How 18 Games Could Change the Game

The "Enhanced Season" is all the rage in the NFL owner circles as they see the large increase in revenue associated with 2 more games and think that is the kind of enhancement they like. As a football fan, two more glorious Sundays of NFL action sounds pretty good to me too. The players have a rather obvious and valid objection to this lengthening of the season though; they are more likely to get injured in an 18 game season than a 16 game season. If we can assume though that players and owners can come to an agreement so that the players feel fairly compensated for the added risk of injury, the issue then shifts to the effect that a longer season would have on the product that is put on the field every week.

The first pass answer is that the product will suffer, because the inevitable increase in injuries will cost teams more missed games by their best players which means more Sundays watching Todd Collins going toe to toe with Jimmy Clausen and frankly, I'm not sure how much more of that modern quarterbacking can handle. This assumes though that teams will not change they way they build rosters and play the game with this shift in the season.

While not all teams will change immediately, I find it hard to believe that GMs and Coaches will not be able to take this fundamental change to the game (more player games lost due to injury) into account when building rosters and playbooks. The question then is not if the product on the field changes, but how it changes. I put my normally data obssessed brain to work on this issue and tried to think through what changes we might see in player personeel and strategy as the league enhances their season. The biggest shift I could come up with was the use of QBs and the type of QB that would be in demand.

Specifically I see more use of the "3rd down specialist" QBs - Do you remember the tail end of Ron Jaworski's career as it conincided with the beginning of Randall Cunningham's? The Eagles dabbled with platooning this QBs so that Cunningham played in certain situations, so that he gained experience in certain limited situations. He was a rookie so he couldn't master the entire playbook, but he could get up to speed on a portion of it, so when they needed to run a play of the type that Cunningham knew, he could go in a run it.

The Eagles did this because they knew that Jaws was soon to be done and they needed to see if the exciting but inexperience Cunningham could develop into a true NFL QB. Teams may like to go more to this model, not just for developmental purposes, but so that there is a built in reason for them to have two QBs taking significant reps during the practice week. Teams will not have pure backup QBs, more QB 1a and QB 1b who more or less split up the practice reps and are both used (while healthy) during the game.

This could take the shape of the "3rd down specialist" ala Cunningham, or it could become more like the baseball model with relievers/closers that come in and either manage a game that their team is winning or start bombing away if their team is losing.

This kind of shift in the perspective of the role of the QB gives coaches significantly more flexibility to set up game plans to maximize the talents of two different types of QBs, forcing defenses to be prepared for both Kolb and Vick or both Young and Montana. In order to get the most out of this shift, then, teams would want their 1b QB to actually be as different as possible from the 1a QB. Pur passing QBs would be paired with non-traditional running QBs, and those running QBs would no longer be taught stop running and look to throw, but to look for those running lanes. There would be less interest in teaching the running QBs to be traditional because teams would not be dependent on one QB whose injury could undermine an entire season. If one of the QBs goes down, you still have someone who is essentially a starting QB - they will just now have to play the entire game.

If this scenario plays out, does it enhance the product? Does the longer season actually create a more engaging product or just more of the same (or more of something that is lesser than it currently is)? I would argue that this could actually be a renaissance in offensive football, forcing/allowing coaches to be more creative and flexible and trying to do things on the field that have not been done previously.

So this little brain exercise has left me hoping that the season is lengthen because it may truly enhance the product.

Week 5 Picks

I had a very poor 39% showing straight up last week, but a rather profitable 62% against the spread (would have been even more profitable with proper risk adjustments on any theoretical wager). Picks (less the Giants and Jets which are only available in the New York WSJ) are below.

Cleveland (14) vs. Atlanta (22) Confidence 55%
Carolina (17) vs. Chicago (20) Confidence 51%
Baltimore (23) vs. Denver (21) Confidence 88%
Washington (22) vs. Green Bay (15) Confidence 56%
Buffalo (20) vs. Jacksonville (18) Confidence 71%
Indianapolis (24) vs. Kansas City (27) Confidence 54%
Arizona (18) vs. New Orleans (20) Confidence 61%
San Francisco 18 vs. Philadelphia (20) Confidence 67%
Oakland (17) vs. San Diego (21) Confidence 88%
Detroit (10) vs. St. Louis (21) Confidence 75%
Cincinnati (22) vs. Tampa Bay (18) Confidence 88% 
Dallas (17) vs. Tennessee (26) Confidence 64%
 

Northern California Symposium on Statistics and Operations Research in Sports

One more plug for my upcoming conference. Tickets can be bought here. And the schedule for the day is:

Schedule

8am – Registration Opens, Continental Breakfast available

9am – Welcome

9:10am – “The intra-match home advantage in Australian Rules football”

By Richard Ryall & Anthony Bedford

9:45am - “Pitcher Accuracy through Catcher Spotting: Assessing Rater Reliability”

By Andrew C. Thomas

10:20am – “Optimal Dynamic Clustering Through Relegation and Promotion: How to Design a Competitive Sports League”

By Martin L. Puterman and Qingchen Wang

10:55am – “Dynamic Effort, Sustainability, Myopia, and 110% Effort”

by Stephen Shmanske

11:30am – Featured Speaker: Sig Mejdal of the St. Louis Cardinals

12:15pm Lunch Provided and Poster Session

1:45pm – “Drafts versus Auctions in the Indian Premier League”

by Tim Swartz

2:20pm – “Reconsideration of the best batting order in baseball: Is the order to maximize the expected number of runs really the best?”

by Nobuyoshi Hirotsu

2:55pm –“Uncovering Football’s Best Goalscorers in the English Premier League, La Liga, Serie A, Bundesliga, Eredivisie, and Ligue 1 for the 2009-2010 Season”

By Joel Oberstone

3:30pm – “An Empirical NFL Draft Value Chart”

By Michael Schuckers

4:05pm – Featured Speaker: Roland Beech of Dallas Mavericks

5:00pm – Concluding Remarks

For further information on each presentation, see the presentation descriptions.

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.

Week 4 Picks

I am in full conference planning mode and the Blind Side Project is in full data gathering mode so I have no time for anything as frivolous as an explanation for each pick. So since the model hit 68% of the games last week (including the Giants and Jets which appeared in the Wall Street Journal) and a confidence weighted 62% against the spread, I'll let the picks speak for themselves.

San Diego (30) vs. Arizona (18) Confidence: 74%

Pittsburgh (26) vs. Baltimore (10) Confidence: 80%

New Orleans (26) vs. Carolina (14) Confidence: 82%

Cleveland (13) vs. Cincinnati (18) Confidence: 81%

Tennessee (23) vs. Denver (21) Confidence: 84%

Green Bay (29)  vs. Detroit (18) Confidence: 85%

Oakland (21) vs. Houston (27) Confidence: 63%

Jacksonville (20) vs. Indianapolis (24) Confidence: 56%

Miami (23) vs. New England (21) Confidence: 63%

St. Louis (18) vs. Seattle (21) Confidence: 57%

Atlanta (18) vs. San Francisco (21) Confidence: 56%

Philadelphia (28) vs. Washington (27) Confidence: 81%