Special Note

Advanced NFL Stats is now Advanced Football Analytics. Sorry for the inconvenience and broken links, but the change was long overdue. Although the old address forwards here, you'll likely need to to update your RSS subscription and bookmarks.

Podcast Episode 24 - Brian Burke

Brian Burke returns to recap his busy summer offseason. After a brief lesson on the rules of Gaelic Football, Dave and Brian discuss what we can learn about NFL win shares from Jimmy Graham’s contract, some new updates to the site (WOPR, and Win Probability Model) and the 2014 season predictions Brian made for ESPN the magazine. Dave also issues a call for podcast contributors, looking for anyone interested in contributing their technical expertise to the show.

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Win Values for the NFL

Jimmy Graham's contract values him at about 0.9 wins per season. Here's how I came to that estimate.

In 2013 the combined 32 NFL teams chased 256 regular season wins and spent $3.92 billion on player salary along the way. In simple terms, that would make the value of a win about $15 million. Unfortunately, things aren't so simple. To estimate the true relationship between salary and winning, we need to focus on wins above replacement.

Think of replacement level as the "intercept" term or constant in a regression. As a simple example think of the relationship between Celsius and Fahrenheit. There is a perfectly linear relationship between the two scales. To convert from deg C to deg F, multiply the Celsius temperature by 9/5. That's the slope or coefficient of the relationship. But because the zero point on the Celsius scale is 32 on the Fahrenheit scale, we need to add 32 when converting. That's the intercept. 32 degrees F is like the replacement level temperature.

No matter how teams spend their available salary, they need to have 53 guys on their roster. At a bare minimum, they need to spend 53 * $min salary just to open the season. We can consider that amount analogous to the 32-degrees of Fahrenheit. For 2013, the minimum salaries ranged from $420k for rookies to $940k for 10-year veterans. To field a purely replacement level squad, a franchise could enlist nothing but rookies. But to add a bit of realism, let's throw in a good number of 1, 2, and 3-year veterans in the mix for a weighted average min salary of $500k per year. The league-wide total of potential replacement salary comes to:

Sun Tzu on Analytics

Apparently he was a fan:

Now the general who wins a battle makes many calculations in his temple ere the battle is fought. The general who loses a battle makes but few calculations beforehand. Thus do many calculations lead to victory, and few calculations to defeat: how much more no calculation at all! It is by attention to this point that I can foresee who is likely to win or lose.
For those of you who may be unfamiliar with Sun Tzu, he was the ancient Chinese general and philosopher who wrote The Art of War. Still required reading around military academies and war colleges around the world to this day, The Art of War is perhaps the most influential military treatise in history. It crystallizes centuries of strategic wisdom into what are essentially tweet-sized chunks of timeless insight. Thus do many calculations lead to victory...I like that part. I think I'm gonna' put it on a t-shirt.

Podcast Episode 23 - John Urschel

John Urschel, professional football player and mathematician, joins the show. John was recently selected in the 5th round by the Baltimore Ravens in this year's NFL draft. Last season, John was a Penn State football co-captain and as a student-athlete he achieved a 4.0 GPA while majoring in math. For his efforts, he won the Campbell Trophy, awarded to the top scholar athlete in Division one football. He recently proved the Urschel-Zikatanov Generalized Bisection Theorem and published his findings in the Journal of Celestial Mechanics and Dynamical Astronomy.

On the show, John explains how he came to love the world of mathematics, and how his passion for football aligns with his commitment to academics. He also shares some of the lessons he's learned from veterans in OTAs and describes how he's preparing for the upcoming NFL season.

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Using Probabilistic Distributions to Quantify NFL Combine Performance

Casan Scott continues his guest series on evaluating NFL prospects through Principal Component Analysis. By day, Casan is a PhD candidate researching aquatic eco-toxicology at Baylor University.

Jadeveon Clowney is thought of as a “once-in-a-decade” or even “once-in-a-generation” pass rushing talent by many. Once the top rated high school talent in the country, Clowney has retained that distinction through 3 years in college football’s most dominant conference. Super-talents like Clowney have traditionally been gambled on in the NFL draft with little idea of what future production is actually statistically anticipated. For all of the concerns over his work ethic, dedication, and professionalism, Clowney’s athleticism and potential have never been called into question. But is his athleticism actually that rare? And is his talent worth gambling millions of dollars and the 1st overall pick on? This article aims to objectify exactly how rare Jadeveon Clowney’s athleticism is in a historical sense.

Jadeveon Clowney set the NFL draft world on fire at this year’s combine when he delivered one of the most talked-about combine performances of recent memory, primarily driven by his blistering 40 yard dash time of 4.53. Over the years, however, I recall players like Vernon Gholston, Mario Williams, and even Ziggy Ansah displaying mind-boggling athleticism in drills. But if each year a player displays unseen athleticism at the combine, who is really impressive enough that we deem them “Once-in-a-decade?”

Probability Ranking allows me to identify the probability of encountering an athlete’s measurable. For instance, I probability ranked NFL combine 40 yard dash times for 341 defensive ends from 1999-2014 (Table 1 shows the top 50). In this case, Jadeveon Clowney’s 40 time of 4.53 had a probability rank of 99.12, meaning his speed is in the 99th percentile of all DEs over this time span.

NFL Prospect Evaluation using Quantile Regression

Casan Scott continues his guest series on evaluating NFL prospects through Principal Component Analysis. By day, Casan is a PhD candidate researching aquatic eco-toxicology at Baylor University.

Extraordinary amounts of data go into evaluation an NFL prospect. The NFL combine, pro days, college statistics, game tape breakdown, and even personality tests can all play a role in predicting a player’s future in the NFL. Jadeveon Clowney is arguably the most discussed prospect in the 2014 NFL draft, not named Johnny Manziel. He is certainly an elite prospect and potentially the best in this year’s draft, but he doesn’t appear to be a “once-in-a-decade” type of physical specimen based exclusively on historical combine performances. From the research I’ve done, only Mario Williams and JJ Watt can make such a claim. Super-talents like Clowney have traditionally been gambled on in the NFL draft with little idea of what future production is actually statistically anticipated. All prospects have a “ceiling” and a “floor” which represent the maximum and lowest potential that a prospect could realize respectively. But what does this “potential” mean and does it hold any importance for actually predicting a prospect’s success in the NFL? In this article I will show how Quantile Regression, a technique used by quantitative ecologists, can clarify what Clowney’s proverbial “ceiling” and “floor” may be in the NFL.

Athletes are a collection of numerous measured and unmeasured descriptor variables. Figure 1 shows a single predictor (40 yard dash time) vs a prospects’ Career NFL sacks + tackles for loss (TFL) per game.

Podcast Episode 22 - Brian Burke

Brian Burke returns to the show to recap the 2014 NFL draft. He describes the Bayesian Draft Analysis tool he created and discusses the value of trades made by teams during the draft. Brian and Dave then discuss their favorite new addition to the league, John Urschel, and make a pitch to get him to contribute to the site. Brian also previews his new project, WOPR, and explains how it'll help generate data for some previously unanswerable questions.

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The AFA Draft Pick of the Year

Was the next Virgil Carter drafted yesterday? Penn State Guard John Urschel was taken with a compensatory pick in the 5th round by Baltimore. John stands out because he has an unusual plan for his time after his playing days are over. He says he's very interested in  "sports analytics. Data analysis for football."

If he does, he'll analyze circles around the rest of us. While playing for PSU, John earned his degree in Math in just three years. Then added a masters degree in math, and is currently working on a second masters in math education. He's published research with names like Instabilities of the Sun-Jupiter-Asteroid Three Body Problem, A Space-Time Multigrid Method for the Numerical Valuation of Barrier Options, and Spectral Bisection of Graphs and Connectedness in which he proved the Urschel-Zikatanov Generalized Bisection Theorem. Man, I wish I had a theorem named after me.

To us, his most interesting research might be this article he wrote for ESPN The Magazine. He looked at "1) how best to predict a lineman's draft position, 2) that prospect's success in terms of NFL starts, and 3) whether a fringe prospect will be selected." Sounds like it would have made a good guest post here.

The Bayesian Draft Model estimated the most likely time Urshel would be taken was pick 167, not very far off from his actual selection at 175. The chance he would be available at 175 was 43% according to the numbers. So almost spot on. Interestingly, Urshel's own selection may have been the result of some sharp analytics. Baltimore is known to have "a proprietary formula—a “special sauce,” assistant GM Eric DeCosta calls it—that factors in potential compensatory picks to the free agency cost-benefit analysis."

Urschel would make a killer impact on the world of football analytics if he chose. However successful his pro career turns out, he'll carry the credibility of a pro-caliber player. Coaches will take what he has to say much more seriously than what an ex-Navy pilot writes on a website.

So, congratulations, John! I'll be rooting for you on the field and off. Play like a Raven!