Anthony So leveled up his skills and landed his dream job with the Phoenix Suns.
“He’s got game” usually refers to someone’s athletic skills or romantic swagger. In Anthony So’s case, he’s got mad game…data science game — arguably, the hottest skills in the business world today.
The difference is, in most industries, after a long day of mining data, analysts don’t “step out of the office and into the game” the way So and his colleagues do at Talking Stick Resort Arena.
As a Basketball Analytics Analyst for the Phoenix Suns, So uses his data science know-how to help give his team a winning advantage. And then there are the perks, like being flown to last month’s MIT Sloan Sports Analytics Conference to mind-meld with other athletic data champions and soak up insight from statistics guru Nate Silver.
So has come a long way from his college days, when he was toiling in a bioengineering lab and questioning his career path.
The 30 year-old is a Jersey boy, raised about 30 minutes east of Philadelphia. He’s always been a sports fan, but basketball is his bae. As a kid, even more than playing hoops and rooting for the Sixers, So was drawn to the stats. He would pull the sports section from his parents’ newspaper and comb through the box scores, comparing the names and numbers to the ones on his NBA trading cards.
Since playing college ball wasn’t in the cards for So, and a data-driven “Moneyball” approach to pro sports wasn’t yet a thing, he studied bioengineering at UPenn. “It sounded really appealing,” he recalls, “because it combined two things I’m really good at – math and science.”
Then reality set in.
So wasn’t passionate about bioengineering, and he felt hemmed in by the rigidity of the program. He longed to explore other topics, to take shots at “applying my skills and analytical mindset toward another industry.” He channeled some of his curiosity into his senior thesis, The Limits of Human Athletics. “This was back when Usain Bolt and Olympic swimmers were setting records at the 2008 Summer Games in China,” So says. “I attempted to predict when the next record would be set, or by how much a record would be broken.”
He finished his degree and promptly pivoted to the world of finance. Despite having no business experience and a steep learning curve, So forged himself into a financial analyst at Delaware-based asset management company BlackRock, providing data and risk analytics to portfolio managers, along with insight into how their funds were performing.
“I saw how valuable data science was to solving the problems that portfolio managers were considering,” says So, who started applying the tools he learned on the job to his true love. In his free time, he created his own data sets to evaluate basketball players the way he assessed portfolio holdings, and blogged his analyses. Then he came across a job posting looking for candidates capable of performing data analysis on basketball-related issues. “I figured if I could level up my skills, I could provide a lot of value to teams that are looking for people with this skill set.”
He first tried to hone his data science game by taking online courses. “I realized that while I was making progress, it was probably a lot slower than if I pursued an actual program with instructors and peers that were in the same boat as me.”
Deterred by the time it would take to pursue a traditional Master’s degree, and “excited for the opportunity to be part of a community where people really understood the type of skills I was looking for,” So bounced to San Francisco and focused on sharpening his data science skills full-time.
Three months later, he graduated from Galvanize with an industry-ready toolset—and his tenacity and the networking opportunities inherent in the Galvanize ecosystem helped him score a data science tryout with the Phoenix Suns.
“It’s easy to find people who excel in data science or love basketball; finding people who fit both characteristics is a bit more challenging,” says Jake Loos, Director of Basketball Analytics & Technology for the Phoenix Suns. “What really drew me to Anthony was his ability to simply talk basketball and understand how analytics can make a lasting impact in an NBA front office.”
Loos offered So a few trial projects, which led to a summer internship and soon after, as So proved to be “the perfect fit,” a full-time position with the Suns’ analytics team. The role requires So to juggle his responsibilities as seamlessly as basketball players transition between offense and defense throughout a game. One minute he might be preparing a post-game breakdown of the Suns’ performance; next he’s scrutinizing an opponent’s strengths and weaknesses. “Let’s say we identify that a particular team is really good at getting corner 3s,” So explains, referring to three-point shots taken from the court’s baseline. “Then we’ll dive deeper to determine how exactly they’re getting those corner 3s, and which players do it particularly well or often. That analysis helps our coaching staff figure out how to neutralize those weapons.”
Another facet of the job is getting a data-driven feel for the assets rival NBA teams may be considering or coveting for potential trades. “It’s kind of like a game of poker,” says So. “We try to determine what hands other teams are holding, and anticipate what moves may happen around the league and how they might affect us.”
It’s not just that So found his sweet spot at the intersection of pro basketball and data science. There is an inherent challenge in applying analytics to athletics that he finds more compelling than dissecting mutual funds or tinkering in a bioengineering lab. “When you’re analyzing financials, you generally know how they’ll behave given an established set of rules and metrics based on data from the past,” he notes. “Evaluating players involves many other factors that aren’t quantifiable. For example, it’s hard to pinpoint a player’s psyche and their approach to the game. At the end of the day, athletes are human like the rest of us. Something outside their work could be affecting them. It’s very difficult to quantify with confidence how those outside factors will affect their performance in a game.”
Immersing himself in these challenges and the data that informs big league decision-making “has made my love for the game stronger,” So says. For a data science all-star living the dream, he’s refreshingly grounded. “Data science helps me answer the questions that I’m curious about,” he adds with a grin, “and luckily, I was able to make a profession out of it.”