
D.Boyd200255100.0%2.790.21100.00
D.Brien20025683.3%2.690.3146.55
G.Anderson2002182378.3%2.460.5447.68
H.Epstein20025955.6%2.410.5934.55
J.Carney2002323688.9%2.470.5368.61
J.Chandler2002111573.3%1.641.3670.60
J.Cortez2002233271.9%2.260.7447.77
J.Elam2002263672.2%2.200.8049.22
J.Feely2002364776.6%2.340.6649.01
J.Hall2002273577.1%2.350.6549.14
J.Hanson2002232882.1%2.390.6156.42
J.Hilbert2002020.0%2.350.650.00
J.Jenkins2002010.0%
J.Kasay20022540.0%2.290.7126.21
J.Nedney2002283580.0%2.080.9267.52
J.Reed2002202483.3%2.170.8370.04
J.Tuthill2002101662.5%2.380.6239.39
J.Wilkins2002192576.0%2.410.5947.27
K.Brown2002172470.8%2.060.9453.60
M.Andersen2002222684.6%2.360.6463.77
M.Bryant2002273479.4%2.100.9065.82
M.Gramatica2002374582.2%2.170.8367.94
M.Hollis2002253375.8%2.330.6748.73
M.Husted200211100.0%2.240.76100.00
M.Stover2002212584.0%2.160.8471.48
M.Vanderjagt2002233271.9%2.350.6545.84
N.Rackers2002151883.3%2.040.9673.89
O.Mare2002243177.4%2.380.6248.85
P.Dawson2002243080.0%2.160.8464.20
P.Edinger2002222878.6%2.110.8963.99
R.Cunningham200211100.0%2.830.17100.00
R.Lindell2002232979.3%2.240.7659.38
R.Longwell2002283677.8%2.370.6349.23
S.Christie2002182669.2%2.250.7546.14
S.Graham2002131872.2%2.220.7848.76
S.Janikowski2002324080.0%2.360.6453.11
T.Peterson2002122157.1%1.831.1746.91
T.Seder200281266.7%2.260.7444.22
A.Elling2003182572.0%2.420.5844.67
A.Vinatieri2003324472.7%2.160.8451.19
B.Conway2003141973.7%2.410.5945.93
B.Cundiff2003243080.0%2.400.6049.98
B.Gramatica20033475.0%2.410.5946.76
C.Hentrich20034580.0%2.001.0069.97
D.Akers2003273381.8%2.290.7161.32
D.Brien2003273284.4%2.210.7970.41
G.Anderson2003293485.3%2.240.7671.06
J.Brown2003243275.0%2.130.8756.66
J.Carney2003223073.3%2.350.6546.82
J.Chandler20036785.7%2.700.3047.61
J.Elam2003283384.8%2.300.7067.42
J.Feagles2003010.0%2.750.250.00
J.Feely2003192770.4%2.380.6244.33
J.Hall2003253375.8%2.200.8054.81
J.Hanson2003222395.7%2.350.6589.97
J.Kasay2003414983.7%2.310.6964.49
J.Nedney200311100.0%1.641.36100.00
J.Reed2003233271.9%2.190.8149.21
J.Wilkins2003444891.7%2.170.8384.97
K.Brown2003182281.8%2.300.7061.16
M.Andersen2003172277.3%2.250.7554.35
M.Bryant2003111478.6%2.140.8662.46
M.Gramatica2003162661.5%2.001.0046.10
M.Knorr200311100.0%2.870.13100.00
M.Stover2003343987.2%2.040.9680.05
M.Vanderjagt20034040100.0%1.981.02100.00
N.Rackers200391275.0%2.050.9560.45
O.Mare2003222975.9%2.300.7049.56
O.Pochman200381553.3%2.340.6634.18
P.Dawson2003182185.7%1.871.1381.00
P.Edinger2003263672.2%2.050.9556.32
R.Lindell2003172470.8%2.450.5543.35
R.Longwell2003263086.7%2.360.6468.65
S.Christie2003152075.0%1.721.2870.80
S.Graham2003222588.0%2.310.6973.94
S.Janikowski2003222588.0%2.210.7977.30
S.Marler2003203360.6%1.941.0646.83
T.Duncan200361060.0%2.390.6137.73
T.Peterson2003121580.0%2.390.6150.48
W.Richey20031250.0%1.441.5651.79
A.Elling20041250.0%2.800.2026.77
A.Vinatieri2004363894.7%2.440.5685.93
B.Cundiff2004202676.9%1.291.7179.76
B.Gramatica200433100.0%2.770.23100.00
C.Hentrich20041333.3%1.231.7740.65
D.Akers2004313686.1%2.160.8475.17
D.Brien2004273577.1%2.370.6348.78
G.Anderson2004172277.3%2.260.7453.73
J.Brown2004252792.6%2.360.6482.60
J.Carney2004222781.5%2.220.7864.25
J.Chandler20045862.5%2.430.5738.62
J.Elam2004303585.7%2.470.5359.65
J.Feely2004202580.0%2.600.4046.20
J.Hall200481172.7%2.500.5043.68
J.Hanson2004242885.7%2.550.4552.49
J.Kasay2004192286.4%2.440.5663.29
J.Reed2004323786.5%2.220.7874.04
J.Scobee2004243177.4%2.090.9162.93
J.Taylor20044580.0%2.390.6150.86
J.Wilkins2004222781.5%2.370.6356.04
K.Brown2004172470.8%1.881.1260.96
L.Tynes2004172373.9%2.330.6747.55
M.Andersen2004192479.2%2.490.5147.73
M.Bryant20043475.0%2.230.7751.43
M.Gramatica2004111957.9%2.500.5034.67
M.Stover2004293290.6%2.410.5976.18
M.Vanderjagt2004212680.8%2.510.4948.20
N.Kaeding2004212777.8%2.220.7857.26
N.Rackers2004222975.9%1.741.2671.26
O.Kimrin200461060.0%1.931.0746.74
O.Mare2004121675.0%2.280.7249.43
P.Dawson2004242982.8%2.360.6459.70
P.Edinger2004152462.5%2.150.8543.58
R.Lindell2004242885.7%2.640.3648.77
R.Longwell2004253083.3%2.350.6561.42
S.Christie2004222878.6%2.430.5748.55
S.Graham2004273187.1%2.310.6972.13
S.Janikowski2004252889.3%2.380.6274.20
T.Peterson2004182281.8%2.210.7965.50
T.Sauerbrun200411100.0%2.610.39100.00
A.Elling2005010.0%1.451.550.00
A.Vinatieri2005222878.6%2.170.8361.27
B.Cundiff20055862.5%1.311.6966.68
D.Akers2005162272.7%2.230.7749.01
D.Brien20051425.0%2.150.8517.47
D.Rayner2005010.0%1.111.890.00
J.Brown2005233369.7%1.591.4167.74
J.Carney2005253278.1%2.540.4646.19
J.Cortez2005121770.6%0.812.1979.87
J.Elam2005273577.1%2.300.7050.96
J.Feely2005354283.3%2.330.6762.78
J.Hall2005131586.7%2.450.5586.45
J.Hall2005131586.7%1.521.4886.45
J.Hanson2005192479.2%2.340.6652.59
J.Kasay2005324080.0%2.310.6956.49
J.Nedney2005262892.9%1.781.2291.23
J.Reed2005273284.4%2.460.5456.21
J.Scobee2005243275.0%2.290.7149.06
J.Wilkins2005273187.1%1.991.0180.86
K.Brown2005263476.5%2.450.5546.85
L.Tynes2005273381.8%2.350.6557.96
M.Bryant2005222684.6%2.390.6162.38
M.Koenen20051250.0%1.311.6955.70
M.Nugent2005222878.6%2.460.5447.91
M.Stover2005303488.2%2.400.6070.54
M.Vanderjagt2005242788.9%2.570.4361.08
N.Kaeding2005212487.5%1.981.0281.58
N.Novak200581080.0%2.650.3545.27
N.Rackers2005404295.2%1.521.4895.17
O.Mare2005253083.3%2.170.8369.77
P.Dawson2005272993.1%2.310.6985.09
P.Edinger2005253473.5%2.450.5545.11
R.Bironas2005232882.1%1.831.1777.02
R.Gould2005212777.8%2.390.6148.89
R.Lindell2005293582.9%1.961.0475.39
R.Longwell2005202774.1%2.340.6647.40
S.Graham2005293387.9%2.180.8277.85
S.Janikowski2005203066.7%2.140.8646.81
S.Suisham20053475.0%
T.France20057977.8%2.230.7756.45
T.Peterson2005232592.0%2.640.3666.52
A.Vinatieri2006394390.7%2.280.7280.72
B.Cundiff2006020.0%1.591.410.00
D.Akers2006222781.5%2.230.7763.98
D.Rayner2006263574.3%2.380.6246.87
J.Brown2006283482.4%2.140.8669.06
J.Carney2006252792.6%2.610.3971.68
J.Elam2006272993.1%2.340.6684.22
J.Feely2006252986.2%2.590.4149.87
J.Hall200691181.8%2.250.7563.48
J.Hanson2006293387.9%2.090.9180.10
J.Kasay2006242788.9%2.280.7277.00
J.Nedney2006293582.9%2.510.4949.50
J.Reed2006202774.1%2.450.5545.37
J.Scobee2006263281.3%2.310.6959.27
J.Wilkins2006323786.5%2.480.5260.99
K.Brown2006192576.0%2.290.7149.81
L.Tynes2006243372.7%2.130.8752.99
M.Andersen2006202387.0%2.580.4253.27
M.Bryant2006172277.3%2.210.7956.68
M.Gramatica200691181.8%1.681.3279.41
M.Koenen20063933.3%2.430.5720.61
M.Nugent2006273090.0%2.190.8181.45
M.Stover2006303293.8%2.560.4478.63
M.Vanderjagt2006131872.2%1.851.1563.63
N.Kaeding2006263086.7%2.430.5764.78
N.Novak200651050.0%2.230.7733.69
N.Rackers2006283775.7%2.380.6247.66
O.Mare2006263672.2%2.430.5744.62
P.Dawson2006212972.4%2.240.7648.53
R.Bironas2006222878.6%2.280.7255.43
R.Gould2006384290.5%2.260.7480.73
R.Lindell2006232592.0%2.460.5477.80
R.Longwell2006212584.0%2.550.4549.36
S.Gostkowski2006283482.4%2.180.8267.57
S.Graham2006253083.3%2.360.6460.74
S.Janikowski2006182572.0%1.951.0559.98
S.Suisham200691181.8%2.440.5651.36
A.Vinatieri2007243080.0%2.690.3144.55
D.Akers2007243275.0%2.310.6948.80
D.Rayner2007152268.2%2.450.5541.80
J.Brown2007323884.2%2.100.9073.66
J.Carney2007121485.7%2.600.4049.37
J.Elam2007273187.1%2.001.0080.69
J.Feely2007212391.3%2.330.6780.47
J.Hanson2007293582.9%2.400.6057.36
J.Kasay2007242885.7%2.440.5661.50
J.Medlock20071250.0%2.790.2126.84
J.Nedney2007171989.5%2.170.8380.91
J.Reed2007242692.3%2.550.4574.36
J.Scobee2007151788.2%2.390.6171.15
J.Wilkins2007243275.0%2.200.8052.89
K.Brown2007252986.2%2.350.6568.17
L.Tynes2007283482.4%2.330.6760.20
M.Andersen2007252889.3%2.630.3756.64
M.Bryant2007283384.8%2.510.4953.44
M.Crosby2007334180.5%2.140.8665.77
M.Gramatica200755100.0%2.180.82100.00
M.Koenen2007020.0%1.791.210.00
M.Nugent2007293680.6%2.480.5248.75
M.Prater20071425.0%2.440.5615.39
M.Stover2007273284.4%2.520.4851.47
N.Folk2007273284.4%1.411.5985.29
N.Kaeding2007293485.3%2.540.4651.65
N.Rackers2007213070.0%2.100.9050.25
O.Mare2007101758.8%2.310.6938.14
P.Dawson2007263086.7%2.280.7272.09
R.Bironas2007374288.1%1.801.2085.07
R.Gould2007313686.1%2.110.8976.54
R.Lindell2007242788.9%2.500.5066.69
R.Longwell2007202483.3%2.150.8570.70
S.Gostkowski2007222684.6%2.440.5658.94
S.Graham2007313491.2%2.310.6980.84
S.Janikowski2007233271.9%1.921.0860.89
S.Suisham2007293680.6%2.030.9770.01
A.Vinatieri2008212680.8%2.460.5449.30
C.Barth2008101283.3%2.560.4448.80
D.Akers2008425084.0%2.330.6764.37
D.Carpenter2008222684.6%2.470.5356.29
D.Rayner200811100.0%2.880.12100.00
G.Hartley20081313100.0%2.590.41100.00
J.Brown2008313686.1%2.400.6065.35
J.Carney2008384388.4%2.470.5367.05
J.Elam2008303293.8%2.590.4177.02
J.Feely2008242885.7%2.590.4149.56
J.Hanson2008212295.5%2.210.7991.41
J.Kasay2008283190.3%2.450.5573.51
J.Nedney2008293387.9%2.160.8478.27
J.Reed2008323688.9%2.450.5569.68
J.Scobee2008192576.0%2.410.5947.30
K.Brown2008293387.9%2.450.5566.75
L.Tynes200811100.0%2.920.08100.00
M.Bryant2008323884.2%2.290.7166.78
M.Crosby2008273479.4%2.400.6049.65
M.Gramatica200861060.0%2.370.6337.91
M.Nugent2008010.0%2.750.250.00
M.Prater2008253473.5%2.450.5545.10
M.Stover2008313783.8%2.290.7165.50
N.Folk2008202290.9%0.952.0593.33
N.Kaeding2008293485.3%2.550.4550.51
N.Novak200861060.0%2.510.4935.79
N.Rackers2008303585.7%2.440.5661.57
O.Mare2008242788.9%2.220.7878.68
P.Dawson2008303683.3%2.390.6158.84
R.Bironas2008303585.7%2.340.6667.32
R.Gould2008262989.7%2.480.5270.00
R.Lindell2008303878.9%2.420.5848.95
R.Longwell2008293485.3%2.440.5660.82
S.Gostkowski2008364090.0%2.580.4263.98
S.Graham2008212487.5%2.200.8076.44
S.Hauschka20081250.0%1.521.4849.50
S.Janikowski2008243080.0%2.320.6856.19
S.Suisham2008263672.2%2.070.9355.23
T.Mehlhaff20083475.0%2.700.3041.65
A.Vinatieri20097977.8%2.590.4145.06
B.Cundiff2009212680.8%2.640.3645.94
C.Barth2009141973.7%2.280.7248.43
D.Akers2009323786.5%2.470.5361.76
D.Carpenter2009252889.3%2.510.4966.99
G.Gano200944100.0%2.540.46100.00
G.Hartley2009141687.5%2.470.5364.38
J.Brown2009192479.2%2.340.6652.84
J.Carney2009131776.5%2.700.3042.50
J.Elam2009121963.2%2.600.4036.40
J.Feely2009334180.5%2.470.5348.89
J.Hanson2009212875.0%2.400.6046.89
J.Kasay2009222781.5%2.480.5249.25
J.Nedney2009172181.0%2.390.6153.33
J.Reed2009273187.1%2.510.4960.65
J.Scobee2009182864.3%2.220.7843.44
K.Brown2009213265.6%2.510.4939.15
L.Tynes2009273284.4%2.660.3447.61
M.Bryant200971070.0%2.390.6143.96
M.Crosby2009283873.7%2.470.5344.78
M.Nugent20094850.0%2.410.5931.16
M.Prater2009303585.7%2.600.4049.40
M.Stover2009151883.3%2.620.3847.68
N.Folk2009182864.3%2.530.4738.09
N.Kaeding2009323884.2%2.530.4749.98
N.Rackers2009172085.0%2.540.4650.84
O.Mare2009242692.3%2.450.5579.00
P.Dawson2009171989.5%2.430.5772.05
R.Bironas2009273284.4%2.170.8371.90
R.Gould2009242885.7%2.400.6064.48
R.Lindell2009283384.8%2.510.4953.31
R.Longwell2009283093.3%2.620.3873.48
R.Schmitt20092366.7%2.670.3337.45
R.Succop2009252986.2%2.400.6065.45
S.Andrus2009010.0%2.190.810.00
S.Gostkowski2009263281.3%2.570.4347.46
S.Graham2009233076.7%2.600.4044.18
S.Hauschka200991369.2%2.580.4240.33
S.Janikowski2009262989.7%2.110.8982.64
S.Suisham2009232979.3%2.600.4045.74
A.Pettrey20102450.0%2.750.2527.30
A.Vinatieri2010293193.5%2.600.4075.97
B.Cundiff2010303390.9%2.620.3864.52
C.Barth2010232882.1%2.510.4949.04
C.Stitser20107887.5%2.670.3349.07
D.Akers2010334180.5%2.470.5348.79
D.Buehler2010243275.0%2.490.5145.26
D.Carpenter2010304173.2%2.310.6947.50
D.Rayner2010131681.3%2.350.6556.68
G.Gano2010243568.6%2.190.8147.07
G.Hartley2010232882.1%2.610.3947.18
J.Brown2010333984.6%2.620.3848.44
J.Carney20105683.3%2.810.1944.53
J.Feely2010242788.9%2.400.6072.42
J.Hanson2010121485.7%2.280.7270.09
J.Kasay2010252986.2%2.400.6065.70
J.Nedney2010111384.6%2.490.5154.31
J.Reed2010243275.0%2.530.4744.45
J.Scobee2010222878.6%2.360.6450.09
K.Brown20104580.0%2.560.4446.80
L.Tynes2010192382.6%2.620.3847.36
M.Bryant2010283190.3%2.630.3761.25
M.Crosby2010253278.1%2.210.7958.33
M.Nugent2010151978.9%2.530.4746.79
M.Prater2010161888.9%2.450.5569.87
N.Folk2010324276.2%2.520.4845.28
N.Kaeding2010232882.1%2.290.7162.16
N.Rackers2010273090.0%2.500.5069.99
O.Mare2010283384.8%2.650.3548.02
P.Dawson2010232882.1%2.550.4548.39
R.Bironas2010242692.3%2.500.5076.85
R.Gould2010253083.3%2.510.4949.72
R.Lindell2010162176.2%2.430.5746.96
R.Longwell2010171894.4%2.700.3072.28
R.Succop2010202676.9%2.490.5146.37
S.Andrus20102450.0%2.520.4829.71
S.Gostkowski2010101376.9%2.560.4445.01
S.Graham20101414100.0%2.670.33100.00
S.Hauschka20106785.7%1.561.4485.10
S.Janikowski2010334180.5%2.120.8866.77
S.Suisham2010172085.0%2.410.5961.64
A.Henery2011242788.9%2.620.3856.03
A.Vinatieri2011232785.2%2.410.5962.45
B.Coutu2011010.0%2.320.680.00
B.Cundiff2011324276.2%2.520.4845.34
C.Barth2011262892.9%2.270.7385.42
D.Akers2011485685.7%2.500.5057.47
D.Bailey2011323786.5%2.580.4251.45
D.Carpenter2011293485.3%2.510.4955.29
D.Rayner2011101566.7%2.480.5240.38
G.Gano2011314175.6%2.480.5245.72
J.Brown2011212875.0%2.490.5145.27
J.Feely2011192479.2%2.560.4446.44
J.Hanson2011242982.8%2.560.4448.52
J.Kasay2011303683.3%2.560.4448.74
J.Scobee2011232592.0%2.360.6481.24
L.Tynes2011273479.4%2.630.3745.24
M.Bryant2011272993.1%2.680.3268.12
M.Crosby2011263086.7%2.500.5060.31
M.Nugent2011344085.0%2.580.4249.44
M.Prater2011243080.0%2.540.4647.15
M.Scifres201111100.0%2.540.46100.00
N.Folk2011192576.0%2.460.5446.34
N.Novak2011273479.4%2.430.5749.11
N.Rackers2011354283.3%2.540.4649.25
O.Mare2011222878.6%2.610.3945.13
P.Dawson2011242982.8%2.270.7364.61
R.Bironas2011293290.6%2.320.6879.31
R.Gould2011283287.5%2.440.5666.45
R.Lindell2011131586.7%2.650.3549.11
R.Longwell2011222878.6%2.490.5147.28
R.Succop2011243080.0%2.450.5548.89
S.Gostkowski2011333886.8%2.620.3849.80
S.Graham20116785.7%2.620.3849.01
S.Hauschka2011253083.3%2.520.4849.62
S.Janikowski2011313588.6%2.250.7577.27
S.Suisham2011263476.5%2.520.4845.55
A.Henery2012273187.1%2.500.5061.60
A.Vinatieri2012293778.4%2.310.6952.66
B.Cundiff201271258.3%2.450.5535.67
B.Walsh2012363992.3%2.490.5177.38
C.Barth2012283384.8%2.260.7469.40
D.Akers2012334770.2%2.560.4441.17
D.Bailey2012293193.5%2.490.5180.86
D.Carpenter2012222781.5%2.450.5549.85
G.Gano201291181.8%2.540.4648.38
G.Hartley2012182281.8%2.620.3846.93
G.Zuerlein2012233174.2%2.200.8051.34
J.Brown2012131492.9%2.440.5681.02
J.Feely2012252889.3%2.490.5168.59
J.Hanson2012323688.9%2.470.5368.30
J.Medlock201271070.0%2.500.5041.98
J.Scobee2012252889.3%2.370.6374.30
J.Tucker2012343791.9%2.490.5176.17
K.Forbath2012171894.4%2.350.6587.10
L.Tynes2012333984.6%2.650.3547.86
M.Bryant2012374288.1%2.590.4156.63
M.Crosby2012233565.7%2.390.6141.31
M.Nugent2012192382.6%2.520.4849.09
M.Prater2012263378.8%2.610.3945.22
N.Folk2012212777.8%2.570.4345.48
N.Kaeding201281080.0%2.570.4346.67
N.Novak2012182090.0%2.470.5371.44
O.Mare20126875.0%2.750.2540.94
P.Dawson2012293193.5%2.530.4779.62
R.Bironas2012253180.6%2.340.6656.06
R.Gould2012212584.0%2.510.4951.18
R.Lindell2012212487.5%2.580.4255.52
R.Succop2012283482.4%2.510.4949.14
S.Gostkowski2012333984.6%2.440.5659.08
S.Graham2012374484.1%2.510.4951.78
S.Hauschka2012273090.0%2.600.4062.79
S.Janikowski2012313491.2%2.350.6579.69
S.Suisham2012283190.3%2.510.4970.07
A.Henery2013243080.0%2.550.4547.00
A.Vinatieri2013384388.4%2.330.6774.07
B.Cundiff2013212680.8%2.420.5850.69
B.Walsh2013263086.7%2.530.4757.60
C.Sturgis2013263476.5%2.460.5446.56
D.Akers2013192479.2%2.590.4145.84
D.Bailey2013283093.3%2.510.4979.44
D.Carpenter2013333691.7%2.420.5878.35
G.Gano2013252889.3%2.490.5168.48
G.Hartley2013223073.3%2.610.3942.22
G.Zuerlein2013262892.9%2.670.3367.76
J.Brown2013232688.5%2.540.4662.39
J.Feely2013303683.3%2.530.4749.39
J.Potter20133475.0%2.590.4143.37
J.Scobee2013232592.0%2.280.7283.30
J.Tucker2013384192.7%2.510.4977.63
K.Forbath2013182281.8%2.520.4848.65
M.Bryant2013242788.9%2.600.4058.83
M.Crosby2013353989.7%2.580.4263.79
M.Nugent2013192382.6%2.450.5552.18
M.Prater2013303293.8%2.510.4981.03
N.Folk2013333691.7%2.540.4672.93
N.Novak2013374190.2%2.640.3659.14
P.Dawson2013394390.7%2.490.5172.53
R.Bironas2013252986.2%2.570.4351.78
R.Bullock2013263574.3%2.440.5645.59
R.Gould2013262989.7%2.350.6576.18
R.Lindell2013232979.3%2.510.4947.46
R.Succop2013253180.6%2.480.5248.71
S.Gostkowski2013394292.9%2.270.7385.35
S.Graham20136875.0%2.450.5546.00
S.Hauschka2013414395.3%2.540.4684.73
S.Janikowski2013213070.0%2.420.5843.30
S.Suisham2013303293.8%2.600.4076.63
A.Henery20141520.0%1.391.6121.58
A.Vinatieri2014353892.1%2.430.5779.25
B.Cundiff2014222975.9%2.550.4544.66
B.McManus201491369.2%2.710.2938.38
B.Walsh2014263574.3%1.841.1666.67
C.Barth2014171894.4%2.630.3777.42
C.Catanzaro2014293387.9%2.580.4256.21
C.Parkey2014323688.9%2.610.3957.03
C.Santos2014253083.3%2.610.3947.97
C.Sturgis2014293778.4%2.570.4345.79
D.Bailey2014263281.3%2.280.7260.83
D.Carpenter2014343889.5%2.510.4967.61
G.Gano2014323982.1%2.540.4648.48
G.Hartley201433100.0%2.630.37100.00
G.Zuerlein2014243080.0%2.580.4246.56
J.Brown2014242692.3%2.610.3970.43
J.Feely20143475.0%2.520.4844.68
J.Scobee2014202676.9%2.220.7855.57
J.Tucker2014333886.8%2.490.5161.35
K.Forbath2014242788.9%2.670.3349.96
M.Bryant2014293290.6%2.490.5172.57
M.Crosby2014344085.0%2.460.5458.64
M.Nugent2014273479.4%2.450.5548.65
M.Prater2014232882.1%2.310.6960.98
N.Folk2014323982.1%2.450.5551.40
N.Freese20143742.9%2.570.4325.04
N.Novak2014222684.6%2.570.4349.38
P.Dawson2014253180.6%2.460.5449.10
P.Murray2014202483.3%2.430.5756.36
R.Bullock2014303585.7%2.520.4855.36
R.Gould201491275.0%2.560.4443.93
R.Succop2014192286.4%2.600.4049.85
S.Gostkowski2014363894.7%2.620.3879.32
S.Graham2014192286.4%2.700.3047.96
S.Hauschka2014333984.6%2.580.4249.16
S.Janikowski2014192286.4%2.310.6970.23
S.Suisham2014323591.4%2.580.4269.23
A.Franks2015131681.3%2.530.4748.25
A.Vinatieri2015252792.6%2.570.4374.07
B.McManus2015404588.9%2.610.3957.11
B.Walsh2015374386.0%2.370.6366.56
C.Barth2015232882.1%2.590.4147.48
C.Boswell2015363992.3%2.590.4171.71
C.Catanzaro2015303390.9%2.670.3358.28
C.Parkey20153475.0%2.660.3442.31
C.Santos2015354283.3%2.490.5151.16
C.Sturgis2015182281.8%2.590.4147.30
D.Bailey2015303293.8%2.610.3976.12
D.Carpenter2015232785.2%2.530.4752.28
D.Hopkins2015262989.7%2.590.4162.41
G.Gano2015344182.9%2.540.4648.92
G.Zuerlein2015203066.7%2.350.6542.54
J.Brown2015303293.8%2.630.3774.90
J.Lambo2015263281.3%2.460.5449.57
J.Myers2015263086.7%2.630.3749.42
J.Scobee201561060.0%2.560.4435.10
J.Tucker2015334082.5%2.480.5249.81
K.Brindza201561250.0%2.390.6131.40
K.Forbath2015101566.7%2.520.4839.61
M.Bryant2015141877.8%2.680.3243.55
M.Crosby2015283287.5%2.540.4659.52
M.Nugent2015242982.8%2.560.4448.54
M.Prater2015222491.7%2.350.6580.78
N.Folk2015131681.3%2.530.4748.24
N.Novak2015182185.7%2.540.4653.60
P.Dawson2015242788.9%2.630.3754.60
R.Bullock2015192382.6%2.620.3847.33
R.Gould2015333984.6%2.480.5255.91
R.Succop2015141687.5%2.440.5666.68
S.Gostkowski2015374092.5%2.540.4675.55
S.Graham2015111384.6%2.510.4952.63
S.Hauschka2015313491.2%2.530.4772.12
S.Janikowski2015212680.8%2.510.4948.36
T.Coons2015283287.5%2.620.3850.73
Z.Hocker2015101471.4%2.650.3540.48

  1. That also includes you DFS players who play at FanDuel.  (back)
  2. Kickers have indeed improved over time.  (back)
  3. In fact, I’ll have a full list at the bottom from 2000 to present.  (back)
  4. Using only the linear term made it over-predict the likelihood of making a 70 yarder, for example.  (back)
  5. I also made sure for games at stadiums with retractable roofs that I determined if the roof was open or closed to determine indoor or outdoor.  (back)
  6. The difference between synthetic turf and AstroTurf is that AstroTurf is the super flat, hard, green surface, whereas synthetic turf is artificial, but made to have the feel and appearance of natural grass.  (back)
  7. Kickers have gotten better over the years in a linear fashion, as shown by Clark, Johnson, and Stimpson.  (back)
  8. Making field goals has a quadratic response to experience.  (back)
  9. Note, categorical terms like field surface will have the number of categories minus one listed. In the below table it doesn’t have synthetic turf listed, but you can read the table as saying grass is significantly different from synthetic turf, and AstroTurf is also significantly different from synthetic turf (and grass due to the direction in the estimate).  (back)
  10. Term Estimate Std Error Wald Statistic P-value
    dist -0.1108 0.0033 1151 <.0001*
    seas 0.0522 0.0051 106.81 <.0001*
    Intercept -98.7131 10.1361 94.84 <.0001*
    surface(Grass) -0.2099 0.0386 29.53 <.0001*
    InOut[temp] 0.0084 0.0016 28.09 <.0001*
    alt 0.2869 0.069 17.28 <.0001*
    InOut[wspd] -0.0183 0.0048 14.79 0.0001*
    surface(AstroTurf) 0.1943 0.0534 13.25 0.0003*
    precip 0.1233 0.0471 6.87 0.0088*
    dist*dist 0.0005 0.0003 4.35 0.0369*
    InOut -0.0046 0.0339 0.02 0.8924

      (back)

  11. With year set to 2015, wind speed to 10 mph, temperature 50°F, low altitude, outdoors, no precipitation, for a kicker with four years experience.  (back)
  12. We went to high school together.  (back)