FNF Coaches Talk — The Home of the Play-Action Pass, The Defense That Made Sam Darnold See Ghosts, Predicting Outcomes with Analytics

Good afternoon, Coaches. We’ve got three good stories for you.

1. The True Home Of The Play-Action Pass (FiveThirtyEight)

We’ve talked in previous FNF Coaches Talk posts about how coaches don’t need to establish the run before using play-action passes effectively. The bottom line is the defense has to account for the run first even if they suspect you might be running play-action.

Pro offensive coordinators often draw creativity from their college counterparts, so it should come as no surprise that nearly the entire NFL lined up last year to pick Oklahoma coach Lincoln Riley’s brain. In terms of volume, the college game is even more reliant on fake handoffs than the pro game.

At its root, play-action is a con. It forces an opponent to honor the threat of choice and lures defenders out of position. By forcing pass rushers to respect a potential designed run — and drawing linebackers toward the line of scrimmage while ceding the middle of the field — QBs are handed enormous passing lanes through which to dissect a defense.

As college football’s regular season marches on, more than one-quarter of dropbacks at the Power Five level have involved play-action. That’s the second-highest mark since at least 2011, and if maintained it will represent a second consecutive season of increased play-action frequency. Fifteen teams are using play-action on at least 35 percent of dropbacks, with Florida State, Louisville, Kansas State and Florida eclipsing 40 percent.

The reason for the increase in volume is clear: Play-action is extremely effective.
“If you’re not a tight end-fullback running team, you’re in a spread, you’re trying to play-action pass and trying to flow it down the field,” Oklahoma State head coach Mike Gundy said at Big 12 Media Days this summer.
Over each of the past eight years, through the fourth game of the season, the average Power Five quarterback’s play-action QBR has been considerably higher than his Total QBR in each season. In 2019, there’s a gap of more than 10 points.

What is your philosophy behind calling play-action passes?

2. What defensive scheme was causing Jets QB Sam Darnold to see ‘ghosts’? (NESN)

The big NFL controversy today is that ESPN showed a mic’d up clip of Jets quarterback Sam Darnold saying he was “seeing ghosts” during the Patriots shutout victory on Monday night.

The Patriots’ defensive success has been, in large part, thanks to their “cover-zero blitz” look. As ESPN analyst and former NFL quarterback Dan Orlovsky explained Tuesday morning, that refers to a Patriots defensive personnel grouping that has no safety on the field. Essentially, Belichick and the Patriots are daring these young, inexperienced quarterbacks to beat their elite secondary. It obviously isn’t easy.

What is your favorite blitz call to confuse the opposing quarterback?

3. From computer screen to field: Students use sports analytics to predict performance outcomes (The Cavalier Daily)

Here’s an interesting story that would affect scouting, player development and in-game decision-making.

With new advances in technology, students and faculty researchers at the University of Virginia have begun applying science to sports by using data analytics to predict the future success of both individual athletes and entire teams.

We all know predicting NFL talent is an inexact science that requires consideration of the player’s make-up — his work ethic, attitude, unselfishness. However, using analytics can help inform these decisions.

One group of students advised by Scherer in recent years developed two models focused on recruiting. The first, the “Diamond in the Rough” model, predicts which lower-ranked high school football players might one day be in the NFL. Results can inform coaching staff about which recruits to pursue because, as Scherer said, outcompeting prominent football schools for high-profile recruits is unlikely.
“There are some players out there with three or two [out of five] stars with incredible potential and we can get them,” Scherer said. “The model can predict whether they will have good forward success in college, and we found that there was actually very little correlation between composite scores provided and actual college performance.”

The second model returns a rating that attempts to measure an athlete’s grit, or how tough a player is. Though this characteristic of a player may appear difficult to measure empirically, Scherer and his students assigned the scores with the assistance of IBM Watson, a supercomputer that incorporates artificial intelligence and analytics. After IBM Watson attached certain personality traits to recruits based on their Twitter feeds, students used their own algorithm to determine the final scores.

“So we try to predict performance, but the other part of the equation is we want to pick who is going to fit well in the current U.Va. system, which is a hardcore, tough system with a rigorous coach that has high standards,” Scherer said.

What information do you use to predict whether individual players will be able to develop into key contributors for your program?