Titans Focused NFL Data Analysis

   Welcome to my page. Use the Contents links below or the top navigation bar to move through the sections. The page is readable, but not optimized for mobile screens. On a large screen, some portions may look better in portrait orientation. Python was used for data analysis, graphs, and page creation itself. See the repository for more information on the code and process. It can also be used to submit feedback.

Contents

Acknowledgements

Regular Season Review - Team Metrics 2022

2022 play-by-play dataset, regular season only

   Team performance for 2022 is gauged in a few different ways. In the first section Expected Points Added (EPA) for Offense, Defense, and Special Teams is considered in total and on a per play basis. Plays with penalties were excluded.

Next, each drive is analyzed for each team on Offense and Defense. The second section focuses on the numerical data, Yards, Points, and Time of Possesion per drive. And the various drive outcomes are examined in the third section. In the future, I might take a similar approach to team performance on a per series basis.

Lastly, penalties and their consequences were tallied. Penalty data is presented from the persepective of the committing team, and not the team benefitting.

2022 season dataset updated on Mon Jan 9 15:24:21 2023

Expected Points Added

   The first section shows, expected points added, EPA, for teams in various situations. Offense was split into passes, runs, and scrambles. Defense was split into pass and runs. And finally Special Teams plays were categorized into "Offense", where a team is trying to score or will be receiving the ball, and "Defense" where a team is doing the opposite. Those values were largely influenced by punter (Def) and kicker (Off) performance.

EPA was calculated on a per play basis, and also as a season total. For most categories, these metrics are interchangeable over the span of the season. But for scrambles, where teams have more spread in play counts, the totals and averages are not proportional. For the most part, EPA data is presented on a per play basis, but totals are shown in some of the graphs. Use the toggle text to switch between EPA/play and Total EPA in the table below.

The graph shows the average EPA/play for each team on Defense and Offense. It is styled after rbsdm.com's Team Tiers. All the EPA data is presented in the following table; and additional graphs are shown after the table.

Team EPA 2022 change to Total EPA

Table is currently sorted by Def_EPA, click other columns to sort table. good OK bad

  Team Off_EPA Off_EPA_pass Off_EPA_run Off_EPA_scramble Def_EPA Def_EPA_pass Def_EPA_run ST_Off_EPA ST_Def_EPA
SF 0.094 0.183 -0.007 0.136 -0.123 -0.104 -0.156 0.031 -0.005
DAL 0.034 0.074 -0.007 -0.119 -0.106 -0.109 -0.101 0.043 0.037
NE -0.043 -0.043 -0.056 0.182 -0.101 -0.120 -0.070 0.135 0.173
WAS -0.082 -0.103 -0.080 0.340 -0.079 -0.058 -0.106 -0.020 0.031
PHI 0.089 0.062 0.079 0.576 -0.073 -0.166 0.062 0.051 0.090
NO -0.048 -0.001 -0.106 -0.073 -0.067 -0.102 -0.024 0.025 0.136
BUF 0.099 0.112 -0.050 0.993 -0.067 -0.046 -0.102 0.145 -0.066
NYJ -0.089 -0.106 -0.087 0.485 -0.060 -0.103 -0.004 0.094 0.065
DEN -0.107 -0.132 -0.105 0.410 -0.057 -0.071 -0.035 -0.105 0.099
CIN 0.061 0.108 -0.056 0.429 -0.053 -0.059 -0.046 0.053 0.106
IND -0.171 -0.185 -0.169 0.217 -0.044 -0.023 -0.069 0.100 0.074
KC 0.179 0.246 -0.023 0.762 -0.022 -0.026 -0.016 -0.154 0.046
JAX 0.034 0.088 -0.066 0.304 -0.012 0.019 -0.058 0.020 0.037
MIA 0.021 0.060 -0.056 0.207 -0.011 0.003 -0.033 0.069 0.128
BAL -0.005 -0.052 0.027 0.243 -0.007 0.027 -0.063 0.160 -0.025
TEN -0.074 -0.093 -0.084 0.530 -0.005 0.056 -0.119 -0.003 -0.012
TB -0.035 0.038 -0.185 -1.331 -0.004 0.022 -0.040 -0.048 0.106
CAR -0.055 -0.114 -0.010 0.375 -0.002 0.036 -0.051 0.068 -0.066
HOU -0.193 -0.197 -0.211 0.261 -0.000 -0.014 0.014 0.102 -0.031
CLE 0.010 -0.042 0.020 0.751 -0.000 -0.039 0.046 0.007 0.021
LAC -0.003 0.022 -0.069 0.244 0.005 -0.056 0.081 0.036 0.028
ARI -0.076 -0.115 -0.060 0.471 0.009 0.025 -0.013 0.040 0.141
LAR -0.088 -0.120 -0.053 0.154 0.018 0.052 -0.027 0.117 0.147
GB -0.005 -0.031 0.006 0.996 0.020 -0.040 0.088 0.056 0.061
PIT 0.005 -0.019 0.017 0.292 0.024 0.083 -0.057 -0.012 0.159
MIN -0.024 0.022 -0.136 0.553 0.024 0.053 -0.018 0.050 0.127
NYG 0.044 0.005 0.018 0.620 0.039 0.006 0.084 0.034 0.056
SEA 0.004 0.013 -0.060 0.634 0.041 0.047 0.034 0.068 -0.087
LV -0.001 0.002 -0.027 0.349 0.061 0.112 -0.011 0.154 -0.037
ATL -0.013 -0.045 0.015 -0.004 0.072 0.121 0.016 0.132 0.045
DET 0.060 0.153 -0.057 -0.295 0.074 0.053 0.102 0.132 0.017
CHI -0.052 -0.229 0.007 0.727 0.118 0.167 0.069 0.025 -0.025
slideshow - use arrows to browse graphs

per Drive Analysis

   Team drive results were analyzed for both Offense and Defense. Yards (Yds), points (Pts), and penalty yards (penalty_yds) per drive are obvious in their intepretation. TOP is average time of possesion in seconds.

Drives resulting in an immediate punt (three_out), without an offensive score (nopts), or without positive yards (noyds) are also presented.

Sadly, the Titans offense was worst in three and outs, not scoring, and penalty yards lost per drive. They were not much better in other metrics.

Team offenses and defenses are presented in the next two tables. They are currently sorted by Pts per drive.
*Note: bigger/smaller isn't obviously better for Off/Def TOP. Pts were estimated by giving 7 for TDs and 3 for FGs, not exactly counted using scores.*

slideshow - use arrows to browse graphs

2022 Team Offense, Drive Analysis good OK bad

  Team Drives Yds Pts TOP three_out nopts noyds penalty_yds
KC 181 38.7 2.68 171 17.1% 54.1% 10.5% -0.2
BUF 189 36.2 2.51 164 16.4% 55.0% 14.8% -0.1
PHI 187 35.6 2.49 167 17.1% 57.8% 15.0% 0.2
DET 177 36.3 2.46 172 16.9% 57.1% 14.1% -0.1
SF 196 34.2 2.40 176 13.8% 56.6% 9.7% -0.1
DAL 207 31.8 2.38 157 23.2% 58.0% 15.5% 0.2
CIN 185 33.1 2.34 176 21.1% 58.9% 17.8% 0.7
LV 173 33.9 2.21 181 19.7% 57.2% 13.3% -0.7
SEA 199 31.4 2.15 158 20.1% 59.3% 16.6% -0.4
JAX 194 33.1 2.10 165 19.6% 60.8% 12.4% -0.2
MIN 209 31.8 2.10 154 22.5% 62.7% 11.5% 0.8
MIA 195 33.2 2.06 161 15.9% 62.1% 12.3% 0.1
NYG 188 31.9 2.04 176 19.7% 61.7% 11.7% -0.6
LAC 203 31.7 2.02 163 22.2% 61.6% 12.3% 0.1
ATL 169 32.2 1.98 181 16.0% 60.9% 13.6% 0.2
GB 178 32.8 1.96 182 15.7% 64.0% 13.5% 0.5
CLE 181 32.8 1.91 179 19.3% 65.2% 14.4% -0.2
BAL 189 32.0 1.90 178 14.8% 61.4% 13.8% -0.4
CHI 182 27.9 1.84 166 23.6% 65.9% 19.2% -0.6
CAR 191 27.0 1.74 152 28.3% 64.9% 17.3% -0.4
NO 186 30.3 1.73 161 24.7% 68.3% 14.5% -0.2
LAR 177 26.8 1.70 171 24.9% 66.7% 22.0% -0.1
PIT 181 29.8 1.65 178 23.8% 65.7% 14.4% -0.4
ARI 188 28.4 1.64 165 24.5% 68.1% 16.0% -0.9
NE 192 27.1 1.63 155 25.5% 67.2% 12.5% -0.8
TB 206 29.9 1.61 154 25.2% 68.4% 13.1% -0.5
WAS 197 28.6 1.55 172 20.3% 70.6% 16.2% 0.2
TEN 192 25.6 1.52 158 30.7% 72.4% 18.8% -1.1
NYJ 197 27.6 1.49 151 25.9% 70.1% 15.2% -0.4
DEN 200 27.6 1.45 152 27.0% 71.0% 16.5% 0.1
HOU 199 24.5 1.44 148 25.6% 70.9% 21.1% 0.3
IND 197 26.7 1.43 158 21.3% 70.1% 14.2% -0.0

2022 Team Defense, Drive Analysis good OK bad

  Team Drives Yds Pts TOP three_out nopts noyds penalty_yds
SF 194 27.7 1.49 157 26.8% 73.7% 20.1% -0.4
NYJ 194 27.3 1.61 165 19.6% 67.0% 17.5% 0.0
DAL 209 28.4 1.63 158 19.6% 67.5% 11.5% -0.2
BUF 185 28.6 1.64 168 21.1% 69.2% 16.2% -0.2
WAS 197 26.4 1.68 140 27.9% 68.5% 22.8% 0.1
NE 193 28.6 1.68 168 20.7% 68.9% 13.5% -0.1
DEN 200 27.6 1.71 159 28.0% 64.5% 13.5% 0.2
NO 186 29.2 1.72 167 21.5% 65.6% 14.0% 0.4
CIN 188 30.4 1.75 155 21.3% 64.4% 8.5% -0.4
BAL 183 31.4 1.79 169 22.4% 64.5% 11.5% 0.0
PHI 181 27.8 1.80 165 21.5% 67.4% 16.6% -0.5
TEN 190 31.2 1.85 167 25.3% 65.8% 16.8% -0.2
CAR 194 30.2 1.90 167 21.1% 65.5% 15.5% -0.5
TB 209 28.3 1.92 163 28.7% 66.0% 17.2% -0.5
HOU 196 32.7 1.92 166 19.9% 63.8% 13.3% -0.1
JAX 195 32.0 1.95 169 21.0% 63.6% 16.4% -0.4
PIT 178 31.0 1.96 168 21.3% 62.4% 15.2% -0.9
IND 198 29.3 1.98 160 22.7% 64.1% 16.7% 0.6
KC 186 29.9 2.03 167 21.5% 64.0% 17.7% 0.0
LAC 201 31.3 2.03 159 19.9% 62.7% 14.9% -0.1
MIA 201 31.1 2.03 165 19.4% 62.7% 17.4% 0.5
GB 177 32.5 2.04 169 24.9% 62.1% 16.4% -0.2
CLE 187 30.6 2.05 156 22.5% 61.5% 17.6% 0.4
NYG 193 33.1 2.06 168 19.7% 59.1% 13.0% -0.2
LAR 174 33.0 2.07 179 21.8% 58.6% 14.4% -0.4
MIN 201 34.2 2.15 167 13.4% 58.2% 11.4% -0.7
SEA 198 33.6 2.15 172 18.2% 60.6% 15.2% 0.0
LV 180 34.4 2.22 171 17.2% 59.4% 13.9% -0.1
ATL 170 34.9 2.25 182 18.2% 57.1% 7.1% -1.3
ARI 192 30.5 2.28 162 22.4% 58.9% 16.7% -0.4
DET 178 37.2 2.31 173 17.4% 59.0% 10.1% -0.3
CHI 177 36.3 2.45 175 16.4% 58.2% 9.0% 0.4

per Drive Results

   Drives were analyzed more simply by their results. Their various outcomes are summarized on the following tables for Teams' Offenses and Defenses. They are currently sorted by Punt rate, which is bad for Offenses, and generally good for Defenses. The first two graphs show how Touchdown and Punt rates varied for the teams and their EPA rankings.

*Note: Return TD column is total and not per drive. Kickoff return TDs were not counted as drives, and punt return TDs were marked as drives resulting in a Punt, instead of an Opp touchdown, and the returning team was also awarded a Return TD.*

2022 Team Offense, Drive Results good OK bad

  Team Punt Turnover Field goal Missed field goal Touchdown Return TD End of half Turnover on downs Opp touchdown Safety
DEN 48.0% 9.0% 14.5% 4.0% 14.5% 0 4.5% 4.0% 1.5% 0.0%
TEN 46.9% 9.4% 10.4% 2.1% 17.2% 0 8.3% 4.7% 1.0% 0.0%
HOU 44.2% 10.1% 15.1% 1.0% 14.1% 1 6.0% 6.0% 3.5% 0.0%
NYJ 42.6% 11.7% 15.2% 3.6% 14.7% 2 5.6% 6.1% 0.0% 0.5%
CAR 42.4% 8.9% 17.8% 1.0% 17.3% 0 6.3% 5.2% 1.0% 0.0%
WAS 42.1% 10.7% 12.7% 2.5% 16.8% 0 7.1% 6.6% 1.0% 0.5%
NE 41.7% 10.9% 16.7% 2.6% 16.1% 0 7.3% 3.6% 1.0% 0.0%
NO 41.4% 10.8% 12.4% 4.3% 19.4% 0 5.9% 3.8% 2.2% 0.0%
NYG 41.0% 6.9% 16.0% 1.6% 22.3% 0 6.9% 4.8% 0.0% 0.5%
TB 40.8% 10.2% 15.0% 3.4% 16.5% 0 7.8% 6.3% 0.0% 0.0%
LAR 40.7% 11.3% 15.8% 1.1% 17.5% 0 9.6% 1.1% 2.3% 0.6%
LAC 38.4% 8.9% 16.7% 1.5% 21.7% 0 5.4% 6.9% 0.5% 0.0%
PIT 38.1% 8.8% 18.8% 6.1% 15.5% 0 9.9% 2.2% 0.6% 0.0%
CHI 36.8% 11.5% 13.7% 1.1% 20.3% 0 6.0% 8.8% 1.6% 0.0%
ARI 36.7% 11.7% 14.9% 2.1% 17.0% 0 5.9% 10.6% 1.1% 0.0%
ATL 36.7% 11.2% 18.9% 3.0% 20.1% 1 5.9% 4.1% 0.0% 0.0%
MIN 36.4% 10.0% 12.9% 3.3% 24.4% 1 6.7% 4.8% 1.4% 0.0%
IND 36.0% 14.2% 16.8% 3.6% 13.2% 0 6.6% 7.1% 2.5% 0.0%
SEA 35.7% 10.6% 17.6% 1.5% 23.1% 0 6.5% 3.5% 1.0% 0.5%
DAL 34.8% 8.7% 14.0% 1.4% 28.0% 0 6.8% 4.8% 1.4% 0.0%
LV 34.1% 10.4% 19.7% 1.7% 23.1% 0 3.5% 5.8% 1.7% 0.0%
MIA 33.8% 10.8% 14.9% 3.1% 23.1% 0 6.2% 6.2% 1.0% 1.0%
CLE 33.7% 11.0% 13.3% 4.4% 21.5% 0 5.5% 9.9% 0.0% 0.6%
CIN 33.5% 8.6% 13.5% 2.7% 27.6% 0 8.6% 4.3% 1.1% 0.0%
JAX 32.0% 13.9% 16.0% 2.1% 23.2% 0 6.7% 6.2% 0.0% 0.0%
BAL 31.7% 11.1% 20.1% 3.2% 18.5% 1 8.5% 5.8% 1.1% 0.0%
SF 31.6% 8.2% 15.8% 2.0% 27.6% 0 8.2% 4.6% 1.0% 1.0%
PHI 30.5% 8.6% 11.8% 1.6% 30.5% 0 9.6% 5.3% 1.6% 0.5%
KC 29.8% 10.5% 13.3% 4.4% 32.6% 0 7.7% 1.7% 0.0% 0.0%
GB 29.8% 10.7% 14.0% 2.2% 21.9% 1 10.1% 10.1% 1.1% 0.0%
DET 29.4% 6.8% 13.6% 3.4% 29.4% 0 6.2% 9.0% 1.7% 0.6%
BUF 26.5% 14.3% 15.9% 2.1% 29.1% 2 7.4% 3.2% 1.1% 0.5%

2022 Team Defense, Drive Results good OK bad

  Team Punt Turnover Field goal Missed field goal Touchdown Return TD End of half Turnover on downs Opp touchdown Safety
TEN 44.2% 10.0% 13.7% 1.1% 20.5% 0 6.8% 3.2% 0.5% 0.0%
WAS 44.2% 6.1% 13.2% 1.5% 18.3% 0 8.1% 6.6% 1.5% 0.5%
TB 44.0% 9.1% 11.5% 1.4% 22.5% 0 6.2% 4.8% 0.5% 0.0%
NO 43.0% 6.5% 17.2% 1.6% 17.2% 0 8.6% 5.4% 0.5% 0.0%
DEN 43.0% 10.5% 19.5% 1.0% 16.0% 0 6.5% 3.0% 0.0% 0.5%
SF 40.7% 13.9% 8.8% 2.6% 17.5% 0 7.7% 7.2% 1.5% 0.0%
PHI 40.3% 13.8% 12.2% 1.7% 20.4% 0 5.5% 5.0% 1.1% 0.0%
CAR 40.2% 6.7% 12.9% 4.6% 21.6% 0 7.2% 5.2% 1.5% 0.0%
BAL 38.8% 13.7% 17.5% 3.8% 18.0% 0 3.8% 4.4% 0.0% 0.0%
NYJ 38.7% 8.2% 17.5% 5.2% 15.5% 2 5.7% 7.7% 0.5% 1.0%
PIT 38.2% 12.4% 16.9% 2.2% 20.8% 0 5.1% 3.9% 0.6% 0.0%
IND 37.9% 9.1% 13.1% 1.0% 22.7% 0 8.6% 6.1% 1.5% 0.0%
HOU 37.2% 11.2% 15.3% 2.0% 20.9% 1 8.2% 4.1% 0.5% 0.5%
KC 37.1% 9.7% 12.4% 0.5% 23.7% 0 7.5% 7.5% 1.1% 0.5%
ARI 37.0% 7.8% 15.1% 1.0% 26.0% 0 6.8% 3.6% 2.6% 0.0%
NE 36.8% 10.4% 12.4% 2.6% 18.7% 3 7.3% 8.3% 3.6% 0.0%
NYG 36.8% 7.3% 20.2% 3.1% 20.7% 0 7.3% 3.6% 1.0% 0.0%
BUF 36.8% 14.1% 13.0% 4.3% 17.8% 0 4.9% 7.6% 0.5% 1.1%
DAL 36.4% 14.4% 16.3% 2.9% 16.3% 0 6.7% 5.7% 1.4% 0.0%
CIN 35.6% 12.8% 18.6% 3.2% 17.0% 0 4.8% 6.9% 1.1% 0.0%
MIA 35.3% 7.0% 14.4% 1.0% 22.9% 1 9.0% 8.0% 2.0% 0.5%
LAC 34.3% 11.9% 14.4% 3.5% 22.9% 0 8.5% 3.5% 0.5% 0.5%
CLE 34.2% 8.6% 16.0% 2.7% 22.5% 0 9.1% 5.3% 1.6% 0.0%
LAR 33.9% 11.5% 20.7% 2.3% 20.7% 0 6.9% 3.4% 0.6% 0.0%
GB 33.9% 12.4% 15.3% 4.0% 22.6% 0 6.2% 4.0% 1.1% 0.6%
JAX 33.8% 11.8% 14.9% 1.0% 21.5% 0 6.2% 8.7% 2.1% 0.0%
SEA 31.8% 11.1% 15.2% 2.5% 24.2% 0 7.6% 6.6% 1.0% 0.0%
ATL 31.8% 9.4% 18.8% 2.9% 24.1% 0 5.9% 4.7% 1.8% 0.6%
DET 31.5% 11.2% 14.0% 2.8% 27.0% 0 6.7% 5.6% 0.6% 0.6%
LV 31.1% 5.6% 15.6% 6.7% 25.0% 0 9.4% 5.0% 1.7% 0.0%
MIN 30.8% 10.9% 19.4% 3.0% 22.4% 1 5.5% 7.5% 0.5% 0.0%
CHI 28.8% 11.9% 11.9% 4.5% 29.9% 1 8.5% 4.5% 0.0% 0.0%
slideshow - use arrows to browse graphs

Penalties

   Committed penalties were tallied for Teams' Offense, Defense, and Special Teams. Total EPA sacrificed to penalties was calculated for each phase, and total penalty yards were added for Offense and Defense. The table is currently sorted by Def_penalty_EPA_given, which is how much EPA a team's defense gave to their opponents on offense. Low numbers show more disciplined / lucky / sneaky teams.
*I did not clean this data thoroughly, declined penalties and other discrepancies likely contribute to penalty metrics.

2022 Team Penalties good OK bad

  Team Off_penalty Off_penalty_EPA_lost Off_penalty_yd_lost Def_penalty Def_penalty_EPA_given Def_penalty_yd_given ST_penalty ST_penalty_EPA
ATL 47 42.0 396 21 26.1 198 9 8.1
MIN 54 39.0 369 37 31.3 327 11 6.7
LAR 42 36.2 313 34 32.8 342 11 7.0
JAX 51 40.6 356 42 33.9 348 9 3.8
PIT 55 41.4 384 39 35.7 327 13 14.8
LAC 53 40.1 377 35 38.0 300 5 3.7
ARI 74 66.8 527 43 38.6 374 14 11.7
CIN 50 45.0 370 38 39.6 295 14 9.2
HOU 41 32.2 299 47 39.7 345 7 5.0
SEA 72 64.8 560 39 40.0 324 7 3.4
CHI 49 38.7 361 31 40.0 393 3 2.6
TB 64 52.6 470 38 43.0 357 1 1.3
GB 48 38.3 381 41 43.3 366 13 9.9
BUF 45 44.3 347 49 43.5 375 5 2.2
LV 72 59.6 569 41 43.9 363 13 6.1
PHI 56 43.1 387 34 44.2 295 5 4.1
BAL 47 34.6 342 39 47.9 372 5 2.5
NYJ 60 51.5 469 31 48.2 397 8 8.1
NE 62 59.9 457 42 48.9 384 8 5.2
DET 47 33.1 394 43 49.7 379 3 2.3
IND 49 37.9 355 40 50.6 439 5 3.2
NYG 64 52.9 455 47 51.4 417 9 7.4
CAR 60 39.7 427 44 51.9 435 15 9.7
TEN 67 50.9 513 44 52.1 443 11 7.2
WAS 52 41.2 363 40 54.0 442 12 12.0
KC 42 33.1 349 45 55.1 489 13 10.2
SF 56 47.3 398 41 57.6 360 8 10.8
DAL 53 43.9 410 53 57.7 447 13 11.3
MIA 63 49.1 424 55 58.5 491 7 4.3
DEN 55 43.0 408 58 62.4 562 8 5.2
CLE 57 47.3 419 45 62.5 446 6 6.4
NO 52 41.8 350 47 65.4 491 8 5.6
slideshow - use arrows to browse graphs

Oilers/Titans Careers by AV

Pro-Football-Reference Oilers/Titans career AV table
Titans Era Group: Player careers starting with or ending after the move from Houston to Tennessee.
Players removed with less than 6 games played
Top 500 Group: Top 500 entries on table (total AV) - analysis not presented, see graphs
Graphs only show players starting during or ending after the '90s.

   Pro-Football-Reference provides an "Approximate Value" metric. It is a measure of seasonal value for any player at any position, and assigned each year. They conveniently provide tables of top Approximate Value (AV) earners for each franchise. I used the Oilers/Titans table, and considered AV normalized by games played.

Processing Details:

I ordered the data by AV per Game Played (AV/G) for each position grouping, and also plotted AV against Games. The top 5 players for AV, Games, and AV/G were labelled on the graphs.

The graphs highlight Titans legends, players with short stints of greatness, and long-time role players. Players that are above the TAN regression line have relatively high AV/G, which is also shown in their marker's shading, darker is higher AV/G. Marker size was also scaled with Games played, though not as distinctly.

Position Groups:

Position Grouping: OL

  Overall Rank Player PFR position From To Games AV AV per game
1 Bruce Matthews LG 1983 2001 296 215 726
90 Mark Stepnoski C 1995 1998 61 37 607
107 Jack Conklin OT 2016 2019 57 34 596
144 Rodger Saffold LT 2019 2021 46 27 587
112 Kevin Mawae C 2006 2009 61 34 557
16 Michael Roos LT 2005 2014 148 77 520
44 Taylor Lewan OT 2014 2022 105 54 514
72 Fred Miller RT 2000 2004 80 41 512
651 Robert Turner G 2013 2013 6 3 500
7 Brad Hopkins LT 1993 2005 194 96 495
37 David Stewart RT 2006 2013 116 57 491
137 Jon Runyan RT 1996 1999 58 28 483
186 Josh Kline OL 2016 2018 46 22 478
340 Gennaro DiNapoli C 2001 2002 21 10 476
179 Quinton Spain OG 2015 2018 50 23 460
178 Justin Hartwig C 2002 2005 50 23 460
133 Jake Scott RG 2008 2011 64 29 453
24 Benji Olson RG 1998 2007 152 67 441
55 Ben Jones C 2016 2022 107 47 439
395 Byron Bell RT 2015 2015 16 7 438
197 Chance Warmack OG 2013 2016 48 21 438
278 Andy Levitre LG 2013 2014 32 14 438
484 Steve Hutchinson LG 2012 2012 12 5 417
291 David Quessenberry OT 2019 2021 33 13 394
192 Jacob Bell LG 2004 2007 55 21 382
615 Joe Looney G 2015 2015 8 3 375
617 Deuce Lutui RG 2012 2012 8 3 375
198 Nate Davis OL 2019 2022 54 20 370
554 Michael Oher RT 2014 2014 11 4 364
503 Ty Sambrailo OT 2020 2021 14 5 357
124 Zach Piller G 1999 2006 87 31 356
536 Andy Gallik C 2015 2015 12 4 333
185 Dennis Kelly OT 2016 2020 74 22 297
103 Eugene Amano C 2004 2011 124 34 274
634 Jeremiah Poutasi OT 2015 2015 11 3 273
231 Kevin Long C 1998 2001 63 17 270
200 Leroy Harris C 2007 2012 75 20 267
268 Brian Schwenke C 2013 2017 57 15 263
334 Fernando Velasco G 2009 2012 49 11 224
568 Byron Stingily T 2012 2014 20 4 200
625 Jamon Meredith T 2014 2015 16 3 188
423 Scott Sanderson T 1997 2000 38 7 184
326 Jason Layman G 1996 1999 61 11 180
621 Kevin Matthews C 2010 2012 17 3 176
891 Tyler Marz T 2018 2018 6 1 167
506 Chris Spencer C 2013 2014 32 5 156
514 Tom Ackerman G 2002 2003 27 4 148
530 Jamil Douglas OG 2019 2020 29 4 138
228 Ken Amato C 2003 2011 125 17 136
469 Aaron Brewer C 2020 2022 37 5 135
386 Michael Otto T 2008 2013 62 8 129
328 Jason Mathews T 1998 2004 86 11 128
694 Aaron Graham C 2002 2002 16 2 125
307 Erik Norgard G 1990 1998 108 12 111
826 Jon Dorenbos C 2005 2006 10 1 100
883 Kendall Lamm T 2021 2021 12 1 83
616 Daniel Loper T 2006 2008 40 3 75
614 Corey Levin OL 2018 2022 41 3 73
922 Dillon Radunz OL 2021 2022 22 1 45
    Notes:
  • Bruce Matthews had an awesome career
  • Taylor Lewan not labelled, he is right beside David Stewart
  • Lewan and Ben Jones are only current Titans with over 100 games played
  • Titans enjoyed some long, productive OL careers early on

Position Grouping: QB

  Overall Rank Player PFR position From To Games AV AV per game
4 Steve McNair QB 1995 2005 139 114 820
354 Ryan Fitzpatrick QB 2013 2013 11 9 818
53 Marcus Mariota QB 2015 2019 63 48 762
78 Ryan Tannehill QB 2019 2022 56 40 714
118 Vince Young QB 2006 2010 54 33 611
251 Jake Locker QB 2011 2014 30 16 533
300 Matt Hasselbeck QB 2011 2012 24 12 500
578 Charlie Whitehurst QB 2014 2015 8 4 500
335 Billy Volek QB 2001 2005 24 11 458
208 Kerry Collins QB 2006 2010 43 19 442
346 Neil O'Donnell QB 1999 2003 25 10 400
689 Blaine Gabbert QB 2018 2018 8 2 250
804 Matt Cassel QB 2016 2017 6 1 167
881 Dave Krieg QB 1997 1998 13 1 77
900 Zach Mettenberger QB 2014 2015 14 1 71
    Notes:
  • RIP Steve McNair
  • Interesting that Tannehill and Mariota show similar rate to McNair (and Moon).
  • Titans legend, Ryan Fitzpatrick!

Position Grouping: RB

  Overall Rank Player PFR position From To Games AV AV per game
23 Chris Johnson RB 2008 2013 95 68 716
10 Eddie George RB 1996 2003 128 87 680
210 DeMarco Murray RB 2016 2017 31 19 613
45 Derrick Henry RB 2016 2022 99 53 535
301 Travis Henry RB 2005 2006 24 12 500
165 Chris Brown RB 2003 2007 54 24 444
438 D'Onta Foreman RB 2020 2021 15 6 400
505 Antowain Smith RB 2004 2004 13 5 385
360 Dion Lewis RB 2018 2019 32 9 281
272 LenDale White RB 2006 2009 58 15 259
705 Skip Hicks RB 2001 2001 9 2 222
454 Bishop Sankey RB 2014 2015 29 6 207
237 Rodney Thomas RB 1995 2000 91 17 187
413 Robert Holcombe RB 2002 2004 39 7 179
832 Darrynton Evans RB 2020 2021 6 1 167
496 Jeremy McNichols RB 2020 2021 30 5 167
540 Shonn Greene RB 2013 2014 24 4 167
763 John Simon RB 2002 2002 12 2 167
603 Dontrell Hilliard RB 2021 2022 20 3 150
807 David Cobb RB 2015 2015 7 1 143
501 Javon Ringer RB 2009 2012 37 5 135
517 Antonio Andrews RB 2014 2016 34 4 118
595 Troy Fleming RB 2004 2005 29 3 103
857 Chris Henry RB 2007 2009 10 1 100
809 Greg Comella FB 2002 2002 12 1 83
916 Jarrett Payton RB 2005 2005 13 1 77
695 Mike Green RB 2000 2002 32 2 62
482 Ahmard Hall FB 2006 2011 85 5 59
841 Quinton Ganther RB 2006 2008 17 1 59
959 Leon Washington RB 2013 2014 21 1 48
851 Jamie Harper RB 2011 2012 23 1 43
794 Jackie Battle RB 2013 2014 32 1 31
    Notes:
  • Titans have had some elite RBs
  • CJ2K and Eddie George are legends
  • Henry has lower AV per game due to low usage in his early years
  • DeMarco Murray was most of the Titans offense for a time

Position Grouping: DE

  Overall Rank Player PFR position From To Games AV AV per game
33 Jevon Kearse LDE 1999 2009 88 60 682
338 Jason Babin LB 2010 2010 16 10 625
11 Jurrell Casey DE 2011 2019 139 86 619
74 Kyle Vanden Bosch RDE 2005 2009 74 41 554
123 Brian Orakpo LLB 2015 2018 61 31 508
119 Kevin Carter LDE 2001 2004 64 32 500
127 Harold Landry OLB 2018 2021 64 30 469
130 Tony Brown DE 2006 2010 69 29 420
57 Derrick Morgan DE 2010 2018 118 47 398
183 Kenny Holmes RDE 1997 2000 58 22 379
587 Jadeveon Clowney DE 2020 2020 8 3 375
217 Zach Brown OLB 2012 2015 49 18 367
246 Carlos Hall RDE 2002 2004 45 16 356
219 Anthony Cook LDE 1995 1998 51 18 353
332 Ropati Pitoitua DE 2013 2015 33 11 333
232 Antwan Odom RDE 2004 2007 52 17 327
249 Jason Jones DE 2008 2011 49 16 327
309 James Roberson RDE 1996 1998 40 12 300
257 Dave Ball DE 2008 2011 51 15 294
597 Reggie Gilbert LB 2019 2019 11 3 273
234 Robaire Smith RDE 2000 2006 64 17 266
383 Pratt Lyons DE 1997 1998 32 8 250
525 Jack Crawford DE 2020 2020 16 4 250
411 Jovan Haye DE 2009 2010 29 7 241
427 Denico Autry DE 2021 2022 27 6 222
304 Travis LaBoy DE 2004 2007 54 12 222
400 Kamalei Correa OLB 2018 2020 32 7 219
341 William Hayes DE 2008 2011 48 10 208
720 Mike Jones DE 1999 1999 11 2 182
467 David Bass DE 2015 2016 29 5 172
533 Sharif Finch LB 2018 2021 24 4 167
827 Marques Douglas DE 2010 2010 6 1 167
786 Kevin Aldridge DE 2002 2002 6 1 167
761 Bo Schobel DE 2004 2005 13 2 154
592 Bud Dupree DE 2021 2022 20 3 150
679 Keith Embray DE 2000 2000 14 2 143
879 David King DE 2017 2017 7 1 143
830 Lavar Edwards DE 2013 2013 7 1 143
969 Jarius Wynn DE 2012 2012 7 1 143
266 Karl Klug DL 2011 2017 109 15 138
777 Erik Walden ROLB 2017 2017 16 2 125
696 Quentin Groves DE 2014 2014 16 2 125
756 Derick Roberson DE 2019 2021 16 2 125
837 Bryce Fisher DE 2007 2007 9 1 111
676 Kevin Dodd DE 2016 2017 18 2 111
954 Cameron Wake LB 2019 2019 9 1 111
556 Juqua Parker DE 2001 2004 41 4 98
811 Sean Conover DE 2006 2007 11 1 91
920 Shaun Phillips LOLB 2014 2014 11 1 91
839 Mike Frederick DE 1999 1999 13 1 77
942 Scott Solomon DE 2012 2012 13 1 77
823 Matt Dickerson DE 2018 2020 18 1 56
784 Olasunkanmi Adeniyi DE 2021 2022 19 1 53
943 Justin Staples DL 2014 2016 21 1 48
    Notes:
  • Exterior DL / EDGE, with a number of names moved from LB
  • Jurrell Casey was so good for so long! If only Titans were better for more of those years
  • Harold Landry narrowly missed labelling, he is by Carter/Orakpo

Position Grouping: LB

  Overall Rank Player PFR position From To Games AV AV per game
132 Randall Godfrey MLB 2000 2002 38 29 763
502 Barrett Ruud MLB 2011 2011 9 5 556
12 Keith Bulluck RLB 2000 2009 157 85 541
437 Moise Fokou MLB 2013 2013 12 6 500
31 Eddie Robinson LLB 1992 2001 127 62 488
141 Rashaan Evans ILB 2018 2021 59 27 458
154 David Thornton LLB 2006 2009 58 26 448
135 Avery Williamson ILB 2014 2021 65 29 446
289 Lonnie Marts LLB 1997 1998 30 13 433
86 Wesley Woodyard MLB 2014 2019 93 38 409
206 Akeem Ayers MLB 2011 2014 50 19 380
157 Jayon Brown ILB 2017 2021 66 25 379
239 Will Witherspoon RLB 2010 2012 46 17 370
60 Joe Bowden LLB 1992 1999 123 44 358
245 Greg Favors LB 1999 2001 47 16 340
145 Peter Sirmon LLB 2000 2006 81 27 333
160 Stephen Tulloch MLB 2006 2010 80 25 312
329 Colin McCarthy MLB 2011 2013 36 11 306
355 Ryan Fowler MLB 2007 2008 30 9 300
373 Rocky Calmus MLB 2002 2004 27 8 296
149 Barron Wortham MLB 1994 1999 92 27 293
250 Brad Kassell MLB 2002 2005 56 16 286
566 Sean Spence LILB 2016 2016 15 4 267
313 Kamerion Wimbley ROLB 2012 2014 45 12 267
673 Zach Cunningham OLB 2021 2022 9 2 222
361 David Long LB 2019 2022 50 9 180
419 Gerald McRath LLB 2009 2011 40 7 175
403 Jacob Ford LB 2008 2010 43 7 163
369 Rocky Boiman ROLB 2002 2005 54 8 148
781 LeVar Woods MLB 2006 2007 15 2 133
824 Zach Diles MLB 2012 2013 8 1 125
770 Josh Stamer LB 2008 2008 16 2 125
680 Justin Ena LB 2004 2004 16 2 125
671 Doug Colman MLB 1999 1999 16 2 125
966 Jamie Winborn RILB 2009 2010 8 1 125
598 Zaviar Gooden OLB 2013 2014 24 3 125
588 William Compton MLB 2018 2020 24 3 125
473 Frank Chamberlin MLB 2000 2002 43 5 116
933 Rich Scanlon LB 2007 2007 9 1 111
815 Rennie Curran LB 2010 2010 9 1 111
803 Jeremy Cain LB 2007 2007 9 1 111
754 Monty Rice LB 2021 2022 19 2 105
504 Tim Shaw MLB 2010 2012 48 5 104
416 Terry Killens LOLB 1996 2000 78 7 90
767 Cody Spencer LB 2004 2005 23 2 87
607 Lenoy Jones MLB 1996 1998 36 3 83
746 Nate Palmer ROLB 2016 2017 24 2 83
723 Stanford Keglar LB 2008 2009 25 2 80
842 Gilbert Gardner LB 2007 2007 13 1 77
956 Aaron Wallace OLB 2016 2018 13 1 77
769 Dennis Stallings LB 1997 1998 28 2 71
519 Patrick Bailey LB 2010 2013 56 4 71
520 Daren Bates LB 2017 2020 58 4 69
660 Colin Allred LB 2007 2010 32 2 62
798 Colby Bockwoldt RLB 2006 2006 16 1 62
868 Steven Johnson MLB 2015 2015 16 1 62
962 Ray Wells LB 2003 2003 16 1 62
753 Robert Reynolds LB 2004 2006 33 2 61
678 Nick Dzubnar LB 2020 2021 33 2 61
871 Joseph Jones OLB 2021 2022 20 1 50
808 Dylan Cole LB 2021 2022 22 1 45
    Notes:
  • Mr.Monday Night!
  • Godfrey was really good in the early NFL 2K games
  • Jayon Brown is to right of Evans/Thornton, within shaeded area
  • Not a lot of recent LBs with long careers here

Position Grouping: S

  Overall Rank Player PFR position From To Games AV AV per game
29 Blaine Bishop SS 1993 2001 126 62 492
27 Marcus Robertson FS 1991 2000 135 63 467
47 Kevin Byard S 2016 2022 110 50 455
26 Michael Griffin FS 2007 2015 141 63 447
98 Chris Hope SS 2006 2011 85 35 412
528 Johnathan Cyprien SS 2017 2017 10 4 400
280 Lance Schulters DB 2002 2004 35 14 400
255 Kenny Vaccaro FS 2018 2020 42 16 381
388 Bernard Pollard SS 2013 2014 21 8 381
205 Tank Williams SS 2002 2005 57 20 351
315 Jordan Babineaux DB 2011 2012 32 11 344
293 Da'Norris Searcy DB 2015 2017 45 13 289
238 Lamont Thompson DB 2003 2006 64 17 266
726 Desmond King FS 2020 2020 9 2 222
606 Rashad Johnson DB 2016 2016 14 3 214
447 Calvin Lowry DB 2006 2007 32 6 188
184 Steve Jackson DB 1991 1999 118 22 186
629 Bobby Myers DB 2000 2001 17 3 176
730 Kendrick Lewis FS 2018 2018 13 2 154
670 Rich Coady DB 2002 2002 14 2 143
751 Daryl Porter DB 2001 2001 14 2 143
387 Perry Phenix DB 1998 2001 59 8 136
414 Amani Hooker S 2019 2022 52 7 135
717 Robert Johnson S 2011 2012 15 2 133
572 Brynden Trawick S 2017 2018 32 4 125
778 Joe Walker DB 2001 2001 16 2 125
356 Vincent Fuller DB 2005 2010 75 9 120
425 Daimion Stafford FS 2013 2016 62 7 113
498 Aric Morris DB 2000 2002 47 5 106
605 Marqueston Huff FS 2014 2015 30 3 100
623 Scott McGarrahan DB 2003 2004 32 3 94
475 Anthony Dorsett DB 1996 1999 56 5 89
711 Chris Jackson S 2020 2022 24 2 83
785 Al Afalava DB 2012 2012 12 1 83
873 Kevin Kaesviharn DB 2009 2009 13 1 77
934 Nick Schommer DB 2010 2010 13 1 77
940 Anthony Smith DB 2011 2011 13 1 77
362 Donnie Nickey DB 2003 2010 123 9 73
834 Matthias Farley DB 2021 2021 17 1 59
895 George McCullough DB 1997 2000 25 1 40
    Notes:
  • Kevin Byard is on his way to the top
  • Blaine Bishop was good
  • Titan's trajectory over Michael Griffin's career was sad
  • Some valuable short time contributors, and some long time legends

Position Grouping: WR

  Overall Rank Player PFR position From To Games AV AV per game
122 A.J. Brown WR 2019 2021 43 31 721
30 Derrick Mason WR 1997 2004 122 62 508
270 Yancey Thigpen WR 1998 2000 31 15 484
150 Corey Davis WR 2017 2020 56 26 464
267 Rishard Matthews WR 2016 2018 33 15 455
87 Drew Bennett WR 2001 2006 87 37 425
548 Julio Jones WR 2021 2021 10 4 400
176 Kevin Dyson WR 1998 2002 58 23 397
85 Nate Washington WR 2009 2014 96 38 396
156 Kendall Wright WR 2012 2016 66 26 394
199 Justin Gage WR 2007 2010 51 20 392
181 Kenny Britt WR 2009 2013 57 22 386
222 Justin McCareins WR 2001 2008 50 18 360
243 Willie Davis WR 1996 1998 45 16 356
121 Chris Sanders WR 1995 2001 97 32 330
474 Eric Decker WR 2017 2017 16 5 312
716 Marcus Johnson WR 2021 2021 7 2 286
483 Adam Humphries WR 2019 2020 19 5 263
539 Dorial Green-Beckham WR 2015 2015 16 4 250
303 Brandon Jones WR 2005 2008 51 12 235
333 Tajae Sharpe WR 2016 2019 47 11 234
380 Justin Hunter WR 2013 2015 35 8 229
367 Roydell Williams WR 2005 2007 40 9 225
574 Bobby Wade WR 2005 2006 18 4 222
522 Isaac Byrd WR 1997 1999 18 4 222
750 Carl Pickens WR 2000 2000 9 2 222
457 Taywan Taylor WR 2017 2018 29 6 207
336 Damian Williams WR 2010 2013 54 11 204
494 Dexter McCluster WR 2014 2015 25 5 200
628 Eric Moulds WR 2007 2007 16 3 188
642 Chester Rogers WR 2021 2021 16 3 188
460 George Wilson WR 2013 2014 32 6 188
476 Harry Douglas WR 2015 2017 27 5 185
471 Tyrone Calico WR 2003 2005 27 5 185
410 Lavelle Hawkins WR 2008 2012 52 7 135
636 Kalif Raymond WR 2019 2020 23 3 130
638 Darius Reynaud WR 2012 2013 23 3 130
907 Randy Moss WR 2010 2010 8 1 125
700 Derek Hagan WR 2014 2014 16 2 125
865 Andre Johnson WR 2016 2016 8 1 125
641 Courtney Roby WR 2005 2006 25 3 120
511 Nick Westbrook-Ikhine WR 2020 2022 43 5 116
714 Darius Jennings WR 2018 2019 24 2 83
662 Cameron Batson WR 2018 2021 27 2 74
724 Joey Kent WR 1997 1999 30 2 67
619 Marc Mariani WR 2010 2016 48 3 62
581 Eddie Berlin WR 2001 2004 57 3 53
    Notes:
  • AJ Brown in 2022 and beyond would be nice. His production rate is extreme relative to Titans WRs
  • Titans are not and have not been a WR team
  • Nate Washington most recent long-time contributor

Position Grouping: DT

  Overall Rank Player PFR position From To Games AV AV per game
42 Albert Haynesworth RDT 2002 2008 90 54 600
167 Jason Fisk RDT 1999 2001 47 24 511
159 Jeffery Simmons DL 2019 2022 53 25 472
148 Gary Walker LDT 1995 1998 62 27 435
69 DaQuan Jones DT 2014 2020 99 43 434
190 John Thornton LDT 1999 2002 51 22 431
285 Sammie Lee Hill DT 2013 2015 38 13 342
61 Henry Ford RDT 1994 2002 129 44 341
512 Sylvester Williams DT 2017 2017 15 5 333
235 Randy Starks RDT 2004 2007 60 17 283
565 Shaun Smith DT 2011 2011 15 4 267
288 Sen'Derrick Marks DT 2009 2012 51 13 255
220 Josh Evans DT 1995 2001 71 18 254
545 Antonio Johnson DT 2013 2013 16 4 250
748 Kyle Peko DT 2021 2021 8 2 250
350 Al Woods DT 2014 2016 42 10 238
580 James Atkins DT 2003 2003 13 3 231
344 Austin Johnson DT 2016 2019 58 10 172
508 Teair Tart DT 2020 2022 31 5 161
553 Larrell Murchison DL 2020 2022 26 4 154
608 Naquan Jones NT 2021 2022 21 3 143
449 Mike Martin DT 2012 2015 46 6 130
490 Rien Long DT 2003 2005 39 5 128
652 Kevin Vickerson DT 2007 2009 24 3 125
453 Joe Salave'a DT 1998 2001 49 6 122
732 Isaiah Mack DT 2019 2020 19 2 105
586 Jared Clauss DT 2004 2005 29 3 103
889 Jesse Mahelona DT 2006 2006 10 1 100
877 Darius Kilgo DT 2018 2018 11 1 91
663 Angelo Blackson DT 2015 2016 29 2 69
886 Bennie Logan DT 2018 2018 15 1 67
935 Malcolm Sheppard DT 2010 2011 16 1 62
    Notes:
  • don't remember Henry Ford well
  • Jeffery Simmons will be a legend in time, to no suprise
  • DaQuan Jones spent a longer time here than I thought

Position Grouping: CB

  Overall Rank Player PFR position From To Games AV AV per game
46 Samari Rolle RCB 1998 2004 101 52 515
54 Cortland Finnegan RCB 2006 2011 93 47 505
277 Adam Jones DB 2005 2006 30 14 467
147 Alterraun Verner LCB 2010 2013 64 27 422
56 Darryll Lewis RCB 1991 1998 113 47 416
161 Denard Walker LCB 1997 2000 61 25 410
224 Logan Ryan CB 2017 2019 45 18 400
264 Nick Harper LCB 2007 2009 38 15 395
353 Perrish Cox RCB 2015 2016 24 9 375
175 Andre Dyson LCB 2001 2004 62 23 371
259 Malcolm Butler CB 2018 2020 41 15 366
247 Adoree' Jackson CB 2017 2020 46 16 348
92 Jason McCourty DB 2009 2016 108 36 333
708 Roderick Hood DB 2009 2009 6 2 333
722 Johnathan Joseph LCB 2020 2020 7 2 286
544 Jackrabbit Jenkins CB 2021 2021 14 4 286
324 Reynaldo Hill LCB 2005 2008 48 11 229
294 Coty Sensabaugh CB 2012 2015 59 13 220
462 Blidi Wreh-Wilson CB 2013 2015 34 6 176
584 Breon Borders CB 2020 2021 17 3 176
479 Kristian Fulton CB 2020 2022 29 5 172
938 Buster Skrine DB 2021 2021 6 1 167
627 Elijah Molden CB 2021 2022 18 3 167
844 Brandon Ghee CB 2014 2014 6 1 167
421 Donald Mitchell DB 1999 2002 44 7 159
493 Brice McCain DB 2016 2017 32 5 156
461 Andre Woolfolk DB 2003 2006 39 6 154
392 LeShaun Sims CB 2016 2019 56 8 143
713 DeRon Jenkins RCB 2001 2001 15 2 133
455 Dainon Sidney DB 1998 2002 48 6 125
573 Michael Waddell DB 2004 2005 32 4 125
887 Greg Mabin CB 2021 2022 8 1 125
668 Chris Carr DB 2008 2008 16 2 125
854 Chris Hawkins CB 2011 2011 9 1 111
961 B.W. Webb CB 2015 2015 9 1 111
552 Ryan Mouton DB 2009 2012 36 4 111
523 Tommie Campbell DB 2011 2013 37 4 108
468 Tony Beckham DB 2002 2005 50 5 100
583 Michael Booker DB 2000 2001 31 3 97
852 Brandon Harris CB 2014 2014 11 1 91
930 Curtis Riley CB 2016 2017 11 1 91
526 Dane Cruikshank CB 2018 2021 44 4 91
646 Tye Smith CB 2017 2020 36 3 83
610 Eric King DB 2006 2008 36 3 83
691 Rich Gardner DB 2004 2005 28 2 71
929 Cody Riggs CB 2015 2016 14 1 71
796 Antwon Blake CB 2016 2016 16 1 62
903 Christopher Milton CB 2019 2020 20 1 50
874 Joshua Kalu CB 2018 2022 40 1 25
    Notes:
  • Samari Rolle was good!
  • did Pacman's returns contribute to his AV?
  • similar to LB, not long careers of recent; Jason McCourty left after 2016

Position Grouping: TE

  Overall Rank Player PFR position From To Games AV AV per game
102 Delanie Walker TE 2013 2019 84 35 417
48 Frank Wycheck TE 1995 2003 137 50 365
262 Jared Cook TE 2009 2012 59 15 254
409 Jackie Harris TE 1998 1999 28 7 250
188 Bo Scaife TE 2005 2010 90 22 244
230 Erron Kinney TE 2000 2005 83 17 205
311 Jonnu Smith TE 2017 2020 60 12 200
321 Anthony Firkser TE 2018 2021 58 11 190
393 Ben Troupe TE 2004 2007 55 8 145
527 Alge Crumpler TE 2008 2009 31 4 129
593 Anthony Fasano TE 2015 2016 32 3 94
703 Ben Hartsock TE 2006 2007 22 2 91
648 Geoff Swaim TE 2020 2022 39 3 77
558 MyCole Pruitt TE 2018 2021 58 4 69
560 Michael Roan TE 1995 2000 62 4 65
624 Shad Meier TE 2001 2004 52 3 58
945 Luke Stocker TE 2017 2018 19 1 53
507 Craig Stevens TE 2008 2015 109 5 46
947 Phillip Supernaw TE 2015 2017 47 1 21
    Notes:
  • Walker and Wycheck are legends
  • Jonnu Smith, Anthony Firkser just under Jared Cook. Smith's production suffered an increased blocking role with poor Tackle play.
  • Titans have had a few TEs with steady production over a lot of years

Position Grouping: ST

  Overall Rank Player PFR position From To Games AV AV per game
75 Al Del Greco K 1991 2000 151 40 265
105 Rob Bironas K 2005 2013 144 34 236
394 Gary Anderson K 2003 2004 30 7 233
236 Ryan Succop K 2014 2019 86 17 198
451 Joe Nedney K 2001 2003 33 6 182
126 Brett Kern P 2009 2021 197 30 152
151 Craig Hentrich P 1998 2009 177 26 147
693 Stephen Gostkowski K 2020 2020 15 2 133
585 Randy Bullock K 2021 2022 27 3 111
370 Beau Brinkley LS 2012 2020 135 8 59
813 Morgan Cox LS 2021 2022 30 1 33

Punter Analysis

Introduction - Analyzing Punt Data 2022 season data updated as of Mon Jan 9 15:24:21 2023

The table shows all punts by Brett Kern and Ryan Stonehouse as Titans against all other punts from the 2009 - 2022 seasons.

Some punt metrics are shown in the table, and are discussed below. All punt data was combined for the introduction section. Focused analyses follow in the next sections.

The last two rows are metrics provided by Puntalytics; these and some of their other metrics are discussed at the end of this section. These "advanced" metrics will be denoted with a *.

  B.Kern R.Stonehouse Rest of NFL
Punts 962 90 32,731
Blocked 5 0 179
Touchback 5.4% 10.0% 7.1%
Inside Twenty 40.5% 33.3% 35.2%
Fair Catch 24.9% 12.2% 25.6%
Out Of Bounds 14.3% 1.1% 9.7%
Returned 44.8% 65.6% 56.0%
EPA/play 0.007 0.055 -0.071
punt EPA* 0.084 0.207 0.013
Adj. Net* 102.7 106.3 100.4

Adj. Net* is a unitless measure (does not correspond to yards). It ranges from 0-160, with some outliers. Most punts have an Adj. Net* of 50-150
2022 Puntalytics data updated as of 12/30/23

To start, I decided to visualize various punter metrics against, "Yards-to-go", which I call distance from the line-of-scrimmage to the opponent's endzone, typically at least 40 yards (~57 yard field goal) for punts. This normalizes the distance metrics, gross and net yards, as a punter can only punt as far as the space they have. The graphs below show how punt distance and punt attempts vary with Yards to go.

Average gross and net yards for a punt are illustrated by the shading, and the smaller bars | | indicate the uncertainty for the interval. Each metric initially increases with yards to go (YTG), then settles at a steady value. This indicates an average distance limitation for punters. In the flat region, there is an area between gross and net yards. It represents the average punt yards lost to returns and other factors, like touchbacks. In the sloping region, at low YTG, punts were more likely to net their distance, as returns (especially longer ones) are less likely.

The second chart shows how punt attempts varied with yards to go. Punts were grouped into small YTG bins, the bin counts are shown by the bars. The solid line approximates this distribution, and is used in later sections. It is helpful to remember if a punt is likely from a certain YTG when considering if data in the charts are meaningful. The first chart shows highest uncertainty at over 95 YTG, where very few punts were attempted. Note that punt distance wasn't presented for less than 40 YTG.

Punt Gross, Net Yards vs Yards to go
Punt Attempts vs Yards to go
Punts going Out Of Bounds (OOB) vs Yards to go

A number of punt metrics are binary stats, whose rates and meanings may change with YTG. These metrics either happened 1 or didn't happen 0. Note the scale of the y-axis in the following charts.

The first example shows Out Of Bounds (OOB) rate. It appears to have little in relation with YTG, and high variation across the YTG range. Within error, it seems to stay within its 10% overall average.

Touchbacks may be undesireable from close (just 30 net yds from the 50), but impressive and very difficult from distance. It seems most punters wouldn't want a touchback in the region where their rate is above overall average (40-60 YTG), and wouldn't mind as much where they are below (>60 YTG).

Downed punt rate also decays with YTG, and may follow similar reasoning as touchbacks. Unless a punt is very short relative to YTG, it being downed implies a more favorable outcome than a touchback.

Touchback, Downed Rates vs Yards to go

Fair catches are most common, and well above overall average for short punts, when a punter is focusing more on pinning the opponent deep instead of maximizing distance.

An "Inside Twenty" metric provides a rough indication of pinning opponents. Landing an opponent inside their twenty is more generally a good thing than some of the other binary metrics, and more impressive from greater distance.

Fair Catch, Inside Opponent's 20 Rates vs Yards to go

The charts above show how the binary punt metrics are distributed with YTG. These data will be presented more simplistically in Part I, by fitting linear regressions to quickly compare punting tendencies. The bar charts show that linear relationships may not describe all the metrics with YTG. The following charts show a logistic regression modelled for the binary metrics, and more clearly show if a linear fit is appropriate for a metric.

Similarly, recall punt distance was not linear over the range of common YTG. Second and Third order polynomial fits were compared to First order (linear). Either of the higher orders are better than fitting a straight line, and are used in place of linear fits when comparing punter distances against YTG.

EPA vs Yards to Go

EPA per punting play may not be very indicative of punter performance, as punters don't chose when to punt. Punts from short distance will never be considered a success, EPA > 0. And punts are generally not deemed favorably by a metric which is about scoring points, not about effectively giving the ball back to opponents. I expect a punter's EPA/play for a given team might be most influenced by their coach's 4th down decision making and surrounding factors, such as kicker/offense ability, game situation, etc. . .

Thankfully people have taken the time and effort to normalize against these factors. The Puntalytics team calculated more realistic expected points for a punting play, and also added ERA adjustement to account for changes in punter behaviour/ability over time. For this page, this metric will be referred to as Punt EPA*. The bar graph indicates it's more normalized about 0, and therefore may be more useful than EPA when comparing punters or assessing success.

Punt EPA* vs Yards to Go

Punt EPA* will be used in place of traditional EPA for the Punting graphs and discussion. Both will be presented in tables. A few other Puntalytics metrics will be presented. These serve to normalize for some of the factors discussed above:

  • RERUN - adjusts Net Yards for returned punts --> expected return distance increases with punt distance, returns longer than expected don't penalize the punter
  • SHARP - adjusts Gross Yards for Yards to go --> accounts for maximum limitation with high YTG and touchback limitation for low YTG, see graph above
  • SHARP RERUN - Net yardage metric considering both SHARP and RERUN, called Adj. Net* in tables
  • Pin Deep/Open Field - classification for punts based on YTG --> provides context to punting situations/goals
      • As noted above, SHARP and SHARP RERUN do not correspond to yards. They approximately range from 0-160, and most punts are 50-150

Titans Punters from 2009-2022

a brief history

  season Punter Punts
2009 R.Bironas 1
2009 C.Hentrich 9
2009 R.Hodges 22
2009 B.Kern 37
2010 B.Kern 77
2011 B.Kern 86
2012 B.Kern 83
2013 B.Kern 79
2014 B.Kern 89
2015 B.Kern 88
2016 B.Kern 77
2017 B.Kern 85
2018 B.Kern 75
2019 B.Kern 93
2020 T.Daniel 5
2020 B.Kern 42
2020 R.Allen 8
2021 B.Kern 51
2021 J.Townsend 11
2022 R.Stonehouse 90
    Timeline
  • Prologue
    Craig Hentrich re-signed with the Titans in 2009, but got hurt in Week 2. He finished the year on IR and then retired after his 17th season. He was the last remaining member of the Titans '99 Super Bowl team.
  • 2009 Titans acquire Brett Kern after he was waived by the Broncos, and steal his magic juice
  • 2010-2016 Kern punts for the Titans, providing a bright spot for fans watching games in the worst of times.
  • 2017-2021 Kern earns Pro Bowl honors (2017-2019) and a 1st-Team All-Pro (2019) as the primary veteran responsible for the Titans resurgence through the Mularkey and into the Vrabel era.
  • 2022 Titans turn the reigns to UDFA Ryan Stonehouse. Kern was acquired by Eagles after Week 14, cementing their claim to the Super Bowl.
2022 updated as of Mon Jan 9 15:24:21 2023

I cannot find the source of this image, if I do I will attribute photographer.

Part I - Brett Kern History

2009-2021 play-by-play data: punt-attempts
Brett Kern's Titans punts were separated from the rest of the dataset for comparison.

   This dataset was used to get a feel for how to assess individual punter data. I will propose interpreations, and demonstrate some interesting trends over Brett Kern's career as a Titan. The section focuses on a subset of the data, 2017-2019, Kern's Pro Bowl years. These will provide a standard of high-quality punting in Part II - Brett Kern vs Ryan Stonehouse.

From 2017-2019, Brett Kern attempted 253 punts and all punters attempted 6,849 punts.

The KDE plot shows a similar distribution of Punts attempts with Yards to go. Therfore the following regression plots may provide fair comparisons. More error at extremes of YTG are expected, due to much lower attempts. Such spread may be amplified for Kern, who is being compared to all other punters at large.

▼ show Binary Metrics vs YTG ▼

Kernel Density Estimation (KDE): Punt Attempts by Yards to go 2017-2019
Binary Punt Metrics 2017-2019

Binary metrics (happened / didn't happen) were considered by estimating linear regressions and confidence intervals.

When the regression lines dip below 0%, they indicate an extremely low likelihood of occurence, and conversely indicate almost all punts achieved the metric if over 100%.

The first two metrics shown are more obvious to interpret, punts that:

  • Land Inside the Opponent's 20
  • Result in a Touchback
From 2017-2019, Brett Kern landed opponents inside their 20 at a higher rate than the rest of the NFL, when achievable (less than 80 Yards to go). This is a good thing.

The graphs paint a less obvious picture for Touchbacks, which are generally undesirable. 2018 may be his best year shown, but in 2019, he may have landed achieved more Touchbacks from great distance (less than 80 Yards to go), which may be assumed a positive result.

The next three metrics shown are less obvious to interpret, and could be considered an indication of punter style. Punts that:

  • Land Out of bounds
  • Are Downed
  • Are Fair caught

Punts that land Out of bounds (OOB) are probably good; Brett Kern was exceptional during this time period. OOB punts from further away might be less impressive if net yards suffer. More on this in the two charts below.

Downed punts and fair catches follow similar reasoning. Although punts resulting in a fair catch may be less impressive from further away.

The three considered together may show a punter's tendencies. Perhaps Kern was confident in his ability to punt OOB and did not need to rely on rolling or hanging punts.

Binary Punt Metrics 2017-2019

Kern Becomes an OOB Technician after 2016

The box plot more directly (refer to binary metrics) shows that Kern punted OOB more often than the rest of the NFL. The larger bars indicate a greater rate, and higher values indicate OOB punts from a greater distance. Interestingly, Kern was not punting like this before 2017.

Punt OOB Rate / Yards to go 2014-2021

Viewing the collection of binary metrics shows that while Kern had a higher OOB rate, the actual rate of his punts returned may be similar to the NFL, considering his lower Downed and Fair Catch (and touchback) rates. To check, I calculated a "returned" binary, for punts that were not fair caught, blocked, OOB, downed, or touchbacks. This graph shows Kern was allowing less returns than the NFL early in his career.

Kern took it to a new level after 2016!

Excepting touchbacks, unreturned punts ensure that net yards will be equal to gross yards. This is great, unless a punt's distance is sacrificed to minimize chances of a return. Did Kern's style result in better net yards, or does it indicate shallower punts (gross yards) in general?

Logistic Regressision:
Punt Return Rate vs Yards to go 2009-2021

Best years by Normalized Net Yards

The relatively high OOB rate started in 2017, where Kern's net yards were still similar to the rest of the pack. But from 2018-2020, Kern performed well in net yards relative the league. This indicates his OOB punts were effective. Although net yards may not always isolate a punter's performance, for example it may suffer with an unfortunate missed tackle, Kern is a team player and takes full responsibility for the unit's performance as a whole.

While 2020 looks to be better than 2017 by net yards, the Pro Bowl years ('17-'19) will continue to be Kern's golden range with the Titans for consideration against Ryan Stonehouse. Also note that the Titans punted relatively little during the 2020 season. What do you think?

Check out the Full Picture.

Net Yards / Yards to go 2016-2021
B.Kern vs NFL, 2017-2019
  B.Kern Rest of NFL
Punts 253 6,849
Blocked 1 38
Touchback 5.5% 6.1%
Inside Twenty 45.1% 37.4%
Fair Catch 19.8% 27.4%
Out Of Bounds 20.9% 10.1%
Returned 44.3% 54.7%
EPA/play 0.070 -0.105
punt EPA* 0.160 0.008
Adj. Net* 105.1 100.2
Kern vs NFL, Adj. Net* and Punt EPA*
Distance, EPA vs YTG     ▼ see more ▼     Historical OOB vs YTG
3rd Order Fit
Gross, Net Yards vs Yards to Go 2017-2019
2nd Order Fit
Punt EPA* vs Yards to Go 2017-2019

Part II - Kern (2017-2019) vs Stonehouse (2022)

2017-2022 play-by-play data: punt-attempts
Ryan Stonehouse '22 vs. Brett Kern '17-'19

   In the previous section, it was shown that Brett Kern outperformed other NFL punters in his Pro Bowl years by comparing their Normalized Net Yards (net yards / yards to go). It was also shown that Kern may have used OOB punts to realize his performance, and that his style was not typical of NFL punters.

This chart may help interpret the actual values for normalized net yards. Consider the following play desription:
(:25) 6-B.Kern punts 58 yards to NE 1, Center-48-B.Brinkley, downed by TEN-29-D.Cruikshank.

It was from 59 YTG, and netted 58 yds (0.98 normalized net yards). That incredible punt was well above average.

Here, the average gross and net yards achieved for all 2009-2022 punts is presented again. The dashed lines indicate the fraction of YTG achieved. This chart may be helpful to interpret the normalized data presented below.

In this section, Kern's high performance years will be compared to Ryan Stonehouse's current season. How has Stonehouse done relative to the recent glory years of Titans punting?

2022 season dataset updated on Mon Jan 9 15:24:21 2023
2022 Puntalytics dataset updated on 12/30/23

Stonehouse punts very differently than Kern. He almost never punts OOB, forces less fair catches, and punts more touchbacks. The first two contribute to his much higher allowed return rate. The KDE shows that Stonehouse is typically punting from greater distance, which might explain some of the differences (and is a sad picture of the Titans 2022 offense).

  B.Kern '17-'19 R.Stonehouse
Punts 253 90
Blocked 1 0
Touchback 5.5% 10.0%
Inside Twenty 45.1% 33.3%
Fair Catch 19.8% 12.2%
Out Of Bounds 20.9% 1.1%
Returned 44.3% 65.6%
EPA/play 0.070 0.055
punt EPA* 0.160 0.207
Adj. Net* 105.1 106.3
Kernel Density Estimation: Punt Attempts by Yards to go

While Stonehouse allows more returns than Kern did, he is punting the ball far. This is shown by his high gross yards. The returns and/or touchbacks bring Stonehouse's net yards to a very similar distribution as Kern's. These are both better than NFL average.

The regression plots show how these tendencies vary with punt distance. As would be expected, Stonehouse's far punts are exemplified by his high gross yards when he has room, high YTG. At lower YTG, when a big punt would just result in a touchback, Kern's OOB punting style gave him more net yards than Stonehouse. But at longer distances, Stonehouse is performing similarly or better than Kern regardless of returns/touchbacks.

Gross, Net Yards / Yards to go distributions
3rd Order Fit: Gross, Net Yards vs Yards to go

The effect of returns on net yards is more closely examined in the scatter plot. All punting data, 2009-2022, was grouped by punter and season, and then all seasons with at least 30 punts were plotted. A slight trend of better net with lower return rate is apparent. Kern's recent seasons are at or below average for return rate, while Stonehouse has allowed one of the higher return rates in the dataset.

Normalized Net Yards vs Return Rate

Kern's seasons show the relatively high net yards and low return rate presented in the previous section. The Pro Bowl subset, '17-'19, were three of his five best by normalized net yards. The other two, 2009 and 2020, were relatively low punting seasons (37,42).

Stonehouse's solitary season exemplifies his abnormally high net yards, despite high return rate. This was accomplished by having high YTG, and booming the ball.

In conclusion, the Titans should be looking forward to a fruitful transition from one punting legend to a promising star. By my metric of choice, Net yards / Yards to go aka Normalized Net Yards, Kern has developed consistent performance over his career. He did this by minimizing opponent return opportunities and punting out-of-bounds. This season, Stonehouse has performed similarly to Kern's best years, and has done so by punting very differently. He has punted far enough that returns or touchbacks have not hurt his normalized net yards.

Binaries vs YTG regressions     ▼ show more ▼     Historical Adj Net*, Normalized Gross

Linear Regression for various Binary Metrics with YTG

These charts provide more context to the averages presented in the table. Note that linear fits are not always appropriate, and may not provide a good view of a metrics relationship with YTG. Charts are best used to roughly compare punter tendencies.

Adj Net* vs Return Rate, note different calculated return rate from Puntalytics
Normalized Gross Yards vs Return Rate, Stonehouse is up there!

Punters are Players - Titans

2009-2022 play-by-play data: plays where a Titan who punted passes/rushes/receives/tackles
EPA percentile is relative to the 2022 NFL play-by-play dataset, QB epa used for passing.

   The tables below show all passing, rushing, receiving, and tackling for Titans punters from 2009-2022. The offensive stats contain an EPA percentile column, which shows the punter's EPA/play relative to all plays in 2022, for convenience. Some of the passes and rushes were on botched plays, click the Punter Name Buttons to see the detailed play descriptions.

Show Tables

dataset updated on Mon Jan 9 15:24:21 2023

Punters are Players!

Punter Passing B.Kern
  Attempts Y/A Comp % TD Int EPA/play EPA percentile
B.Kern 6 6.5 33% 0 0 0.28 61
2016 Week 7 - TEN at IND: (Kick formation) TWO-POINT CONVERSION ATTEMPT. 6-B.Kern pass to 4-R.Succop is incomplete. ATTEMPT FAILS.
2019 Week 7 - TEN at LAC: (8:18) (Punt formation) 6-B.Kern pass short left to 31-K.Byard to LAC 32 for 11 yards (57-J.Brown).
2019 Week 15 - TEN at HOU: (1:14) (Punt formation) 6-B.Kern pass incomplete deep right to 29-D.Cruikshank (32-L.Johnson).
2019 Week 20 - KC at TEN: (6:07) (Punt formation) 6-B.Kern pass short middle to 37-A.Hooker to 50 for 28 yards (17-M.Hardman).
2020 Week 7 - TEN at PIT: (:21) (Punt formation) 6-B.Kern Aborted. 48-B.Brinkley FUMBLES at TEN 18, recovered by TEN-6-B.Kern at TEN 18. 6-B.Kern pass incomplete deep left to 87-G.Swaim. Penalty on TEN-48-B.Brinkley, Ineligible Downfield Pass, declined.
2020 Week 8 - CIN at TEN: (Kick formation) TWO-POINT CONVERSION ATTEMPT. 6-B.Kern pass to 94-J.Crawford is incomplete. ATTEMPT FAILS. DEFENSIVE TWO-POINT ATTEMPT. 56-J.Bynes intercepted the try attempt. ATTEMPT FAILS.
Punter Rushing B.Kern
  Attempts Y/A First Down % TD Fumbles EPA/play EPA percentile
B.Kern 3 0.0 0% 0 2 -2.96 1
2013 Week 3 - TEN at LAC: (7:48) (Punt formation) 6-B.Kern FUMBLES (Aborted) at TEN 33, and recovers at TEN 33. 6-B.Kern to TEN 30 for -3 yards (37-J.Addae).
2013 Week 6 - SEA at TEN: (1:01) (Punt formation) 6-B.Kern FUMBLES (Aborted) at TEN 38, and recovers at TEN 38. 6-B.Kern to TEN 38 for no gain (42-C.Maragos).
2019 Week 8 - TEN at TB: (3:45) (Field Goal formation) 6-B.Kern left end to TB 28 for no gain (45-D.White).
Punter Tackling R.Bironas B.Kern J.Townsend R.Stonehouse
  Solo Asst for Loss
R.Bironas 1
B.Kern 2
J.Townsend 1
R.Stonehouse 2
2009 Week 11 - HOU at TEN: 2-R.Bironas kicks 67 yards from TEN 30 to HOU 3. 11-A.Davis to HOU 40 for 37 yards (2-R.Bironas). (#44 Leach returned to the game for Houston)
2012 Week 13 - TEN at HOU: (2:21) (Punt formation) 6-B.Kern punts 52 yards to HOU 38, Center-48-B.Brinkley. 82-K.Martin pushed ob at TEN 20 for 42 yards (6-B.Kern).
2018 Week 4 - TEN at PHI: (1:54) (Punt formation) 6-B.Kern punts 54 yards to PHI 19, Center-48-B.Brinkley. 16-D.Carter to TEN 39 for 42 yards (6-B.Kern).
2021 Week 4 - NYJ at TEN: (9:27) 8-J.Townsend punts 42 yards to NYJ 29, Center-46-M.Cox. 10-B.Berrios pushed ob at NYJ 47 for 18 yards (8-J.Townsend).
2022 Week 11 - GB at TEN: (7:14) 4-R.Stonehouse punts 46 yards to GB 32, Center-46-M.Cox. 25-K.Nixon pushed ob at TEN 44 for 24 yards (4-R.Stonehouse).
2022 Week 14 - TEN at JAX: (5:29) 4-R.Stonehouse punts 53 yards to JAX 42, Center-46-M.Cox. 39-J.Agnew ran ob at JAX 49 for 7 yards (4-R.Stonehouse). PENALTY on JAX-6-C.Claybrooks, Offensive Holding, 10 yards, enforced at JAX 47.

Ryan Stonehouse vs. Rest of NFL - 2022 season

2022 play-by-play data: punt-attempts
Titans rookie punter, Ryan Stonehouse, was separated from the rest of the dataset for comparison.

   This section evaluates Titans rookie Ryan Stonehouse against all other NFL punters in 2022. The first part compares him to the NFL average, where he performs well. As the 2022 season ends, I will add definitive individual punter analysis to see how he compares to the league's best. For now, some charts are displayed to evaluate individual punters in 2022. All graphs are updated with the displayed date, but the words may lag behind until the season ends.

2022 season dataset updated on Mon Jan 9 15:24:21 2023
2022 Puntalytics dataset updated on 12/30/23
Punt Attempts across League
  Punter Punts Team
C.Waitman 96 DEN
R.Stonehouse 90 TEN
C.Johnston 88 HOU
T.Way 83 WAS
B.Mann 83 NYJ
J.Hekker 81 CAR
J.Camarda 79 TB
B.Gillikin 77 NO
J.Gillan 75 NYG
R.Wright 74 MIN
R.Dixon 73 LAR
J.Scott 73 LAC
M.Haack 71 IND
P.Harvin 69 PIT
B.Anger 68 DAL
A.Lee 68 ARI
T.Gill 67 CHI
M.Dickson 66 SEA
B.Pinion 62 ATL
M.Wishnowsky 61 SF
C.Bojorquez 61 CLE
T.Morstead 61 MIA
A.Cole 59 LV
L.Cooke 58 JAX
J.Stout 57 BAL
P.O'Donnell 54 GB
T.Townsend 53 KC
J.Fox 52 DET
S.Martin 46 BUF
A.Siposs 45 PHI
M.Palardy 43 NE
J.Bailey 37 NE
K.Huber 31 CIN
D.Chrisman 28 CIN
B.Kern 10 PHI
J.Elliott 1 PHI
  Rest of NFL R.Stonehouse
Punts 2,110 90
Blocked 12 0
Touchback 7.1% 10.0%
Inside Twenty 37.1% 33.3%
Fair Catch 28.3% 12.2%
Out Of Bounds 8.9% 1.1%
Returned 42.3% 65.6%
EPA/play -0.099 0.055
punt EPA* 0.010 0.207
Adj. Net* 100.3 106.3
Distribution of Punts by Yards to Go

Unfortunately, the Titans offense has provided Ryan Stonehouse with a lot of punt opportunities. As shown above, he is currently #2 in the league with 90 punts. He has a similar touchback and inside twenty rate as the rest of the NFL, but has less punts that are fair caught or land OOB. This suggests that more of his punts are returned than average. Comparing punts' gross to net yards will give a sense of yards lost to returns.

Stonehouse punts the ball further than the rest of the NFL, as shown by his normalized gross yards. Currently, this is his most impressive trait. However his net yards are not as high. The strong punting of Stonehouse makes him really good, but when he finds a way to maintain distance and minimize returns, he will become elite. As shown in Part I, Kern sent his punts OOB to achieve high net yards and inside twenty percentage.

Punt Distance Distributions
Punt Distance vs Yards to Go

Individual Punter Analysis

As discussed in the previous section, Stonehouse has a good normalized net yardage desipite allowing a lot of returns. By contrast, Kern achieved his best net yardage with low return rates. And it seemed that better performance with lower return rate was an, at least weak, historical trend in the NFL.

The chart below shows no trend for the 2022 season. The best and worst punters by normalized net yards span the range of return rate allowed. This may be expected with varied punting styles and varied punting situations, as a single season's data is more influenced by team performance. Stonehouse has allowed the most returns on his punts, but is still amongst the top of the league in netting his YTG.

Other punters show similar performance, indicating similar styles. These will be examined in subsequent graphs. To start, let's see if the best netters with returns allowed are the ones punting far AKA gross.

Normalized Net Yards vs Return Rate, Table sorted by normalized net yards (Norm.Net)

Punters with at least 30 punts on a single team in 2022. Marker size scaled with # punts and colored by team. Grey line and area show linear fit and confidence interval.
  Team Name Norm.Net Return% Punts
JAX L.Cooke 0.698 50% 58
KC T.Townsend 0.694 42% 53
CAR J.Hekker 0.687 33% 81
SF M.Wishnowsky 0.683 28% 61
LV A.Cole 0.676 42% 59
WAS T.Way 0.668 41% 83
HOU C.Johnston 0.667 51% 88
SEA M.Dickson 0.666 53% 66
LAC J.Scott 0.666 26% 73
CLE C.Bojorquez 0.662 43% 61
NO B.Gillikin 0.660 34% 77
BAL J.Stout 0.659 39% 57
TEN R.Stonehouse 0.657 66% 90
MIA T.Morstead 0.653 51% 61
MIN R.Wright 0.652 51% 74
DAL B.Anger 0.649 47% 68
BUF S.Martin 0.647 35% 46
ATL B.Pinion 0.646 40% 62
PHI A.Siposs 0.636 44% 45
GB P.O'Donnell 0.633 37% 54
DEN C.Waitman 0.629 46% 96
NYG J.Gillan 0.629 37% 75
IND M.Haack 0.628 37% 71
DET J.Fox 0.625 58% 52
PIT P.Harvin 0.623 35% 69
TB J.Camarda 0.621 41% 79
NYJ B.Mann 0.621 39% 83
ARI A.Lee 0.599 54% 68
CHI T.Gill 0.598 46% 67
LAR R.Dixon 0.596 49% 73
NE J.Bailey 0.590 43% 37
CIN K.Huber 0.574 35% 31
NE M.Palardy 0.572 42% 43

As expected, punters who net good yards despite high return rates also punt far. Stonehouse has been the best in the league by this metric, which measures how much of the available room (YTG) a punt travels.

Normalized Gross Yards vs Return Rate, Table sorted by normalized gross yards (Norm.Gross)

Punters with at least 30 punts on a single team in 2022. Marker size scaled with # punts and colored by team. Grey line and area show linear fit and confidence interval.
  Team Name Norm.Gross Return% Punts
TEN R.Stonehouse 0.757 66% 90
JAX L.Cooke 0.746 50% 58
KC T.Townsend 0.742 42% 53
LV A.Cole 0.738 42% 59
CLE C.Bojorquez 0.733 43% 61
HOU C.Johnston 0.728 51% 88
CAR J.Hekker 0.726 33% 81
MIA T.Morstead 0.725 51% 61
SF M.Wishnowsky 0.719 28% 61
DET J.Fox 0.714 58% 52
SEA M.Dickson 0.709 53% 66
WAS T.Way 0.707 41% 83
MIN R.Wright 0.707 51% 74
ATL B.Pinion 0.704 40% 62
NO B.Gillikin 0.702 34% 77
DAL B.Anger 0.700 47% 68
BAL J.Stout 0.695 39% 57
BUF S.Martin 0.690 35% 46
PHI A.Siposs 0.690 44% 45
TB J.Camarda 0.686 41% 79
GB P.O'Donnell 0.685 37% 54
DEN C.Waitman 0.680 46% 96
NYJ B.Mann 0.680 39% 83
NYG J.Gillan 0.678 37% 75
LAC J.Scott 0.678 26% 73
ARI A.Lee 0.665 54% 68
IND M.Haack 0.665 37% 71
LAR R.Dixon 0.664 49% 73
CHI T.Gill 0.654 46% 67
NE J.Bailey 0.650 43% 37
PIT P.Harvin 0.649 35% 69
CIN K.Huber 0.649 35% 31
NE M.Palardy 0.608 42% 43

As shown in Part I, return rate increases with YTG, as some punters seek to maximize distance above all alse. The same relationship is seen for the 2022 season. The regression line/area shows the expected return rate, given a punter's average YTG. While Stonehouse has had relatively high YTG, his return rate is still much higher than is typical for the league.

Let's look at Wishnowsky (SF) and Townsend (KC), the tops in the league by normalized net yards, as an another example. Wishnowsky has been the best in the league at minimizing returns, and on the surface it seems his style may be similar to Kern's. But the graph shows his return rate is within expectation, given his shallower punts. Townsend has allowed an average return rate overall, but it is lower than expected for his average punting room. His performance may be considered more impressive, as he is achieving similarly from greater distance. To his credit, Wishnowsky is maximizing his opportunity, relative to someone like Bailey (NE).

Return Rate vs YTG, 2022 punts

Punters with at least 30 punts on a single team in 2022. Marker size scaled with # punts and colored by team. Grey line and area show linear fit and confidence interval.

The regressions shown on the these charts were fit using the table data, which has already grouped and averaged punts by 2022 punters. Despite this limited data source, the fit shown for Return Rate vs YTG is basically the same as the logistic regression fit to all punting data from 2009-2021, within the YTG range presented.

Additional charts with limited or no analysis are presented in the advanced metrics and extras. See Punters are Players for punters who went above and beyond in 2022.

▼ advanced metrics ▼

Puntalytics era adjusted Punt EPA and their Adj. Net, "SHARP_RERUN", can be used interchangeably to compare punters. My simpler, Normalized Net, is not a bad approximation to their Adj. Net.

Punt EPA* vs Adj. Net*
Normalized Net vs Adj. Net*

Classification of Pin Deep (shorter YTG) vs Open Field (longer YTG) territory shows Punter performance in different situations.

Adj Net*: Pin Deep vs Open Field

Table sorted by Punt EPA*. Open Field and Pin Deep columns refer to the Adj Net* score for the two punting territories.

  Team Name Punt EPA* Adj. Net* Punts Open Field Pin Deep
KC T.Townsend 0.233 107.2 44 100.8 110.2
TEN R.Stonehouse 0.207 106.3 87 101.6 107.9
LV A.Cole 0.190 106.4 55 104.9 107.3
CAR J.Hekker 0.180 105.9 70 109.6 103.9
CLE C.Bojorquez 0.179 105.5 53 103.1 107.3
JAX L.Cooke 0.122 104.1 49 100.2 106.1
HOU C.Johnston 0.115 104.6 78 111.8 101.3
DAL B.Anger 0.093 102.7 58 101.2 103.5
SEA M.Dickson 0.089 102.9 57 102.4 103.0
WAS T.Way 0.085 102.9 74 101.1 103.7
NO B.Gillikin 0.077 102.2 66 99.0 104.2
BUF S.Martin 0.060 101.1 43 97.9 102.6
MIA T.Morstead 0.060 100.3 53 102.3 99.4
DET J.Fox 0.059 102.0 45 101.1 102.4
TB J.Camarda 0.044 101.2 69 97.7 102.4
SF M.Wishnowsky 0.034 101.6 54 105.8 96.4
MIN R.Wright 0.030 101.1 71 97.0 102.9
GB P.O'Donnell 0.021 101.6 49 107.8 98.0
ATL B.Pinion 0.004 100.5 56 103.7 98.7
LAR R.Dixon -0.022 99.1 62 90.0 101.0
NYJ B.Mann -0.029 99.2 75 104.0 97.1
BAL J.Stout -0.039 98.0 50 90.0 103.8
PHI A.Siposs -0.055 98.4 45 103.0 95.0
DEN C.Waitman -0.056 98.8 86 99.9 98.4
ARI A.Lee -0.057 98.1 63 99.3 97.7
LAC J.Scott -0.066 97.7 64 97.2 98.0
NYG J.Gillan -0.070 97.1 69 92.8 99.9
CHI T.Gill -0.074 97.8 57 97.9 97.8
IND M.Haack -0.125 95.8 65 98.1 94.6
PIT P.Harvin -0.128 96.2 64 101.4 94.4
CIN K.Huber -0.158 95.2 31 88.5 98.4
NE J.Bailey -0.283 90.9 37 92.8 89.1
NE M.Palardy -0.345 89.5 34 88.9 89.8
Adj Net* vs Pin Deep / Open Field. Higher are better overall. Left/Right is greater contribution from Open Field / Pin Deep punts to overall score.
Adj Net* vs Return rate
Return Yd vs Gross Yd
Return Yd vs Return Rate
▼ extras ▼
Net Yards vs Yards to go
Gross Yards vs Yards to go
Punt Distribution by YTG. Wishnowsky vs Townsend vs Others
Inside Twenty rate vs Yards to go
Out-of-bounds rate vs Yards to go
Downed rate vs Yards to go
Fair Catch rate vs Yards to go
Touchback rate vs Yards to go
Normalized Net Yards vs Touchback rate

Punters are Players - 2022

2022 play-by-play data: plays where a punter passes/rushes/receives/tackles
EPA percentile is relative to the entire dataset's distribution, QB epa used for passing.

   The tables below show all passing, rushing, receiving, and tackling for punters in 2022. The offensive stats contain an EPA percentile column, which shows the punter's EPA/play relative to all plays in 2022. For example, rushing EPA/play is measured against all rushes with non-null EPA.

Show Tables

dataset updated on Mon Jan 9 15:24:21 2023

Punters are Players!

Punter Passing B.Mann R.Dixon R.Wright A.Lee T.Townsend J.Gillan J.Fox
  Attempts Y/A Comp % TD Int EPA/play EPA percentile
B.Mann NYJ 2 8.5 50% 0 0 0.45 65
R.Dixon LAR 2 9.0 100% 0 0 2.69 95
R.Wright MIN 2 6.5 50% 0 0 -0.20 49
A.Lee ARI 1 4.0 100% 0 0 2.86 96
T.Townsend KC 1 0.0 0% 0 0 -3.52 2
J.Gillan NYG 1 0.0 0% 0 0 -3.92 1
J.Fox DET 1 6.0 100% 0 0 2.95 96
Week 2 - CLE at NYJ: (1:40) (Punt formation) 7-B.Mann pass short right to 16-J.Smith pushed ob at CLE 37 for 17 yards (38-A.Green).
Week 12 - NYJ at CHI: (9:25) (Field Goal formation) 7-B.Mann to CHI 26 for -8 yards. FUMBLES, and recovers at CHI 26. 7-B.Mann pass incomplete short right [36-D.Houston-Carson].
Week 5 - LA at DAL: (2:29) (Punt formation) 11-R.Dixon pass short middle to 43-J.Gervase to LA 37 for 12 yards (57-L.Gifford).
Week 12 - KC at LA: (6:01) (Punt formation) 11-R.Dixon pass short right to 87-J.Harris to LA 47 for 6 yards (21-T.McDuffie).
Week 4 - NO at MIN: (2:00) (Punt formation) 14-R.Wright pass short right to 83-J.Nailor to NO 34 for 13 yards (25-D.Sorensen).
Week 15 - MIN at IND: (11:06) (Punt formation) 14-R.Wright pass incomplete short left to 83-J.Nailor.
Week 16 - ARI at TB: (10:45) (Punt formation) 14-A.Lee pass short right to 57-K.Grugier-Hill to ARI 49 for 4 yards (30-D.Delaney).
Week 3 - IND at KC: (13:34) (Field Goal formation) 5-T.Townsend pass incomplete short left to 83-N.Gray. Penalty on KC-74-G.Christian, Ineligible Downfield Pass, declined.
Week 18 - PHI at NYG: (:06) (Field Goal formation) 6-J.Gillan sacked at PHI 40 for -11 yards (27-Z.McPhearson).
Week 4 - DET at SEA: (3:07) (Punt formation) 3-J.Fox pass short left to 87-Q.Cephus to DET 41 for 6 yards (37-X.Crawford). Seattle challenged the pass completion ruling, and the play was Upheld. The ruling on the field stands. (Timeout #1.)
Punter Rushing M.Dickson T.Townsend J.Gillan B.Anger
  Attempts Y/A First Down % TD Fumbles EPA/play EPA percentile
M.Dickson SEA 2 -9.0 0% 0 1 -4.23 0
T.Townsend KC 1 0.0 0% 0 0 -0.95 10
J.Gillan NYG 3 -1.0 0% 0 1 -1.99 1
B.Anger DAL 2 0.0 0% 0 1 -2.44 1
Week 5 - NO at SEA: (4:28) (Punt formation) 4-M.Dickson right end to SEA 13 for -8 yards (96-C.Granderson, 24-D.Washington).
Week 6 - SEA at ARI: (:59) (Punt formation) 4-M.Dickson up the middle to SEA -2 for -12 yards (47-E.Turner). FUMBLES (47-E.Turner), RECOVERED by ARI-31-C.Banjo at SEA -4. TOUCHDOWN.
Week 17 - KC at DEN: (Kick formation) TWO-POINT CONVERSION ATTEMPT. 5-T.Townsend rushes right end. ATTEMPT FAILS.
Week 1 - TEN at NYG: (Kick formation) TWO-POINT CONVERSION ATTEMPT. 6-J.Gillan rushes. ATTEMPT FAILS.
Week 5 - GB at NYG: (:15) (Punt formation) 6-J.Gillan left end ran ob in End Zone for -3 yards, SAFETY (81-J.Deguara). Penalty on NYG-27-J.Pinnock, Offensive Holding, declined.
Week 14 - NYG at PHI: (8:32) (Punt formation) 6-J.Gillan to NYG 33 for -10 yards. FUMBLES, touched at NYG 33, recovered by NYG-52-C.Coughlin at NYG 33. PENALTY on NYG-6-J.Gillan, Illegal Kick/Kicking Loose Ball, 10 yards, enforced at NYG 43.
Week 5 - LA at DAL: (Kick formation) TWO-POINT CONVERSION ATTEMPT. 5-B.Anger rushes. ATTEMPT FAILS. Penalty on DAL-5-B.Anger, Illegal Kick/Kicking Loose Ball, declined.
Week 18 - WAS at DAL: (11:50) (Punt formation) 5-B.Anger FUMBLES (Aborted) at DAL 19, and recovers at DAL 19. 5-B.Anger to DAL 20 for 1 yard (46-M.Eifler).
Punter Tackling R.Dixon M.Haack J.Bailey T.Morstead J.Gillan R.Stonehouse J.Fox A.Siposs M.Wishnowsky J.Camarda
  Solo Asst for Loss
R.Dixon LAR 2 1
M.Haack IND 2
J.Bailey NE 2
T.Morstead MIA 3 1
J.Gillan NYG 1
R.Stonehouse TEN 2
J.Fox DET 1
A.Siposs PHI 2
M.Wishnowsky SF 1
J.Camarda TB 5 1
Week 9 - TB at LA: (14:01) 11-R.Dixon punts 41 yards to TB 27, Center-42-M.Orzech. 1-J.Darden pushed ob at TB 44 for 17 yards (11-R.Dixon).
Week 15 - GB at LA: (5:43) 11-R.Dixon punts 41 yards to GB 33, Center-42-M.Orzech. 25-K.Nixon pushed ob at 50 for 17 yards (11-R.Dixon). PENALTY on LA-21-R.Yeast, Face Mask, 15 yards, enforced at 50.
Week 4 - SF at LA: (13:32) 11-R.Dixon punts 56 yards to SF 23, Center-42-M.Orzech. 3-R.McCloud to SF 45 for 22 yards (11-R.Dixon, 34-J.Funk).
Week 9 - NE at IND: (7:05) 6-M.Haack punt is BLOCKED by 31-Jo.Jones, Center-46-L.Rhodes, RECOVERED by NE-41-B.Schooler at IND 8. 41-B.Schooler to IND 2 for 6 yards (6-M.Haack).
Week 15 - MIN at IND: (10:42) 6-M.Haack punts 43 yards to MIN 25, Center-46-L.Rhodes. 5-J.Reagor to IND 24 for 51 yards (6-M.Haack). PENALTY on MIN-29-K.Boyd, Face Mask, 12 yards, enforced at MIN 25.
Week 3 - NE at BAL: (6:33) 7-J.Bailey punts 56 yards to BAL 13, Center-49-J.Cardona. 13-D.Duvernay pushed ob at NE 44 for 43 yards (7-J.Bailey).
Week 5 - NE at DET: 7-J.Bailey kicks 65 yards from NE 35 to DET 0. 15-M.Alexander ran ob at DET 47 for 47 yards (7-J.Bailey).
Week 5 - NYJ at MIA: 4-T.Morstead kicks 73 yards from MIA 20 to NYJ 7. 10-B.Berrios pushed ob at NYJ 49 for 42 yards (4-T.Morstead).
Week 6 - MIA at MIN: (7:29) 4-T.Morstead punts 62 yards to MIN 26, Center-44-B.Ferguson. 5-J.Reagor ran ob at MIA 49 for 25 yards (4-T.Morstead).
Week 12 - MIA at HOU: (11:58) 4-T.Morstead punts 52 yards to HOU 16, Center-44-B.Ferguson. 1-T.Smith pushed ob at HOU 33 for 17 yards (4-T.Morstead).Week 12 - MIA at HOU: (7:25) 4-T.Morstead punts 55 yards to HOU 15, Center-44-B.Ferguson. 1-T.Smith ran ob at HOU 41 for 26 yards (4-T.Morstead; 43-A.Van Ginkel).
Week 3 - NYG at DAL: (7:44) 6-J.Gillan punts 41 yards to DAL 37, Center-58-C.Kreiter. 9-K.Turpin to NYG 35 for 28 yards (6-J.Gillan).
Week 11 - GB at TEN: (7:14) 4-R.Stonehouse punts 46 yards to GB 32, Center-46-M.Cox. 25-K.Nixon pushed ob at TEN 44 for 24 yards (4-R.Stonehouse).
Week 14 - TEN at JAX: (5:29) 4-R.Stonehouse punts 53 yards to JAX 42, Center-46-M.Cox. 39-J.Agnew ran ob at JAX 49 for 7 yards (4-R.Stonehouse). PENALTY on JAX-6-C.Claybrooks, Offensive Holding, 10 yards, enforced at JAX 47.
Week 9 - DET at GB: 3-J.Fox kicks 65 yards from DET 35 to GB 0. 25-K.Nixon to GB 33 for 33 yards (51-J.Woods; 3-J.Fox).
Week 2 - PHI at MIN: (3:26) 4-J.Elliott 41 yard field goal is BLOCKED (7-P.Peterson), Center-45-R.Lovato, Holder-8-A.Siposs, RECOVERED by MIN-29-K.Boyd at MIN 43. 29-K.Boyd to PHI 30 for 27 yards (8-A.Siposs).
Week 6 - PHI at DAL: (6:59) 8-A.Siposs punts 59 yards to DAL 5, Center-45-R.Lovato. 9-K.Turpin to DAL 39 for 34 yards (8-A.Siposs). PENALTY on DAL-1-K.Joseph, Offensive Holding, 7 yards, enforced at DAL 14.
Week 11 - ARI at SF: (5:48) 18-M.Wishnowsky punts 51 yards to ARI 29, Center-46-T.Pepper. 83-G.Dortch pushed ob at SF 33 for 38 yards (18-M.Wishnowsky). PENALTY on ARI-23-C.Clement, Illegal Block Above the Waist, 10 yards, enforced at ARI 44.
Week 4 - TB at KC: 5-J.Camarda kicks 65 yards from TB 35 to KC 0. 10-I.Pacheco to KC 42 for 42 yards (5-J.Camarda).
Week 9 - TB at LA: 5-J.Camarda kicks 67 yards from TB 35 to LA -2. 19-B.Powell to LA 27 for 29 yards (5-J.Camarda).
Week 9 - TB at LA: (14:17) 5-J.Camarda punts 53 yards to LA 23, Center-97-Z.Triner. 19-B.Powell pushed ob at TB 35 for 42 yards (5-J.Camarda). PENALTY on LA-21-R.Yeast, Offensive Holding, 10 yards, enforced at LA 35.
Week 13 - TB at NO: (5:05) 5-J.Camarda punts 50 yards to NO 18, Center-97-Z.Triner. 89-R.Shaheed pushed ob at TB 40 for 42 yards (5-J.Camarda). NO-53-Z.Baun was injured during the play. His return is Questionable.
Week 17 - TB at CAR: 5-J.Camarda kicks 63 yards from TB 35 to CAR 2. 20-R.Blackshear to CAR 38 for 36 yards (5-J.Camarda).Week 5 - TB at ATL: 5-J.Camarda kicks 63 yards from TB 35 to ATL 2. 35-A.Williams to ATL 32 for 30 yards (5-J.Camarda; 41-K.Kieft).

Playground

Section for small-form analyses . . .

Third Down Rushing Conversions, 2022

High conversion rates were related to less required yards for a first down. Rush attempts were grouped by team.

To be added: What were possible causes and consequences of Derrick Henry's low 3rd down usage?

Receivers by CPOE/target or reception

utilize a passing metric, completion percent over expected, to identify most QB-friendly WRs

QB/Team by percentage of passing EPA from YAC

investigate some narratives surrounding offenses that create YAC opportunities or QBs whose stats are inflated by production after the catch

Titans drive stallage / negative plays

covered in 2022 review, see per Drive Analyses

Derrick Henry

to be determined . . .