A visit to Pro Football Focus’ palatial Cincinnati mega-lab two years ago did wonders for Beechwood High School’s staff, including its intensely-driven analytics guru Jeremy Fisher. But oddly enough, it was more for what it confirmed rather than revealed.
Over the course of a day, this small school out of suburban Fort Mitchell, Kentucky was shown virtually every step of how the magic is made. The revered football analytics data-miner is majority-owned by Sunday Night Football analyst Cris Collinsworth and counts more than 60 major Division I football programs and all 32 NFL teams among its clientele. Fisher and his fellow coaches periodically found themselves chuckling amongst themselves throughout the demonstrations — when asked what was so funny by PFF staffers, they replied, “Oh nothing, we’ll show you at the end.”
The end came about. Now it was Fisher’s turn to take the sabermetric wizards through his workflow process. Project manager for Citibank by day, football coach by afternoon, Fisher has melded those two disciplines into a remarkably efficient database that has kept dozens upon dozens of data columns on every single one of the Tigers’ plays for nearly two decades. And the data-hunting process — the tendencies, the acute attention to details on formations and positioning — nearly mirrored PFF’s own.
The first thing the PFF staffer remarked? “You guys don’t really need us, do you?”
The second thing was, “Do you want a job?”
And, well, that’s just the thing. Fisher, who specializes in Management Information Systems and database designs for one of the world’s largest banks, loves his day job. Loves the numbers. Loves what he can do with the numbers. Loves the limitless possibilities they hold.
And playing with numbers all day has only inflamed his passion for football analytics over the years.
The marriage of these two principles goes all the way back to 2002, when a coaching change elevated then-defensive coordinator Noël Rash into the head coach role. Rash came from a vastly different background than his predecessor — an assistant at one of the most storied high school programs in Ohio state history, Archbishop Moeller — and wanted to look at things in a new way.
He wanted to take a deeper dive into the data, and then he wanted to look at it from a different perspective. To Fisher, Rash laid out all of his thoughts one day, and then said the magic words, “Can you make this work?”
Goodness gracious, did he ever.
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Years before Hudl had even gotten its footing in the market, the Beechwood staff was already keeping anywhere from 20 to 44 pieces of data on every single play, exporting the information into a Microsoft Access database that hosted all specific play data obtained in breakdowns going back to 2006. From there, Rash got a reporting platform with all the information in the exact format he wanted. And with Fisher’s background in a plethora of reporting software, Rash could get this information however he wants, with whatever fancy visuals he tastes.
Fisher was then (and still is) challenged by his superiors to go a little further down the rabbit hole. That means going beyond the box score to chart out a number of additional items ranging from the simple (field vs. boundary, strong/weak running by formation and down) to the complex (charting individual player touches by position, formation and targets). Fisher even goes as far as to use a stopwatch app on his computer to time how long it takes the quarterback to release the ball once it’s snapped. The Tigers adjust their pass coverage based on the length and timing he measures.
By any standard in the high school stratosphere, what the Tigers have in place is astonishing. Thanks to an acute attention to tendencies and tells, they often find they know what to expect when opponents break the huddle. In 2016, their first of three straight state championship-winning seasons, Fisher estimates that his defensive staff accurately guessed their opponent’s exact play nearly 63 percent of the time.
The next season, that number dipped to 48, and then last year it was 52 as they made it a Class 1A three-peat.
Just how valuable is that information? Consider the Tigers’ final two opponents of the 2018 season.
In the Class 1A state semifinals, their touch-tracking data, which Fisher typically compartmentalizes into personal touches, position touches and formation touches (including targets into those figures), concluded that Campbellsville’s star athlete touched the ball on 80 percent of the offense’s snaps. By the end of the first half against Beechwood, that star athlete had totaled minus-24 yards from scrimmage, as the Tigers went on to win in a romp.
The next week, in the 1A state final on the University of Kentucky campus, Fisher’s data found that Pikeville’s star quarterback was not only an exceptional runner who got the ball 70 percent of the time, but was also used aggressively to command pace early. Pikeville’s average game-opening drive lasted only six plays, with their star getting five touches. But against the Tigers, their first series ended in a three-and-out — and no touches for their primary playmaker. Pikeville’s QB also never kept the ball on third down, which influenced Beechwood to show completely different packages on first and second downs. All of it added up to a nail-biting 21 – 20 win.
To harden itself for the state playoffs, the Tigers will routinely schedule opponents during the regular season that are three or four classes above them. And while in some years they may have luxuries in size or athleticism, those are mostly exceptions. Often, they are undersized against opponents. But this deep well of information allows them play fast, without having to think too much out there.
“We don’t want to come across like we feel we’re better than anybody,” Fisher said. “But at the same time, we also don’t want people to realize how much information we really know and understand. There are very few games that we go into and say, ugh, we don’t have a chance.”
Fisher estimates that the Tigers’ database has data on more than 50,000 individual plays going all the way back to 2002. As the game continues to evolve in so many abstract ways, and as your typical high school offense becomes less and less predictable in this age of mega-data, the Tigers will continue to tweak and enhance this elaborate workflow.
“I hate to come across as though I have some sort of specific knowledge,” Fisher said. “But I’m not gonna lie, I’m fairly proud with myself of the fact that I’ve been able to find some success relating between the two jobs that I have, to take what I do at Citibank and be able to apply data analysis, to be able to interpret results and information, and be able to apply it to football.”