I can’t stand unsupported arguments. I’m a pretty logical guy, but when someone makes an argument based on their own opinion, fairy dust and unicorns, I flip my lid. You’ll notice a trend here at the Seahawks Asylum, that I’m crazy (ha! ha! these puns get me every time) about supporting arguments. With real data.
AdvancedNFLStats is a kick ass website. For about ten years, they’ve been translating advanced stats used in baseball (commonly referred to as Sabermetrics) to football. These metrics are complex, deep and most importantly: contextual. The latter is what current NFL metrics lack. For a great example, read my post about legendary Batman villian Two Face, played by Seneca Wallace.
Below are some of the metrics that I’ll commonly refer to within my posts. But don’t worry about having to come to this page every time I quote; I’ll always summarize what the stats mean in the context of the post.
I know this looks like a lot of text, but I guarantee you that it’s easy to follow and simple.
Expected Points (EP) and Expected Points Added (EPA)
These two put a numerical value on the likelihood of affecting the score of the game. And these values are tied to real football scoring. If you see 7.0 for example, that’s 7 real scored points.
Expected Points (EP). If the Seahawks are pinned on their own 1 yard line, how many points do you expect them to score on the very next play? Not many. Hell, zero. EP is that value, based on historical data. AdvancedNFLStats uses this historical data and looks at ALL plays that have started on the 1 yard line and what they led to on the very next play. We don’t see it often, but we’ve all seen a team score from their 1 yard line on the next play. EP is the numerical value, in points likely to be scored, on the very next play. Since teams HAVE scored from their 1, but VERY rarely, the EP value isn’t zero points. We’ll call the EP for that drive 0.10 at that moment in time.
Now, put the Seahawks on the opponent’s 1 yard line and it’s the opposite. The Seahawks are damned likely to score a TD, aren’t they? The EP for that situation would be something like 6.9 (because again, it’s a weighted average of historical plays on the opponent’s 1 yard line; not every 1 yard goal line offense has resulted in a TD, but most have). Make sense?
Therefore, Expected Points Added (EPA) is the contribution of one play to the EP. If Marshawn Lynch rushes for 30 yards from midfield, he just put the Seahawks in position to score, right? So if midfield has an EP of 2.0 (outside realistic range of a field goal, thus being less than 3.0, the value of a field goal) and the opponent’s 20 yard line has an EP of 3.5, (because they’re in range to score at least a field goal, but also realistic range for a TD) the EPA was +1.5, right? Damned fine running there, Marshawn.
But if instead he’d ran for a 5 yard loss, putting the Seahawks on their own 45 yard line and thus hurting the team’s chance the score, the EPA would be negative, something like -0.2, bringing the Expected Points from 2.0 to 1.8.
Visit AdvancedNFLStats to read a full explanation of Expected Points Added (scroll down the page).
Win Probability (WP) and Win Probability Added (WPA)
Win Probability (WP) is very similar to Expect Points, except instead of assigning an amount of points to a game situation, it determines how likely a team is to win (or lose) in that very same situation. To determine this, AdvancedNFLStats has put together a Win Probability model that takes into account contextual details other than down and distance, but also: score of the game, time left on the clock, etc., based on historical data.
Win Probability uses percentages. If a team’s WP is 99% (or .99) means a team has 99% chance of winning the game (a WP this high likely means they’re leading, they have the ball, it’s 1st and 10 and there are 4 seconds on the clock; virtually every time a team was in this position in the past, they’ve won 99% of the time).
Or, a game tied at halftime would be something like .48/.52 (it’s not fifty fifty because team receiving the ball after halftime will give have a slight edge).
So, similar to EPA, Win Probability Added (WPA) determines a play’s likely affect on the outcome of the game. Let’s layer some context to Marshawn’s 30 yard rush, from above: the game is tied, it’s the 4th quarter, Seahawks have possession on the 50 yard line, it’s 1st and 10 and only 1 minute remains. Seahawks have 3 downs and don’t need a lot of yards to get within field goal range, so we’re gonna say they’re pretty highly likely to win, right? Let’s call their Win Probably .75 (or 75% for those of you skimming, not reading).
Marshawn’s run puts the Seahawks at the 20 yard line, thus in easy range for a field goal. That run elevated the Seahawks’ WP to something like .85 (because how many kickers miss 37 yard field goals?), thus meaning his rush was worth .10 WPA (that’s a TON for a single play). Seahawks, while on the 20 yard line, then knee the ball twice (each knee is likely worth .02 WPA, not necessarily because it means they’re more likely to score, but because the opponent is less likely to score with less and less time remaining) and then the kicker nails the field goal with only seconds remaining to finish the game and put WP up to 1.00 (technically when the clock expired).
Visit AdvancedNFLStats to read a full explanation of Win Probability Added (scroll down the page).
+WPA and +EPA are a bit sticky. These numbers attempt to measure a DEFENSIVE players’ contributions to Expected Points and Win Percentage. Now, understand that it is much more difficult to assign values to situations that can’t be assigned a numerical value. If a pass is completed, on which defensive player do you lay the blame? The corner back playing zone that didn’t make the play? The safety that didn’t come to support? Or was it the lack of pass rush?
Instead, +EPA and +WPA attempt to put numerical values on things that CAN be measured that prevent the opposing team from scoring: tackles, tackles for a loss, interceptions, sacks, passes defensed, etc. Now just like a player’s actual salary, we know how much he’s earning (WPA+), but we don’t know how much he’s spending (WPA-). Now here’s where it gets tricky, so follow closely.
Now since there are a set number of snaps within a game, we can extrapolate negative contributions, right? If a player made six positive plays out of ten snaps in a game, we’ll assume the other four plays were neutral or negative. His overall contribution was positive, right? Now you might be shaking your head thinking this stat is incomplete, and is assuming too much. But again, think of the bell curve. With enough data, it will complete itself.
If you look at the 2009 season +WPA/+EPA stats for defensive players, you’ll see guys like Derrell Revis ranked #1 among cornerbacks, Jon Vilma ranked #1 among linebackers, Jared Allen #3 among defensive ends, etc. Enough data points (both number of players and number of snaps) will build an eventual bell curve that is accurate and robust.
So, if a cornerback picks a pass and returns it for a touchdown with a successful PAT, what is his EPA+? This is an easy one, because it led to actual points: 7.0! If Chris Clemons sacks Alex Smith for a loss of 10 yards, what’s his EPA+? If you answered “it depends” you win! The value will change based on down and distance from the goal. If it was fourth down, and the 49ers turned the ball over with one minute to go in the game, with the Hawks up, the Win Percentage (WP) probably went from something like .85 to .95, awarding Clemons with a WPA+ of 0.10. Huge!
Visit AdvancedNFLStats to read a full explanation of +WPA/+EPA (scroll down the page).