Comeback Wins/Losses: The Comeback Kings

by Clark Heins

For every comeback win there is a corresponding comeback loss, and one cannot be considered without the other. A comeback win occurs when the winning team overcomes a deficit at the start of the fourth quarter, at sometime during the fourth quarter or, if necessary, in overtime. Comeback wins have little to do with “comeback opportunities,” as the latter deal specifically with a point spread of eight or fewer points and include games that are tied in the fourth quarter. A comeback win can occur from any deficit and doesn’t deal with ties.

For the purposes of this study, I have made no attempt to credit a QB’s total of comeback wins/losses based upon whether or not he deserves them, as luck always plays a role. My totals are entirely based upon one criteria---who was the QB of record when the comeback win or loss was attained, regardless of how it was attained. As an example, I didn't credit Dan Marino, John Elway, Kerry Collins, and Warren Moon with comeback wins when they were injured during a game-winning drive and replaced by another QB. To do so would ignore the element of luck. Also, my calculations are based upon “QB starts” rather than “games played”, as it would be extremely unfair to use the latter stat for many of the QBs. “QB starts” isn’t perfect either, as several of the QBs mentioned here scored comeback wins or losses in relief.

Continue reading...

Spygate: The Effectiveness of Cheating

by Paul Benjamin

Enough time has passed to evaluate the effect of Bill Belichick's cheating. The cheating took place from 2000-2006, and was ended early in 2007, giving 5 years of data since.

The known cheating consisted of two components, as revealed by Eric Mangini. First, the Patriots would tape opponents defensive hand signals. This permitted coaches to correlate the signals with the defensive alignments and figure out what each signal meant. Second, the Patriots used unregistered radio frequencies, so that the second time they played that team the offensive coordinator could watch the defensive signals and choose the perfect play to tell the quarterback. Normally, the referee cuts the registered radio frequency 15 seconds before the snap, so the offensive coordinator cannot communicate with the quarterback after the defense makes its substitutions, but the Patriots were the only team in the league that had radio equipment that could broadcast on multiple frequencies simultaneously. After the referee would cut the registered frequency, the quarterback could still hear the coordinator on the other frequency, so he could be told the defensive alignment he was facing and what play to call.

So the plays and blocking schemes were always perfect ones to exploit each defensive alignment.

Continue reading...

Ring Probability Added

by Joe Harris
When arguing a QB's "greatness" people often quote the number of rings a player has as a definitive conclusion to a debate. For example, Dilfer is obviously greater than Marino because he has a ring. And there is nothing else to it. That statement takes the logic a bit further than most people would, but that is essentially how a lot of fans view the world. Whilst I am not a fan of this form of logic, I thought that it would be interesting to look at this concept through the lens of WPA or, more specically, RPA - Ring Probability Added.

Ring Probability Added
The basic idea is to take Brian's WPA stats and weight it depending on the magnitude of the game. For example, in 2004 when New England beat Carolina, Tom Brady essentially did enough to win the game single-handedly with +0.97 WPA. This corresponds to +0.49 RPA - he increased New England's chances of winning the Superbowl by 49%.

Continue reading...

Betting Market Power Rankings – Conference Finals Edition

by Michael Beuoy

Editor's Note: Michael submitted this earlier this week and I was late in posting it. EA

Here are the final Betting Market Power Rankings of the season, updated with the results of the prior week and the lines for this week. As promised last week, I will also revisit my predictions for the lines and over/unders for the conference final games.

Refer to last week’s post for more detail on the weights used.
Here is a glossary of terms:

LSTWK - The betting market rank as of the prior week
GPF - Stands for Generic Points Favored. It’s what you would expect a team to be favored by against a league average opponent at a neutral site.
oGPF – Offensive Generic Points Favored. The component of a team’s total GPF attributable to its ability to score points.
dGPF – Defensive Generic Points Favored. The component of a team’s total GPF attributable to its ability to prevent the other team from scoring points
O RANK – The team’s oGPF ranking.
D RANK – The team’s dGPF ranking.
GWP - Stands for Generic Win Probability. I converted the GPF into a generic win probability using the following formula: GWP = 1/(1+exp(-GPF/7)).

Continue reading...

Best (And Worst) Post-Season Coaching Records Since 1950, via a Binomial View

by Jim Glass

Vince Lombardi's post-season coaching W-L record of 9-1, .900, is the best ever. Or maybe not, some say Joe Gibb's record of 17-7 is standard to beat - "only" .708, but over a run of 2.4 times as many games it is much harder to keep a big winning record. How can we compare them?

One way is to compute the binomial probability of a coach attaining a given won-loss record by random chance. For instance, in Herm Edwards' first year coaching my Jets, he went 10-6. I was happy. That .625 winning record was better than Tom Landry's .607 and our former coach Bill Parcells' .570 - better than a Hall of Famer's and sure future Hall of Famer's! It looked like we had a great coach. Except that Parcells earned his .570 over 303 games and Landry earned his .607 over 418, while Herm had earned his .625 over only 16.

The binomial calculation can give the probability of winning a given number of games out of any number played, and so providea a common standard to apply to the W-L performances of different coaches with different W-L percentages over differing numbers of games. It told me that, assuming game outcomes were random with a 50% chance of winning/losing, Herm's record of .625 (or better) had only a 22% probability of occurring by random chance, which was pretty encouraging - but Bill's .570 record had only a 1% chance, and Tom's .607 had only a 0.001% chance. So perhaps it was premature to declare Herm a better coach than Tuna and Tom (as indeed it turned out to be).

Continue reading...

Betting Market Power Rankings – Divisional Round Edition

by Michael Beuoy

In last week’s post, I showed how one can use the betting over/under in conjunction with the point spread to decompose team strength into an offensive and defensive Generic Points Favored (GPF = oGPF + dGPF). The post was essentially a redo of the Week 16 rankings, and unfortunately, I did not have enough time to apply the new method to the Wildcard Round of the playoffs. This week, I do have time, so here is a peek into the mind of the Betting Market for the Divisional Round of the Playoffs. In addition, I’ve laid out a table of the predicted lines and over/unders for each possible matchup in the Conference Finals and Superbowl. I’ll return to the predictions in the following weeks to see how the model did (testable predictions! science!).

For those of you that are interested, I’ve decided to start a blog for the purposes of publishing these rankings for various sports. I’ll start off with the NBA (see the first set of rankings here). After that, I’ll take a crack at NCAA Basketball, and then hopefully move on to Major League Baseball (which presents some interesting opportunities for decomposing team strength into offense, defense, and pitching, and creating a separate set of starting pitcher rankings). The blog will probably be pretty rough in the early going (i.e. ugly and confusing), but I hope to learn quickly.

Continue reading...

NFL Coach Quality: A Bayesian Approach To Approximating the Value of Coaches - UPDATED

by David Durschlag

You are currently viewing version 1 of this article. To view version two, please click here.

Summary

Evaluating NFL coaches is a difficult task, popular among fans and vitally important to franchises. This is a brief attempt at the task, using purely quantitative data.

Data

The numbers of regular season games each team won each year are treated as data points. No information beyond number of regular season wins was used.

While the "metagame" of the NFL continues to evolve, the data used herein is from 1993 onward, when the last Collective Bargaining Agreement was signed. While victories now come in different environments, they are all under (roughly) the same rules. Data from before this period could be skewed based on the different rules for control of players, so it was excluded.

Also excluded was the performance of any team in a year in which it had multiple head coaches. This was done to ensure that credit for a season was easy to assign.

Continue reading...