10 Blind Player Comparisons From the 2015 NFL Season

Would Russell Wilson's success have been as obvious this year if not for his name?

Stats are fun, and game-watching is fun.

Sometimes, those things work together to reinforce the same idea. Occasionally, they work in opposite directions, and one contradicts the other.

If we remove the names and just look at the metrics (i.e. Net Expected Points (NEP), which compares a player's performance to historical expectation level), we can see some different stories unfold.

Here are 10 such cases from the 2015 NFL season.


Player Drop Backs Passing NEP Passing NEP/P Passing Success Rate
Player A 299 19.17 0.06 45.48%
Player B 229 141.09 0.62 60.26%

Okay, you might be asking yourself why these two instances were chosen, and that's mainly because I'm tricking you. But just know that Andy Dalton led the NFL with a Passing NEP per drop back of 0.35 this year and that the best Passing NEP per drop back of any player since 2000 with at least 200 drop backs was Peyton Manning's 0.45 in 2004. Tom Brady's 58.93 percent Success Rate (the percentage of drop backs that led to expected points gains) was the highest among that group. Basically, Player B was absolutely phenomenal.

Conversely, Player A is a big-name quarterback whose passing metrics declined each year since he entered the league but who earned a monstrous offseason contract before the year began.

Player A is Russell Wilson through Week 10, and Player B is Russell Wilson after Week 10.

Player Drop Backs Passing NEP Passing NEP/P Passing Success Rate
Player A 249 -6.98 -0.03 40.56%
Player B 347 -7.95 -0.02 42.94%
Player C 307 -9.15 -0.03 42.02%

These players, listed by Passing NEP, are an uninspiring bunch. But what makes it even worse is that the average Passing NEP per drop back of the 37 quarterbacks with at least 200 drop backs this year wasn't zero; it was 0.12. Oof.

You wouldn't know it by the numbers, but these guys all have different career arcs. Player A is a youngster still trying to find his way and avoid traffic issues. Player B's best days are behind him. And Player C, well, let's just say it's Blaine Gabbert.

Player A is Ryan Mallett, Player B is Peyton Manning, and Player C is Blaine Gabbert.

Running Backs

Player Rushes Rushing NEP Rushing NEP/P Success Rate
Player A 125 18.41 0.15 50.40%
Player B 196 -12.76 -0.07 37.76%

Pretty big gap here, eh? Well, it's funny, but Player B was actually a big reason why Player A didn't see the field for much of the year. In all, they were about 31 points apart in expected points based on rushing. Among 44 backs with at least 100 carries, Player A was first in Rushing NEP and Rushing NEP per carry and second in Success Rate.

Player B was 34th, 33rd, and 28th among the group. Talk about an injustice.

Player A? David Johnson. Player B? Chris Johnson.

Player Rushes Rushing NEP Rushing NEP/P Successes Success Rate
Player A 167 21.98 0.13 81 48.50%
Player B 97 -14.52 -0.15 29 29.90%

Player A was unstoppable. His Rushing NEP per carry would have placed him second in the league among 100-plus carry backs, and his Success Rate was a monumental improvement from his mark the year before (24.62 percent).

Player B's awful metrics somehow improved upon his prior season as well. In 2014, his Rushing NEP  was -18.80, and his Rushing NEP per carry was -0.23. His Success Rate ranked 1,077th among 1,079 running backs with at least 50 carries since 2000.

Let that sink in. 1,077 of 1,079.

Player A is Devonta Freeman through Week 11, and Player B is Devonta Freeman after Week 11.

Player Rushes Rushing NEP Rushing NEP/P Successes Success Rate
Player A 154 2.28 0.01 67 43.51%
Player B 223 -13.39 -0.06 91 40.81%

You wouldn't know it by these end-of-year numbers, but this was a hard-to-peg backfield entering the season. Well, at least in the sense that Player B had dominated last year, analytically, and Player A struggled.

Last year, Player A posted a Rushing NEP of -6.62 on 168 carries. Player B was at 20.63 on 222 totes. They reversed roles this year.

Player A is Giovani Bernard; Player B is Jeremy Hill.

Wide Receivers

Player Rec Rec NEP Targets Tar NEP Rec NEP/Tar Catch Rate Success Rate
Player A 105 107.73 177 23.96 0.61 59.32% 81.90%
Player B 80 106.18 132 62.71 0.80 60.61% 88.75%

These former teammates finished eighth and ninth, respectively, in Reception NEP (did that give it away? Sorry). How they did it, though, is a vastly different story. Player A was a volume-dependent player: among 32 receivers with at least 100 targets this year, he ranked 26th in Reception NEP per target. Player B ranked seventh.

I'm sure it's obvious, but Player A is Demaryius Thomas, and Player B is Eric Decker.

Player Rec Rec NEP Targets Tar NEP Rec NEP/Tar Catch Rate Success Rate
Player A 111 143.89 192 71.55 0.75 57.81% 95.50%
Player B 64 82.92 105 42.90 0.79 60.95% 95.31%

Okay, the volume here is vastly different, and you probably know who Player A is based on his target count, but Player B's rate stats are pretty darn close to Player A's. Of course, keeping up that efficiency over 90 more targets wouldn't be a guarantee, but on a per-target and per-catch basis, Donte Moncrief (Player B) was actually pretty similar to DeAndre Hopkins.

For what that's worth.

Player Rec Rec NEP Targets Tar NEP Rec NEP/Tar Catch Rate Success Rate
Player A 44 63.19 79 25.37 0.80 55.70% 90.91%
Player B 50 60.27 92 18.01 0.66 54.35% 86.00%

Okay. Okay. This one is rough. Well, for a lot of people at least. Player A had a career year.

In 2014, his Reception NEP was 54.89, and his Reception NEP per target was 0.64 on 86 targets. His Reception Success Rate was 88.68 percent. His Catch Rate was 61.63 percent. Basically, his 2014 was Player B's 2015. And his 2015 saw an increase in efficiency, most notably in Reception NEP per target.

That would be fine if it didn't make us reconsider everything we thought we knew. Player A is Markus Wheaton, and Player B is Martavis Bryant.

Tight Ends

Player Rec Rec NEP Targets Tar NEP Rec NEP/Tar Catch Rate Success Rate
Player A 52 72.15 74 55.98 0.98 70.27% 92.31%
Player B 72 61.27 103 31.48 0.59 69.90% 80.56%
Player C 75 58.55 112 16.37 0.52 66.96% 74.67%

Whew. One of these is not like the other, but each of these guys has the physical tools (and hype) to be the next big thing at the tight end position. But take a look at these numbers. A long look.

These guys rank seventh through ninth in Reception NEP among 28 tight ends with at least 50 targets this year, but there's a big difference in the actual scores. Per target? Forget about it. Player A led the 28 tight ends (second place was Rob Gronkowski at a distant 0.88). Player B was 14th of 28. Player C was 20th, worse than Vernon Davis.

Success Rate? Psh. Player A led the group. Player B ranked 16th, and Player C ranked 24th, suggesting a lot of inflated numbers that didn't actually make a meaningful impact on the game.

They are Tyler Eifert (A), Travis Kelce (B), and Zach Ertz (C).

PlayerRecRec NEPTargetsTar NEPRec NEP/TarCatch RateSuccess Rate
Player A7478.8411050.720.7267.27%82.43%
Player B4854.827431.650.7464.86%79.17%

Different usage but similar efficiency. Player A had a better Catch Rate and Success Rate, but it was pretty much a wash after that. For what it's worth, Player B's cap hit was fifth highest among all tight ends this year. Player A was just 32nd, tied with Jeff Cumberland and Jim Dray.

Player A is Benjamin Watson, and Player B is Jimmy Graham.