Box scores in the National Basketball Association look far different than they did thirty years ago—or even ten, for that matter. these days, they’re canvassed in acronyms such as PER (Player efficiency rating) and 3PAr (3-Point Attempt Rate), which look more like robot names than a way to measure a basketball game. teams in the NBA have followed the paths of their Major League Baseball counterparts and now are taking a hard look at advanced analytics when it comes to most aspects of running an organization.

Advanced basketball analytics— also known as APBRmetrics—is a rather broad term. In a nutshell, it refers to the movement toward looking past traditional statistics and breaking down nearly every aspect of the game imaginable, such as the percentage of shots a player blocks when he’s on the court or how well he shoots from a particular spot behind the three-point arc. Basketball is following the trend baseball pioneered with its form of advanced statistics called sabermetrics, a movement chronicled in Michael Lewis’ best seller Moneyball.

Though the field is still growing, a trio of Duke alumni—Brett Greenberg ’08, Ken Catanella M.B.A. ’06, and Drew Cannon ’12—have managed to land gigs in the NBA as number-crunching experts. Each took his own path during his time in Durham—only Cannon pursued a major in statistics. Yet the trio shared a deep-rooted love for watching basketball from an outside-of-the-box-score perspective, long before stats like win shares and usage percentage were a part of a basketball fan’s jargon.

It’s a field shrouded in secrecy. Although the range of statistics is becoming more public, the way teams use them to win games is proprietary. There are sharp parameters to what these so-called “stat geeks” could share outside the team. (New commissioner Adam Silver '84 talks stats.)

What’s transparent is that by fostering their passions with invaluable apprenticeships and a germane curriculum, they’ve managed to become colleagues in one of the pro game’s most insular communities.


Brett Greenberg

It was obvious that Brett Greenberg wasn’t your typical ten-year-old basketball fan. Each time he’d watch his beloved Washington Wizards (née Bullets), the Baltimore County, Maryland, native made mental notes, observing the game with a “mathematical mind.” While other kids were gawking at slam dunks, Greenberg calculated the plus-minus of players, which measures how a team performs when they’re on and off the court. It was a statistic that wouldn’t appear in box scores for years.

Once he got to high school, Greenberg knew he wanted to work in the NBA. There was just something about basketball he couldn’t get away from. He considered coaching, but after arriving at Duke, he began to explore the ins and outs of the analytical world. And for Greenberg, having the opportunity to work as student manager on the basketball team solidified his desire to spend his days in the hoops world.

“Basketball was basically my major,” Greenberg recalls, adding that when he was at Duke he took every sports class he could, including a physical education class taught by legendary college coach and Los Angeles Clippers scout Jerry Welsh.

Being a student manager at Duke was a humbling experience. “We worked tirelessly seven days a week cleaning up sweat, handing out drinks, breaking down video, and anything else that was asked of us. I learned the importance of a strong work ethic, attention to detail, and countless life lessons.”

The 2013-14 season marks Greenberg’s first year as director of basketball analytics/salary-cap management for the Wizards. Since graduating with a major in sociology nearly six years ago, Greenberg has worked his way up the NBA ladder. He spent a year as a video intern with the Miami Heat before joining Washington, where he would serve as basketball operations assistant and video coordinator.

Directing an NBA team’s analytics initiative is a mixture of short-term and long-term projects, Greenberg says, because some statistics need day-to-day evaluation, while others have to be looked at in chunks. At one point, he may be using data from SportVU technology—a series of cameras that capture data from the court, such as the speed at which a player moves—to pinpoint a particular statistic. “There’s no typical day,” he says.

In addition to his work with on-the-court analytics, Greenberg wears another hat: directing the team’s salary-cap management. Working with the NBA’s salary cap isn’t like balancing a checkbook. You must be aware of the ramifications of every transaction. Guiding the cap is the league’s Collective Bargaining Agreement, a manuscript that shares the girth of a James Joyce novel and, some might argue, with language that’s even more complex.

“You have to know the rules, and we’re constantly updating our salary numbers, as every team in the NBA does,” says Greenberg. “You have to know every team and every player in the NBA inside and out. It’s a lot of short-term and long-term planning. One mistake can impact a team for years. It’s probably the most complicated salary cap. The NFL [National Football League] has a hard cap that you can’t exceed, while the MLB has no cap, but only a luxury tax. With the NBA, there is a soft cap and all these exceptions. You really have to know every intricacy.”

Both APBRmetrics and salary-cap management require a high level of analysis to tackle, and Greenberg has the CV to do it. He says he “fell in love” with the cap during his days at Duke. In fact, he spent his last year in Durham working on an independent study of the ins and outs of the league’s cap, guided by law professor Paul Haagen, codirector of the Center for Sports Law and Policy. He says Haagen was critical in helping with his understanding of the cap.

And whereas others might find cap management daunting and downright vexing, the NBA’s salary cap is like a puzzle Greenberg can’t stop working on.

“You have to figure out every piece of it and put it together,” he says.

Ken Catanella

Greenberg isn’t the only analytics guru who has visited Haagen’s office in the last few years. Haagen recalls getting a call from a Fuqua student named Ken Catanella almost a decade ago. Catanella was interested in developing analytical models for basketball and wanted advice on how to structure his ideas; he was looking for someone with whom he could talk out various problems of the game.

Yet the story of Catanella becoming the director of basketball operations for the Detroit Pistons starts earlier than that. His is a story of destiny. He was born in Indiana, where “basketball is king”; he jokingly professes to being born with a basketball in his hands. Catanella played the game at Amherst College before landing a spot on Bundesliga’s Cologne 99ers in Germany’s basketball league. When the combo guard’s career was over, he served as the team’s assistant general manager. And what did the Indiana native do once he got to Durham? He found his way onto the men’s basketball coaching staff, of course, as a graduate assistant.

He says his time at Duke was formative, solidifying his pursuit of a career in basketball analytics. It was only natural. Before working in the hoops world and attending Fuqua, Catanella was a Wall Street man. He would value publicly traded companies, in addition to working on analytics regarding stadium and arena financing for professional sports teams. But he always had his eyes on the pro basketball ranks.

“I saw an opportunity in the NBA with analytics,” says Catanella. “At the time, it was much more prevalent among Major League Baseball teams. When I was at Duke, I was on a staff with such intelligent and hard-working coaches, and being there helped crystallize these hypotheses I had. First and foremost was that the use of analytics could be used by coaches to evaluate their teams if you make the info accessible by putting in the time and effort. For instance, how to maximize a player’s minutes on the court.”

After getting his M.B.A. at Duke, Catanella jumped to the NBA. He took a job with the New Jersey Nets from 2006 to 2008, where he was in charge of the team’s analytics department. Before joining Detroit in December 2011, he spent nearly three years directing the salary cap and basketball-analytics efforts of the NBA’s League Office, and he worked closely with its Labor Relations Committee during the league’s collective-bargaining negotiations with the National Basketball Players Association.

In Catanella’s eyes, analytics are nothing new. But in the early days, it was just simple box scores that guided general managers. He says a movement toward more complex metrics happened for two reasons. “First is the availability of information. Second, we have seen an exponential increase in the processing power of computers.” Given the nearly endless ways of breaking down basketball statistics, finding a novel approach to talent evaluation is crucial for gaining an advantage. “You need creativity. If you don’t find ways to analyze the data in an executable fashion, it doesn’t add much value to a team.”

Catanella also says that part of basketball’s adoption of the “moneyballmovement has been because of a new wave of owners predisposed toward the use of analytics. “These owners have seen analytics benefit their [other] businesses. They’ve used analytics to make million- and billion-dollar decisions.”

Like Greenberg, Catanella has the task of managing the team’s salary cap and day-to-day operations, as well as its analytics. “Each day is never the same, but I’d say there’s always some combination of salary-cap budgeting, with player evaluation and live game analysis and video game analysis.”

On a given day, he might be sorting through information to figure out when the coaching staff should tell players to commit intentional fouls at the end of the game or what playing styles are conducive for success when going up against certain teams. “The more information you have, the better you’ll be able to develop your conclusions and ultimately decide what is best for the team.”

That’s why Catanella says one of the hardest parts of the job is to admit when a trend isn’t working.

“It takes a certain amount of humility,” he says of his position. “There are times you need to acknowledge and understand that you don’t have the answer to everything. You have to always stay grounded and be open to yourself.”

Drew Cannon

Boston Celtics basketball operations analyst Drew Cannon knew he wanted to work in basketball analytics since he was nine, long before his junior-high graduation—and long before APBRmetrics was in the mainstream consciousness. “It’s the only proof anyone has in sports,” Cannon says of the draw of analytics. “They give you something to work off of.”

That first job was an internship with Dave Telep, a prep basketball scouting guru who ran a scouting service used by 230 Division I schools. Cannon would help with Telep’s database and assist with anything else he needed. Learning under the wings of Telep—who was hired by the San Antonio Spurs’ scouting department last year—was a transformative experience for Cannon. The Raleigh native and college basketball fanatic was getting paid to work in basketball, taking a job that would turn into a summer-after-summer gig. Once at Duke, Cannon set his sights on the program that fit his dream: statistical science. As he worked on his course requirements during the day, Cannon began writing for national outlets such as Basketball Prospectus when he had free time. His articles covered myriad topics, from how to objectively incorporate intangibles to why Virginia Commonwealth was able to make a Final Four bid in 2011. He’d even make up his own stats. Consider an article he wrote for called “Introducing 3-Point Score.” The math would look tricky to most people: 3 x Three Pointers Made + 0.3 x (Three Pointers Attempted – Three Pointers Made) + (Team Possessions x Percent of Minutes – Three Pointers Attempted)/Team Possessions x Percent of Minutes. For Cannon, it was yet another cutting-edge perspective on the game.

He even took his love of basketball analytics into the classroom. For his senior thesis, Cannon wrote a paper titled “Projecting the Basketball Careers of High School Prospects Through Multiple Regression Series.” It was a continuation of his outside-the-classroom work as he strived to figure out a way to project the next LeBron James.

Once he graduated, Cannon moved to Chicago to live with family, not completely sure of his next career stop. But a move to Indiana was going to follow soon after.

Among those who read Cannon’s writings was a young basketball coach in Indianapolis who happened to take his team to back-to-back NCAA title games—including one against Duke in 2010. That coach was Butler’s Brad Stevens. And he had an idea: bring Cannon to his staff.

“It didn’t occur to me” to pursue a job in basketball analytics, says Cannon of receiving the call from the pro-analytics Stevens. He viewed himself as a scribe on the topic, not as an in-house analyst. “I thought I’d be writing forever.”

Cannon spent the 2012-13 season in Indianapolis, earning $1,000 a month as Stevens’ analyst while taking M.B.A. classes at Butler. When he ran the numbers for Stevens, Cannon pieced together what lineup configurations worked best—for example, is player X used best when he is out there with player Y or player Z? Butler would end up cruising to a 27-9 mark and another NCAA tournament, though the Bulldogs failed to make the Sweet Sixteen.

Cannon’s time in Indianapolis would be short. Last July, the NBA world was shocked when Stevens was given a six-year deal by the Boston Celtics to be the team’s new head coach. A few weeks later, Stevens brought Cannon to Beantown with him. It was only fitting. After all, Sports Illustrated declared Cannon to be Stevens’ “secret weapon.”

The move marks Cannon’s first work with pro ball. His first love was always the college game, especially given his Tobacco Road roots. In the job, he covers a range of tasks throughout the day. Sometimes he’s spending his time on computer programming. Other times he’s working on his now-famous lineup analytics. Cannon has made the transition with ease, though he does note there are a few changes between college and the pros.

“The biggest difference is the data available. When I was at Butler, I would be slowly watching game film over and over to get my information. Now I have access to a lot more data and a lot better data.”



Clark’s work has appeared in The New York Times, The Boston Globe, The Washing- ton Post, and other publicatrions.

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