Showing posts with label numbers. Show all posts
Showing posts with label numbers. Show all posts

The Watchtower Review

>> 1.10.2012

the Lowenbrau Lion, by Adrian Valenzuela

The point of the Watchtower posts was to forecast the performance of the Detroit Lions against their weekly opponents. From the start, I’ve used historical performance data of the Lions coordinators against their opposition’s. By controlling for the relative talent of the players, I tried to isolate systemic advantages at the X-and-O level. I then tried to apply those advantages to the teams’ current skill levels, and project a result.

The Watchtower is one of my most popular features; people really dig it. It’s fun to write, especially researching every coordinator’s coaching tree, and picking the picture. However, after three years, I’m no longer satisfied with The Watchtower an alternative “game preview,” or as a predictive tool.

Watchtower Problem #1: heavy reliance on per-game team averages.

When I use average yards per attempt and average yards per carry, it gives a pretty accurate picture of those players’ performance levels. Whether a quarterback has 25 or 50 attempts, or 200 or 400 yards, dividing one by the other tells you at what rate the quarterback is generating offense, every time. But dividing “points scored in a season” by “games in a season” doesn’t work. A “game” is not a fixed unit of measure; there’s a wide variance in the number of possessions and plays in a “game.”

In every pass attempt, there is exactly one pass attempt, one bite at the apple. In every game, there’s a wide variance in possessions, time of possession, and plays. Example: when the Lions hosted the Vikings, they scored 34 points. When they hosted the Chargers, they scored 38. On the face of it (and in terms of the “points per game” numbers I’ve been using), the offense was very effective in both games.

However, in that Minnesota game the offense netted just 280 yards and 20 points from ten possessions. Against San Diego, the offense netted 440 yards and 31 points from eight possessions. This is a massive difference in effectiveness and it’s almost completely uncaptured by the current Watchtower methodology.

Dropping the "per game" team averages would allow me "tell the story" more effectively; I thought there was a very high chance that the first Packers game would be shockingly conservative—and the rematch a track meet. But using season average against season average, there’s no way to project either of those outcomes.

Finally, that "track meet" effect means something: there is a tendency for points to follow points, and that speaks to a very real offense/defense interaction effect that isn’t accounted for, either in traditional analysis or in The Watchtower. When one offense puts the pedal to the metal, the other one follows—and both defenses, apparently, just let it happen. Why? What’s going on here?

Watchtower Problem #2: No real accounting for turnovers or special teams.

This is one that’s bothered several readers from the get-go. The Watchtower is a study of offense-defense interaction: what happens when offensive scheme A meets defensive scheme B. But special teams and turnovers play a huge role in the final score.

In the Thanksgiving Day game, when the Lions and Packers played to a stalemate for most of the first half, a tipped pass fell into enemy hands and the Packers’ offense got to start deep in the heart of Lions territory. That was the game-changing play both teams desperately needed. Despite incredible down-to-down play by the defense, the offense was really the unit that put the Packers in position to score.

On special teams, the Lions’ coverage units struggled mightily throughout the first two thirds of the season, and it regularly hung the defense out to dry. Moreover, the iffy upfield blocking for Stefan Logan (and his own iffy fair catch decisions on kickoffs) failed to make the field shorter for the offense.

Watchtower Problem #3: The Human Element.

I project ranges for points, passing effectiveness, and running effectiveness for each side—then basically use the “Mitigating/Aggravating Factors” and “Conclusions” section to winnow those down to the final score I deem “most likely,” usually via talking-out-loud thought experiment.

There are several layers of my own bias involved here—and even though I work hard to follow where the data leads me, a little bias on top of a little bias on top of a little bias makes a big difference. I can definitely lead the statistical horse to water if I want to—and sometimes I do even when I’m trying not to.

What I’d love to be able to do is project a range of possible outcomes and their probabilities, so when I say “The most likely outcome is . . .” my a hand won’t be moving the data’s mouth.

Thoughts?

Read more...

Detroit Lions 2011 Regular Season: Halfway There

>> 11.03.2011

Everyone is furiously trying to prove that this 6-2 start capped by a blowout of the Broncos is not the same as 2007’s 6-2 start capped by a blowout of the Broncos. I have something different in mind.

In the Old Mother Hubbard series, I attempt to contextualize individual Lions performances. We watch these guys all season long year after year after year, and we lose perspective on their strengths and weaknesses. I use Pro Football Focus data and radar charts to give you an at-a-glance impression of how Lions are performing against the high, low, and average NFL performances at the same position.

So, if we’re taking the temperature of the Lions at the bye/halfway point . . . why not do the same thing?

image

Here are the offensive team grades through Week 8. The dark red line is the New England Patriots, #1-graded offense in the NFL. The bright green line is the Seattle Seahawks, #32-graded offense in the NFL. The thick black line is, as always, the NFL average, and the Honolulu Blue line is the Lions.

This is going to surprise some folks, because we perceive the Lions offense to be one of the best in the NFL—and indeed it is the 4th-best, scoring 29.9 points per game. Keep in mind PFF’s “consistency bias,” as I call it: PFF’s system prefers consistently above-average play to streaky home-run hitters. It’s true for individual players like Ndamukong Suh and Jahvid Best, and it’s true for the Lions as a whole.

No surprise, the Lions’ pass offense was graded 8th-best, at +33.2. Also unsurprisingly, the Lions’ rushing game was well below average; the third-worst in fact. But look: the difference between the best running grades and the worst running grades is miniscule.  Having a very poor running game doesn’t grade out much worse than having an average running game. This is a recurring theme this season.

As far as the offensive line goes, it's no surprise to anyone who’s listened to me or PFF over the years: the Lions do an above-average job of pass blocking. They graded –3.7 (average –6.18) over the course of the season. Also no surprise: they can’t run block for crap. The Lions have the fourth-worst run-blocking line in the NFL to this point, at -40.8 (average –18.15).

On offense, the Lions have taken more penalties than most; they’re ranked 24th with a –5.5 penalty grade. However, since the NFL average is –3.11, that’s not crippling. On the whole, the grades show the Lions have a very good passing offense, a decent pass-blocking offensive line, a terrible running game and a terrible run-blocking offensive line. Add it all up and it’s surprisingly mediocre for a team scoring 30 points per game. Once again, we see: the running game doesn’t matter.

image

The 49ers have a ridiculous defense. I mean, geez. Just look at that. Also: Indy NOOOOOO!

But check out the Lions: 8th-best graded defense overall, graded +33.7. This jibes with their 6th-lowest scoring defense (18.4 PpG). The run defense is ranked 24th, just –2.2 overall—and the average is +14.8, meaning that’s truly not good. The pass rush, again, is what you’d think: 5th-best in the NFL, graded +21.7 (avg. +8.06).

The jawdropper, though: The Detroit Lions have the best pass coverage grade in the NFL. Not pass defense, not pass rush, not statistical derivation: the play of their corners and safeties grades out better than any other team in the NFL. At +22.1, they’re well ahead of the 49ers’ second-place unit (+14.4), and have lapped the rest of the field (avg. –7.74).

The Lions defense is, as it was last season, heavily penalized. Their -7.9 grade is ranked 27th, well below the –3.4 league average—but not as horrific as it’s been. Special teams-wise, the Lions grade out at +7.4—but that’s not all that, because the average is +12.65.

On the whole, we’re left with a promising, but mixed bag. The Lions offense is struggling to move the ball consistently, but is generating points through the air with home run plays. The run blocking is awful, as is the running game as a whole. The defense is a top ten unit, despite poor run-stopping and penalty grades, because they rush the passer better than most—and cover the pass better than anyone.

At the moment, the Lions are in fantastic shape for the playoffs. My favorite predictive football model, the Simple Rating System, LOVES what the Lions have done this year. It’s a combination of strength of schedule and points differential, and at the halfway point the Lions are the second-highest-rated team in the NFL. Given the teams they’ve played and the results of those games, SRS expects the Lions to be the second-hardest out in football (after the Packers) going forward.

Of course, the Lions play the Packers twice throughout the rest of the season, so SRS would project a final finish of 12-4. Could that really happen? Bizarrely, yes. The road games against Chicago and New Orleans are possible (if not likely) losses—but the Lions should be able to split with the Pack, considering they did so last season without Matthew Stafford. The games at Oakland and against San Diego are worlds less scary than they were a few weeks ago, too.

Let’s be clear: I’m not projecting, or claiming, or promising a 12-4 season. I AM promising, projecting, and claiming that the Lions are going to make the playoffs, as I have since May, and have never wavered from. The Lions are only halfway there, but right now that Lions Kool-Aid tastes sweeter than ever.

Read more...

The Hidden Detroit Lions Offense: 1st and 2nd Down

>> 10.26.2011

The Lions lost to the Falcons on Sunday, due to an astonishingly poor performance by the offense, and particularly Matthew Stafford. Many noticed the Lions seemed to be “in a lot of third-and-longs,” and blamed the lack of a power running game that could keep the Lions offense on schedule.

It’s been my contention the Lions use their backs in nontraditional—but effective—ways. If they can run for three or so yards on first down, that gives Stafford and the 7+ yard-per-attempt passing attack two attempts to get seven yards. If they can mix in the screens and draws on which Best and Morris are varyingly effective, they can move the ball very well and score points in bunches.

This has been empirically obvious: through five weeks the Lions had the #2 offense in the NFL, racking up an impressive 31.8 points per game. Subsequently, I have been directing all parties inquiring RE: fat guards and white running backs to talk to that statistical hand.

However, something is not adding up. Maurice Morris and Keiland Williams combined for over five YpC against the Falcons, yet indeed the Lions were constantly facing second- and third-and-long.

Chart?

Chart.

1ST DOWN RUN MM KW NB PASS CJ NB BP TS WH
22/8/6.59 12/2/3.8 7/0/2.0 5/2/6.4 0/0/0.0 10/6/9.9 5/3/16.2 1/0/0.0 1/1/9.0 1/1/6.0 1/1/8.0
2ND DOWN RUN MM KW NB PASS CJ NB BP TS WH
20/7/5.3 7/2/6.6 3/1/12.3 2/0/-1.0 2/1/5.0 13/5/4.62 4/1/6.25 1/0/1.0 3/2/5.0 2/2/13.0 0/0/0.0
TOTAL RUN MM KW NB PASS CJ NB BP TS WH
42/15/5.98 19/4/4.8 10/1/5.1 7/2/4.43 2/1/5.0 23/11/6.91 9/4/11.8 2/0/1.0 4/3/6.0 3/3/10.7 1/1/8.0

The Hidden Game of Football is a seminal book which tops every serious football analyst’s reading list (but which I still haven’t read). In it, so I am told, the authors outline a new way of defining a successful football play. On first down, a successful play gains four yards. On second down, a successful play gains half the remaining distance to converting the first down. On third down, a successful play converts first down. This theory informs the analysis at awesome websites like Football Outsiders and Advanced NFL Stats.

The chart above is a breakdown of the Lions first- and second-down plays against the Falcons. The first number in each box is the number of plays in that category. The number after the first slash is the number of “successful” plays, and the number after the second slash is the average yards-per-play rate of categorical plays. So.

The Lions faced 22 first-and-10 situations Sunday (including plays wiped out by penalties). They gained at least four yards 8/22 times, and averaged 6.59 yards per play. That sounds kinda okay-ish until you look at the run/pass breakdown: the Lions ran on first down 12 of 22 times, were successful twice, and averaged 3.83 YpC. This meshes with my “3-to-4 yards on first down is okay” theory until we go a little deeper.

Maurice Morris ran seven times on first down, never successfully, and averaged 2.0 yards per carry.

Keiland Williams fared a little better. He gained 4+ yards twice on five carries, including a long one that swelled the average up 6.4 YpC. However, neither could compare to the first-down passing game, which was successful six of ten attempts and averaged 9.9 YpA.

Megatron was targeted five times on first down, successfully three times, for a 16.2 average (yes, the 54-yard touchdown was on first down). Non-Megatron receivers were successful on 3 of 4 targets, for 5.75 YpA.

On second down, things were not much better. The running game chewed up half of the yards needed for conversion just twice on seven carries, though the YpC was an impressive 6.57. Part of that is due to a long run by MoMo, but part of it is the “on schedule” effect: the Lions average distance-to-conversion on second down was eight yards. This includes sacks, penalties, etc., but those count in the game, too. The Lions simply aren’t getting enough yards on first down, and it’s making second down much harder to convert.

The Lions running game was successful on first- and second-down just 4 of 19 carries, despite an apparently-excellent 4.84 YpC. The passing game was a better-but-still-not-great 11 of 23 plays for 6.91 YpA. Here’s the interesting bit, though: non-Megatron receivers were successful on 7 of 11 first- and second-down targets, for 6.09 YpA.

This points towards something else I’ve been saying: Stafford is pressing. He’s trying to force it to Calvin (see CJ’s second-down success rate above).  Despite the totally ineffective running game, when Stafford spreads the ball around the offense works. I’m wrong about Maurice Morris being a solid first- and second-down tailback, but I’m right that if Stafford does his job that doesn’t matter.

Read more...

Detroit Lions Offensive Line Analysis: Part I

>> 12.15.2010

Detroit Lions offensive line: offensive tackle George Foster (72), center Dominic Raiola (51), guard Edwin Mulitalo (64), and offensive tackle Jeff Backus (76) line up in the red zone in the Atlanta Falcons 34-21 victory over the Detroit Lions.  Sigh.In the 1993 offseason, the Lions attempted to compensate for the tragic death of All-Pro guard Eric Andolsek—and freak paralysis of G Mike Utley—by signing three free agent linemen: Dave Lutz, Bill Fralic, and Dave Richards.  I clearly remember the newspaper headline that echoed a quote from a coach: “Lions Add ‘900 Pounds of Beef’.”  The gambit didn’t work, and the Lions have been frantically sandbagging the offensive line ever since.

Those who’ve been reading since the beginning might remember that I wrote about that memory last spring, while contemplating the additions of Gosder Cherilus, George Foster, Jon Jansen, Ephraim Salaam, and Daniel Loper over the preceding year.  Four of those five are gone—yet I’ve noted several times this season that the offensive line is better than you think it is, especially in pass protection.  Many have rampantly bashed Cherilus, as well as usual suspects like Backus and Raiola all year long.  Many and called for drastic action to overhaul the offensive line, theoretically to protect the Lions’ investment in Matthew Stafford.  Few, however, seem to realize that the Lions’ O-line is keeping its quarterbacks clean as well as any in the NFL.

Sean Jensen, Bears writer for the Chicago Sun-Times, posted the latest “New York Life Protection Index” stats, while lamenting the Bears’ position on that table (dead last).  This metric, per the creators:

“ . . . was created by sports information leader STATS to provide a composite gauge for this undervalued component of the game. While the New York Life Protection Index is calculated using a proprietary formula, the fundamentals are comprised of the length of a team’s pass attempts combined with penalties by offensive linemen, sacks allowed and quarterback hurries and knockdowns.”

Okay, so check out the New York Life Protection Index, and check out where the Lions rate: ninth.  Ninth?  Yes: ninth-best in the NFL, first-best in the NFC North.  Yes, in pass protection.  Yes, the Lions.  I’m tempted to crow about how all of my suspicions have been confirmed, and how my own eyes have been seeing the truth while all others’ have been clouded with lies and suspicion, and on and on and on, except . . .

The Colts are first.  The Colts, whose profound struggles on the offensive line are a matter of fact, are ranked #1 by this metric.  This reminds me of that year the Lions allowed the fewest sacks in the NFL, at least in part because Joey Harrington was throwing the ball into the stands on every third dropback.  Something similar has to be happening here with Peyton Manning and the Colts’ patchwork line . . . but how do we capture it?  Let’s examine another advanced offensive line metric, one that’s far less of a “black box.”

Football Outsiders’ Offensive Line Rankings feature a variety of interesting stats.  First and foremost, there’s Adjusted Line Yards, which their attempt to mathematically isolate yards gained because the offensive line got good push from yard gained because the running back broke a play open.  The mathematical methods Football Outsiders uses to isolate offensive line yards are detailed here, but for now let’s just see how the Lions stack up.

  • Adjusted Line Yards: 3.25 per carry, ranked dead last in the NFL.
  • Power Success: 59%, ranked 17th in the NFL.
  • Stuffed: 25%, ranked 27th in the NFL.
  • 2nd Level Yards: 1.00, ranked 25th in the NFL.
  • Open Field Yards: 0.43, ranked 28th in the NFL.

So, the Lions’s offensive line isn’t doing great; it’s actually the worst run-blocking line in football.  Detroit running backs have the least daylight to work with of any in the NFL.  This, unlike the Colts being #1 in pass protection, jibes with what we’ve seen on the field.  But Football Outsiders’ ALY stat can drill down even deeper.  They’ve actually broken down the Adjusted Line Yards by gap: “A” gap (between center and either guard), left and right “B” gap (between guard and tackle), and “C” gap (outside tackle/between OT & TE).  Here’s what they came up with for the Lions:

    L END L TACKLE C/GUARD R TACKLE R END
RNK TEAM ALY Rnk ALY Rnk ALY Rnk ALY Rnk ALY Rnk
32 DET 4.65 13 3.7 25 3.33 30 2.73 32 1.61 32
- NFL 4.31 - 4.19 - 4.06 - 4.03 - 4.06 -

This table has two rows: the Lions, and the NFL average.  Working from left to right, we see that runs to the outside of Jeff Backus, or between Backus and the tight end, get the benefit of slightly-above-average run blocking.  Runs between Backus and Sims have get below-average help from the line.  Runs on either side of Dominic Raiola get poor help from the offensive line, runs between Stephen Peterman and Gosder Cherilus are at a steep disadvantage to the rest of the NFL, and . . . well, just don’t run to the outside of Gosder.

This is both surprising and unsurprising.  First, remember when the Lions passed on Michael Oher to take Brandon Pettigrew?  Some subscribed to the notion that Pettigrew’s size and blocking would result in improved pass protection and running lanes anyway—getting “offensive line help” without actually drafting a lineman.  Football Outsiders’ stats show this is exactly what’s happening, which is surprising and exciting.  What isn’t surprising is the total lack of daylight in the A gaps.  Stephen Peterman has been playing hurt, and Lord do we ever see it here.  And Gosder?  I had no idea the Lions were struggling so much to run behind him.  These numbers are flatly appalling for a 6’-7”, 325-pound RT with a legendary mean streak.

Okay, so the left side of the Lions' line is average at run blocking, and the center and right side are butt-naked last in the NFL.  So how did the Lions end up with the ninth-best offensive line by the New York Life Protection Index?  Well, because that looked only at pass protection.  Well, what does Football Outsiders have to say about pass blocking?

Adjusted Sack Rate (ASR)/Sack Rate: Sack Rate represents sacks divided by pass plays, which include passes, sacks, and aborted snaps. It is a better measure of pass blocking than total sacks because it takes into account how often an offense passes the ball. Adjusted Sack Rate adds adjustments for opponent quality, as well as down and distance (sacks are more common on third down, especially third-and-long).

The Lions rank 4th in the NFL in adjusted sack rate, having allowed just 24 sacks while passing constantly against tough competition.  Above them are the Saints, the Giants, and . . . at number one . . . the Colts.  Okay, so this metric has its limitations, too—it’s still derived almost entirely from sacks.  Clearly, we can’t just measure pass protection by sacks allowed, because sacks are as often taken by quarterbacks as they are allowed by the offensive line.  Aaron Schatz ended the 2003 “Fun With Sacks” article where he conceived of Adjusted Sack Rate with the following plea:

Consider this a public request: If you have an idea for another statistic to measure pass blocking/pass rushing, please let me know. The never-ending quest for knowledge marches forward!

Coming up in Part II: we march.


Read more...

The Detroit Lions, the NFL, and Luck

>> 11.30.2010

Two weeks ago, Michael David Smith of the Wall Street Journal’s online edition wrote that the Detroit Lions may be the unluckiest team in NFL history.  Despite, at the time, outscoring their opponents, the Lions had won only 2 of 9 games.  Certainly, Lions fans expected better—and hoped for much better.  Infuriatingly, the Lions seem much improved, but there’s been no change in the bottom line.  However, it’s hard not to consider Bill Parcells’ famous line, “You are what your record says you are.”  Many fans, bloggers, and media pros subscribe to this idea: no matter how much more competitive the Lions look, they are not actually better until they have more Ws next to their name.

So, what do we make of this?  Do we ignore what our eyes tell us?  Do we disregard increased production on both sides of the ball as window treatments on the Titanic?  Or, do we foolishly embrace false “progress” because we’re so desperate to believe?  How much of the Lions’ 2-9 record can be blamed on happenstance, and how much of it is just the Lions’ lack of ability?  Fortunately, Brian Burke of Advanced NFL Stats recently wrote an article exploring exactly how random win-loss records are in the NFL.

Imagine flipping a perfectly fair coin 10 times. It would actually be uncommon for the coin to come out 5 heads and 5 tails. (In fact, it would only happen 24% of the time). But if you flipped the coin an infinite number of times, the rate of heads would be certain to approach 50%. The difference between what we actually observe over the short-run and what we would observe over an infinite number of trials is known as sample error. No matter how many times you actually flip the coin, it’s only a sample of the infinitely possible times the coin could be flipped.

As a prime example, the NFL's short 16-game regular season schedule produces a great deal of sample error. To figure out how much randomness is involved in any one season, we can calculate the variance in team winning percentage that we would expect from a random binomial process, like coin flips. Then we can calculate the variance from the team records we actually observe. The difference is the variance due to true team ability.

I strongly, strongly encourage you to read “The Randomness of Win-Loss Records” at Advanced NFL Stats in its entirety.  Go ahead, I’ll wait.

Okay, back?  Great.  Lost?  Don’t worry: I’ve got you covered with some bullet points:

  • 42% of an NFL team’s regular season record can be accounted for by randomness, otherwise known as sample error.
  • The correlation coefficient (r) between observed team records and a team’s true ability the square root of 0.58, which is 0.75.
  • After a full season of 16 games, your best guess of a team's true team strength should regress its actual record one quarter of the way back to the league-wide mean of .500.
  • The theoretical maximum accuracy of any predictive model is about .75. (from the comments, and Burke’s earlier work about luck & NFL outcomes).

If 42% of the Lions’ 2-9 record can be accounted for by randomness, that’s 4.62 games’ worth out of the eleven.  Assuming that the Lions have had nothing but bad luck to this point—they’re at the very nadir of randomness—then we flip it to nothing but good luck, we can see the theoretical maximum given this talent.  So, if Lions had gotten all the bounces: no Stafford injury, no Megatron Referee Fail, no Wendling/McCann freak TD return, no Alphonso Smith Disasters, Drew Stanton competes that pass, Shaun Hill doesn’t airmail that two-pointer (neither of which would happen anyway because Stafford would’ve been healthy, remember?), a few fewer specious penalties for the Lions, a few more for the opponents, recover a few more of the forced fumbles, catch a couple of dropped INTs . . . the Lions could be as good as 6-5 right now.

Before you freak out: that assumes both a 16-game season, and that the Lions are currently having the rottenest luck possible.  An 11-game sample isn’t the same as a 16-game sample; there may yet be some regression to the mean—that is, if the Lions really aren’t what their record says they are, their luck will turn before we get to the end of the season.  Well, either that, or next season will be a 16-game dip in the strawberry river:

Let’s assume for a second that there’s no sudden switch in the Lions’ fortunes, and they don’t sweep the NFC North at home during these next five games.  Let’s also assume they maintain their current pace: a winning percentage of .182.  Applied to 16 games, that’s 2.912 wins.  What’s the “best guess at their true strength,” if we regress them one-quarter of the way back to the mean?  If I understand this correctly, the difference between .500 and .182 is .318—and a quarter of that is .0795.  So, the Lions’ “true strength” should be a winning percentage of .262: just over four wins.

Again: this assumes the Lions only win one more game.  If the Lions finish 3-13, we’ll have no business saying “well this was really a 7-win team that got screwed.”  Sure, if everything had broken the Lions’ way, and they’d been the beneficiary of some truly rare luck, then maybe they’d have won six or seven games—but as they are, busted-up Stafford and all, if the Lions only win one more game, they really are only a 3-to-4 win team.

So, again, perspective: this is applying Brian Burke’s analysis of win/loss randomness in the NFL to the Detroit Lions’ current record.  All it can do is tell us, at the end of the season, what role “the Football Gods” have played in making the Lions’ record what it is—it is a redictive system, giving us a way of understanding what's already happened.  It can’t tell us which games were the result of randomness, if “the randomness” has already happened, or if the Lions are “due” for a hot streak.  It can’t tell us what we really want to know: how many games the Lions will win going forward. 

Let’s attack this from the other direction: with a predictive model, one that can actually assess teams' relative strengths and project a winner.  I’m choosing the Simple Ranking System, as published by Doug at Pro Football Reference.

Yes, this is required reading too.  Yes, I’ll wait.

Fortunately, it is as simple as the name implies, so it only requires one bullet:

  • Every team's rating is their average point margin, adjusted up or down depending on the strength of their opponents.

Okay, so average point differential, adjusted by strength of schedule, which adjusts the rankings, which adjusts the strengh of schedule, which adjusts the rankings, which adjusts the strength of schedule, over and over and over until the numbers stop changing.  Very simple indeed, yes—but as Doug says, “As it turns out, this is a pretty good predictive system.”

Chart?

Chart:

Team W L T W-L% PtDif SoS SRS
Green Bay Packers 7 4 0 0.636 103 1.2 10.6
New England Patriots 9 2 0 0.818 68 2.2 8.4
Pittsburgh Steelers 8 3 0 0.727 73 1.5 8.1
New York Jets 9 2 0 0.818 77 1 8
Atlanta Falcons 9 2 0 0.818 67 -0.1 6
Philadelphia Eagles 7 4 0 0.636 53 1.1 5.9
Baltimore Ravens 8 3 0 0.727 62 0.3 5.9
San Diego Chargers 6 5 0 0.545 85 -2.1 5.7
Tennessee Titans 5 6 0 0.455 39 0.5 4
Chicago Bears 8 3 0 0.727 50 -1.1 3.5
Indianapolis Colts 6 5 0 0.545 30 0.5 3.3
New York Giants 7 4 0 0.636 37 -1.4 1.9
Miami Dolphins 6 5 0 0.545 -20 3.5 1.7
Kansas City Chiefs 7 4 0 0.636 54 -3.3 1.6
New Orleans Saints 8 3 0 0.727 68 -4.5 1.6
Cleveland Browns 4 7 0 0.364 -13 1.4 0.2
Detroit Lions 2 9 0 0.182 -24 2.1 -0.1
Houston Texans 5 6 0 0.455 -23 1.5 -0.6
Oakland Raiders 5 6 0 0.455 -1 -1.9 -2
Minnesota Vikings 4 7 0 0.364 -50 2.5 -2.1
Buffalo Bills 2 9 0 0.182 -66 3.7 -2.3
Washington Redskins 5 6 0 0.455 -47 2 -2.3
Dallas Cowboys 3 8 0 0.273 -45 1.3 -2.8
Tampa Bay Buccaneers 7 4 0 0.636 -4 -3.1 -3.5
Cincinnati Bengals 2 9 0 0.182 -63 2 -3.7
Jacksonville Jaguars 6 5 0 0.545 -54 0.9 -4
St. Louis Rams 5 6 0 0.455 -18 -4.1 -5.8
Denver Broncos 3 8 0 0.273 -73 -0.1 -6.7
San Francisco 49ers 3 7 0 0.3 -59 -2.5 -8.4
Seattle Seahawks 5 6 0 0.455 -66 -3 -9
Arizona Cardinals 3 7 0 0.3 -104 -1.6 -12
Carolina Panthers 1 10 0 0.091 -136 -0.9 -13.3

Guess how this chart is sorted?  By SRS rank.  You can see the Packers, Patriots, Steelers, and Jets up there at the top, and Seahawks, Cardinals, and Panthers scraping the bottom of the barrel.  But wait, that team in bold, the one that’s darn near in the center?  That’s the Lions, ranked 18th overall.  When we take into account who they’ve played—per SRS, the Lions have played the 6th-hardest schedule in the NFL to this point—and how their offense and defense has performed, the Lions are the 18th-strongest team in the NFL.

This isn’t “with Stafford,” “with that Megatron touchdown,” “with that Drew Stanton pass,” or with anything imaginary added or subtracted.  Quite literally, it’s the scoreboard of every Lions game so far this year; it’s simply been adjusted by the scoreboards of everyone they’ve played.

Ah, but how accurate is this method?  It’s a predictive model, but how predictive is it?  Clearly, if it says the 2-9 Lions are near the middle of the pack in relative strength, it can’t be good at predicting who’ll win and who’ll lose, right?  Well, I regressed the SRS rankings against win percentage, and this is what I got:


image

Check out the correlation factor there: .7449205, or if you round up .001, .745.  What was the theoretical maximum for a predictive model again?  Well, if Brian Burke is right, it’s approximately .75.  That means that given the inherent randomness in NFL outcomes, the Simple Ranking System is as good as it gets when it comes to assessing relative strength of NFL teams, and thereby predicting future NFL outcomes.  Again, according to this system, the Lions are the 18th-best team in the land.  Further, if I’m not mistaken, they’re the biggest outlier on the chart: they’re the lowest, rightest dot (-0.1 SRS, .186 W-L).  Nobody’s getting screwed harder, or helped out more, by Lady Luck than the Lions.  Just trace the Y axis up to the line of best fit (the diagonal one), and you’ll know what the Lions’ win percentage ought to be: .500.  That’s right, SRS expects the Lions to have 5 wins by now.

So what does this all mean?  It means that if the Lions keep playing like they’ve been playing, they’re either going to pick up multiple wins in these last five games—or next season, they’ll be tubing down the strawberry river of regression to the mean.



Read more...

Barry Sanders and Emmitt Smith: By the Numbers

>> 10.27.2010

Forget what you know.  Forget what you’ve heard.  Forget what you thought you understood about the circular arguments that have swirled ‘round and ‘round these men for nearly two decades.  Here are the numbers; this is the truth.



Age Year Year Att Att Yds Yds TD TD Y/A Y/A Rnk Rnk PBL PBL
21 1989*+ 1990* 280 241 1470 937 14 11 5.3 3.9 2nd 10th 0 0
22 1990*+ 1991* 255 365 1304 1563 13 12 5.1 4.3 1st 1st 1 0
23 1991*+ 1992*+ 342 373 1548 1713 16 18 4.5 4.6 2nd 1st 1 2
24 1992* 1993*+ 312 283 1352 1486 9 9 4.3 5.3 4th 1st 1 3
25 1993* 1994*+ 243 368 1115 1484 3 21 4.6 4 5th 3rd 1 3
26 1994*+ 1995*+ 331 377 1883 1773 7 25 5.7 4.7 1st 1st 1 4
27 1995*+ 1996 314 327 1500 1204 11 12 4.8 3.7 2nd 8th 1 4
28 1996* 1997 307 261 1553 1074 11 4 5.1 4.1 1st 12th 1 2
29 1997*+ 1998* 335 319 2053 1332 11 13 6.1 4.2 1st 5th 1 2
30 1998* 1999* 343 329 1491 1397 4 11 4.3 4.2 4th 4th 0 2
T 10* 6+ 8*, 4+ 3062 3243 15269 13963 99 136 5 4.3 - - - -


These numbers are from Pro Football Reference.  “Y/A” is raw yards-per-attempt, “Rnk” is ordinal rank amongst NFL running backs, by yards.  “PBL” is the number of Pro Bowl offensive linemen on the each player’s team that season.  A “*” denotes Pro Bowl selection. “+” denotes First Team All-Pro.  Barry’s Pro Football Reference page; Emmitt’s Pro Football Reference page.  As you see, I have aligned the stats to begin at their rookie years, and end when Barry retired.  I have also bolded the “better” of each statistic pair.

A few talking points:

  • Barry Sanders went to the Pro Bowl at the end of every season he played in the NFL, and was first-team All Pro in six of those ten seasons.  Emmitt went to Hawaii eight of fifteen seasons, and was first-team All Pro four times.
  • In 1993, Barry missed five games due to a season-ending injury, but was still 5th-best in the NFL with 1,115 yards on 243 attempts.
  • Omitted are Emmitt’s last five seasons, where he added 4,392 yards on 1,166 attempts (3.77 YpC).  He finished 13th, 15th, 20th, 61st, and 21st in the NFL in rushing in those seasons.
  • Lately I have heard talk of Barry having run behind “two Pro Bowl offensive linemen,” and this is true—but never both at the same time.  Left tackle Lomas Brown was a Pro Bowler from 1990 until the Lions let him walk in 1995.  Center Kevin Glover was a Pro Bowler in '96 and ‘97—and then the Lions let him walk.

Let’s discuss in the comments.

Read more...

The Defensive Line and the Secondary, Part III

>> 7.23.2010

Throughout the offseason, it’s been speculated that the Lions’ woeful pass defense will get a boost from the revamped defensive line.  With Kyle Vanden Bosch and developing Cliff Avril on the ends, and Corey Williams and Ndamukong Suh joining Sammie Hill on the inside, the pass rush should be greatly improved.  This should, in turn, take pressure off the unproven secondary . . . right?

I set out to investigate this in part one, using the NFL’s league-wide data over the past two decades or so.  I tried to find correlation between seasons when sacks were up, and seasons when passing offense was down.  I think I learned more from the comments about how statistics and regression analysis work, than I did about the correlation between pass rush and pass defense—but my early results suggested that there is not a correlation between pass rush and pass defense.

I tried again with part two, blending pro-football-reference.com’s official and official-derived data for 2009, with profootballfocus.com’s manually film-reviewed defensive stats and grades.  I came up with a stat I called “pass rush rate,” which was opponent pass dropbacks (attempts + sacks) divided by cumulative sacks, hits, and pressures.  Then, I ran a simple correlation between every team’s pass rush rate for 2009, and their yards-per-attempt allowed.  The correlation was weak, –0.152. When squared to get the effect size, it was a negligible .023.

Importantly, the real-world analysis bore this out: the Jets had an extraordinary pass defense, by far the best in the NFL.  While their pass rush was solid, ranking 9th of 32 in pass rush rate, it was just that—solid, not phenomenal like the overall pass defense was.  Amazingly, the Cleveland Browns generated pressure on the quarterback more often than every team except Dallas and Minnesota—and yet, they were the fifth worst pass defense in the NFL!  Pass rush alone doesn’t make a defense effective.

For this installment, I wanted to get even more specific: I wanted to isolate defensive line pass rush from everything else.  After all, the idea is that getting an effective rush with just the front four will allow much greater flexibility in coverage and blitzing.  I aggregated the stats of just the defensive linemen, and compared them to what I already had.

Now, let me tell you a legendary tale . . . or, well, I guess, just a legend:

  • Name: The name of the team.
  • A: The primary defensive alignment.
  • Pass Rush Rate: The percentage of opponent dropbacks (Attempts + Sacks) on which the defense achieved a pressure stat (Sacks + QB Hits + Pressures + Batted Passes).
  • DL Pass Rush Rate: The percentage of opponent dropbacks (Attempts + Sacks) on which the defensive line achieved a pressure stat (Sacks + QB Hits + Pressures + Batted Passes).
  • % of rush from DL: The percentage of defensive pressure stats (Sacks + QB Hits + Pressures + Batted Passes) generated by defensive linemen.

NAME A Pass Rush Rate DL Pass Rush Rate % of rush from DL
Dallas Cowboys 3-4 48.2% 18.2% 37.8%
Minnesota Vikings 4-3 47.7% 38.9% 81.7%
Cleveland Browns 3-4 44.0% 19.1% 43.4%
Miami Dolphins 3-4 43.3% 36.0% 83.1%
Philadelphia Eagles 4-3 43.1% 35.4% 82.2%
New York Giants 4-3 43.0% 35.3% 82.0%
Atlanta Falcons 4-3 42.9% 35.1% 81.8%
Green Bay Packers 3-4 41.6% 13.5% 32.5%
Pittsburgh Steelers 3-4 41.3% 10.1% 24.4%
Houston Texans 4-3 41.2% 32.7% 79.4%
New York Jets HYB 40.3% 19.3% 47.9%
Denver Broncos 3-4 39.7% 13.1% 33.0%
Tennesee Titans 4-3 39.6% 36.0% 90.9%
San Francisco 49ers 3-4 39.4% 16.5% 41.9%
Washington Redskins 4-3 39.2% 29.2% 74.5%
Carolina Panthers 4-3 39.2% 35.4% 90.3%
Arizona Cardinals 3-4 38.5% 18.7% 48.6%
New England Patriots HYB 37.8% 18.8% 49.8%
Chicago Bears 4-3 37.3% 29.9% 80.1%
San Diego Chargers 3-4 37.3% 12.5% 33.5%
Kansas City Chiefs 3-4 37.1% 12.8% 34.5%
St. Louis Rams 4-3 37.0% 28.7% 77.5%
Oakland Raiders 4-3 36.6% 30.5% 83.3%
Tampa Bay Buccaneers 4-3 36.3% 29.6% 81.6%
Indianapolis Colts 4-3 35.8% 33.2% 92.8%
Baltimore Ravens 4-3 35.6% 25.2% 70.7%
Seattle Seahawks 4-3 34.2% 27.2% 79.4%
New Orleans Saints 4-3 34.2% 23.6% 69.2%
Buffalo Bills 4-3 32.5% 27.6% 84.9%
Cincinnati Bengals 4-3 32.2% 26.2% 81.3%
Detroit Lions 4-3 29.2% 23.5% 80.2%
Jacksonville Jaguars HYB 27.9% 10.3% 37.0%

We can see a few things in action here.  First, the Lions were terrible: second-worst in the NFL in Pass Rush Rate.  Second, the numbers get more wildly varied from left to right.  Most teams generate a pressure stat on 30-40% of the time their opponents drop back to pass, with the extremes at 27.9% and 48.2%.  Most teams generate pressure from the defensive line between 15-35% of the time, with extremes at 10.1% and 38.9%.  The percentage of the pass rush that comes from the defensive line is all over the board, from 92.8% all the way down to 24.4%.  What does this mean?

Given the amazingly wide range of percentage-of-pass-rush-from-defensive-line stats, and the zero (okay –.120, R-squared .014) correlation between them and Pass Rush Rate, I knew that scheme was a major factor.  The Colts generated almost all of the pass rush from the defensive line, just as a Tampa 2 is supposed to.  Their two ends, Robert Mathis and Dwight Freeney, accounted for 24 of Indy’s 33 sacks, 23 of 45 hits, 78 of 139 pressures, and 1 of 4 batted balls.  That’s right, Mathis and Freeney were fifty-seven percent of the Colts’ pressure statistics; they were the Colts’ pass rush.  Meanwhile the Steelers, despite having one of the league’s better pass rushes, got only 24.4% of their rush from their line.

  I separated the teams out by scheme, grouping 4-3 teams together, and 3-4 and hybrid teams together.  Since the Lions are a 4-3 team, and that’s what this exercise is all about, I discarded the 3-4s and the hybrids, and set about correlating PRR with Y/A, for just 4-3 teams:

2009 NFL 4-3 Defense Pass Rush Rate vs. Yards per Attempt

Okay, so these are the 2009 4-3 defenses, and their overall Pass Rush Rate regressed against opponent Yards per Attempt.  Look at the R-squared; there is literally zero correlation between these two statistics.  Okay, we expected that to an extent—but what if we do it for just defensive line?  If the rush is getting there without blitzing, that should make coverage better—so, we should see a tighter correlation when we regress DL-only Pass Rush Rate against Y/A Allowed:

2009 NFL 4-3 Defensive Lines Pass Rush vs. Yards per Attempt

That’s a little itsy bit better, but there’s still no real correlation happening here.  Okay, what if we do it for percentage of pass rush that comes from the defensive line?

2009 NFL 4-3 Defensive Line Pressure vs. Yards per Attempt

Okay, we’re making tiny, tiny incremental progress, but this is still nothing we can call correlation.  Yards per Attempt, my favorite measure of per-play passing effectiveness, is completely disconnected from pass rush, DL-only pass rush, and percentage of pass rush generated by the DL.  But we know for a fact that teams with good pass rushes have good defenses, right?  I mean, the Vikings have a good defense, right?  Right.

2009 NFL 4-3 Defensive Line Pressure vs. Points Allowed

Okay, now we’re talking.  In all of my pass rush data mining, the strongest meaningful correlation I could find was between what percentage of pass rush comes from a 4-3 defensive line, and how many points that defense surrendered on the year.  As I said way back in part one:

We're left with the depressing conclusion that the only good pass defense is good pass defense. However, that's not really the case, either. Sacks and interceptions, though they don’t affect the interplay of pass offense and pass defense outside of themselves, are still extremely important in terms of total defense. Stopping drives and preventing scoring is the primary job of a defense; a third-down sack or a red-zone INT can erase sixty or seventy yards’ worth of Montanaesque passing effectiveness.

So again, as I’ve been saying: an improved pass rush won’t improve a team’s pass defense—but it will improve the team’s scoring defense.  Here’s the second-strongest correlation I found: percentage of PRR from a 4-3 DL regressed against Passing 1st Downs Allowed:

image

Okay, again, this makes sense: the more pass rush you can generate from your 4-3 defensive line, the fewer passing first downs you allow . . . but we’re not done yet.  I calculated the simple correlation factors for every offensive stat I thought might be illuminating.  Note that these are NOT the R-squared effect sizes you see in the charts above—since that eliminates the direction of the correlation, which is important here.  To get those effect-size figures, square the amounts in this table:

Category %DB/P %DB/DLP %P/DL Att/PD
points -0.133 -0.360 -0.547 -0.237
total first downs -0.007 -0.210 -0.431 -0.262
passing first downs -0.015 -0.241 -0.482 -0.132
running first downs -0.187 -0.257 -0.232 -0.114
yards per attempt -0.064 -0.139 -0.190 -0.062
yards per completion -0.147 -0.316 -0.418 -0.293
completion percentage 0.135 0.269 0.325 0.356
interceptions -0.133 -0.081 0.037 0.165
touchdowns 0.175 0.205 0.123 0.134
passer rating 0.156 0.148 0.049 0.012

Look at completion percentage: there is a weak, but positive correlation between PRR, defensive line PRR, and percentage of PRR from DL and completion percentage.  So, as the defensive line gets more pressure, generally quarterbacks complete more of their passes—but, at what cost?  Look again at yards per completion; there’s a moderate negative correlation between increased DL pressure and average completion length.

There is a definable “cringe effect!”  When the defensive line generates more pressure, offenses generally tend to complete more and shorter passes—“going into a shell,” as it’s called.  It’s this mechanism, completing more passes for fewer yards, that explains why yards-per-attempt allowed doesn’t change as the pass rush rate increases.  Teams will dink-and-dunk in the face of the rush—meaning they convert fewer third downs, and score fewer points.

So.  How much better will the Lions’ defensive line have to be?  Well, as we saw, their pass rush numbers are terrible.  In order for the Lions to improve their Pass Rush Rate to the league average, they’d have to increase it from 29.2% of snaps to 37.7%.  To increase DL PRR to league average, they’d have to increase it from 23.5% to 30.7%.  The percentage of PRR from the DL is about right, 80.2% versus 81.3%.

The league average team faced 567 dropbacks last year, compared to the Lions’ 571, so I’ll normalize the Lions’ pressure stats to 99.3%: 22.83 QB sacks, 34.76 QB hits, 100.29 QB pressures, and 7.94 batted passes.  I’ll do the same for the DL pressure stats, from 18 to 17.86, from 26 to 25.82, from 82 to 81.43, and from 8 to 8.94.  Now, to compare to the NFL average, find the difference, and voila:

Team/Data %DB/P %DB/DLP %P/DL QBSk QBHt QBPr BP DLSk DLHt DLPr DLBP
Detroit Lions (normalized) 29.2% 23.5% 80.2% 22.83 34.76 100.29 7.94 17.86 25.82 81.43 7.94
NFL Average 4-3 37.7% 30.7% 81.3% 33.00 52.00 118.00 10.00 25.00 40.00 98.00 9.00
Delta (absolute) 8.5% 7.2% 1.1% 10.17 17.24 17.71 2.06 7.14 14.18 16.57 1.06
Delta (percentage) 29.1% 30.6% 1.4% 44.5% 49.6% 17.7% 25.9% 40.0% 54.9% 20.4% 13.3%

We can conclude that, in order to bring their pass rush up to NFL average levels for a 4-3, their defensive line will have to increase their sack rate by 40%, their hit rate by 54.9%, their pressure rate by 20.4%, and their batted-ball rate up by 13.3%—and they’ll need a few more sacks and hits from the linebackers and secondary, as well.  I’m still working on projecting all that data out into points allowed, first downs allowed, etc., but there you have it.  If the Lions face the same number of dropbacks in 2010 that the average NFL team did in 2009, the difference between KVB/Avril/Williams/Suh and Avril/Hunter/Cohen/Hill will have to be worth an improvement of 7 sacks, 14 hits, 17 pressures, and 1 batted ball over 2009’s 18, 26, 82, and 8 to get back to average.


Read more...

  © Blogger template Simple n' Sweet by Ourblogtemplates.com 2009

Find us on Google+

Back to TOP