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:

2006 AFC and NFC Favorites

If you look below at Week 2's highest win projections for AFC and NFC teams, San Diego and Chicago already began to emerge as the highest ranked teams, and they both went on to be #1 seeds in the playoffs. 

Game-by-Game Models

Up to now, I had used linear regression to predict the total number of season wins based on efficiency stats. It was, and still is, very useful in helping to understand the importance of various phases of the game in terms of winning. But this method has its limits, notably in predicting the outcomes of individual games, and taking into account the strength of future opponents.

By using a different technique, we can use the same stats that we've established as the best measures of performance and strength of a team in the 4 primary dimensions of the sport plus turnovers.

By using a form of non-linear regression called "logit" regression, we can calculate the probability of a dichotomous outcome, i.e. that one team will beat another in individual games. The independent variables remain the same:

Off Pass Eff
Off Run Eff
Def Pass Eff
Def Run Eff
Turnovers

In the model there will be 2 sets of variables, those efficiency stats for Team A and those for Team B. The outcome variable would be which team won, technically speaking AWon = 1 if Team A won, and = 0 if Team B won.

The new model needs a database of games to analyze, so I prepared a data set of all the outcomes of every regular season game in 2005 with each team's corresponding efficiency stats. Each game is a "case," statistically speaking. There are 256 regular season games each year in the NFL, so I was confident that one year of data would be enough to establish significance of each variable. This also assumes that NFL football doesn't drastically change in nature from year to year, which I would come to learn is not a good assumption.

I also added home field advantage to the model. If Team A was the home team, the variable AHome = 1, otherwise it was 0.

After running the regression, I was amazed at how well the model predicted winners. For the 2005 season, it predicted 74% of all regular season games correctly. By adding variables such as penalties or sacks, the model improved only very marginally to about 75% correct. Because I had to rerun the numbers each week during the season, every additional variable I used added effort to the task without enough of a benefit to be worthwhile.

The regression results look like this:

Mean of A_Won = 0.500
Number of cases 'correctly predicted' = 380 (74.2%)
Log-likelihood = -262.404
Likelihood ratio test: Chi-square(11) = 184.974 (p-value 0.000000)

            Predicted
              0     1
  Actual 0  190    66
         1   66   190

2006 Season Predictions

Yes, I know it's 2007 now. But back in the early weeks of the 2006 NFL season the model I developed could use each team's efficiency stats to predict the team's number of wins. After accounting for strength of opponent, it started to make sense as early as week 2. Teams like Baltimore, Chicago, Philadelphia, and San Diego looked like division winners. By week 5, 7 of the 8 division winners were correctly predicted and 2 of the 4 wildcards were predicted.

Here is how the predicted wins looked after week 4.

The only division winner the model missed was New Orleans, which was actually predicted to win the division by Week 3, but not by Week 4. Still it was close, plus, Atlanta suffered a nearly unprecedented let-down that got their coach fired.

The wildcard predictions are tougher by nature because it's not 4 teams competing for 1 spot, it's usually 6 teams competing for 2. Still, the NFC wildcards were accurately predicted as the Cowboys and Giants. In fact, the model accurately predicted the top to bottom rankings of the NFC-East. I'm not sure how many people thought the Eagles would win the East when the Giants were 6-0 and then Tony Romo caught fire in mid-season. The model predicted too many wins for the NFC-East in total, becuase it could not account for head-to-head match-ups between division members. Obviously, a division is extremely unlikely to produce 4 teams that average 12 wins. But what's important here is the order in which the teams fall within their division.

The AFC wildcard predicitions were incorrect. But remember this was from week 4. Cincinnati would have slipped in if not for a missed extra point in week 16 or a missed short field goal against the Steelers in week 17. Additionally, Denver controlled its own destiny at the end. Had they won at home in week 17 against the 49ers, they would have made the playoffs.

Again, the main purpose of the model is to understand the inner-workings of the game, not to predict outcomes. But by comparing the predictions of the model against actual outcomes, we can qualititatively verify the validity of the model. Besides, predicting winners is fun.

Week 4 was the last week I used this model. Realizing that the model could not take into account head-to-head match-ups, I switched to a game-by-game model.