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Players with the best NFL Combine results for fantasy football success

The NFL Combine has come and gone, leaving in its wake a wave of winners, losers and shifting big boards. Not to be left out of the fun, I dove in with an analytical look at the 2020 prospects based on which Combine metrics matter for offensive and defensive players.

The technique I used in those analyses is called ridge regression, which is great at quantifying the importance of each metric for comparison. We can now say with some confidence which drills matter most for each position group based on historical data. In this analysis, my process changes in a few ways. 

First, the target variable, i.e. what we’re predicting, is going to be the fantasy football success of prospects, not draft position and player value (PFF WAR). Second, fantasy success is measured as a binary variable (yes/no), based on whether each player achieved a particular season-end scoring threshold in his first three seasons. These thresholds are top-12 scorer for quarterbacks, top-20 for running backs, top-30 for wide receivers and top-eight for tight ends. Lastly, the algorithm employed here doesn’t assume there is the linear relationship between performance in each feature variable and the target, and there’s no assumption that features are independent. 

[Editor's Note: PFF's new college-to-pro projection system is powered by AWS machine learning capabilities.]

This analysis projects the likelihood of fantasy success in a prospect's first three seasons using the random forest algorithm. Random forest predicts classifications (success or not) by building many decision trees, randomizing samples and features, creating a “forest” of decision trees whose collective predictions are more accurate than an individual tree. 

This technique differs from the more linear relationships in classification algorithms like logistic regression. A linear relationship would tell us that a prospect who runs 0.2 seconds faster than average would have a feature effect twice that as one who runs 0.1 seconds faster, but we know that relationships aren’t that simple and the benefits of being faster, bigger, quicker can see diminishing returns as some point. The downside of using random forest versus logistic regression comes in interpretability. While forest-based algorithms often produce better predictions, the exact manner in which they have split data and assigned probabilities is opaque. 

I’ll illustrate the process below for every position by including an example decision tree first, then detailing results of the random forest predictions on historical prospects and the 2020 class. You’ll see that a single decision tree contains some useful insights but with rigid cutoffs that are likely overfit to past data. 

Quarterbacks

Our sample decision for the quarterback tree includes the 110 prospects in our 2007-2017 data who participated in the majority of Combine drills. The first split is by broad jump at 118 inches, with those at or above this number falling into the highest success node. This is the node in the lower right corner, that includes three numbers: the ratio of data points that fall into the node classified as successful (0.41), the total number of prospects who fall into the node (n=22) and the rough percentage that fall into the node of the entire population (20%). 

Those who don’t make the 118-inch cut for broad jump are then sorted by short shuttle times, first at less than 4.3 seconds and then greater than or equal to 4.5 seconds. This is a perfect example of how using a single decision tree can overfit the data. There’s no logical reason why a quarterback would be more successful with a short shuttle at or above 4.5 seconds than at 4.4 seconds, which is the case for the last split in the bottom left. The random forest we use for the results below is informed by so many trees that overfitting is less of an issue. 

Top-10 Historical Success Probabilities: Quarterbacks (2006-2017)

Year Pick Player Success Success Prob Height Weight Forty Bench Broad Vertical 3cone Shuttle
2014 3 Blake Bortles Yes 62.6% 77 232 4.93 NaN 115 32.5 7.08 4.21
2016 26 Paxton Lynch No 60.0% 79 244 4.86 NaN 118 36 7.14 4.26
2010 25 Tim Tebow No 59.9% 75 236 4.71 NaN 115 38.5 6.66 4.17
2009 17 Josh Freeman Yes 58.1% 78 248 4.9 NaN 119 33.5 7.11 4.43
2017 300 Mitch Leidner No 56.4% 75 226 4.93 NaN 123 34.5 6.96 4.25
2009 5 Mark Sanchez Yes 56.3% 74 227 4.93 NaN 116 32.5 7.06 4.21
2015 103 Bryce Petty No 55.2% 75 230 4.87 NaN 121 34 6.91 4.13
2016 135 Dak Prescott Yes 54.3% 74 226 4.79 NaN 116 32.5 7.11 4.32
2017 2 Mitchell Trubisky No 50.7% 74 222 4.67 NaN 116 27.5 6.87 4.25
2011 300 Jerrod Johnson No 49.3% 77 251 4.75 NaN 119 29 7.28 4.31

As I mentioned earlier, it’s more difficult to interpret random forest results, but the consistent themes in the top-10 are strong broad jumps and middling-to-strong performance in the agility drills. In addition, all 40-yard dash times were under five seconds, and all weights are over 220 pounds. 

The model was fairly poor at picking out actual success, with only fleeting successes like Blake Bortles, Josh Freeman and Mark Sanchez qualifying. The results tell us what we already knew: a lot more goes into being a successful quarterback than athleticism.

2020 Quarterbacks

Year Player Success Prob Height Weight Forty Bench Broad Vertical 3cone Shuttle Rank
2020 Justin Herbert 50.0% 78 236 4.68 NaN 123 35.5 7.06 4.46 10
2020 Cole McDonald 42.5% 75 215 4.58 NaN 121 36.0 7.13 4.52 21
2020 James Morgan 34.5% 76 229 4.89 NaN 112 29.0 7.51 4.64 40
2020 Nate Stanley 33.1% 75 235 4.81 NaN 108 28.5 7.26 4.48 42
2020 Jalen Hurts 31.5% 73 222 4.59 NaN 125 35.0 NaN NaN 44
2020 Jake Fromm 30.2% 73 219 5.01 NaN 111 30.0 7.27 4.51 48
2020 Kelly Bryant 27.3% 75 229 4.69 NaN 125 35.0 7.33 4.51 56
2020 Kevin Davidson 26.5% 76 224 5.00 NaN NaN NaN 7.13 4.37 61
2020 Jordan Love 20.0% 75 224 4.74 NaN 118 35.5 7.21 4.52 83
2020 Jacob Eason 19.6% 77 231 4.89 NaN 110 27.5 7.50 4.75 84
2020 Shea Patterson 18.7% 72 212 4.71 NaN 116 31.0 7.14 4.50 91
2020 Steven Montez 7.3% 76 231 4.68 NaN 117 33.0 7.25 4.43 144
2020 Brian Lewerke 7.2% 74 213 4.95 NaN NaN NaN 7.14 4.40 145

Justin Herbert checks all the Combine boxes as a tall, big, relatively fast and quick quarterback. Even then, the model only gives him a coin-flip chance at success based only on measurables. Jalen Hurts is shockingly fifth in the 2020 class, though he’s likely penalized significantly for skipping the agility drills despite his top-level speed and jumps. 

Running Backs

The sample decision tree for running backs is more complex, mostly due to the larger sample of players at the position who participate at the combine in comparison to quarterbacks. It’s also the case that athleticism matters more for running backs, with the top three leafs (final nodes) of the tree with average success rates between 0.44 and 0.6. The first (most important) split is at 4.4 seconds in the 40-yard dash, with players below that mark finding fantasy success 60% of the time. Next, the broad jump, weight, three-cone drill and vertical jump all factor into various splits. Again, some of the splits directionally go against intuition, but that’s why we build a forest of decision trees for our final numbers below.

Top-10 Historical Success Probabilities: Running Backs (2006-2017)

Year Pick Player Success Success Prob Height Weight Forty Bench Broad Vertical 3cone Shuttle
2007 7 Adrian Peterson Yes 77.5% 73 217 4.40 NaN 127 38.5 7.09 4.40
2008 73 Jamaal Charles Yes 76.1% 71 200 4.38 NaN 122 30.5 6.80 4.22
2016 73 Kenyan Drake Yes 74.9% 73 210 4.45 10 123 34.5 7.04 4.21
2008 55 Ray Rice Yes 73.6% 68 199 4.42 23 119 31.5 6.65 4.20
2017 8 Christian McCaffrey Yes 73.3% 71 202 4.48 10 121 37.5 6.57 4.22
2008 44 Matt Forte Yes 72.5% 73 217 4.44 23 118 33.0 6.84 4.23
2006 30 Joseph Addai Yes 71.8% 71 214 4.40 18 125 38.5 7.09 4.47
2013 48 Le'Veon Bell Yes 71.8% 73 230 4.60 24 118 31.5 6.75 4.24
2011 73 Stevan Ridley Yes 70.0% 71 225 4.65 18 118 36.0 6.78 4.21
2016 45 Derrick Henry Yes 69.8% 75 247 4.54 22 130 37.0 7.20 4.38

The random forest model is much more accurate at running back, with all of the top-10 in success probability being actual successes in the NFL. Some of this is the fact that the model was fit on the same data that it is now predicting, but it’s also due to the strength of athleticism in predicting positional success. The tenth-highest predicted probability for running backs (Derrick Henry, 69.8%) is higher than the top probability at quarterback.

2020 Running Backs

Year Player Success Prob Height Weight Forty Bench Broad Vertical 3cone Shuttle
2020 Jonathan Taylor 45.8% 70 226 4.39 17 123 36.0 7.01 4.24
2020 AJ Dillon 37.6% 72 247 4.53 23 131 41.0 7.19 NaN
2020 Brian Herrian 33.8% 71 209 4.62 18 126 38.5 7.12 4.40
2020 Darius Anderson 32.2% 70 208 4.61 19 128 36.0 NaN 4.19
2020 Cam Akers 29.6% 70 217 4.47 20 122 35.5 NaN 4.42
2020 JaMycal Hasty 29.4% 68 205 4.55 15 123 39.0 NaN 4.03
2020 Anthony McFarland 26.9% 68 208 4.44 NaN 116 29.5 NaN NaN
2020 Darrynton Evans 25.4% 70 203 4.41 20 125 37.0 NaN NaN
2020 Rico Dowdle 24.0% 71 213 4.54 NaN 127 38.0 NaN NaN
2020 James Robinson 22.4% 69 219 4.64 24 125 40.0 7.03 4.19
2020 Scottie Phillips 21.7% 68 209 4.56 29 114 30.0 7.40 4.53
2020 Joshua Kelley 20.0% 70 212 4.49 23 121 31.0 6.95 4.28
2020 Raymond Calais 19.6% 67 188 4.42 20 120 37.5 NaN NaN
2020 Eno Benjamin 19.0% 68 207 4.57 12 122 39.0 6.97 4.25
2020 Javon Leake 17.8% 72 215 4.65 NaN 125 28.5 NaN NaN
2020 LeVante Bellamy 16.9% 68 192 4.50 16 125 39.5 NaN NaN
2020 Clyde Edwards-Helaire 16.9% 67 207 4.60 15 123 39.5 NaN NaN
2020 Patrick Taylor 16.5% 73 217 4.57 15 123 34.0 NaN 4.34
2020 Tony Jones Jr. 14.6% 70 220 4.68 13 119 32.5 7.18 4.21
2020 Sewo Olonilua 13.8% 74 232 4.66 25 123 36.0 NaN 4.28
2020 D'Andre Swift 12.7% 68 212 4.48 NaN 121 35.5 NaN NaN
2020 J.J. Taylor 12.6% 65 185 4.61 19 118 34.5 7.00 4.15
2020 Ke'Shawn Vaughn 9.7% 69 214 4.51 NaN 117 32.0 NaN NaN
2020 DeeJay Dallas 6.6% 70 217 4.58 NaN 119 33.5 7.18 4.32
2020 Salvon Ahmed 4.1% 70 197 4.62 NaN 120 34.5 NaN NaN
2020 Benny LeMay 3.8% 68 221 4.75 24 112 34.0 NaN NaN
2020 Lamical Perine 3.7% 70 216 4.62 22 118 35.0 7.13 4.31
2020 Zack Moss 1.6% 69 223 4.65 19 NaN 33.0 NaN 4.37

Jonathan Taylor performs best in the fantasy success model, though at 45.8% probability perhaps not as well as you would have suspected. A.J. Dillon isn’t far behind in the class, while PFF’s pre-Combine RB1, Zack Moss, was given the lowest probability of success by the model at a paltry 1.6%.

Wide Receivers

The sample tree for wide receivers splits first at weight (200 pounds), then broad jump (120-121 inches), and for lower leaves short shuttle and bench press. Looking back to the 2006-2017 sample could be overvaluing receiver size, as it’s become less of a driver in today’s space-seeking NFL offenses. Still, the big prototype WR1s are probably the best bets to attract targets and see fantasy success early in their careers.

Top-10 Historical Success Probabilities: Wide Receivers (2006-2017)

Year Pick Player Success Success Prob Height Weight Forty Bench Broad Vertical 3cone Shuttle
2018 91 Trequan Smith No 62.2% 74 203 4.49 12 130 37.5 6.97 4.50
2018 174 Marquez Valdes-Scantling No 56.6% 76 206 4.37 15 124 30.5 NaN NaN
2012 43 Stephen Hill No 52.8% 76 215 4.36 14 133 39.5 6.88 4.48
2014 90 Donte Moncrief No 51.2% 74 221 4.40 13 132 39.5 7.02 4.30
2007 2 Calvin Johnson Yes 48.8% 77 239 4.35 NaN NaN NaN NaN NaN
2013 34 Justin Hunter No 45.8% 76 196 4.44 NaN 136 39.5 NaN 4.33
2015 7 Kevin White No 44.2% 75 215 4.35 23 123 36.5 6.92 4.14
2008 84 Harry Douglas No 42.7% 71 176 4.51 NaN 120 31.0 6.57 4.12
2007 157 David Clowney No 42.5% 72 188 4.36 NaN 123 32.5 7.00 4.15
2009 124 Louis Murphy No 41.8% 74 203 4.32 12 NaN NaN NaN NaN

The wide receiver success model was fairly awful at finding successful wide receivers, only hitting on Calvin Johnson. The other athletic marvels on the top-10 list weren’t able to translate those gifts into fantasy production, another sign that we shouldn't read too much into wide receiver results from the Combine.

2020 Wide Receivers

Year Player Success Prob Height Weight Forty Bench Broad Vertical 3cone Shuttle
2020 Denzel Mims 46.3% 74 207 4.38 16 131 38.5 6.66 4.43
2020 Chase Claypool 44.1% 76 238 4.42 19 126 40.5 NaN NaN
2020 Isaiah Hodgins 39.2% 75 210 4.61 9 124 36.5 7.01 4.12
2020 Michael Pittman Jr. 36.8% 76 223 4.52 13 121 36.5 6.96 4.14
2020 Dezmon Patmon 29.9% 75 225 4.48 15 132 36.0 7.28 4.38
2020 Juwan Johnson 29.3% 76 230 4.58 14 124 33.0 6.94 4.37
2020 Brandon Aiyuk 28.6% 71 205 4.50 11 128 40.0 NaN NaN
2020 Jalen Reagor 26.5% 70 206 4.47 17 138 42.0 7.31 4.46
2020 Tyrie Cleveland 23.1% 74 209 4.46 13 126 39.5 NaN NaN
2020 Jerry Jeudy 22.4% 73 193 4.45 NaN 120 35.0 NaN 4.53
2020 Henry Ruggs III 22.3% 71 188 4.27 NaN 131 42.0 NaN NaN
2020 Antonio Gandy-Golden 21.9% 76 223 4.60 22 127 36.0 7.33 4.55
2020 Quez Watkins 21.3% 72 185 4.35 NaN 125 36.5 7.28 4.36
2020 Darnell Mooney 20.6% 70 176 4.38 9 124 37.0 NaN NaN
2020 Isaiah Coulter 19.6% 73 198 4.45 NaN 121 36.0 7.28 4.62
2020 Donovan Peoples-Jones 19.2% 73 212 4.48 NaN 139 44.5 NaN NaN
2020 Devin Duvernay 19.1% 70 200 4.39 NaN 123 35.5 7.13 4.20
2020 Gabriel Davis 16.8% 74 216 4.54 14 124 35.0 7.08 4.59
2020 Justin Jefferson 15.9% 73 202 4.43 NaN 126 37.5 NaN NaN
2020 Antonio Gibson 15.7% 72 228 4.39 16 118 35.0 NaN NaN
2020 Kalija Lipscomb 14.7% 71 207 4.57 16 127 32.0 NaN NaN
2020 K.J. Osborn 14.7% 71 203 4.48 18 123 37.5 7.00 4.35
2020 Kendrick Rogers 13.8% 76 208 4.51 17 124 35.5 7.13 4.48
2020 Chris Finke 13.4% 69 186 4.57 7 NaN 40.0 NaN NaN
2020 Binjimen Victor 11.5% 75 198 4.60 9 128 35.0 7.10 NaN
2020 Jeff Thomas 10.7% 68 170 4.45 NaN 125 36.5 NaN NaN
2020 Stephen Guidry 10.6% 75 201 4.47 NaN 125 34.0 7.31 4.46
2020 Omar Bayless 10.6% 72 212 4.62 11 123 36.0 7.35 4.50
2020 Joe Reed 10.5% 72 224 4.47 21 123 38.0 NaN NaN
2020 Trishton Jackson 9.9% 72 197 4.50 NaN 117 36.0 NaN NaN
2020 Marquez Callaway 9.8% 73 205 4.55 NaN 126 38.0 NaN NaN
2020 Jauan Jennings 9.6% 75 215 4.72 NaN 119 29.0 NaN NaN
2020 Cody White 8.8% 75 217 4.66 NaN 120 35.5 7.19 4.52
2020 Malcolm Perry 8.5% 69 186 4.63 10 122 36.0 7.12 4.31
2020 CeeDee Lamb 8.5% 73 198 4.50 11 124 34.5 NaN NaN
2020 Aaron Parker 8.0% 73 209 4.57 12 112 26.5 6.94 4.23
2020 Tony Brown 6.9% 72 198 4.65 14 119 33.5 7.21 4.27
2020 Quintez Cephus 5.7% 72 202 4.73 23 124 38.5 7.20 4.33
2020 Freddie Swain 5.5% 72 197 4.46 16 124 36.5 7.05 4.26
2020 John Hightower 5.5% 73 189 4.43 NaN 122 38.5 7.07 4.21
2020 Aaron Fuller 3.9% 70 188 4.59 NaN 118 34.0 7.14 NaN
2020 Quartney Davis 3.0% 73 201 4.54 NaN NaN 35.5 NaN NaN
2020 Laviska Shenault Jr. 2.7% 72 227 4.58 17 NaN NaN NaN NaN
2020 Austin Mack 2.4% 73 208 4.59 NaN 117 31.5 NaN 4.42
2020 K.J. Hill 0.8% 71 196 4.60 17 114 32.5 NaN NaN

Buzz is building post-Combine for names at the top of the list like Denzel Mims and Chase Claypool, but the wide receiver model still sees them at less than 50% for fantasy success. I’m not suggesting that you ignore the results of the Combine, as they’ll at least affect draft positions. But caution is probably in order before thinking about adding them to dynasty rosters ahead of more accomplished receivers.

Tight Ends


The sample decision tree for tight ends first splits at 40-yard dash (4.5 seconds), then for the tight ends who don’t make that steep threshold at three-cone drill (7.2 seconds), and broad jump. In fantasy football, we don’t care much about tight-end versatility or how well they block, but the ability to stretch the field and get open with quickness are paramount.

Top-10 Historical Success Probabilities: Wide Receivers (2006-2017)

Year Pick Player Success Success Prob Height Weight Forty Bench Broad Vertical 3cone Shuttle
2017 19 O.J. Howard No 58.9% 78 251 4.51 22 121 30.0 6.85 4.16
2008 141 Gary Barnidge No 50.4% 78 243 4.61 22 117 31.0 6.92 4.23
2015 204 Darren Waller No 46.9% 78 238 4.46 12 125 37.0 7.07 4.25
2013 222 Chris Gragg No 43.4% 75 244 4.50 18 125 37.5 7.08 4.51
2012 34 Coby Fleener No 42.7% 78 247 4.51 27 NaN NaN NaN NaN
2007 31 Greg Olsen No 40.8% 78 254 4.51 23 114 35.5 7.04 4.48
2006 72 Leonard Pope No 39.0% 80 258 4.62 22 118 37.5 7.47 4.67
2008 300 Darrell Strong No 38.9% 76 268 4.78 17 110 25.0 6.86 4.36
2008 158 Kellen Davis No 38.2% 78 262 4.59 22 118 28.0 7.25 4.38
2015 117 Blake Bell No 36.7% 78 252 4.8 14 116 33.0 6.85 4.32

The threshold of fantasy success for tight ends in this analysis is tougher at TE8, but the model going 0-for-10 isn’t a great endorsement. It has identified move tight ends, some of whom were eventually successes later in their careers, like Greg Olsen and Darren Waller. If anything, the model’s lack of accuracy points to some of the randomness of the position and how closely it’s linked to quarterback and touchdown scoring.

2020 Tight Ends

Year Player Success Prob Height Weight Forty Bench Broad Vertical 3cone Shuttle
2020 Adam Trautman 37.6% 77 255 4.80 18 114 34.5 6.78 4.27
2020 Albert Okwuegbunam 35.7% 77 258 4.49 NaN NaN NaN NaN NaN
2020 Dalton Keene 23.9% 76 253 4.71 21 125 34.0 7.07 4.19
2020 Stephen Sullivan 22.9% 76 248 4.66 NaN 123 36.5 7.51 4.62
2020 Charlie Taumoepeau 22.5% 74 240 4.75 18 121 36.5 7.00 4.27
2020 Charlie Woerner 15.1% 76 244 4.78 21 120 34.5 7.18 4.46
2020 Josiah Deguara 14.0% 74 242 4.72 25 115 35.5 7.15 4.35
2020 C.J. O'Grady 13.8% 75 253 4.81 16 119 34.0 7.30 4.34
2020 Devin Asiasi 13.7% 75 257 4.73 16 115 30.5 NaN NaN
2020 Cole Kmet 13.2% 77 262 4.70 NaN 123 37.0 7.44 4.41
2020 Colby Parkinson 12.6% 79 252 4.77 18 109 32.5 7.15 4.46
2020 Dominick Wood-Anderson 11.4% 75 261 4.92 NaN 119 35.0 NaN NaN
2020 Hunter Bryant 9.1% 74 248 4.74 23 115 32.5 7.08 4.46
2020 Brycen Hopkins 6.2% 75 245 4.66 21 116 33.5 7.25 4.28
2020 Jared Pinkney 4.9% 76 257 4.96 23 NaN NaN NaN NaN
2020 Harrison Bryant 2.9% 76 243 4.73 13 110 32.5 7.41 4.37
2020 Mitchell Wilcox 2.2% 75 247 4.88 NaN 112 31.0 7.37 4.43

You wouldn’t think a tight end with a 4.80 40-yard dash would top the list over one who posted a sub-4.5 time, but that’s what numbers say. Adam Trautman likely benefits significantly from his 6.78 three-cone time at 255 pounds, while Albert Okwuegbunam is probably hurt by skipping drills other than the 40-yard dash. Outside of those two, the model doesn’t see much chance of success for the rest of the class, at least only based on their Combine metrics.

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