Blind Side Project : How Good is D'Brickashaw Really

D'Brickashaw Ferguson is the left tackle for the New York Jets. He provides an interesting example to begin the ranking of offensive linemen because he has been in the league for four years, was the fourth pick in the 2006 draft, was selected to his first Pro Bowl last season after starting every game of his NFL career. Expectations for D'Brickashaw were high coming out of college and he appears to be realizing them. He also protects the blind side of a second year QB who many feel is below average and believe that his gaudy 8.5 adjusted yards per attempt and 0% interception rate is due more to short quick passes and loads of YAC than his true QB skill. This suggests that D'Brickashaw is not serving an insurance role as much as he is creating value for the Jets offense.

To get an idea of how effective a LT D'Brickashaw really is I used the data set that I have collected with the tremendous assistance of Keith Goldner. We now have 9 teams in the data set and enough pass attempts to make a first pass at evaluation. In order to evaluate linemen we will borrow from those that came before us at footballoutsiders.com (as well as many others) and compare performance to average. In this case though, what we are going to measure as performance, is the probability that, for a certain length of time, the lineman will be able to successfully hold their block.

Warning: Technical Stats Jargon in Use: Trying to estimate the average probability that a LT can hold a block for 2.5 seconds is tricky because the data is censored data. In this case, the phrase "censored data" means that on any play that a lineman holds his block successfully and the QB throws the ball after 2.5 seconds, we do not know how long the lineman could have held that block. The lineman may have been about to lose the battle, or maybe he could have continued to protect the QB for 2 or 3 more seconds...we will never know. This censoring of the data can lead to a biased estimate of the probability of success. Happily statisticians have built tools to deal with this problem, namely Cox Regression which is used in this first pass. Estimates are done by position.

Ok onto the results. Remember the linemen score reflects the  probability that the player adds or subtracts from the estimated average probability of success. The results below are for the Jets offensive line as this is the team that I have the most data for and thus the best estimates. I have also included a consistency score which is the standard deviation of a player's performance on a play-by-play basis (player predicted performance - average performance). Lower consistency scores mean the player is more consistent.

Player	Ferguson	Slauson	Mangold	Moore	Woody
Grade	2.2%	-1.8%	0.0%	2.0%	-1.0%
Consistency	0.07	0.09	0.15	0.07	0.07

The 2.2% grade for D'Brickashaw is the highest of any linemen in the data set and his consistency score of 0.07 is tied for the best consistency score in the data set. Overall, the interpretation here is that Ferguson (LT) and Moore (RG) are consistently outstanding, Mangold's performance has been about average though highly variable and Slauson and Woody have performed well below average for their positions.

As the data set continues to get bigger, these estimates will be more precise and the effects will be more clear, and more teams and players will be ranked. This analysis as is though suggests that Sanchez gets Blind Side pressure on 2.2% fewer plays than the average QB. I think Jay Cutler is probably jealous.

Time in the Pocket and the Effects on a QB

The stake in the ground of the Blind Side Project is that time in the pocket, which is increased by good offensive linemen, has a significant effect on a QBs ability to complete passes. The natural first pass at trying to establish this would be to look for a direct correlation between completion percentage and time in the pocket while adjusting for the distance of the throw. While a small positive correlation can be found, it does not seem to be of the magnitude one might expect, or at least I would not expect. This could be a function of the size of the data set, and that more data will demonstrate a stronger correlation, but I think that the complexities of the game of football, are more the culprit. There are a host of factors that could effect the relationship between time in the pocket (even adjusted for distance of the throw) and completion percentage. One area that is clear however, is that when a QB is under pressure, their completion percentage (adjusted for the distance of the throw) drops. In our 2007 data, when Peyton Manning is under pressure, his adjusted completion percentage drops from 86% to 46% and when Tom Brady is under pressure, his adjusted completion percentage drops from 71% to 39%. There is also a very direct connection between how long a QB holds the ball and the probability that they are under pressure. The graph below demonstrates the "survival function" or the estimated probability that a QB will not be under pressure, for any given length of time in the pocket.

This graph looks about like we could expect it to. As time in the pocket increases, the probability that the QB is not under pressure drops and the probability drops the fastest between 2 and 3 seconds. This suggests that the average offensive line has a fairly high probability of keeping their QB free of harassing defensive players for about two seconds, but there is only about a 40% chance that the QB will have 3 seconds without pressure.

This helps provide a baseline for the overall quality of an offensive line. Lines (or individual linemen) that can help increase this probability, can help increase the overall efficiency of the offense. Additionally, linemen that can increase the No Pressure probability, also provide the playcallers with increased flexibility in areas such as depth of routes and number of  receivers running routes (as opposed to staying in to help block).