Season Projections Visualization

I put together a season projection visualization that illustrates the playoff probabilities and win totals for each team. The numbers are based on the results of the season prediction project I did for ESPN The Magazine.

The method used to create the projections is explained here.

The viz is intended one-stop shopping for the season outlook. The top window shows the probabilities each team will make the playoffs. Dark green indicates a playoff berth by winning the division, lighter green indicates a wildcard berth.

The three windows below are team-specific. Hover the cursor over (or tap) a team's column in the top chart to see its details below. The window on the left is a chart of win totals. The bars represent the probability the selected team will finish with a corresponding number of wins. The second window shows the same information presented in a different way. It's the cumulative probability of each win total. In other words, it's the probability the selected team will win at least that many games. The third window is a pie chart. (Yes, I know pie charts are the unloved orphans of the chart world.) It illustrates the probability each team will win its division.

2014 Season Predictions for ESPN the Magazine

ESPN asked me to predict the 2014  season for their NFL Preview edition of their magazine. I was very hesitant because predicting the season to any degree is extremely difficult. I'm even on record as proclaiming that all pre-season predictions are "worthless." (More on that below). "You want me to predict which teams make the playoffs?" "Yes," they said, "in fact, we want you to predict the winner of all 267 games."

Then it got worse. "We want you to predict every score of every game."

I started doing some math in my head. There's 267 games in the season, including the playoffs, which means there's 2^267 different possible combinations of game outcomes in the season. While that might sound like a lot of different possibilities, it's even more than a human being could possibly fathom. Physicists and astronomers estimate there are about 10^80 atoms in the universe (that's 100 quinvigintillion to you and me). And the NFL season's 2^267 possible outcomes comes to 2.4x10^80, or about 240 quinvigintillion. Put simply, there more than twice as many possible outcomes to the NFL season than there are atoms in the universe. And that just refers to wins and losses, and doesn't even consider scores.

So how hard could it be?

2013 Seahawks Defense: In the Conversation for Best Ever?

The SEA defense dominated the league's best offense in years to take home the championship. Where should they stand in relation to some of the great defenses in recent times? Could they be the best defense ever?

One of the best things about Expected Points Added is that it separates the contributions of offenses, defenses, and special teams. A defense with a very good offense will appear better in terms of other metrics because their opponents would tend to get possession in poor field position. Conversely, a defense sharing a locker room with a below-average offense won't seem as dominant.

Another feature of EPA is that it's measured in net points. It's not just a klugey stat transformed into an analog of net points. It is net point potential. When EPA says a defense is worth 5.0 points per game, that's universally understandable and comparable.

One drawback, at least in its current general implementation, is that EPA doesn't account for the changing nature of the NFL. The league is a moving target, as offenses consistently gain an ever firmer upper hand over defenses. Even over the the last dozen years, offenses have gained several points of advantage. (How do we know exactly how much? EPA, that's how.) So defenses from a decade ago might appear better than today's defenses only because of how the league has evolved.

It's a trivial matter to account for the average EPA by year. That would allow us to compare apples to apples based on the "scoring environment" of the season. I'll do that below and see where SEA '13 fits in. But there's one other notion we should at least consider.

Thomas Bayes Would Approve of Seattle's Defensive Tactics

The following is a guest article by Gary Montry, a professional applied mathematician. Editor's note: Gary uses net yardage as the measure of utility, and we might prefer something like EP or WP, I think the general point of the article stands, and its strength is in the construction and solution to the problem. It's also a great refresher on conditional probabilities and Bayes' theorem.   

Last week a WSJ article about the Seahawks' defensive backs claimed that they "obstruct and foul opposing receivers on practically every play."  I took a deeper look in to the numbers and found that as long as referees are reluctant to throw flags on the defense in pass coverage (as claimed in the article), holding the receiver is a very efficient defensive strategy despite the risk of being penalized.

The following is an analysis using the concepts of expected utility, expected cost, and bayesian statistics.

The reason defensive holding is an optimal strategy comes down to one word. Economics. The referee's reluctance to call penalties on the defensive secondary is analogous to a market inefficiency. The variance in talent on NFL rosters, coaching staffs, and front offices between the best and worst teams in the league is probably very small. Successful teams win within a small margin. Seattle has found a way to exploit a relaxation in marginal constraints within the way the game is called that their competitors have not, and turned it into a competitive advantage.

If you think about committing a penalty in the same way as committing a crime, the expected utility is essentially the same. The expected utility (EU) for defensive holding is (opponent loss of down due to incomplete pass - probability of being penalized x cost of penalty). In other words, EU is the benefit of an incomplete pass minus the cost of the penalty times the probability of getting caught.

Can Late-Season Momentum Explain Playoff Upsets?

Outcome bias is a powerful habit in forming mainstream opinion, and one of the most common NFL-related ones entails "momentum."  It's easy to look at a team that wins the Super Bowl and point to a late-season three-game winning streak as a sign that the future champs "figured things out" or "got the ball rolling."  Fill in your favorite cliche and voila, instant storyline.

But how often does so-called momentum play a role in playoff success?  This isn't a new question, and many analysts and fans have realized that success at the end of the regular season is not a prerequisite for a deep postseason run.  Last season's Baltimore Ravens won it all despite losing four of their final five regular season games.

Special Playoff Team Viz

I put together a special version of the team stat visualization for the playoff field. It's a good, quick way to get a feel for how each contender compares on offense and defense, in the passing game and in the running game. The first three tabs depict the three key team stats: EPA, WPA, and SR. Two additional tabs break out run and pass EPA production.

What might be particularly useful is the week selector slider. It's handy for isolating recent trends or for isolating subsets of games, such as GB's games with and without a healthy Aaron Rodgers.

The dashed average lines for each stat represent the 2013 overall league average, not just the playoff field. Here's a version below, but a larger version can be found here.

What Kind of Teams Are Super Bowl Winners?

What's the profile of a Super Bowl winner in the modern era? Does defense win championships? Are they predominantly elite offenses? Do they have to be above average on both sides of the ball? Are champions always dominant in the regular season? Is your team out of the mix for the Lombardi Trophy?

Here's the plot of every team's regular season Expected Points Added (EPA) for every team from 1999-2013. The horizontal axis represents their offensive EPA per game, and the horizontal axis represents their defensive EPA per game. The best teams are in the upper-right quadrant, while the worst are in the lower-left. (Click to enlarge...it's suitable for framing!)

The 2013 Buccaneers Really Know How to Blow It

This post at Reddit noted that the 2013 Buccaneers have blown 4 games in which they had at least a 0.95 Win Probability (WP). This is the most blown games for any team since at least 1999, and there are still 8 games left to blow this season.

Most readers are familiar with the Comeback Factor (CBF) stat. It measures the unlikelihood of the win at the lowpoint of the game for the eventual winning team. For example, if a team comes back to win from a 0.05 WP, that would be a CBF of 20 (1/.05 = 20). A team that comes back from a 0.01 WP earns a CBF of 100.

On the flip side of that equation is the Blown Game Factor (BGF), a stat which measures how badly a team blows a game. If a team has a 0.95 WP and goes on to lose, its BGF is a 20. It's really no different than Comeback Factor--it's just measured from a different perspective.

TB already has 4 games with a BGF of 20 or higher, meaning at one point they had at least a 0.95 WP. The table below lists all the teams in the database (since 1999) with 2 or more games with a BGF of at least 20. That's not the only way to measure total heartbreak, so I included some other numbers.

The Worst 8 - 0 Team of all Time?

CBS's Pete Prisco recently sent out a tweet saying the Chiefs "might be the worst 8 - 0 team I've ever seen."  I thought I would take him up on that by compiling some basic team stats of every 8 - 0 team in a Google spreadsheet and comparing them.

Regardless of what the stats say, you should familiarize yourself with this amazing gif which is probably the only reason the internet needs to exist and is proof-positive that Andy Reid's Chiefs are worth every bit of 8 and OHHH YEEEAAAA.

The spreadsheet will contain the following stats on each team:

Which Teams Should Abandon the Run?

Yeah, yeah, yeah. It's a passing league. We got it. And still, according to the numbers, teams aren't passing enough. In the cases of some teams, it's painfully obvious that they should be passing more and running less. As a Ravens fan, I watched another game where nearly every run was simply a wasted down. Most of their paltry positive rushing yards seem to come from trash draw plays on long distances to gain, intended to mitigate very poor field position prior to a punt. It's like they're playing with two or three downs when everyone else gets four.

I wonder if, at some point, when an offense is so much better at passing than running, should it abandon the run almost altogether. On top of the general imbalance in the league, some teams are just throwing away downs when calling conventional run plays. Of course, running and passing generally play off of each other in a game-theory sense. To be successful, passing needs the threat of running, and vice versa. But sometimes, the cost of running is so high for some offenses, that it would be worth the trade-off to forfeit the unpredictability and just pass nearly every down.

It sounds crazy, but take a look at the Expected Points Added per play so far this season (through the 1pm games on Sunday 10/13). The right-most column is the pass-run split. The bigger that number, the greater the imbalance. Pay particular attention to the teams highlighted in red:

Can the Steelers Afford to Run the Ball?

It's been a rough year for running the football in the NFL so far. The league is averaging just 3.8 yards per rush through two weeks, a full half-yard behind the pace set in 2012. With passing offense steady at 5.4 yards per play, the league continues to tilt towards the aerial attack.

The running game has suffered the most in Pittsburgh, where the 0-2 Steelers have averaged an AFC-worst 2.4 yards per carry and a league worst 16 percent success rate on the ground. The Steelers are the classic "hard-nosed identity" team and as such have a reputation as a team that wins with its work on the ground. This has been a bit exaggerated during the Ben Roethlisberger era -- the 2010 AFC Champion team, for example, finished just 18th in yards per carry, and the Super Bowl champion squad in 2008 finished 29th.

Still, the club has always presented the run. The 2008 Steelers were ninth in rushing attempts at nearly 30 per game. Surely this is partially due to clock running, but the 15-1 Packers in 2011, for example, finished 26th in rushing attempts despite a large amount of garbage time.

Through two games this year, Pittsburgh's rushing attack has been too feeble to even be an option. Observe, the Steelers' 31 rushing attempts in 2013 by location and yardage gained:

Win Probability Forfeited 2012

by Matt Meiselman

Matt has been helping me crunch some 4th down numbers this off-season. He is a senior at the University of Maryland studying broadcast journalism. He's originally from New Jersey, but loves New York sports. Matt aspires to work in sports media and has a passion for sports statistics. -BB

Fourth down decision making is one of the most controversial aspects of coaching in the NFL. Act too aggressively and miss? You get blamed for a loss. Act too timid? The fans will be calling for your head. Most NFL coaches are operating with the mindset that whatever puts their team (and their job) at the least amount of risk is the right choice to make. This has been, and will always be, the wrong way to try to win a football game.

In last year's article on this subject, Brian talked about how coaches aren’t just saving wins if they’re more aggressive; they are simultaneously forfeiting wins by being too meek. In 2011, the average team forfeited .65 wins for the year on 4th down decisions alone. The NFL has started to become more risk-friendly instead of risk-averse, and you’d expect that with more innovative minds in the game, like Bill Belicheck and Jim Harbaugh, the league would be trending towards more optimum game management. This was not the case in 2012.

During the 2012 season, the average team forfeited .73 wins, a significant increase from the year before. The average win probability forfeited per opportunity also rose, jumping from 1.6% of a win to 1.9% of a win. Below are the calculations for each team:

Don't Overlook the Effect of Penalties

One of the more overlooked aspects of team performance is the tendency for being penalized. Penalty rate, defined as penalty yards per snap is one of the more reliably stable team stats. Compared to things like running or passing efficiency, the absolute size of penalty rate's effect is small, but because it tends to be consistent, it can be fairly predictive.

This Sunday's conference championship games feature teams on opposite sides of the penalty spectrum. ATL has by far the league's lowest team penalty rate at 0.21 penalty yards per snap. For context, the league average is 0.41, and the next best team is NYG at 0.29. ATL is 3.9 standard deviations better than the 2012 mean! SF is third worst in the league 0.46 penalty yds per snap.

On the other side of things is BAL. They're averaging 0.53 penalty yards per snap, the league's worst rate. That's 1.9 standard deviations worse than the mean. NE is tied for 6th best, at 0.39 penalty yds per snap.

Year-to-Year Improvement and Decline at QB

This is intended as a companion to the recent Post article on team improvements and declines at the quarterback position. I looked at the year-to-year change in total EPA at the QB position for each team since digital records began in 2000. The table below lists the change in total EPA from the previous season. For example, Arizona improved by 47 EPA in 2001 compared to the 2000 season.All numbers are from the regular season only. The table is color coded so that the biggest improvements are in green and the biggest declines are in red. The 2012 numbers are adjusted for a 16 game season.

Which Teams Should Run More?

If I read one more of these articles in the Baltimore Sun, I think I'm going to throw up.

...Ray Rice consistently ranks as one of the most productive and dynamic running backs in the NFL, capable of eluding defenders on the ground and through the air. He benefits from running behind bruising All-Pro fullback Vonta Leach, who's regarded as the most devastating lead blocker in the game. Yet, the Ravens have fallen to 19th overall in the NFL in rushing yards per game with an average of 104 yards per contest. And Rice ranks 12th on the rushing chart individually with 524 rushing yards...

The article goes on to add:

Blah blah blah. Run more blah. Need to find their identity blah blah.

Ok, I made that second part up. Actually, I have to give the author credit for attempting at least a surface level statistical analysis. But the fetish that sports columnists have with the running game has been one of the most enduring false narratives in the sport of football. It's time to put it to rest. I get that offenses have to run to keep defenses honest and to set up the passing game, but they don't have to run as often as they generally do to accomplish those things.

Let's take a look at offensive Expected Points Added per Play by play type. EPA/P accounts for all play outcomes--sacks, interceptions, fumbles, gains, losses, first down conversions, and so on--in other words, everything. Naturally, an offense would want to do more of the kinds of plays that gain them more and do less of the kinds of plays that gain less. Although this should be intuitively understood, zero-sum game theory proves that the optimum total production is when payoffs are equalized across strategy options.

Teams with high differences between pass payoffs and run payoffs should probably be running more often. And although you might think that teams with higher run payoffs than pass payoffs should be running more, that may not be true due to the passing paradox: Underdog teams that are poor at passing may need to do it more often to generate high variance outcomes.

The table below lists each offense's passing and running EPA per play, their total EPA per play, the difference between pass and run EPA/P, and their proportion of pass plays. I limited the analysis to 'normal football' where the score is relatively close and time is not yet a factor in play selection. The teams are ranked by the difference between pass and run payoffs. Teams at the top of the list should be passing more, and the teams at the (very) bottom should be running more. (Click on the table headers to sort.)

WP Forfeited

It's 4th and 2 from the opponent's 42. The score is tied at 17. There's 1 minute and 17 seconds left on the clock. What would you do? If you're Ken Wisenhunt, you send in Dave Zastudil to punt.

One thing I've learned about human nature is that to help convince someone of something, I should frame the issue in terms of fearing a potential loss. That's usually a stronger motivation than expecting a potential gain. For example, I wouldn't suggest to a coach that he could improve his chances of winning by going for it on 4th down more often. Instead, I'd tell him he's forfeiting a significant chance of winning by not going for it. Nobody likes forfeiting stuff. My wife suggested using the phrase "leaving points on the table." Coach Wisenhunt forfeited 13% chance of winning that game.


By far, the most common question I get from reporters is whether teams are going for it more often. My answer is almost always "it's complicated, but I think so." The difficulty in measuring 4th down aggressiveness is that it's so dependent on the situational variables. You can't just count how often teams go for it rather than kicking. To-go distance, score, and time all weigh heavily on the decisions, and there are just too many possible combinations to compare rates from year to year or even decade to decade.

If we constrain the analysis to certain parameters--inside opponent territory and when the score difference is within reason, for example--we'd get an incomplete picture. We've learned over the last few years that there can be many situations outside traditional 'go-for-it' limits in which it can be beneficial to go for the conversion rather than kick or punt. And each situation can have a drastically different magnitude of effect on a team's chances of winning. Also, why would we reward a coach if he goes for it on 4th and whatever when he's down by 5 with a minute left to play? Coaches always do that.

Here's my stab at the problem. With every 4th down situation in which it would usually make sense to go for it but a coach decides to kick, he forfeits some amount of win probability. We can total all the WP forfeited to measure the degree to which teams are erring on the side of conservatism.

WP: Moving beyond Grossman

This week's post at the Washington Post's Redskins Insider site takes a look at the Redskins quarterback situation going into the 2012 off-season.

...with so many needs heading into the off-season, it might be tempting for the Redskins to think that Grossman is a medium-term solution at quarterback. But this is a trap...

“Best season ever”, “31st in the league”, and “leads the league in interceptions” is a combination that does not bode well for the future of a franchise that would consider standing pat at quarterback. 

Packers Demonstrate Value of Explosiveness Against Bears

There was one striking statistic which made its way over viewers' screens during Christmas night's game between the Packers and Bears: Green Bay, despite rolling to a 14-1 record and the NFC's top seed, has actually allowed more yards than they have gained. The game against Chicago was a microcosm of this phenomenon, as the Bears outgained the Packers and had a much better success rate despite losing by a final score of 35-21.

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The Bears had great success in the running game, with Kahlil Bell picking up 123 yards on 23 carries and a whopping 63% success rate and the team overall sporting a 57% success rate. On the other side, the Packers managed only 81 rushing yards on 21 carries, with the Bears holding the Packers to a paltry 32% success rate with the run. As Al Michaels reminded us, running the ball well and stopping the run is often considered the be not only a winning formula, but the winning formula.

And it's true, for much of the first half in particular, the Packers seemed quite vulnerable. The Bears and their second-string quarterback and third-string running back controlled the ball and racked up yards against the Packers. But the Bears simply couldn't muster the big play until it was too late, and all too often drives stalled around midfield or in deep field goal range.

EPX

The Packers' defensive efficiency ranking is a lot lower than their rankings in terms of other advanced statistical yardsticks including Expected Points Added (EPA), Win Probability Added (WPA), and Success Rate (SR). One explanation for the discrepancy is that GB's offense is so good that the defense can afford to guard the sidelines at the end of games, allowing teams to move the ball while burning clock. Because they know teams need to throw the ball to keep up, they can create big plays, particularly interceptions and big sacks. Their very high interception rate bolsters this theory.

Trash-time distorts the relationship between true team strength and team statistics, be they conventional or advanced, total stats or per play. To determine true team strength, we need to weed out the random outcomes and discount trash-time performance.

WPA is probably the ultimate explanatory statistic. EPA is less explanatory and more predictive, because it's not subject to the leverage of time and score, but it's also subject to the random outcomes of a bouncing or tipped oblong ellipsoid.

One way to eliminate trash time from the data would be to simply throw out the fourth quarter. As it turns out, there is a lot of baby in that bathwater. A better way might be to throw out data based on Win Probability (WP). A statistic that's based on EPA, but limited to when the game is still in play, could be the answer.

There's still the problem of the bouncing ball. There are sometimes huge EPA plays--James Harrison's 99-yard TD return in the Super Bowl a couple years ago comes to mind. A play like that  represents almost a 12-point swing in EP, but it's the kind of event that's so rare that it makes little sense to project future team performance on such a distorting play. Put simply, it does not have the equivalent predictive value of two solid 80-yard offensive drives.

But it's representative of something. We don't want to throw plays like that out. What we can do is limit their statistical impact. We can cap their EPA value at a certain amount, so that no single play will have more or less than a chosen value.

Tebow by Quarter

By request, here is how Denver quarterback Tim Tebow's performance breaks down by quarter. It's nothing short of amazing. He's a very poor performer until halftime, at which point he turns into a mediocre player. But then, in the fourth quarter, he comes alive.

Tebow has broken into positive territory in WPA for the season, including the blowout against DET, but has yet to do so in EPA.

The two tables below list Tebow's Expected Points Added (EPA) and Win Probability Added (EPA) by quarter for 2011 through week 14.