Match Up Pages Are Back

One of the least well-known features on this site are the Match Up pages. They're a one-stop shop for comparing all the relevant stats between upcoming competitors. I created the pages originally as just a tool for myself, needing a quick and easy way to research the numbers for my posts at the NY Times and Washington Post.

There are tables to compare each team's conventional rate stats, offensive and defensive advanced stats, and offensive and defensive player stats. Unfortunately, it's quite an eye chart, so unless you're familiar with the stats and the tables, it might just seem like a wall of numbers.

You can get to them by clicking on the upcoming games in the scoreboard banner above, or you can always navigate to them directly at this address.

Washington Post: What Does Grossman's Good Start Mean?

Today's post at the Washington Post's Redskins Insider looks at Grossman's performance and its context in his career. I also grade the o-line, look at a 4th and 5 conversion attempt, and show that a 14-point margin of victory can be misleading.

Damn, I need to get a new picture to those guys. That's just awful.

When Will Ray Lewis Slow Down?

Lewis entered the league in 1996, and 15 years later at age 36 he's still producing. Last Sunday, all Lewis did was notch 7 tackles (for a 1.3 TF) , a forced fumble, an interception, and two passes defended, all good for 8.5 points of +EPA. That's all.

As a Baltimore guy, I fear the day the day when Ray hangs up his cleats. So I plotted his performance by year (since 2000 for regular season games) in terms of +WPA per game, +EPA per game, and Success Count (SC) per game, expecting to find some sort of noticeable decline. And that's exactly not what I found.

Success Count

Most readers are familiar with the Success Rate (SR) stat. It's pretty straightforward. SR is the percentage of plays in which the result of a play results in an increase in net scoring potential, defined by an increase in Expected Points (EP). SR makes sense as a metric for running backs, quarterbacks, and receivers, and it certainly makes sense for team offenses and team defenses. But it doesn't make sense for individual defensive players. In its place, a slightly different variant of the success metric, called Success Count (SC), has been added.

On all the individual defender stat pages here, I had included SR simply because I already had it coded for teams and offensive players. I thought it might be interesting and possibly reveal something, but no insight materialized. SR for defenders doesn't make sense for the same reason that total WPA or EPA, as derived from play-by-play descriptions, doesn't make sense.

Grossman's Game-Clinching Drive

The Redskins stunned the Giants on Sunday, winning 28-14 after shutting out Coughlin and crew in the second half.  After a Rex Grossman fumble to start the 4th quarter, the Redskins blocked a 38-yard Lawrence Tynes field goal attempt and the momentum swung in their favor.  Starting from their own 30, Washington went on a 6-minute, 10-play drive, culminating in a game-clinching touchdown pass to Jabar Gaffney.

Using a Markov model of a football drive, we can calculate the probability of a drive ending in any possible way: TD, field goal, turnover, punt, etc...

Here is a look at the Redskins final scoring drive:

The biggest play of the drive? Play 4.  Backed into a 3rd-and-9 on their own 45, Grossman completes a 7-yard pass to Fred Davis - short of the first down.  Enter Antrel Rolle: a 15-yard unnecessary roughness penalty keeps the drive alive and vastly shifts the probabilities.  Before the penalty, there was over a 60% probability the drive would end in a punt and only a 12% chance of a TD.  After the penalty, the punt probability drops to 5% and TD probability jumps to 37%.

The Redskins converted three 3rd-downs on the drive (plays 4, 7 and 10), ultimately scoring on a 3rd-and-4.  We see these 3rd-downs highlighted in the expected points:

Note: These expected points are based on the probability of scoring on one drive.  This expected points model differs from Brian's expected points model in a few ways, the most notable of which is that it does not include the interaction between drives.

Keith Goldner is the creator of Drive-By Football, and Chief Analyst at numberFire.com - The leading fantasy sports analytics platform. 

Please Welcome Keith Goldner

Keith has agreed to be a contributor here at Advanced NFL Stats this season. He's been doing his own modeling and analysis at his site Drive-By-Football for several months. I was so impressed, I sought out Keith before my call for writers last week.

Keith is a true stat-head. Aside from Drive-By, he is the chief analyst at numberFire, a slick and smart fantasy-oriented site. He graduated Magna Cum Laude from Northwestern with a B.A. in math. Keith has worked with two NBA franchises - Oklahoma City Thunder and Philadelphia 76ers - doing statistical analysis and data management. In addition, he has worked with ESPN and the Wall Street Journal, contributing analysis across multiple platforms, and his sports analytics research has been featured at conferences across the nation.

Keith won't be confined to the GWP, WPA, AYPA environment. He has adapted his own Expected Points model (which has a clearer name--Net Expected Points).

So please welcome Keith. We can expect his first post very soon.
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On another note, I have only begun reviewing all the responses for my call for writers. I was overwhelmed with all the interest. I intend to select an additional writer or two later this week.

Will the New Kickoff Rules Really Reduce Injuries?

The NFL play-by-play reports when players are injured on each play, or at least when the injury stops play so trainers can attend to the injured player. These are far from 100% all injuries suffered in the course of play, but they are the ones that tend to be significant or severe--ACLs, broken bones, separated shoulders, concussions--the kind of things that really worry players, teams, and the league.

With that information in hand, we can see the injury rates for each type of play, including kickoffs.

The Weekly League: Notes and Ideas for Week One

Jay Cutler has fallen and doesn't want to get up.

This week's edition of The Weekly League features

1. Heat-seeking previews for the Atlanta-Chicago, Minnesota-San Diego, and New England-Miami football games.

2. A total and purposeful omission of the Sunday night game between Dallas and the Jets.

and

3. A photo of Jay Cutler looking groggy -- i.e. basically the only type of photo of Jay Cutler on the internet.

Atlanta at Chicago | Sunday, September 11 | 1:00pm ET
Four Factors


Notes
• Against his better judgment, the author remains the sort of person who's pretty sure the Cutler-Martz Experiment will succeed.
• In fact, it sorta did succeed last year -- in that Cutler finished seventh among 31 qualified quarterbacks in yards per attempt, at 7.6 (compared to a league average of 7.0).
• Unfortunately, when considering net yards per attempt -- which also accounts for sacks -- Cutler finished 21st overall, at 6.0 (relative to the league average of 6.2).
• The Bears, of course, finished with a league-worst 10.7% sack rate.
• That Cutler's career sack rate is just 5.9% (i.e. about league average) and the Bears drafted lineman Gabe Carimi from Wisconsin bodes well for the team's passing attack (i.e. the thing that wins games).