• Dylan Fadden

What PFF's Unique Quarterback Stats Say About Draft Prospects

In pursuit of finding the secret to predicting an NFL player's success, I took a look at the most analyzed position in all of football—the quarterback. To do this, I utilized Pro Football Focus’s unique quarterback statistics. Pro Football Focus, better known as PFF, is a media outlet that prioritizes the usage of analytics in the football world. PFF tracks and grades every football game from low-level D1 college football, all the way into the NFL. This in-depth analysis offers grades on every single play of every game, as well as the entire season as a whole. For the quarterback position, their unique statistics include:

BTT (Big Time Throws): A pass with excellent ball location and timing, generally thrown further down the field and/or into a tighter window.

BTT%: The percentage of attempts that are BTTs.

TWP (Turnover Worthy Plays): A pass that has a high percentage chance to be intercepted. Or, a poor job of taking care of the ball and fumbling.

TWP%: The percentage of attempts that are TWP.

ADJ% (Adjusted Completion Percentage): The % of aimed passes thrown on target (completions + drops / aimed).

*pulled from PFF.com*

Score = each statistical category rank added together. Similar to golf, the lower score is better. (BTT%rank + TWP%rank + ADJ%rank)

By doing this, PFF eliminates the, “Oh, well, he played against weaker competition in college,” argument. These stats are specifically based on aspects of the game that are completely in the quarterback's control.

For the pool of players, I chose quarterbacks that were first-round draft selections and had received significant playing time in the NFL. First sorting it by year, I ranked each quarterback by their performance in BTT, TWP, ADJ%, and overall pass grade. After that, I moved every quarterback in the player pool into a group as a whole and ranked them best to worst in the same categories. I then added together each ranking in each category for every player to come to a combined score. For example, Russell Wilson finished second in BTT%, third in TWP%, and second in ADJ% to come to a final score of seven (2+3+2=7).

Are PFF’s unique stats an indicator of overall career success?


Defining success in the NFL is rather difficult due to the subjective nature of the question. Where do you draw the line between failure and success? Is it winning a Super Bowl? Winning an MVP? Or simply sustaining a long career? These different levels all mean “success” to some degree, and of the player pool, nearly all of them fill in somewhere between these different levels of success.

To disprove the original thought of being able to determine NFL success with PFF’s unique stats, we can easily identify players at each tier of success randomly throughout the pool of players. My findings indicate that there is NO clear correlation between where a player ranks in these statistics and significant success in the NFL.

In his first three years, Patrick Mahomes quickly became the face of the NFL—winning a Super Bowl and the MVP award. Oddly enough, he did not fare too well in these rankings. Of the 30 players that were analyzed in this study, Patrick Mahomes finished 16th with a score of 50. This was worse than Brandon Weeden, Dwayne Haskins, Paxton Lynch, and Blake Bortles.

Even with winning the Heisman Trophy at Louisville, Lamar Jackson faced harsh criticism when preparing for the NFL draft; he was an afterthought in the quarterback-packed draft of 2018. Starting the final seven games of his rookie season, Jackson went 6-1 and gained the momentum needed to propel himself to an MVP in 2019. If you were to only base your opinion of Jackson off of my formula, you would be shocked at his performance in the NFL. Jackson ranked 29 out of 30, and 19 or lower in the three statistical categories.

Can PFF’s unique stats be helpful in deciphering between players in a specific draft class? (finding a “sleeper?”)


While determining specific levels of NFL success cannot be determined from these statistics, I did find that in some specific years, there is some trend of lower draft picks becoming better than those picked before them.

The first year we can see this in is the 2012 quarterback draft class.

Andrew Luck and Robert Griffin III were the unanimous number one and two draft selections; there was no disputing that. But aside from them, why not Russell Wilson? Across these three statistical categories, Wilson performed extremely well in comparison to his draft counterparts. This is not to say that it was clear and obvious that Wilson would become the player he is today; however, it is worth noting that he should’ve had much more hype around him during the draft process.

Sure, there was a lot to question with Wilson, but to be as proficient as he was in these statistics meant that he possessed elite-tier accuracy, decision making, and playmaking ability. By the numbers, there is no explanation for why Wilson was selected behind Brock Osweiler and Brandon Weeden. In fact, it wasn’t even close. Aside from scoring the best, Wilson distanced himself from these two by seven and 14 points—proving he was far better than both of them in every category. This is the perfect example of why scouts need to close their mouths, stop drooling over height and physicality, and start paying attention to the numbers.

The second year I want to look at is the 2017 draft class.

This topic of conversation has gained plenty of traction in recent years due to the success of both Patrick Mahomes and Deshaun Watson. Honestly, it’s been exhausted to this point. The central point of discussion has been, “Why was Mitch Trubisky drafted over Deshaun Watson and Patrick Mahomes?” Statistically, there is not an argument to be made for Mahomes, as he performed shockingly close to Trubkisy in this formula. However, for Watson, there is a clear argument to be made. As if winning a national championship wasn’t enough, the quarterback from Clemson finished first in his draft class for BTT%, TWP%, and ADJ%. If a quarterback is notably proficient in all three of the statistical categories compared to the rest of the prospects in the given class, it is reasonable to assume that he should be drafted higher than what he is projected. While this example isn’t as drastic as the Russell Wilson scenario, it still provides a similar trend and suggests that Watson should have been taken a