If I were to venture into the consciousness of the typical football/Fantasy Football fan, I feel like one of the general misconceptions that one would be able to find in their overall outlook on the game is their perception of the relationship between offensive lines and running backs. Or more generally, the relationship between a team’s run blocking ability and their running back’s performance.

Over time, I think the public perception has become more aware of the actual value of running backs relative to other players on the field. However, I feel like this mental re-calibration hasn’t shifted the value that was taken away from running backs to where it should be placed—offensive lineman and the other blockers on the field.

This post will look at three issues.

First, we will look the relationship between a team’s run blocking ability and their running back’s performance. Then we will try to come up with a metric that controls a team’s run blocking when we look at the performance of running backs.

Lastly, we will look at two stats and decide which is the best predictor of future performance for NFL running backs.

How to judge run blocking?

As far as freely available analytics that objectively judge the performance of team run blocking ability, there isn’t much information. Football Outsiders has looked at the value of offensive lines and their influence as a unit, but this doesn’t help us much. While they are responsible for most of the value attributed to a team’s aptitude at run blocking, there are more players responsible for run blocking beyond the offensive line: tight ends, fullbacks and wide receivers.

Pro Football Reference has approximate value (AV), which attempts to put a single number on a player’s overall worth. While AV does a good job generally, we need a stat that objectively analyzes the performance of offensive lineman and more specifically, how they run blocked. An offensive lineman may be a good player overall, but that doesn’t mean that they are a good run blocker (e.g. Anthony Castonzo and Ricky Wagner last year). This also doesn’t solve for the team performance angle that we need resolved.

Enter Pro Football Focus Player Grades: PFF is able to provide us with an objective number to use as a value metric for a team’s ability to run block. Beyond the general grades that they use to judge offensive players for every play that they are on the field, PFF is able to delineate between an offensive player’s performances on plays where they were asked to run block. When it comes to the objectivity of the grades, each player is graded on the execution of their assignment, so there isn’t a need to worry about the impact a team of bad players might have on a good player.

Because we know that PFF grades judge players (in this case, teams on run blocking plays) outside the influence of other teammates and their performance, we can now quantify the relationship between a team’s run blocking ability—what they did outside the ability of their running back—and the performance of their running backs—what he did with the help of his team. We’ll use each team’s run block grade as one variable, and we’ll use yards-per-carry as our value metric for running backs.

When we look at the relationship between team run blocking value and yards-per-carry by their running backs, we will look at all player seasons from 2007 to 2014 where a running back had thirty or more carries in a season. Unfortunately, we can’t widen our sample beyond these years, because these are the only years that team run block grades are available. This leaves us with a population of 670 running back seasons and 257 team seasons.

Results

When I looked at the correlation between team run blocking and running back yards-per-carry, I found that there is a .276r (graph below), which is a modest positive relationship. As we would expect, the better your team blocks for your running back, the more yards they will rush for.

This is exciting, because we can now show that there is an approximate amount of value that a team provides that a running back has no control over.

Build off of this…

Now that we know that there is linear relationship between a team’s run blocking ability and their running back’s yards per carry, we can now try to separate what the team and the running back were each responsible for when it comes to a running back’s yards-per-carry.

To do this, I developed a linear regression around a team’s run blocking grade, which translates a team’s run blocking grade into a yards per carry scale (i.e. if a team had a run blocking grade of “x,” that would correlate to a yards-per-carry of “x”) (the chart below gives a range of examples).

Once we have calculated a team’s expected yards-per-carry (xYPC) based off of their run blocking grade, we can then subtract a running back’s yards-per-carry from the expected yards-per-carry that their team’s run blocking ability suggests they should produce; this difference is the surplus or negative value that a running back provides beyond or below what their team’s run blocking ability implies their yards-per carry-should be. We will call this excess or lack thereof, for players that underperformed their team’s xYPC, a player’s baseline score, because it encapsulates how much beyond or below their team’s run blocking grade the player performed.

It’s easy to think about how a running back might perform above, at, or below their team’s expected yards-per-carry. Players that performed below the rate at which their team’s run blocking suggests are players that are not athletic enough to get through the holes or run lanes that they are provided with and are hit in the back field; or, they can’t find the holes or run lanes to begin with (Trent Richardson). Players that meet the expectation of their team’s run blocking are players that are able to take advantage of the blocks that their team provides, but aren’t able to make defenders miss in the open field (Frank Gore). Running backs that excel above the rate at which their run blocking suggests are players who can make the opposition miss in space and break tackles (Jamaal Charles).

The chart below lists all running backs from 2007 to 2014 with a minimum of 150 carries in a season; the default sort shows the players with the highest baseline score.

Player Year Team RK Avg. RK BSLN C.J. Spiller 2012 BUF 3 6 1 1.8417 Jamaal Charles 2010 KC 1 6.3 2 1.8285 Jamaal Charles 2009 KC 4 5.9 3 1.79885 Adrian L. Peterson 2012 MIN 2 6 4 1.5789 Fred Taylor 2007 JAX 9 5.5 5 1.375 Fred Jackson 2011 BUF 10 5.5 6 1.36735 Adrian L. Peterson 2007 MIN 6 5.6 7 1.3175 Chris D. Johnson 2009 TEN 5 5.7 8 1.3104 DeMarco Murray 2011 DAL 11 5.5 9 1.2904 Lamar Miller 2014 MIA 25 5.1 10 1.21395 Justin Forsett 2014 BLT 13 5.4 11 1.1544 Jamaal Charles 2014 KC 27 5.1 12 1.14465 Ryan Grant 2007 GB 28 5.1 13 1.1433 DeAngelo Williams 2011 CAR 12 5.5 14 1.13785 Chester Taylor 2007 MIN 15 5.4 15 1.1175 LeGarrette Blount 2010 TB 32 5 16 1.0163 Ben Tate 2011 HST 14 5.4 17 0.99105 Derrick Ward 2008 NYG 7 5.6 18 0.9836 Jamaal Charles 2012 KC 16 5.3 19 0.96575 DeAngelo Williams 2008 CAR 8 5.5 20 0.95605 LeSean McCoy 2010 PHI 21 5.2 21 0.94225 Jeremy Hill 2014 CIN 23 5.1 22 0.9372 Tim Hightower 2010 ARZ 33 5 23 0.9335 Willis McGahee 2011 DEN 48 4.8 24 0.92295 Darren McFadden 2010 OAK 18 5.2 25 0.90625 DeMarco Murray 2013 DAL 19 5.2 26 0.87475 Reggie Bush 2011 MIA 26 5.1 27 0.8697 Matt Forte 2011 CHI 40 4.9 28 0.85285 Ryan Mathews 2011 SD 39 4.9 29 0.8398 LeGarrette Blount 2013 NE 34 5 30 0.8327 Marion Barber III 2007 DAL 49 4.8 31 0.79875 DeAngelo Williams 2009 CAR 20 5.2 32 0.77575 Marshawn Lynch 2014 SEA 56 4.7 33 0.755 Brandon Jacobs 2007 NYG 31 5 34 0.7517 Chris Ivory 2013 NYJ 74 4.6 35 0.74635 Marshawn Lynch 2012 SEA 29 5 36 0.73235 Jamaal Charles 2013 KC 36 4.9 37 0.72325 Frank Gore 2009 SF 38 4.9 38 0.69805 Eddie Lacy 2014 GB 58 4.7 39 0.6974 Jonathan C. Stewart 2009 CAR 24 5.1 40 0.67575 Ray Rice 2009 BLT 17 5.3 41 0.67055 C.J. Anderson 2014 DEN 61 4.7 42 0.65915 Jonathan C. Stewart 2014 CAR 75 4.6 43 0.65635 Arian Foster 2014 HST 47 4.8 44 0.63045 C.J. Spiller 2013 BUF 71 4.6 45 0.62935 Le'Veon Bell 2014 PIT 55 4.7 46 0.6173 Rashad Jennings 2013 OAK 90 4.5 47 0.59505 Brian Westbrook 2007 PHI 44 4.8 48 0.56115 Adrian L. Peterson 2010 MIN 66 4.6 49 0.5515 Justin Fargas 2007 OAK 69 4.6 50 0.5416 Steven R. Jackson 2007 SL 137 4.2 51 0.51915 Alfred Morris 2012 WAS 43 4.8 52 0.5004 Michael Turner 2009 ATL 41 4.9 53 0.47665 Maurice Jones-Drew 2007 JAX 76 4.6 54 0.475 Frank Gore 2007 SF 103 4.4 55 0.46355 Ray Rice 2011 BLT 54 4.7 56 0.43595 Chris D. Johnson 2010 TEN 110 4.3 57 0.4297 Steve Slaton 2008 HST 46 4.8 58 0.42705 Doug Martin 2012 TB 64 4.6 59 0.4111 LeSean McCoy 2013 PHI 22 5.1 60 0.41025 Arian Foster 2010 HST 35 4.9 61 0.4087 LaDainian Tomlinson 2007 SD 53 4.7 62 0.40715 Jamal Lewis 2007 CLV 94 4.4 63 0.401 Ryan Torain 2010 WAS 89 4.5 64 0.39885 Maurice Jones-Drew 2011 JAX 52 4.7 65 0.3986 DeMarco Murray 2014 DAL 51 4.7 66 0.3968 Rashard Mendenhall 2009 PIT 68 4.6 67 0.38995 Chris D. Johnson 2008 TEN 37 4.9 68 0.388 LeSean McCoy 2011 PHI 45 4.8 69 0.38565 Brandon Jacobs 2008 NYG 30 5 70 0.3836 Matt Forte 2010 CHI 85 4.5 71 0.3795 Alfred Morris 2013 WAS 67 4.6 72 0.36655 Tre Mason 2014 SL 124 4.3 73 0.36445 Michael Turner 2011 ATL 79 4.5 74 0.35925 Chris D. Johnson 2014 NYJ 129 4.3 75 0.3505 Reggie Bush 2013 DET 86 4.5 76 0.33765 Fred Jackson 2013 BUF 119 4.3 77 0.32935 Beanie Wells 2009 ARZ 62 4.7 78 0.32255 Chris D. Johnson 2012 TEN 82 4.5 79 0.3012 Adrian L. Peterson 2009 MIN 92 4.4 80 0.29705 Larry Johnson 2008 KC 72 4.6 81 0.2914 Matt Forte 2013 CHI 65 4.6 82 0.29095 Steven R. Jackson 2011 SL 100 4.4 83 0.2831 Adrian L. Peterson 2011 MIN 60 4.7 84 0.2771 Fred Jackson 2009 BUF 84 4.5 85 0.2706 Peyton Hillis 2010 CLV 99 4.4 86 0.2561 Ahmad Bradshaw 2012 NYG 70 4.6 87 0.2518 Ahmad Bradshaw 2010 NYG 83 4.5 88 0.24855 Roy Helu 2011 WAS 147 4.2 89 0.24645 LeGarrette Blount 2011 TB 121 4.3 90 0.2461 Mark Ingram 2014 NO 117 4.3 91 0.2452 Adrian L. Peterson 2008 MIN 42 4.8 92 0.243 Adrian L. Peterson 2013 MIN 81 4.5 93 0.22875 Ryan Mathews 2013 SD 96 4.4 94 0.2129 Willis McGahee 2012 DEN 107 4.4 95 0.21245 Marshawn Lynch 2013 SEA 133 4.2 96 0.20415 Matt Forte 2012 CHI 102 4.4 97 0.1931 Marshawn Lynch 2007 BUF 169 4 98 0.19045 Ahmad Bradshaw 2009 NYG 50 4.8 99 0.1899 Alfred Morris 2014 WAS 154 4.1 100 0.1685 Julius Jones 2008 SEA 108 4.4 101 0.1625 Kenny Watson 2007 CIN 106 4.4 102 0.1571 Maurice Jones-Drew 2009 JAX 78 4.5 103 0.1536 Marshawn Lynch 2011 SEA 135 4.2 104 0.15285 Chris Ivory 2014 NYJ 161 4.1 105 0.1505 Bilal Powell 2013 NYJ 176 4 106 0.14635 Fred Jackson 2010 BUF 138 4.2 107 0.13845 Stevan Ridley 2013 NE 125 4.3 108 0.1327 Reggie Bush 2012 MIA 116 4.3 109 0.1309 Laurence Maroney 2007 NE 88 4.5 110 0.1302 Ray Rice 2012 BLT 101 4.4 111 0.12875 Ben Tate 2013 HST 123 4.3 112 0.12145 Knowshon Moreno 2010 DEN 122 4.3 113 0.11785 Lamar Miller 2013 MIA 175 4 114 0.11305 Clinton Portis 2008 WAS 109 4.3 115 0.09715 Steven R. Jackson 2009 SL 91 4.4 116 0.08645 Willis McGahee 2007 BLT 151 4.1 117 0.07625 Ryan Grant 2009 GB 97 4.4 118 0.0761 Travis Henry 2007 DEN 163 4.1 119 0.0668 Jonathan C. Stewart 2008 CAR 73 4.6 120 0.05605 Frank Gore 2014 SF 112 4.3 121 0.0535 DeAngelo Williams 2012 CAR 127 4.3 122 0.04225 Frank Gore 2008 SF 115 4.3 123 0.0328 Jonathan C. Stewart 2010 CAR 126 4.3 124 0.031 Eddie Lacy 2013 GB 152 4.1 125 0.0308 Ryan Mathews 2010 SD 128 4.3 126 0.0265 Knowshon Moreno 2013 DEN 114 4.3 127 0.00805 Ricky Williams 2009 MIA 59 4.7 128 -0.00145 Willie Parker 2007 PIT 150 4.1 129 -0.00205 Marshawn Lynch 2008 BUF 158 4.1 130 -0.0034 Ricky Williams 2010 MIA 146 4.2 131 -0.00555 Jonathan Dwyer 2012 PIT 178 4 132 -0.00755 Arian Foster 2011 HST 98 4.4 133 -0.00895 Jerome Harrison 2009 CLV 87 4.5 134 -0.00975 Beanie Wells 2011 ARZ 113 4.3 135 -0.02075 Clinton Portis 2007 WAS 179 3.9 136 -0.02655 Kevin Smith 2008 DET 159 4.1 137 -0.02815 Shonn Greene 2011 NYJ 136 4.2 138 -0.03165 Steven R. Jackson 2012 SL 156 4.1 139 -0.04615 Michael Turner 2008 ATL 77 4.5 140 -0.05385 Steven R. Jackson 2008 SL 157 4.1 141 -0.05425 Sammy Morris 2008 NE 63 4.7 142 -0.05815 Rashard Mendenhall 2010 PIT 168 4 143 -0.062 Maurice Jones-Drew 2010 JAX 93 4.4 144 -0.06835 Joique Bell 2014 DET 188 3.9 145 -0.072 Chris D. Johnson 2011 TEN 170 4 146 -0.07955 DeAngelo Williams 2013 CAR 141 4.2 147 -0.08475 Rashad Jennings 2014 NYG 210 3.8 148 -0.1027 Cadillac Williams 2009 TB 191 3.9 149 -0.1053 Giovani Bernard 2013 CIN 162 4.1 150 -0.106 Ron Dayne 2007 HST 173 4 151 -0.1079 LeSean McCoy 2012 PHI 142 4.2 152 -0.11265 Joseph Addai 2007 IND 155 4.1 153 -0.12445 Branden Oliver 2014 SD 223 3.7 154 -0.1289 Ray Rice 2010 BLT 132 4.2 155 -0.1347 Earnest Graham 2007 TB 172 4 156 -0.1349 Ronnie Brown 2008 MIA 118 4.3 157 -0.1364 Edgerrin James 2007 ARZ 201 3.8 158 -0.1432 Arian Foster 2012 HST 148 4.1 159 -0.1519 Ahmad Bradshaw 2011 NYG 195 3.9 160 -0.15615 Terrance West 2014 CLV 196 3.9 161 -0.16155 Giovani Bernard 2014 CIN 177 4 162 -0.1628 Steven R. Jackson 2010 SL 200 3.8 163 -0.1702 Rashard Mendenhall 2011 PIT 160 4.1 164 -0.1753 Steven R. Jackson 2014 ATL 220 3.7 165 -0.1757 Frank Gore 2011 SF 111 4.3 166 -0.1796 DeMarco Murray 2012 DAL 164 4.1 167 -0.1879 Frank Gore 2010 SF 140 4.2 168 -0.19005 Michael Bush 2010 OAK 166 4.1 169 -0.19375 BenJarvus Green-Ellis 2012 CIN 183 3.9 170 -0.19935 Cedric Benson 2011 CIN 185 3.9 171 -0.20385 Frank Gore 2012 SF 57 4.7 172 -0.20935 Zac Stacy 2013 SL 187 3.9 173 -0.2286 BenJarvus Green-Ellis 2010 NE 104 4.4 174 -0.22945 Brian Westbrook 2008 PHI 171 4 175 -0.23345 Maurice Jones-Drew 2008 JAX 143 4.2 176 -0.2337 Bishop Sankey 2014 TEN 212 3.8 177 -0.2341 LeSean McCoy 2014 PHI 131 4.2 178 -0.24495 Thomas Jones 2008 NYJ 80 4.5 179 -0.24555 LeSean McCoy 2009 PHI 167 4.1 180 -0.25135 Brandon Jacobs 2011 NYG 213 3.8 181 -0.25615 Michael Turner 2010 ATL 149 4.1 182 -0.25945 Joique Bell 2013 DET 198 3.9 183 -0.26235 LenDale White 2007 TEN 214 3.7 184 -0.263 Matt Forte 2014 CHI 186 3.9 185 -0.2682 Frank Gore 2013 SF 153 4.1 186 -0.2716 Mike Tolbert 2010 SD 174 4 187 -0.2735 Stevan Ridley 2012 NE 95 4.4 188 -0.2749 Felix Jones 2010 DAL 120 4.3 189 -0.293 Ryan Mathews 2012 SD 207 3.8 190 -0.29305 Maurice Jones-Drew 2013 JAX 244 3.4 191 -0.2957 Cedric Benson 2009 CIN 134 4.2 192 -0.29805 Vick Ballard 2012 IND 190 3.9 193 -0.30195 LaDainian Tomlinson 2010 NYJ 139 4.2 194 -0.31875 Shonn Greene 2010 NYJ 145 4.2 195 -0.31875 Michael Bush 2011 OAK 203 3.8 196 -0.32365 Matt Forte 2008 CHI 180 3.9 197 -0.32895 Kevin S. Jones 2007 DET 211 3.8 198 -0.3322 Ricky Williams 2008 MIA 165 4.1 199 -0.3364 Warrick Dunn 2008 TB 144 4.2 200 -0.3435 Julius Jones 2009 SEA 208 3.8 201 -0.34435 Justin Fargas 2008 OAK 189 3.9 202 -0.36585 Thomas Jones 2007 NYJ 224 3.6 203 -0.38775 DeShaun Foster 2007 CAR 228 3.6 204 -0.38865 Mark Ingram 2012 NO 199 3.9 205 -0.39105 Shonn Greene 2012 NYJ 184 3.9 206 -0.3969 Julius Jones 2007 DAL 230 3.6 207 -0.40125 Chris D. Johnson 2013 TEN 182 3.9 208 -0.41265 Marion Barber III 2009 DAL 105 4.4 209 -0.44185 Joseph Addai 2009 IND 205 3.8 210 -0.44425 Darren McFadden 2014 OAK 248 3.4 211 -0.4505 Reggie Bush 2007 NO 232 3.6 212 -0.46335 Mikel Leshoure 2012 DET 218 3.7 213 -0.47585 Peyton Hillis 2011 CLV 231 3.6 214 -0.48315 Steven R. Jackson 2013 ATL 241 3.5 215 -0.49405 Knowshon Moreno 2009 DEN 204 3.8 216 -0.49465 Thomas Jones 2009 NYJ 130 4.2 217 -0.50145 Ronnie Brown 2010 MIA 219 3.7 218 -0.50555 Ryan Grant 2008 GB 181 3.9 219 -0.51345 Michael Turner 2012 ATL 229 3.6 220 -0.52275 Brandon Jackson 2010 GB 221 3.7 221 -0.5276 Le'Veon Bell 2013 PIT 234 3.5 222 -0.52825 Larry Johnson 2007 KC 240 3.5 223 -0.53185 Marshawn Lynch 2010 SEA 237 3.5 224 -0.5494 Andre Ellington 2014 ARZ 253 3.3 225 -0.5685 Cedric Benson 2007 CHI 247 3.4 226 -0.5729 Adrian Peterson 2007 CHI 249 3.4 227 -0.5729 Matt Asiata 2014 MIN 239 3.5 228 -0.5773 Shaun Alexander 2007 SEA 236 3.5 229 -0.58585 Andre Williams 2014 NYG 251 3.3 230 -0.6027 Trent Richardson 2012 CLV 226 3.6 231 -0.60465 LenDale White 2008 TEN 193 3.9 232 -0.612 Le'Ron McClain 2008 BLT 192 3.9 233 -0.63045 Willis McGahee 2008 BLT 197 3.9 234 -0.63045 Willie Parker 2008 PIT 206 3.8 235 -0.63685 Matt Forte 2009 CHI 227 3.6 236 -0.6663 Cedric Benson 2008 CIN 235 3.5 237 -0.7006 Darren McFadden 2012 OAK 252 3.3 238 -0.7125 Daniel Thomas 2011 MIA 238 3.5 239 -0.7303 Rashard Mendenhall 2013 ARZ 256 3.2 240 -0.73555 Marion Barber III 2008 DAL 216 3.7 241 -0.7499 LaDainian Tomlinson 2008 SD 202 3.8 242 -0.76465 Joseph Addai 2008 IND 242 3.5 243 -0.76675 Dominic Rhodes 2008 IND 243 3.5 244 -0.76675 Thomas Jones 2010 KC 215 3.7 245 -0.7715 BenJarvus Green-Ellis 2011 NE 222 3.7 246 -0.77195 BenJarvus Green-Ellis 2013 CIN 245 3.4 247 -0.806 Jamal Lewis 2008 CLV 225 3.6 248 -0.80715 Laurence Maroney 2009 NE 194 3.9 249 -0.81045 Ray Rice 2013 BLT 259 3.1 250 -0.8108 Jahvid Best 2010 DET 254 3.3 251 -0.82275 Trent Richardson 2014 IND 257 3.2 252 -0.82465 Cedric Benson 2010 CIN 233 3.5 253 -0.83695 Warrick Dunn 2007 ATL 255 3.2 254 -0.844 Thomas Jones 2011 KC 258 3.2 255 -0.844 Mike Bell 2009 NO 209 3.8 256 -0.8632 Brandon Jacobs 2009 NYG 217 3.7 257 -0.9101 Kevin Smith 2009 DET 246 3.4 258 -0.9347 Bernard Pierce 2013 BLT 263 2.9 259 -1.0108 LaDainian Tomlinson 2009 SD 250 3.3 260 -1.0329 Alfred Blue 2014 HST 260 3.1 261 -1.06955 Trent Richardson 2013 IND 262 2.9 262 -1.07965 Rudi Johnson 2007 CIN 261 2.9 263 -1.3429

Top Players

Players at the top change a few spots here and there, and Jamaal Charles moves down one spot because of how good his run blocking was in 2010. One of the major shifts that we see at the top of the chart are the fifteen spots that Lamar Miller jumps; despite the 2014 Dolphins having the 13th worst run blocking grades of all time, Miller managed an incredible 5.1 yards-per-carry.

Bottom Players

Call it a conformation bias, but I knew that I had a stat that was worthy of the public when I looked at the players with the worst baseline scores and Trent Richardson was near the bottom. Brandon Jacobs and Mike Bell are close to the bottom of this list for two reasons. First, the 2009 Saints and Giants are the 10th and 14th best run blocking teams of all time. Second, if my memory serves correct, Jacobs and Bell were both short yardage backs and were put in the game against defenses that were predisposed to stop the run. This metric also doesn’t like LaDainian Tomlinson’s 2008 season where he rushed for 3.8 yards-per-carry, while the Chargers from the same year rank as the 18th best run blocking team since 2010.

Risers

Steven Jackson has the biggest difference—86 spots—between his yards-per-carry rank and his baseline score rank. This makes sense when you see that Jackson mustered 4.2 yards-per-carry, while the 2007 Rams grade out as the worst run blocking team of all time!

Best Predictor For Future Success

To identify whether a player’s current baseline score or yards-per-carry is the best predictor of future success, we need to find out which metric has the highest correlation with the following year’s yards-per-carry total.

But, this is where I am sad to say that sample size becomes a huge issue.

If we look at all players with consecutive seasons of 30 carries in each season from 2007 to 2014, we have a list of 409 players, which is a small sample. This is our first problem. Our second problem is that 30 carries isn’t enough time for a player’s yards-per-carry to stabilize fully (since 2007, Felix Jones is the all time yards-per-carry leader for all players with a minimum of 30 carries at 8.9 YPC: fun fact for all of the Cowboys fans). For those 409 pairs of seasons, a player’s current season baseline score has a .08r with his next season’s yards-per-carry, and a player’s current yards-per-carry has a .07r with his next season’s yards-per-carry. Both of these numbers are not statistically significant.

To solve for the issue of yards-per-carry stabilization, I upped the minimum threshold of carries for players in the population to 100 carries in consecutive seasons. This lowers our population to 210 pairs of seasons; we almost cut the group in half from the previous data set. These 210 players and their baseline score have a .14r with their next season’s yards-per-carry. The same group’s current season yards-per-carry has a .17r with their next season’s yards-per-carry.

As far as the strength of the relationship, these totals are more significant, but instead of coming up with a narrative around why a player’s current yards-per-carry might be the best predictor for future yards-per-carry (narrative that explains potential noise: it takes time to turn around a bad offensive line, and a player’s true talent yards per carry totals may take longer become actualized), we should just settle for the fact that our sample size is too small to make any definitive statements.

Conclusions

While we weren’t able to find the best predictor for yards-per-carry, we were able to establish a connection between a team’s run blocking ability and their running back’s yards-per-carry. We were also able to come up with a metric that adjusts a running back’s yards-per-carry by their team’s run blocking ability.

This approach obviously isn’t perfect, and it doesn’t try to account for every piece of noise that inhibits our ability to objectively asses a running back’s true talent; it just tries to control for a team’s ability to block for their running back.

Altogether, this research should help come up with a quantifiable measure of a running back’s ability that is independent of his team’s performance. However, there are several other variables that we need to control before we can come up with a more holistic number that statistically measures a running back’s true talent.