When the going gets tough, the tough simply reach into the vault.
It's a slow week, so I thought I'd revisit a subject I've touched upon several times before--namely, the subject of luck. Which NFL teams got lucky in 2010 and which teams got the shaft?
Before I jump into numbers, let me preface everything I'm about to show you with the notion that defining luck is problematic by itself. One man's missed 26-yard field goal is another man's great push from the interior of the defensive line. And as we've all seen, stripping the ball may very well be a skill, but recovering the fumble is almost entirely random chance. So we should accept going into the topic that we'll likely not agree on the premise.
This may look like I'm already backing from the data. So be it. I'm fine with that. I've become much less of a stats guy over the last two years anyway. Thus, I've lost my attachment to the outcome, you might say. Very zen of me, isn't it?
The view is also grounded in several points. First, as I've broken more tape down over the last several years, I've realized that individual stats in the NFL are somewhat limited. Let me give you an easy example.
This year, the Broncos' running backs were widely criticized for their lack of production via the widely used statistic of yards per carry. By the end of the season, Knowshon Moreno had driven his average to a respectable 4.28 yards per carry. He ended up 23rd in the league in this statistic.
Earlier in the season, however, the Broncos were so brutal in the run game, this average was well below 4 yards. The easy thing to do at the time was to blame Moreno and cite this mindless stat (which I'm sure we've all done). However, as I become more engrossed in watching tape, I realized that Moreno just had no room with which to work until late into the season. Perhaps the best example of this was the Week-5 game against the Ravens, in which I watched Haloti Ngata repeatedly utilize the same swim move on multiple Broncos offensive linemen in order to penetrate into the Broncos' backfield. Given that there is no stat for "times Ryan Harris is beaten by a swim move," I've realized the limited value of some statistics. I still use them (and other better stats), but I don't mindlessly draw conclusions from them.
The second point is that as a former linebacker, I'd rather talk about schemes and players than stats. Since I'm now in the company of Doug Lee, who is a "statisticoholic," I don't have to peg myself as a statistical genius. It's not that I ever did, it's simply that I ended up doing so many regressions and probability equations, I began to find them as stale as a conversation with a Raiders coed or an Oakland-area stripper (the two worlds collide often).
So now I'm bringing the stats for both enlightenment and entertainment. The good news? I rarely find myself arguing about stats anymore. Besides, watching stats guys argue is like watching roller derby girls fight without body oil or hair pulling. It's simply not worth the cover charge. The bad news? Stats are hard to make entertaining, sort of like a Winger reunion tour.
Before I get going on an 80s hair-metal rant, I'm bringing the conversation back to luck. How do we take a non-OJ stab at quantifying it? Let's wax poetic for a moment.
Billy James, the plump capo of baseball sabermetrics, tooled around with a nice little equation that does a decent job of predicting a team's winning percentage in baseball over a 162-game schedule. According to professor Wayne Winston, who wrote the excellent book Mathletics, between 1980-2006, the equation was off on average by only 2% per team. That's an average of only 3 games per year. In a schedule as long as Major League Baseball's (162 games), that's quite impressive.
The equation that James came up with is remarkably similar to the famous Pythagorean Theorem, so similar in fact, that James called the result of his equation a team's "Pythagorean Wins." Here was his original equation:
James later came back and said that the exponent should really be about 1.81, but at that point, the geeks didn't care too much. They had a nice little tool to determine those teams that won or lost more games than they should have. In other words, they could now quantify, at least by their definition, the concept of luck.
It didn't take long for others to take this equation and apply it to other sports like football. Daryl Morey, a real stats guy, and now General Manager for the Houston Rockets, developed a similar equation for the NFL to predict wins, which is currently used by The Football Outsiders today:
Points Scored2.37 + Points Allowed2.37
The reason I point this out is that many people associate The Football Outsiders with having developed this predictive tool, but Morey and James are its true creators.
Morey's also does a decent job over time. Over a 16-game season, it predicts winning percentage within about 6%. That's less than 1 game per year; it does a better job of predicting future wins than most current win-loss models when backtested. How do I know this? Because I've backtested a lot of them myself (my lost stats years).
Morey tested his exponent of 2.37 over a 10-year period. I decided to do a little research myself and I backtested the equation over an even longer period of 23 years, and found that the best exponent for predicting winning percentages over that same period was actually 2.61.
Now that we've exhausted all the geometry that you'll ever need to know to watch football again, let's apply our equation to this year's teams. We'll use our new equation (let's call it The Dude's Worthless Wins Projector or DWWP for short) based on 23 years of data:
Points Scored2.61 + Points Allowed2.61
This DWWP equation will allow us to compare how many games a team won with how many games they "should" have won. So who got lucky in 2010? Who got shafted? Let's take a look:
The Shaft in 2010
|NFL Team||Points Scored||Points Against||Projected Winning %||Projected Wins||Actual Wins||Luck (Shaft)|
|New England Patriots||518||313||0.7883||13||14||1|
|New Orleans Saints||384||307||0.6420||10||11||1|
|New York Jets||367||304||0.6205||10||11||1|
|Green Bay Packers||388||240||0.7779||12||10||(2)|
|Kansas City Chiefs||366||326||0.5749||9||10||1|
|New York Giants||394||347||0.5821||9||10||1|
|Tampa Bay Buccaneers||341||318||0.5454||9||10||1|
|San Diego Chargers||441||322||0.6944||11||9||(2)|
|St. Louis Rams||289||328||0.4181||7||7||0|
|San Francisco 49ers||305||346||0.4184||7||6||(1)|
Let's start with the obvious. The Broncos weren't very good. The DWWP suggests that the Broncos should have won an extra game, but when you reach rock bottom, who's counting?
I've also taken the liberty of highlighting the teams that got shafted. It's just more fun to focus on the negatives. First, it shouldn't surprise you that the Green Bay Packers are in the Super Bowl. According to the DWWP, Green Bay should have won 12 games and taken the NFC North Division. But your eyes told you that all year if you watched Green Bay's defense of swarm. Second, according to the DWWP, both the Chargers and the Lions got the shaft. If you watched some of their games this year, you "felt" this to be true. The Chargers gave up several fluke plays in the area of special teams, while the Lions found ways to give away games late. Lastly, the Titans were the unluckiest team in the NFL, according to this formula. Denver fans saw this firsthand in their game against the Titans when the Broncos benefitted from a pass interference call late in the game.
I'd mention the Bengals, but it would be two minutes of your life you'd not be getting back.
All of these shafted teams seem to have the injury bug in common. So do we consider injuries part of the luck of a football season? I'd say yes, but again - what exactly is luck in the NFL? I consider every successful catch by Darrius Heyward-Bey to be a lucky one, but that's just me.
Or are you what you are, as the football prophet Bill Parcells says?
Feel free to give us your thoughts below. Who got the shaft in your mind this year? Feel free to use stats, body oil, or pull hair if you must.