- Home Archives for September 2013
Cards on the table, I'm a huge Eagles fan. As an NFL stats nerd, I could not have been more excited for Chip Kelly to make the transition to the big leagues. While I did not expect him to immediately institute his Oregon trademarks, I did expect to see him going for it more often on fourth down, especially in situations where the numbers called for it -- and generally, making decisions to maximize the Eagles win probability.
It's four weeks into the season, and too many times I've asked my TV, "What are you doing, Chip?" Today against the Broncos, there were a couple of questionable decisions. Down 14-3, the Eagles were moving the ball very well to start the game. Vick and company strung together a 15-play drive that ended up with a 4th-and-4 from the Broncos 7-yard line. Using our Markov model, we can look at the progression of the drive:
I made a Tableau viz to illustrate last season's ANS Player of the Year Awards. The idea was to see just how far from the pack the very top performers stood at each position. For fun I also included previous seasons. Now I've finally gotten around to setting up an automated update process so that we can see the position leaders as the season develops. The viz is permanently available under the Graphs | Visualizations | Position Leaders menu link. Like the other visualizations, it will
It's no surprise where Peyton Manning ranks, but check out LeSean McCoy on the RB page. One thing you can use this plot for is to see which teams, players, and squads are likely to regress. for example. If you look at the QB chart, you can see that Cutler, Tannehill, and Locker have WPA numbers that far exceed what we'd expect given their EPA numbers. Recall that they each had game-stealing TD passes so far this season, inflating their WPA. It's likely that they'll return to earth over the course of the season and we'll see their respective logos drift down toward the trend line.
Likewise, we would expect guys who can produce yards and points but not a lot of WPA to drift upward on the WPA axis. This would include Newton, Romo, Eli Manning, and Ponder (if he recovers from his rib injury and gets his job back). I guarantee it!*
*Guarantee not an actual guarantee.
Sam Waters is the Managing Editor of the Harvard Sports Analysis Collective. He is a senior economics major with a minor in psychology. Sam has spent the past eight months as an analytics intern for an NFL team. When he is not busy sounding cryptic, he is daydreaming about how awesome geospatial NFL data would be. He used to be a Jets fan, but everyone has their limits.
One of the new features this season will be a partnership with the Harvard Sports Analysis Collective. I've teased these guys in the past about their vaguely Maoist name, but I've always been a fan of their straightforward analytic style. Some of the brightest minds in analytics are coming from places like HSAC.
In case you're not familiar with them, HSAC is an undergraduate organization at Harvard College dedicated to the quantitative analysis of sports strategy and management. It was founded in 2006 under the guidance of Professor Carl Morris. HSAC's work has focused on applying some scientific rigor to sports analysis. Their previous work on the NFL has ranged from identifying inefficiencies in the NFL draft to criticizing poor in-game decision making, which you can find on their new website. Many of its members have worked in the sports analytics industry in both the media and on the team side. According to Kevin Meers, co-president of the group, says that right now they're focused on getting our bench press reps up so they can get off the computers and onto the field. Good luck with that.
Kevin will be coordinating weekly contributions from HSAC members here at ANS. He's majoring in economics with a minor in statistics. His work has focused on the NFL draft, but he is currently studying applications of game theory on in-game decisions. He has also spent the past two years as an intern in the NFL, and he may have been the only person from Washington DC who wasn't excited about the RG III trade.
Please welcome Kevin and HSAC, comrades! Look for their first post shortly.
Game probabilities for week 4 are up at the New York Times. With the demise the Fifth Down Blog, the probabilities are now on the NYT website proper. This week I explain why the randomness of turnovers makes the Giants-Chiefs game a closer match-up than their win-loss records suggest.
In retrospect, turnovers explain a great deal of a team’s fortunes. But prospectively, team turnover statistics don’t predict game outcomes as much as you might think. The reason behind this distinction is something called auto-correlation. Put simply, turnovers are very random. Only a small portion of a team’s past turnover rate carries forward to be predictive.
Friend of ANS Aaron Gordon used the Excitement Index (EI) and Combeback Factor (CBF) to find out if the Thursday night games really are more boring than most games. From Aaron's article at Sports on Earth:
What NFL Network games have sorely missed are big comebacks. NFL Network games average a Comeback Factor 3.32 -- half the league average -- and only five games with a CBF of 5 or above (where the winning team had a win probability below 20 percent). By definition of the win probability model, 20 percent of the games played should feature a comeback with a CBF of 5 or above (which the larger data set confirms). For NFL Network games, its only 13 percent. For comparison, Monday night games --which often feature hand-picked matchups -- have an average CBF of 8.15, but are right about where they should be in terms of CBF games of 5 or above: 23 percent.
Keith Goldner, chief analyst at Numberfire and a regular contributor to Advanced NFL Stats, joins Dave to discuss his recent research. Keith begins by outlining his Markov model for football, explaining how to use historical data to calculate the probability of an offensive drive ending in a particular "end state". The two then discuss the concept of "net expected points", and compare and contrast its value as a tool in combination with other efficiency stats. The show ends with a detailed analysis of two fourth down decisions from last week's games, highlighting the frustrating difference between good process and good results.
For more of Keith's work, and to check out some of the visualizations and data sets discussed in the episode, visit his site: Drive By Football
Make sure to never miss an episode of the Advanced NFL Stats Podcast by subscribing on itunes.
That brings us to the first team efficiency rankings of the 2013 season. For those who need a refresher, these rankings consider passing, running, turnover and penalty efficiency to create a logistic regression model. Using these values of team efficiency, we can determine the Generic Win Probability (GWP), a theoretical measure of a team's long-term true winning percentage. For more details, check out Brian Burke's explanation from the start of last season.
Again, three games is hardly a significant sample size. And as you might expect, that has led to some wacky results thus far.
Within about thirty seconds of real time, both two NFC North teams were faced with the same dilemma: 4th-and-1 in "field goal range," up three points with four minutes left in the game.
1. Detroit Lions: The Lions faced off against the Redskins on Sunday and went for it from the 12-yard line. Matt Stafford kept it and went up the middle, gaining two yards and converting. The decision here is pretty straight forward from a numbers perspective - especially on a 4th-and-1 - but so many coaches make the wrong choice.
In the show’s first episode, host Dave Collins is joined by Brian Burke, founder of AdvancedNFLStats.com, to talk about his background and why he began researching the NFL. Brian breaks down some of his most powerful metrics, and provides context into the current state of pro football data analysis. They also preview some early season trends, and try to figure out just how bad the Jaguars really are.
The feed for the podcast is here. It's permanent link is here. And look for it on iTunes soon.
First up is Dave Collins, who will be hosting the Advanced NFL Stats podcast. This will be a fun new feature that I'm really excited about. The podcast will come in weekly installments and will include a different guest from week to week. The first episode is already in the can and ready. Look for it shortly.
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.
Next up is Sterling Xie. He's going to be doing the weekly write-up for the team efficiency rankings. Sterling is 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 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.
Andrew Carroll will be doing some general analysis. Andrew is a contributor at NinersNation.com and considers himself a 49ers/Chiefs fan. Less of a statistician and more of a wide-eyed, data wonking glutton, Carroll enjoys spreadsheets, numbers, stats, and otherwise-trivial data that provide a different perspective to the NFL landscape.
The new guys will join Jack and Keith who are returning for 2013.
Lastly, a very sincere thank you goes to all the guys who applied to be contributors at ANS this season. The response to the call for writers was overwhelming. This season I received three times as many submissions than I did for the last open call two years ago. I wish there was a way for everyone to have their football research read...which is why I created the ANS Community site. It never took off the way I envisioned, but it's still ready and waiting for interesting research and analysis.
Here they are from last season, and here they are for 2013.
Not spectacular. It's a team sport, and these are team numbers. But Richardson's stats don't show any indication he's a game changer. He was 72nd in the league with -19.7 EPA and 58th with -0.03 EPA/P. When you watch him, he's hard to bring down, and occasionally gets a yard or two more than a typical RB. But when a team spends a first round pick on a RB, it should expect more than just a couple of extra yards per game than typical.
Last season IND was 7th in run EPA/P and 9th in run SR, so this trade could be a fun experiment in isolating the value of a RB from the rest of the offense. We'll see.
The season is still young so this trend may not last, but with two weeks on the books, the number of rushes-per-game is down 4.8% from last year.
But what’s really dramatic is that average yards-per-carry is down by 10.7% (from 4.262 to 3.805).
Combine the two effects, fewer runs going not as far, and the rushing-yards-per-game figure is down 15% from last year. That number has been fairly steady for the last 25 years.
On the passing side, attempts are up 7.8%, completions are up 10.0% and passing yards are up 10.5%. The league-wide passer rating now stands 87.3. In 1994, that would have qualified as fourth-best in the league.
I'm surprised league-wise rushing yards per carry is so low. I don't recall anything below 4.1 YPC, even this early in the season. Aside from that, everything else is part of a continuing trend--something game theory predicts will continue.
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:
by Matt Meiselman
Matt has been helped me crunch some numbers this off-season. He is graduate of the University of Maryland with a degree in 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.
We're trying hard not to be broken records on 4th down decisions, but this one is special. -BB
Jason Garrett is not typically regarded as a savvy 4th down decision maker, and his clock management skills aren’t the greatest either. Garrett consistently makes blunders in way too many of his strategic decisions. But even after all the mistakes he’s made as the Cowboys head coach, he somehow found a way to top every one of them with a single mistake in Week 2 against the Chiefs.
With 3:50 to play in the 4th quarter, Dallas trailed Kansas City 17-13. It was the Cowboys’ ball, but they faced a 4th and 10 from the Chiefs 35. They held all three of their timeouts and undoubtedly still had a decent opportunity to come away with a win. Garrett had a decision to make: should he go for it? Punt? Or a kick a field goal?
Based on the 4th down calculator, the Cowboys chose the worst of the three options, and it wasn’t even close.
The Jets stuck with Sanchez for too long, even giving him a puzzling contract extension. I feel for Jets fans, but in Baltimore Brian Billick stuck with Kyle Boller as the starter for over 4 years. I'm curious who the biggest team albatross is recent memory really is? Is it Joey Harrington? David Carr? JaMarcus Russel? Is Blaine Gabbert in the running?
So I'm throwing it out to the smartest readership in football. Who do you nominate for biggest albatross? I'll run the numbers. I'm looking for guys whose team stuck with them for far too long as they did unmentionable levels of damage to their team.
The #1 worst game is one that's hard to forget. Baltimore's defense mauled a Welker-less NE in the 2009 playoffs. Joe Flacco needed to throw for a whopping 34 yards to win the game. That's not a typo. As a Baltimore guy, I still thought the Ravens would somehow manage to blow the game. That's just how we think.
Here's the list, including a link to each game WP graph and advanced stat boxscore.
This is a guest post from Ed Feng. Ed founded The Power Rank to bring more analytics and visualization to sports, football in particular. It all started when he got inspired to apply his Ph.D. research in physics to ranking sports teams. Now he hopes to get young people interested in math through sports.
How good is an NFL coach?
How do you compare established coaches like Bill Belichick with upstarts like Jim Harbaugh?
As a reader of Advanced NFL Stats, you know the answer is not Super Bowl rings. In the playoffs, a team plays at most 4 games, a small sample size.
The best coaches will not always win the Super Bowl. Most who saw the two Super Bowl wins by the New York Giants or last year's run by Baltimore would agree.
Instead, let's look at winning percentage in the regular season to evaluate coaches. However, sample size is still an issue.
Let me share a story about why.
The importance of field position in football is, despite its position as one of the indomitable "little things," still an understated facet of the game. Just take a look at last year's field position data from Football Outsiders. All of the top six teams in net drive start position -- average offensive initial field position minus average defensive initial field position -- won at least 10 games. In 2012, every two extra yards of net field position added an extra win, and net field position explains a whopping 31 percent of the team-by-team variation in winning percentage.
Sean Payton is back in the decision-maker chair for the Saints after his one-year sabbatical, and he is typically known as one of the more analytical coaches. The Saints pass more often than other teams, they go for it on fourth down more often, and even try the occasional surprise onside kick.
On Sunday, Payton faced a couple of fourth down decisions, testing his analytical mind. On their second drive, the Saints ran the ball on 4th-and-1 from their own 47-yard line -- a sizable gamble to the traditional football mind. Mark Ingram was stuffed and the Falcons would turn good starting field position into a field goal. This was definitely the right decision, despite the outcome (+0.84 EP going for it vs -0.30 EP with a punt).
The much bigger decision, though, came with 3:30 left in the game up three. The Saints faced a 4th-and-2 from the 4-yard line after a 5-yard completion to Lance Moore. Drew Brees lined up to attempt a 4th-down conversion but Payton ultimately decided to take a timeout to think things over. After the timeout, the Saints came out in field goal formation, made the chip shot and went up 23-17.
So, what do the numbers say?
It is possible that you do not think Quentin Tarantino is a genius. It too is possible that you have little interest in that small group of people that are Judd Apatow's friends. When a celebrity embarrasses his or herself, it is possible you do not want to scold that person, joke about that person or even joke about others joking about that person. It is possible even that you didn't know it happened when it happened, and upon knowing, wonder why you should give a damn.
Thanks to everyone who responded to the call for writers. Please be assured that all responses have been collected and will get a thorough look. I've combed through the spam filter so nothing gets lost. I'm over a week behind where I wanted to be in terms of reviewing them, but I'll get there. I intend to contact everyone over the next two weeks to finalize things.