Today, let’s go back to talking about fumbles and luck.  It occurred to me (or was pointed out), that there are two sides to the equation, and I’ve only really looked at one.  To refresh, I previously explained that “fumble problem” is largely overblown.

For individual players, there is some year-to-year persistence in fumble rate (fumbles/rushes), but it’s small.  Overall, there is a large amount of luck and natural variation in any particular player’s rate of fumbling.  Here is the post.

Additionally, I explained that while not all fumbles are equal, over the long-term we should expect every team to recover approximately 50%.  The counterpoint to this argument is that it’s about “hustle” and “heart”.  As you can imagine, I have little patience/regard for that side of things (in this specific scenario, not necessarily in everything).  However, I do not believe I actually took a look at the team-level statistics to back that assertion up.

Let’s look at that now.

First up, we’ll tackle the OVERALL team fumble recovery percentages.  Then I’ll break it into recovery percentages for giveaways and takeaways (much different situations which carry different expected recovery rates).  NOTE: I’ve decided to push the breakdowns until tomorrow, after reaching nearly 1000 words on the first section.

Team Fumble Recovery Percentage

This statistic (data taken from Teamrankings.com) covers the percentage of ALL fumbles that a team recovers.  This is very important for Eagles fans.

Last year, the team lost a historic amount of fumbles (22) and gained just 5 (NFL long-term average is 11).  Not coincidentally, the Eagles recovered just 35% of all fumbles last year, 4th worst in the league (Buffalo was worst with a rate of just 30.6%).

If Fumble Recovery % is truly random, then the Eagles can be expected to regress to the mean next year (50%), which would result in a better TO differential (which is highly correlated with Wins/Losses).

To get a look at the randomness, I’m going to look for year-to-year persistence.  This is where it gets tricky.  Due to the amount of players on NFL rosters and the constant turnover in personnel, just looking at a team’s year-to-year persistence isn’t enough to conclusively prove anything about fumble recovery %.  As usual, we will have to settle for a strong indication, since equalizing the data for every player and every play is far beyond the scope of anything I can (or want) to do.  Just know that this is an issue inherent in almost every NFL analytic breakdown or long-term analysis.

Before we look at the whole league, here are the Eagles’ yearly total fumble recovery percentages from 2003-2012:

As expected, the annual totals seem to fluctuate relatively narrowly around the 50% mark.  Also of note is just how much worse last year was when compared to the previous 9 seasons.  This is a major reason why I expect improvement next year.  It’s not just that the Eagles were below the expected recovery %, it’s that they were FAR below it; it’s difficult (though far from impossible) to get much worse.

Now let’s look at the entire league.  Here is a graph showing the One Year persistence of Fumble Recovery Percentage for the entire league over the last 10 seasons.

As you can see, there is no correlation (officially its -.06).

Due to the factors I cited above (roster changes, personnel differences, etc…), we can’t say definitively that fumble recovery % is all luck.  However, we can absolutely say that a team’s % for one year doesn’t tell us anything about it’s expected % next year.  In other words, the Eagles 35% recovery rate in 2012 doesn’t tell us anything about the 2013 season.

Mean Regression

The flip side of what I just said is that just because the Eagles were below the expected mark last year in no way means the team will be at or above the mark next year.  That’d be a bit like playing roulette and expecting the next roll to be black because the last 3 were red.  The overall idea is that each season’s recovery percentage is entirely independent.

So why do I expect the Eagles to recover?

Looking at the overall data, the standard deviation of all team recovery percentages over the last 10 years is 7.46%.  That means, if the data is random and Normally distributed, we should see approximately 68% of all individual team rates fall between 42.5% and 57.46% (1 standard deviation above and below the mean).

In our data, 220 teams fall within that range.

220 divided by 320 (our sample size) = .6875 or 68.75%

Additionally, we should expect 95% of all team rates fall within the range of +/- 2 standard deviations (35.07% – 64.927%).

In our data, we have 306 teams that fall within that range.

306 divided by 320 = .956 or 95.6%

Lastly, we should see 99.7% of teams to fall within 3 standard deviations of the mean.

In our data there is just 1 team that does not fall within the expected range (2011 Saints).

319/320 = .9968 or 99.7%

Summing up a bit:

– The data is random (no year-to-year persistence).

– The data is Normally distributed (hits the 68%/95%/99.7% mark almost exactly)

The 2012 Eagles recovery rate of 35% is almost exactly on the 2 standard deviation line.

Therefore, since we expect 95% of teams to fall within 2 standard deviations:

The 2013 Eagles have a 97.5% chance of recovering fumbles at a higher rate than they did last year.  (95% + half of the 5% distribution beyond 2 standard deviations)

One last bit for today, how does this rate business effect actual fumbles?

Here is a chart showing the correlation between fumble recovery rate and fumbles differential (Gained – Lost).  In other words, to what degree are “creating” fumble turnovers and “protecting the ball” a result of fumble recovery percentage (which is luck)?

I’ll have more on this tomorrow, when I’ll take a more detailed look.  The chart above should give you a strong hint of where this is going though.

I’m going to stop there for today.  This section is a lot longer than I intended and I’m guessing very few people have the time/patience to read through a 3000 word post on expected fumble recovery rates.

Bill Barnwell posted an article today at Grantland discussing which losing teams from last year are most likely to rebound next year.  He has the Eagles ranked 2nd, behind the Lions, in terms of which teams (6-10 or worse last year) are most likely to make the playoffs.

The case he makes is very similar to the case I’ve been making on this site for a long time, however, it’s interesting nonetheless.  Here is the Eagles section, I’ve bolded the most important points.  Afterwards, I’ll highlight something that isn’t getting enough discussion.

2. Philadelphia Eagles

Arguments in favor: Coaching voice change, massive turnover differential, fumble recovery rate, hidden special teams bounce back

Arguments against: Uncertainty related to coaching change, quarterback play

Although it seems difficult to remember, the Eagles were actually a passable team during the first half of last season. It was after firing defensive coordinator Juan Castillo during the team’s bye week that the Eagles quit on Andy Reid and collapsed, finishing 1-9. There’s still a good amount of the core that made the playoffs from 2008 to 2010 here, and they chose the high-risk, high-reward coaching option this offseason when they added Chip Kelly. Of all the teams on this list, it seems like the Eagles have the highest upside.

The statistical case backing them up is built upon an impossible turnover rate. Philadelphia was the other team with a minus-24 turnover margin, and by recovering 35 percent of the fumbles in their games, they finished just ahead of Kansas City, at 29th. Of course, Kelly has already become the first coach to teach Michael Vick how to avoid fumbling, so that should solve a good chunk of the problems there.

In all seriousness, Kelly’s insistence on getting the ball out quickly should reduce the likelihood of fumbles, and some simple variance should help push the Philadelphia offense back toward the middle of the pack. The defense should also deliver more than the eight interceptions it produced last year, so it’s not difficult to imagine the Eagles actually winning the turnover battle in 2013.

As for the “hidden” special teams numbers, that’s a Football Outsiders statistic that encapsulates how teams were impacted by special teams performance out of their control, including such obvious ones as how reliable field goal kickers and kickoff artists were against them. The Eagles were the fourth-most impacted team in football last year by those figures, with kickers notably going 27-for-29 on field goals against them in 2012.

Beating a dead horse:  The Eagles were VERY unlucky last year.  You’ll recognize the points above from previous articles here as well as from the dashboard prototype I threw up a couple of weeks ago.

However,

I wanted to also highlight the coaching risk.  As Bill says towards the top, the Eagles selected the high-RISK/high-reward option.  Eagles fans should not overlook the first part of that trade-off.  Kelly is a very exciting coach, and I’m encouraged by the fact that he’s willing to tear down conventional football wisdom in search of advantages.   The flip-side, though, is that he carries a higher risk of failure.

Make no mistake, if Kelly goes down, odds are he’s going down BIG-TIME.  This is not an approach that seems to lend itself to a series of mediocre seasons.  The problem with challenging convention is that if things start slowly, the external noise will be excruciatingly loud.  Old-school beat writers, ESPN talking-heads, and Joe Banner will attack Kelly and the Eagles like sharks around a bait-ball (Discovery channel reference!).

That risk and uncertainty poses the biggest potential hurdle for the team this year.  All things considered, the Eagles now look like an 8 or 9 win team (Vegas has the O/U at 7.5, so I’m bullish by one win).  Unfortunately, the Eagles also have perhaps the WIDEST expected range of outcomes.  It is completely conceivable for this team to win anywhere from 3 to 13 games next year.

I’m certainly betting on success; Kelly seems to know what he’s doing and is willing to adapt (rather than just trying to impose “his” way on the game, a la Steve Spurrier).  Just know that the natural payment for Kelly’s potential upside is a corresponding risk of spectacular failure…

One of the more widely reported reasons for the 2012 Eagles’ ineptitude was the number of significant injuries the team sustained.  Vick, Shady, D-Jax, and pretty much the entire Offensive Line were hit, each missing at least several games.  While it must have had a negative effect on last season, it’s reasonable to believe that the injuries from last season are ALSO a reason to be optimistic this year.  Surely this year’s team will not suffer as greatly.  With a healthy offensive line and full seasons out of the major playmakers, the 2013 Eagles should be in a position to rebound strongly.  Right?

Maybe, but caution is advised.

As is the often the case with anecdotal or out-of-context evidence, the complete data set tells a somewhat different story.  To get an idea of whether we should expect the Eagles to be “less injured” this year, we need to answer two questions:

– How injured were the 2012 Eagles?

– Is there any persistence in year-to-year injuries?

How injured were the 2012 Eagles?

I’m going to use two different numbers to illustrate.  The first is straightforward; it’s just the number of injury games lost by starters.  Rick Gosselin of the Dallas Morning News put together this chart that shows the relevant data for every team in the league during the 2012 season.  I’m going to assume his numbers are accurate.

The 2012 Eagles’ starters lost 63 games to injury last year.  That sounds like a lot, and while it is a lot in absolute terms, it is good enough (bad enough?) for just 25th in the league.  In other words, 7 teams lost starters to injury MORE often than the Eagles did last year.

Still, the Eagles were close to the bottom. As a result, it may be the case that we can expect the team to regress towards the league average next year, meaning fewer injuries and presumably better on-field performance.  Unfortunately, to get a look at this requires more than 1 years’ data, and I don’t have/can’t find charts like the one above for previous years.

For that we need to turn back to the Football Outsider’s Adjusted Games Lost measure that I referenced two weeks ago.  As I explained then, the AGL measure is a bit muddled.  It uses games missed as well as the injured player’s relative importance to quantify the effects of injury.  It also makes adjustments based on the injury report (Questionable, Probable, etc…) to account for the effect of a player participating at less than 100%.

As you can probably tell, there’s likely to be a lot of noise in that data.  Without seeing the exact formulation, I don’t have a sense for the specific weaknesses of the stat, but we can assume it’s far from perfect.

However, the data is available for the past 5 years and I think we can assume that the statistic has been measured consistently over that time period (i.e. no changes to the formula).  Therefore, we can use the stat to get an answer for both questions mentioned above.

– According to AGL, the 2012 Eagles measured 73.3 on the AGL scale.  The average AGL over the past 5 seasons is 56.4.

– The standard deviation for AGL over the past 5 years is 23.1.  Roughly 65% of teams over that span fall within 1 standard deviation of the average and 97% fall within 2 standard deviations, meaning the overall distribution is somewhat Normal.

What does this tell us?

Basically, it means the 2012 Eagles did suffer a relatively high number of serious injuries.  However, the team’s injury count was FAR from out of the ordinary.  Therefore, unlike other measures I’ve highlighted (fumbles, field position), we should NOT expect to see a big improvement to the 2013 Eagles purely as a result of mean-regression (better luck).

We’re not done yet though.

Remember that second question?  The one about persistence?

Well it turns out that, for reasons I haven’t fully analyzed, Adjusted Games Lost is a surprisingly persistent statistic.  That means there is a mildly strong (.30) correlation between a team’s AGL measure one year and its AGL count the following year.  The most obvious reason for this would be that certain players are “injury-prone” and are therefore likely to make continuous contributions to the AGL count each season. For example, if Mike Vick is your Quarterback, you will likely get a few bumps from him each season, as opposed to someone like Eli Manning, who has now started 146 consecutive games (including playoffs).  Regardless of the reasoning, the persistence tells us that since the Eagles were injury prone last year, they are somewhat likely to be injury-prone again this year.

Wrapping everything up, here’s the key takeaway:

There are a number of reasons to feel very good about the Eagles going into this year.  Assuming the team will be healthier, though, is not one of them.

A commenter suggested I take a look at Points per Play, so that’s what I’m doing today.  Note that I haven’t fully thought this through, so I’m going light on the analysis until I feel comfortable with the real meaning behind the data.

For starters, here are the highest points per play measures from the last ten years.  I should also note here that my data is different from the points/play and points/game data from teamrankings.com, and I haven’t yet figured out why.  For now, I’m going with mine, since I know how that was compiled, just know that there may be some small discrepancies (definitely some rounding differences).

As a reminder, Red highlighting means a team LOST the Super Bowl, Yellow means it Won, and the Eagles are in Green.

Somewhat surprisingly, the 2007 Patriots do not rank #1, though a .559 mark is amazing.  Green Bay’s 2011 team sets the mark for offensive efficiency, in terms of maximizing the scoring impact of each play.

Regarding the Eagles, the 2009 team sets the mark for the Andy Reid Era, though the 2010 team was not far behind (.425).  Remember, both the 2009 and 2010 teams were 25% better than league average on offense.  The defense, on both teams, performed worse (+2%, -7%).

Is a high points per play measure good?

Obviously, it is.  However, the question I want to raise here is if the goal on offense should be two-fold.  Scoring points is, by far, the priority goal.  Given the choice, though, shouldn’t teams want to run a lot of plays?

In theory, running more plays takes more time off of the clock, meaning the opposing team’s offense gets less time on the field.  Commentators also frequently cite the fatigue impact of keeping the opposing defense on the field, but I don’t see a big advantage there, since presumably the offensive players get tired too (and don’t rotate like the defenders do).  It’s interesting that just 2 teams in the table above even reached the Super Bowl, with just 1 winning it.

What about the data?

Despite the logic above, there is a modest negative correlation between points per play and points allowed.  Here is the chart:

The correlation value is -.24.

So teams that score efficiently, despite presumably giving the ball back to the other team fairly often, do not appear to allow more points as a result.

But why?

Perhaps scoring so efficiently puts a lot of pressure on the other team to keep up.  Therefore, despite receiving a lot of possessions, the other team is forced to make higher risk plays, leading to more negative plays, and potentially more turnovers.

Let’s take a look.  Here is the chart showing Points Per Play against Turnovers Created.

Bingo.  The correlation value is a fairly strong .38.  UPDATE: There are definitely some causation issues here (i.e. forcing turnovers helps teams to score with fewer plays).  However, given the other offensive data I looked at, I feel comfortable saying it runs more in the direction cited above then in the reverse.  Just know that there is a kind of positive feedback loop at work here.

This goes a long way to explaining the apparent discrepancy I discussed above.  Teams that score frequently also give the opposing team a lot of possessions.  However, those opposing teams do not score more often as a result.  The reason (or A reason, at least), is that the opposing teams end up turning the ball over as they try to keep up.

This goes back to a post I did way back in January about team’s Pass Play Percentage.  The gist of that post was that teams may alter their game-plans too early when losing.  Even down by 2 touchdowns, teams should not abandon the run game until well into the 4th quarter.  By passing more to keep up, losing teams force themselves to successfully execute a strategy they’re not prepared for against a defense that is expecting pass.  The result, predictably, is a lot of turnovers, which obviously hurts the comeback effort.

That brings up the whole concept of Game Theory in run/pass selection, which I think is an extremely fertile area for NFL analytics and research.  Can’t address it in any more detail today though, so we’ll stop there.

Lastly, Happy Independence Day. I’m probably taking the rest of the week off since I won’t be near my computer much anyway.  Enjoy.

Little to no NFL news to really comment on, other than the Aaron Hernandez saga (which is so outrageous I’d just as soon never mention it).  However, rookies report to Eagles training camp on July 22, just 3 weeks from now; so things will pick up soon.

Today, I wanted to take a brief look at  “big-play” offenses.  In short, I think Chip Kelly’s offense will be far less reliant on big plays than Andy Reid’s did.  Andy Reid, despite being known as a “West-Coast” guy, emphasized the deep ball.  Going purely off memory, I believe this began with the TO acquisition, then continued thereafter.

I expect Chip Kelly’s offense to function much differently.  I think the Eagles will use D-Jax on deep routes mainly to stretch the defense and create space for both the run game and underneath TE routes.  Pure speculation at this point, but Andy liked to set things up for a deep shot, whereas I expect Chip to use the deep ball to open things up underneath.

Let’s back up for a second.

What makes a “big play” offense?

The easiest answer would be the obvious one: big plays.  It’s hard not to like deep strikes and long touchdowns.  With the Eagles’ offense though, they typically came at the expense of the ability to consistently gain first downs.  Is that a good trade-off?

Is there a way to quantify this?

Maybe.  As usual, I’ve attempted to create a shortcut.  Rather than look at the number of long scoring plays, passes over 20 yards, etc., I’ve taken total offensive yards and divided by the number of first downs.

For example, a 20 yard pass will only pick up 1 first down (20 yds/1st Down).  In contrast, three running plays of 4 yards each will also pick up 1 first down, but with just 12 yards (12 yds/1st Down).

Easy right?  There’s obviously other noise in here.  Penalties distort the overall numbers.  Negative plays (sacks) change the yards/1st down equation.

I should probably just attach the usual disclaimer: measure is not perfect, but I believe it gets at what we’re looking for.

The Results

From 2003-2012, the NFL average offensive yards per 1st down was 17.6.

So were the Eagles as “big play” dependent as many believe?

From 2003-2012, the Eagles averaged 18.2 yards per 1st down, the 3rd highest average in the league, behind only Tampa Bay and Tennessee (18.4).  Here is a table showing the entire league.

Of note here, besides the Eagles high placement, are the teams ranked 31st and 32nd.  Over the past 10 years, the Colts and Patriots, teams known for their high-powered offense, have the lowest yards/first down averages.

The 2012 Patriots actually recored the LOWEST measure in the last 10 years; just 15.4 yards per first down.

Thinking through our perceptions of these teams, this makes a lot of sense.  When I picture Peyton Manning and Tom Brady offenses, I think of methodical movement down the field.  I do NOT think of spectacular deep throws (though both QBs can obviously do that as well).

The magnitude of the difference in averages above seems small.  Here, though, is another table.  This one shows the 15 HIGHEST averages of the past 10 seasons.

The Eagles feature prominently, as do the 49ers and Titans.  The 2004 Eagles (Super Bowl with TO), averaged 18.7 yards per 1st down, a high measure, though not high enough to crack the table above.

So the numbers match our expectations.  Andy Reid’s Eagles did, in fact, rely more heavily on big plays than nearly any other team in the league.  Note that this is not inherently good or bad.  The correlation value of Yards/1st down to Points Scored is just .065, negligible and an indication of little to no connection.

UPDATE: Here is table showing yards/first down for each team during the Andy Reid era (’99 not included)

So what does this mean going forwards?

The most drastic difference between Andy Reid and Chip Kelly is likely to be offensive philosophy (stating the obvious).  We don’t know what system Kelly will run, and it will likely depend on which QB wins the starting job.  However, as I’ve previously stated, I think the best proxy for what the new Eagles offense will look like is Penn State’s, followed closely by New England.  Going back to our league-wide chart above, we can see that New England’s offense, on a yards per 1st down basis, is about as far from the Eagles’ recent offense as possible.

Practically, this means:

– Longer drives

– Deep balls to set up the short game, rather than the opposite

– Lots of underneath/slot passes

– Fewer “exciting” plays, but more plays overall.  Learn to love the 5 yard pass.

– D-Jax will need to be a “team player”…he will likely be a secondary focus (at least his deep routes)

However, it does NOT mean:

– An expected difference in points scored.