Why is that important? When a new head coach is hired, he may get to hire his coaching staff but he doesn’t get to hire the general manager (unless he is the general manager) and for the most part, he inherits the players from the previous coach. Therefore, I could argue that in a coach’s first year, the expectations should be very low and gradually ramp up from there. Normally, I would say that a “fair shot” for a head coach would consist of four or five seasons but again, not all situations are normal. For instance, in his first four years as a head coach with their current team, Coach A was 22-42 and made one playoff appearance while Coach B went 39-25 and won the Super Bowl twice.
Coach B (Bill Belichick) inherited an 8-8 team that was the team’s worst record in the previous four years. Coach A (Jim Schwartz) inherited a team that from 2001-2007 went 31-81 before bottoming out in 2008, going 0-16.
At the end of the day, the question is very simple: how do you win in the NFL? There is an easy answer for those of you out there that are more sarcastically inclined and yes, the way to win in the NFL is to score more points than the other team. Now that it’s been said, we can move on with life… and this blog post.
If we were to liken football history to that of baseball, we are certainly in the “Live Ball” era of football. For much of football’s history, three yards and a cloud of dust was the norm and the way to win was to get 3 ½ yards every play while holding your opponents to 2 ½ yards per play. There has been a gradual shift from the running game to the passing game over the past 40 years starting with players like Johnny Unitas and Joe Namath and continuing today with Tom Brady and Peyton Manning but nothing jumpstarted passing football like the rule changes following the 2004 season. The rule changes enacted were in favor of the quarterback and the receivers as fans seemed to tire of watching defensive backs mugging receivers every play.
Since then, quarterbacks have set just about every relevant record that matters with regards to playing the position. Yards and touchdowns in a season along with yards and touchdowns in a career all fell. In 2012, three rookies and three second year quarterbacks made up half the playoff field. One of the more senior quarterbacks, Aaron Rodgers, sat for three years waiting for Brett Favre to take his sideshow to New York and Minnesota. In 2012, two of those three rookies were the starting quarterbacks from the word “go” and Russell Wilson was the starter coming out of training camp.
All of this leads me to believe that success in the NFL revolves around the quarterback position. While this is completely logical (look at the win-loss records for Tom Brady and Peyton Manning over the past ten years), I wanted to find something more. Would there be a way to come up with a formula with which a football team could be built to win?
SAMPLE SIZE
One of the biggest problems with the NFL is the fact that there are only 16 games per season. Attempting to predict something like winning based on that sample is problematic at best. By comparison, there are 162 games in every baseball season so in many ways, one season (or even half a season) is enough to draw some significant conclusions. Essentially, what I’m trying to say is outliers will exist in any analysis that looks at patterns of success in the NFL. This just needs to be accepted so we can move on.
For this set, I used 2002 to 2011, a ten year period that, for the most part, occurred after that 2004 cutoff I spoke of earlier. The rules may not have changed yet at the beginning of that window, but some of the elite quarterbacks that are still dominating the league today were either in their prime or coming into their prime by the time the window started.
MEASUREABLES
I believe that the NFL is currently a passing league first, so one of the first things I wanted to include in any analysis is quarterback play. There are many ways to measure quarterback play but of all the data I have access to, I chose to use the passer rating metric. The new metric that has recently been introduced, QuarterBack Rating (QBR) is a better overall measure of how well a quarterback plays but data is only available for the past five seasons (2008 onward) and in an attempt to draw more significant conclusions, I want a larger sample size.
Turnovers are a very large part of football but especially so in the NFL. When it comes to the success of a quarterback, the pass rush of the opposing team can have a drastic impact so it would seem that sacks would also be an important piece of this analysis. Finally, while the rushing game has been devalued in the past ten years, it can still be very important. This year, the Vikings made the playoffs with a (putting it nicely) suspect quarterback and it was because they had a very reliable running game. So, rushing yards per game will also be included in this analysis.
At this point we need to remember one critical thing; there is another team on the field during every game. Therefore, I’m going to break this analysis into three parts:
OFFENSE – The goal of the offense is to score points. Therefore, how do passer rating, sacks allowed, turnovers committed, and rushing yards per game come together in terms of points on the scoreboard?
DEFENSE – How do those statistics (defensive instead of offensive) go into the number of points that a team’s opponent scores in a season?
EFFECTIVE WINNING PERCENTAGE (more on this in a moment) – How do the number of points scored and allowed go into a team’s winning percentage?
THE ONE-POSSESSION CONUNDRUM
When I first started looking at the mountain of data that I obtained, I noticed that no matter what I did, there were a number of stubborn outliers that I simply could not explain. After who knows how long of staring at numbers and manipulating them every way I could imagine it, it finally occurred to me; one possession games.
I don’t know if I was watching a football game or reading a piece about the 2012 incarnation of the Indianapolis Colts but it got me to thinking and I eventually asked whether I should even include one possession games in my analysis.
Off the top of my head, the average NFL game is comprised of anywhere from 100-150 plays, including special teams. Essentially, a one possession game (where the margin is 8 points or less) could have swung the other way if just one of those plays had happened differently. Given this logic, it follows that over time, a team’s record in one possession games would regress towards the mean, or a .500 winning percentage.
To calculate the “effective” winning percentage, I took the number of one possession games each team played, divided it in half and added that number to the wins and losses a team had from 9+ point games. So, if a team was 12-4 (for a .750 winning percentage) but went 6-2 in one possession games, I normalized this record by putting in a .500 record in one possession games so I removed the 6-2 record and put in a 4-4 record to go with the team’s 6-2 record in 9+ point games. The result is a record of 10-6 for an “effective” winning percentage of .625.
When I did this, all of my regressions started to look much better than they originally had but while this solved one problem, it created another one. I appear to be getting closer to explaining how NFL teams can win but for the life of me, I have no idea how some teams do better in one possession games than others.
Out of all the regressions I conducted, the best r2 value I could get was about 0.19 and while the more statistically inclined of you might know that implication, for those of you who don’t, that’s not good. Essentially, in my search for an indicator, I was only able to find variables that could explain 19% of the variability of the dependent variable. For a perfectly linear relationship, the independent variable explains all of the variability in the dependent variable. When searching for a correlation, a regression isn’t worth much with an r2 value below 0.5.
I know that something dictates performance in close football games and if pressed, I’m inclined to say that it’s something subjective such as the quality of coaching. I don’t believe that it’s a coincidence that the only three teams that have won more than 60% of such games are New England Patriots (.708), Indianapolis Colts (.683), and the New York Giants (.600), while the only two teams to win less than 40% of such games are the Buffalo Bills (.375) and Detroit Lions (.338).
BY THE NUMBERS
For all of the regression analysis, I used the Ordinary Least Squares method and the advantage of this method is the r2 statistic I mentioned earlier. Using this method, I’ll be able to see how my variables mentioned above affect points scored and points allowed (and therefore winning percentage) and I’ll also be able to see how much of the picture is revealed by this analysis and how much is still murky.
Without further ado, the following equation determines how many point an offense will score in a season:
OFFENSE
Points = (RTN x 4.86222) – (Sck-All x 1.05405) + (Give x 1.5772) + (RYPG x 0.812841) – 144.606
RTN = passer rating of the team
Sck-All = sacks allowed
Give = turnovers committed
RYPG = rushing yards per game
DEFENSE
The following equation determines how many points a team will allow in a season:
Points = (OPP-RTN x 4.53191) – (Sck x 0.596579) + (Take x 1.42401) + (RYAPG x 0.89086) – 141.051
OPP-RTN = passer rating of the opponent
Sck = sacks
Take = turnovers a team creates
RYAPG = rushing yards allowed per game
WINNING
Finally, throw those together with effective winning percentage and you get the following equation:
EW% = (PF x 0.0214484) – (PA x 0.0208863) + 7.80691
EW% = effective winning percentage
PF = points scored
PA = points allowed
THAT’S A NICE BIT OF NUMERICAL NONSENSE, BUT WHAT DOES IT MEAN?
Fantastic question. First of all, the r2 values are all relatively high, which is desired. For points scored, the value was 0.750041 and for points allowed it was 0.743349 while for effective winning percentage, it was 0.928130. This tells me that there is more to scoring points than what I put into these equations (special teams contributions, third down efficiency, etc.) but for relatively straightforward analysis, I’d say this methodology was successful.
If we dig a little deeper into the equations, some interesting things emerge about the relative importance of the variables. For this following section, you have to keep in mind that when I’m talking about the effects of changing one variable, I’m holding all the others constant.
PASSER RATING
Given by the constant in the winning percentage equation, an “average” team in every part of this analysis would win 7.8 games. I realize that truly the average team wins 8 games but there isn’t one team that would have all of the league average statistics used in this analysis.
If a team’s passer rating improves by 10 points, from a league average 80.5 to 90.5 (a moderate but significant jump), that would imply that the offense would score 48.6 more points (4.86 x 10). Multiplying this by 0.02145 means that holding all else constant, an increase in 10 points of passer rating leads to 1.04 more wins, improving a record from 7.8-8.2 to 8.84-7.16.
SACKS ALLOWED
This season, the average number of sacks allowed was 36.5 while the fewest allowed was 20. Going from league average protection to the best pass protection in the league this season would increase a team’s point total by 17.39 points, or a bit more than a third of a win.
GIVEAWAYS
This is the one that doesn’t make intuitive sense. Given that the coefficient has a positive value, this implies that as a team turns the ball over more, they score more points. This is backed up by the same sign on the coefficient in the points allowed equation. The only possible explanation that I have is that a team that turns the ball over more is more aggressive and is more likely to score more points (call it the Brett Favre Effect).
Even though it makes no intuitive sense, the p-value for this variable was essentially zero. This means that the variable is statistically significant; in other words, it’s in the equation because it should be, it adds more descriptive value to the equation.
If we accept that it belongs, we can see that committing 10 more turnovers will lead to 15.77 more points scored. In 2012, this means that the difference between the best and worst performances for turnovers committed (23 turnovers) was worth 36.28 points, or 0.778 wins.
RUSHING YARDS PER GAME
When I started, I didn’t originally have any rushing stats in the analysis. Including this is basically a shout out to the incredible season that Adrian Peterson is having. Given that I’ve added a key component of my analysis because of one player, perhaps that shows just how good of a season he’s had.
Anyways, a league average offense rushed for 115.9 yards per game in 2012. The Redskins led the league (yes, even with Peterson, the Vikings didn’t lead the league in rushing) with 169.31 yards per game. That difference is worth approximately 43.4 points, or 0.93 wins.
SPELL IT OUT FOR ME…
Turnovers are important. Protecting the passer is important. Having a good running game is important. Nothing is nearly as important as having a good quarterback. In other words, if you were to replace a novelty quarterback with a passer rating of 72.9 (for sake of argument, let’s refer to him as Tim Tebow) with a first ballot Hall of Famer with a passer rating of 105.8 (let’s call him Peyton Manning), and everyone else plays just as well as they did over both years, the team will score 159.97 more points and win 3.43 more games.
OK, YOU’VE BEEN AVOIDING IT THIS WHOLE TIME… DID YOUR METHOD WORK?
Let’s put it this way… it shows what I wanted it to show and that’s that the team goes as the success of their quarterback goes. A quarterback’s play while throwing the ball is far more important than sacks allowed, turnovers, or rushing yards per game. On average, from 2002 to 2011, my equations were off by 1.4 wins on average and 73% of the time, it was within two wins of how a team actually did.
In 2004, the Tampa Bay Buccaneers went 5-11 when my method would have predicted a record of 8.9-7.1 due to quality play from the quarterback position. While it was predicted that the Bucs would have scored 362.8 points, they actually scored 301 (perhaps as a result of their 36 turnovers).
In 2009, the Colts went 14-2 but their numbers predicted a record of 9.3-6.7. That season, the Colts were outgained 126.5-80.9 in rushing yards per game, a deficit they most likely overcame with the help of a certain quarterback.
2012 RESULTS
This is how the 2012 season shaped up division by division with the records predicted by my methods next.
As can easily be seen, there were some hits and some misses but I would argue (as I mentioned before) that 16 games is a small enough sample size that it is easy to get outliers. So what happens when we start looking at teams over a longer time scale, say, 10 years?
Before I show you the results, I took the “different than expected” records in one-possession games and added that to the predicted number of wins. For instance, over the past ten years, the Patriots should have a record of 99.9-60.1 but their record in one-possession games was 46-19, or 13.5 games better than an even 32.5-32.5 record. When you add that in, the Patriots’ predicted record becomes 113.4-46.6. When you compare this to their actual 123-37 record, it compares favorably. While a difference of 9.6 wins might sound like a lot, you have to remember that over 10 years, that’s only one win per year. Here’s how the league looks:
The average difference between the team’s actual records and those predicted by my method? 3.31 wins per team over the ten year period so an average of a third of a win per year.
At the end of the day, if you take away only one thing from this post, you should take away that today’s NFL does indeed revolve around the quarterback position and there’s a reason why simply adding a player like Peyton Manning immediately makes the Denver Broncos Super Bowl contenders.
Essentially, this analysis confirms what we know simply watching games every Sunday during the fall: a good defense can take you far, a great running back can get you to the playoffs, but quarterbacks are ones that carry you to the Lombardi Trophy.
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