Last week I started posting the expected points added (EPA) for Week 1 by quarterback. The total figure aggregates the EPA on all the different types of play a quarterback was involved in: passes, interceptions, scrambles and runs. Sam Bradford led all Week 1 passers in total EPA and EPA per play, and his efficiency mark still stands at the top after missing Week 2.

One of the more well-known quarterback statistics that uses EPA in its calculation is ESPN’s Total Quarterback Rating (QBR). But QBR isn’t as simple as totaling up the various EPA figures, it adjusted credit/blame for the quarterback on various plays and also has a “clutch-weighting” on individual plays based on how close the score is at that time. I like QBR in concept, and I believe the analysis built into the calculation is likely sound. But, when there is a confusing or counterintuitive result in the final QBR numbers, we’re left guessing as to why QBR differs so much from fan impressions, or even the raw EPA figures. Continue reading “How Is Deshaun Watson Currently Second in ESPN’s QBR?” →

Here are the expected points added (EPA) numbers through Week 2 for quarterbacks with at least 20 passes. This week I arranged the leaderboard by expected points added per play (EPA/P), since efficiency, not volume, is my preferred way to view the impact quarterbacks have on the game.

Sam Bradford still sits atop after only facing the Saints, but now familiar names like Matt Ryan, Derek Carr and Tom Brady join him near the top. The most surprising name is Jay Culter, who performed well in his first start, especially in terms of limiting turnover and only losing a combined five yards (-2.3 EPA) on two sacks.

Continue reading “Quarterback Expected Points Added Through Week 2” →

Earlier this week I posted the Week 1 expected points added (EPA) for quarterbacks. What’s even more exciting than quarterback EPA? Kicker EPA!!

Seriously, I had some kicking EPA code lying around and decided to run the 2017 numbers through the model and calculate how much actual value these guys are providing so far this year.

Expected points is truly a great way to judge kickers, whether a kick is good or not is almost entirely on the kicker, as opposed to passing and rushing where 11 offensive and defensive players are all influencing the results to varying degrees.

The kicker EPA calculation is based on the actual points scored on each kick versus your league-average expectation for a play from that down, distance, yard line and time of game.

Kickers have vastly improved their kicking over the years, making anything under 50 yards a high probability make.

Continue reading “Week 1 Expected Points Added: Kickers” →

After putting together a quick analysis of Deshaun Watson‘s Week 2 performance from an expected points added (EPA) perspective, I decided that I might as well put all the Week 1 numbers out there.

This is a good way of judging a quarterback’s total impact by breaking out his EPA generated in various phases of the game: passing, sacks (negative), scrambling and rushing. Scroll the table to the right on to see all the EPA components.

To get some perspective on how much a point added is worth, Max Mulitz has estimated that roughly 36 points – as part of point differential – equal out to one additional win versus an eight win baseline. Keep that number in mind when looking at these numbers. Continue reading “NFL 2017 Week 1: Quarterback Expected Points Added” →

Deshaun Watson, the No. 12 overall pick in the 2017 NFL draft, took the field for his first start in a tough situation: playing on the road, on a short week and with a slight ankle sprain. Surprisingly, the Texans prevailed 13-9, but the consensus reaction on football Twitter was that Watson looked out of place, with some going as far as to say that his career might already be doomed.

I'd be worried about Deshaun Watson if I was #Texans. This guy started 38 games in college and he's lost. Does not have NFL skill level.

— Evan Silva (@evansilva) September 15, 2017

With all the opinions flying around on Watson, we can dig a little deeper than the box score to see what Watson’s impact was on the game. My preferred method for doing this is using expected point added (EPA). Continue reading “Exactly How Great/Good/Bad/Doomed Was Deshaun Watson’s First Start?” →

I’ve spend a lot of time recently messing around with the excellent air yards data you can pull from the NFL.com API using the nflscrapR package in R. After the kickoff game last week, I used air yards to show that Alex Smith has, in fact, not morphed into a bomb throwing passer. But there are many more applications for air yards data. The most interesting being for receivers.

If we know where a player was targeted on the field, we build league-wide historical averages for anything from expected yards, touchdowns, and receptions. Then, we can compare those expected number to what was actually posted in the box score. The differences between expected and actual stats can help us identify receivers who may be under- or overvalued based on unsustainable levels of efficiency.

I’ll say this in bold letter: Receivers will not all regress to league-average numbers! I’m not foolish enough to think that equal opportunity will eventually lead to equal stats for Antonio Brown receiving passes from Ben Roethlisberger and an unheralded wide receiver playing with one of the most inefficient quarterbacks in the league.

That said, these numbers can give up an idea of receivers’ potential upside, or downside, if notoriously volatile efficiency moves in the other direction. Continue reading “Using Air Yards to Enhance Expected Fantasy Points” →

There was a lot of talk over the offseason about which young quarterback insiders and outsiders would want to start their franchises. Bleacher Report found that 42 current and former NFL insiders put Derek Carr and Carson Wentz at the top. The MMQB’s Andy Benoit had Wentz as his top pick.

While the “football people” like the pocket presence of traditional throwers like Carr and Wentz, the numerically inclined tend to favor Marcus Mariota and Dak Prescott. Field Gulls’ Ben Baldwin makes a compelling stats-based case for Prescott over Carr, and doesn’t think they’re particularly close.

In previous posts I’ve shown how we can use Bayesian updating to project future passing efficiency. A quick refresher: we can calculate the best future estimate by adjusting a knowledge-less, league-average expectation for each piece of evidence, or pass attempt. In that way, we don’t overreact to small samples by incorporating the fact that variance plays a big role in the final numbers of any drive, game or even full season. Continue reading “Finding the Best Historical Comps for the NFL’s Top Young Quarterbacks” →