Evaluating Kicker Performance since the PAT Change

Main Takeaway: A probability-based model as in the one described here for pass rushers appears to offer a sound framework for the evaluation of kickers. 


Sample Size Requirement: Minimum of 30 FG Attempts in 2015 and 2016 combined

Step I: Establish the league average success rate for each distance chunk (1-19, 20-29, 30-39, 40-49, 50+) to create a performance baseline. In order to do this, data on all field goals attempted from 2012 to 2016 was used. A downward adjustment was made to the raw league average success rate for 50+ yards given that above average kickers tend to take the vast majority of such attempts, biasing the raw success rate upwards.

1-19: 0.9950
20-29: 0.9700
30-39: 0.9100
40-49: 0.8000
50+: 0.5000 (down from 0.6200)

PAT: 0.9100 (assumed to be equal to the 30-39 success rate)

Step II: Impose a “uniform kicking distribution” on all kickers given that differences in kicking distribution between kickers can significantly muddy the waters when it comes to performance evaluation. The uniform kicking distribution took the following form:

1-19: 1 Attempt
20-29: 8 Attempts
30-39: 9 Attempts
40-49: 10 Attempts
50+: 5 Attempts

PAT: 40 Attempts


Top 5 in Points Above Expected per Season

1) Justin Tucker (BAL): 13.03 PAE
2) Ryan Succop (TEN): 10.55 PAE
3) Adam Vinatieri (IND): 10.17 PAE
4) Dan Bailey (DAL): 9.46 PAE
5) Josh Brown (FA): 7.74 PAE

Bottom 5 in Points Above Expected per Season

27) Dan Carpenter (FA): -3.85 PAE
28) Connor Barth (CHI): -3.90 PAE
29) Mike Nugent (FA): -4.38 PAE
30) Andrew Franks (MIA): -6.31 PAE
31) Randy Bullock (CIN): -7.87 PAE


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