You can contact Brian by email at brian@mail.advancednflstats.com. Questions about the content of the posts are best as comments. All comments get read, and those with questions often get a quick response.
I'm always amazed at the overwhelming response this site has received. I appreciate all the feedback. Please keep it coming--the good and the "constructive." While I don't want to discourage anyone from writing me, I'd like to encourage everyone to use the site comments feature for any items you think might be of interest to other readers. You may get a answer to your question quicker from another reader than from me. Also, my spam filter can trap some comments and emails, so I apologize if you haven't received a response.
FAQ
Where did you get your data?
Most of my team data comes from open online sources such as espn.com, nfl.com, myway.com, and yahoo.com. It's easy for anyone to grab whatever they're interested in from those sites.
My play-by-play data comes from a source that's not publicly available, and at this time I regret that I cannot share it. However, I have been able to create a database of nearly all NFL plays since 2002 free for anyone to use for research.
Why don't you do predictions Against The Spread (ATS)?
While I don't object to gambling, it doesn't really interest me. I realize a lot of football fans are interested in it, and I'm happy if anything here helps inform fans of all kinds. I do however use Vegas odds and spreads as consensus yardsticks for my own models, and sometimes it's fun to see if some math can beat "the system." I just lack that gene for risk, and cheering for my favorite team is excitement enough for me.
I heard you're not a math professor or professional statistician, but actually a former Navy fighter pilot. Is that true?
Yes. I flew F/A-18 Hornets off carriers. The Navy sent me to graduate school where I picked up a knack for analytics. Only now do I realize that much of the combat tactics we used as pilots were derived using the same analytical techniques I use for my football research.
Why aren't you working for a team?
I'm very reluctant to relocate my family. I often do freelance consulting for NFL teams and media outlets.
What's it like flying an F/A-18?
The best way I ever heard someone describe flying a fighter plane was this: It's like simultaneously racing in the Indianapolis 500 while playing a video game and holding two telephone conversations...all while people are shooting at you. Then the hard part begins--landing on a pitching carrier deck. In reality flying a plane is a true team sport. Thousands of brave sailors and everyone else who supports them. I was very fortunate to be part of such a great organization as the United States Navy.
Contact / FAQ
About the Founder
Brian Burke
Reston, Virginia
Brian is a football fan and closet math enthusiast. He has a BS in aerospace engineering and an MS in management and leadership. After spending 15 years in the Navy, most of them as an F/A-18 carrier pilot, Brian has taken up the less dangerous hobby of advanced NFL statistics.
Originally from Baltimore, Brian spent his youth either on the sidelines of Maryland Terrapin football games or in 50-yard line seats at Colts games, both thanks to his dad. His football career topped out playing tight end for the Towson High Generals. His only college football experience was playing in the Naval Academy's annual "2.7" game, an intramural game that featured the "geeks" vs "rocks" divided by the Academy's average GPA. You can guess which team Brian played for.
After graduating from Annapolis in the Class of '93, Brian attended flight school and went on to fly the F/A-18C Hornet until leaving the Navy. He attended the Naval Postgraduate School and returned to Annapolis as an instructor. Brian flew 21 combat missions and was awarded the Air Medal, the Navy Commendation Medal, and numerous other personal and unit commendations.
He now resides with his family in northern Virginia.
Is There 'Momentum' During a Season?
The 2006 season continued and I made incremental improvements to the model. Beginning at week 8, I emphasized the efficiency stats from each team's most recent 4 weeks. I hoped this would help account for a team's improvement over the course of the season and for injuries to key players. I set this to be a changable variable which I called an "emphasis factor" that I could set to 0 to see how much the predicted probabilities changed from the baseline.
What I found was that the probabilities changed, but not by much, and rarely enough to swing one team from underdog to favorite. It made intuitive sense to me, and still does, so I kept the "emphasis factor" in the model.
But this is actually a question ripe for research. Teams appear streaky. Commentators, analysts, and fans accept that teams can be on a roll or in a rut. But over the course of a season it would be reasonable that wins and losses can naturally come in bunches on occasion.
Just as if you flipped a coin 16 times, you wouldn't expect the outcome to perfectly alternate heads and tails. If you did this several times, you would find that some of the 16-coin-flip "seasons" have several instances of consecutive heads "streaks." If we were betting on heads, our human intuition would tell us that the coin is "hot" or on a hot streak. In some rare, but totally natural, cases we might even expect 8 straight heads followed by 8 straight tails, or vice-versa.
The same principal holds for sports. The 16-game NFL season is particularly short and is susceptable to natural "streaks" without any actual change in performance. In theory, an average team destined to go 8-8 could start the season 8-0 followed by an 0-8 "collapse." Admittedly this would be extremely rare. In this case the luck factor (calculated below) for this team would be +4 during the 1st half of the season, and -4 for the last half of the season.
This phenomenon may explain why the emphasis on recent performance I applied in the model didn't change the probabilities very much. Teams appear streaky in terms of wins and losses, but win/loss records are naturally more erratic than the efficiency stats, so we should expect teams to appear that they're on "a roll" or in "a rut" when winning or losing streaks are really just part of a natural distribution of outcomes.
Below is a table of Week 11 comparing the predicted probabilities using both the standard stats and the stats with the recent 4-weeks performance weighted.
Meet Our Writers
Keith Goldner
Keith is a true stat-head. Aside from Drive-By, he is the chief analyst at numberFire, a slick and smart fantasy-oriented site. He graduated Magna Cum Laude from Northwestern with a B.A. in math. Keith has worked with two NBA franchises - Oklahoma City Thunder and Philadelphia 76ers - doing statistical analysis and data management. In addition, he has worked with ESPN and the Wall Street Journal, contributing analysis across multiple platforms, and his sports analytics research has been featured at conferences across the nation.
Jack Moore
Jack is a recent graduate of the University of Wisconsin-Madison with a bachelor's degree in mathematics and economics. He is a contributor at FanGraphs and also writes about baseball at Disciples of Uecker (a Milwaukee Brewers blog) among other places, and his work has also been featured on ESPN and ESPN Insider. His love for the Wisconsin Badgers has instigated a foray into the world of football analysis as well, with a blog named Badger of Honor. His introduction to game theory as part of his college education opened whole new doors for analyzing the game of football, adding a completely new dimension to watching the game besides simply tallying fantasy points. Jack is a master of vizualizations.
Dave Collins
Dave is the master of ceremonies of the Advanced Football Analytics Podcast. He's a pro broadcaster and has a sharp mind for sports analytics. Dave hosts The Opening Play Podcast where he covers sports and comedy "in an intelligent way." His first summer job was as a tour guide for the Red Sox, and since then his love of sports, statistics and corny jokes about the Green Monstah has never waned. He graduated from Northwestern University with degrees in communications and economics and spent the 2009 football season interning with the Chicago Bears. Now in California, he works at StubHub.com and produces the Bay Area Sports and Tech Meetup . Dave can also be found performing improv comedy throughout San Francisco. Though he currently resides in the Bay Area, he remains a die-hard Patriots fan.
Sterling Xie
Sterling does the weekly write-up for the team efficiency rankings. He's the type of devoted stats guy that makes former players and coaches roll their eyes while gushing spiel about the good ol' days. Sterling is currently enrolled in Hamilton College, working towards a degree in English. His work is featured on Bleacher Report and its partner websites, where he writes about all things Boston sports. He's a sucker for a good third-down back, and greatly misses rooting for Danny Woodhead.
Rob Hendryx
Rob will be handling the playoff projections this season. Rob is an engineering graduate student slave at the University of Texas at Austin. He has been researching sports statistics for a few years, mostly in the search of evidence that Tony Romo is not cursed. (Results are still inconclusive.) Recently he has posted his own research to reddit.com/r/NFLstatheads under the username kloverr, and moderated the small community there as well.
Which teams were luckiest in 2006?
When we are engaged in any endeavor, we know we shouldn't think about how much luck has to play in the outcome. It is usually counterproductive to dwell on luck because that tends to reduce our efforts towards our goal. Think about it--Why should I try so hard if it comes down to luck anyway?
In the NFL, if teams thought that way they'd probably be dead meat. But luck is a factor in all sports. Think about a very simple example game. Assume both PIT and CLE each get 12 1st downs in a game against each other. PIT's 1st downs come as 6 separate bunches of 2 consecutive 1st downs followed by a punt. CLE's 1st downs come as 2 bunches of 6 1st downs resulting in 2 TDs. CLE's remaining drives are all 3-and-outs followed by a solid punt. Each team performed equally well, but the random "bunching" of successful events gave CLE a 14-0 shutout.
The linear efficiency model I've began using early in the season has an r-squared value of almost 0.75. That means that 75% of the variance in the outcome (season wins) can be explained by the model's variables. But by including additional variables such as penalty yards or special teams the r-squared only marginally increases and they are largely insignificant. Those factors are fairly random and chaotic anyway, which is one way to define luck. So we could conjecture that a measurable part of a team's win-loss record, but something less than 20% is due to luck.
So how can we determine how lucky a team is? By using the model and estimating the number of wins a team "should" have based on its stats, the number of "expected wins" is calculated. The difference between a team's actual wins and its expected wins reveals how lucky a team is.
Seattle appears to have been the luckiest team this year, winning about 4 more games than we would expect given their stats. They squeaked out 1 game over .500 in a very weak NFC-West to make the playoffs. If they were in the AFC they probably would not have made the post season at all.
Minnesota appears to be the unluckiest. This is probably due to their league-leading run defense, but they didn't have the wins to show for it.
Notice Pittsburgh's expected win number--10.24. They played well enough this year to win 10 games but, according to most analysts "collapsed" and didn't make the playoffs a year after winning the Super Bowl. But their regular season record last year was 11-5. One could make the case that the Steelers played only slightly worse than they did last year, but just got unlucky.
Jim Mora, Jr., former coach of the Falcons, or other fired coaches might have used this to save his job. "Mr. Blank, look, we actually played well enough to win 9 or 10 games and would have made the playoffs!" But somehow I don't think that would fly, no matter how sound the math.
Efficiency Rankings
By creating a notional league-average team, i.e. a team that had the average offensive and defensive passing and running efficiency stats, and turnover margin, I could determing the probability that any NFL team would beat the notional average opponent. I could simulate a neutral site by setting the home field advantage variable to 0.5 (instead of 1 or 0).
Then by sorting the teams by their generic win probability vs. the average team, we can create an efficiency ranking, similar to the common "power" rankings but far more objective. Here is how the efficiency rankings ended for the 2006 season:
By the way, SS is for the Seahawks and NY is for the Jets. I had to use non-standard abbreviations so that the teams sorted in the same alphabetical order in abbreviated and non-abbreviated form.
The 2006 Season Continues
The model was never as accurate as it was in week 5. In week 6 it was 7-7, only 50% correct. My 5-year old son regularly does better than that just by picking the team with more wins (and breaks a tie with home field advantage). Over the rest of the season the model came out to be less accurate than I had hoped, based on how well it could predict (retroactively) the 2005 season. I would understand why after the season concluded.
But at the time, I kept using the model for more analysis. I modified the model somewhat to emphasize the latest 4 weeks of games more than those early in the season. I also could tell that home field advantage appeared too powerful as a predictor, so I reduced it slightly.
In the end the model was accurate only about 65% of the time.
2006 Game Predictions
The first week I tested the game-by-game model was week 5 of 2006. I was initially encouraged because it correctly predicted all 14 games that week. That streak of luck would not last long, however.
But keep in mind it doesn't really predict a winner, it produces probabilities. So for the BAL vs. CIN game, it might say BAL 0.60 CIN 0.40. Baltimore is favored with a 60% chance of winning. So if CIN wins, that doesn't necessarily mean that the model was wrong. But for the statistical purpose of judging the "fit" or validity of the model, we'll say that if the predicted favorite wins, the model was correct.
For week 5 the model produced the following probabilities: