What’s it like to work in sports analytics?

Note: This piece was written with Noah Davis, and stemmed from a survey we put together roughly a year ago. 

In the past decade, sports analytics moved from the fringes of popular consciousness to the mainstream. The typical media narrative tells us that data is changing the game. To some extent, that’s true. The majority of professional teams in the five major sports leagues have at least one person on staff or on retainer tasked with delving into details and applying numbers to performance, and nearly all NBA, NHL and MLB franchises have sent at least one representative to the Sloan Sports Analytics Conference.

Noah and I wanted to find out more details about the job, the lifestyle and how analytics are being used, so we developed an informal survey and asked people who work or had worked on the sports analytics staffs of professional teams to participate. We used a combination of social media and personal email to contact staffers who we knew worked with teams or who were mentioned in ESPN’s analytics feature. A total of 61 respondents answered questions anonymously. A pdf of the survey can be found hereWe also interviewed a half-dozen by phone, either on or off the record, to get anecdotes about what it’s like to be part of a professional franchise.

Some of the responses were predictable. Our survey wasn’t perfect, and our sample isn’t necessarily representative of the industry, but the respondents were 95 percent male, 95 percent white, and 92 percent fell into the 19-to-45 age group. Additionally, respondents from Major League Baseball teams report the highest average number of full-time staffers working for their teams – 3.6 – compared with roughly two apiece per team in the NFL, NHL, and NBA. (These numbers could be skewed because teams without any current or former analytics staffers would not be able to respond to the survey.) That makes sense, considering that everyone we spoke with believes that baseball teams are generally the furthest along in the development of their analytics departments. But professional teams across all major sports are increasingly investing in their analytics departments, slowly but surely adding to their budgets as executives place more trust in numbers.

Respondent breakdown:

NBA 18

MLB 16



Professional soccer 5

Other/multisport 4

(Five respondents left “sport” blank.)

For the love of the game

Working for a team isn’t a 9-to-5 job. The hours are long, with analytics staffers reporting that they work anywhere from an average of 53 hours per week in soccer to 66 in the NHL, with MLB, the NFL and the NBA averaging 60. (One MLB staffer reported working 95 hours per week.) “There are no holidays,” Bill Petti, a consultant for a number of MLB teams, said about the life of an analytics staffer. “You’re working nights. You’re sitting in the office at 10 p.m. in case somebody has a question.”

Aaron Barzilai, who worked as director of analytics at the Philadelphia 76ers until last February, agreed. “Salaries are depressed. You can’t be working for a team if you don’t love it because you need to be getting some psychic benefit from working for a team.” 

Salaries are decent and occasionally well above the typical white-collar worker. According to our survey, medians ranged from $75,000 (NFL) to $100,000 (MLB) per year. Three respondents said their annual wages were greater than $200,000. For most, it’s not a bad living, but consider that nearly anyone with the skills to get one of the few jobs as an analytics person on a professional sports team could also make significantly more working at Google, Facebook, Microsoft, or dozens of other firms. This leads to lots of turnover, especially at more junior positions, which are lower paid and there’s little, if any, opportunity to advance into a more senior role because those jobs rarely become available. Many recent college graduates work for teams for a few years before moving on to tech firms or other companies where they can earn more for their skill set.

Determining value

The people we spoke with said that teams undervalue their analytics staff and invest accordingly, not unlike employees anywhere who think their departments deserve more resources.. While any reasonable employee would say that their department deserved more money and more resources, it’s not unreasonable to think that more computing power or another data set could produce results that were cheap by comparison. “They are spending hundreds of millions on players, tens of millions on coaches and staff, but $10,000 is a large expenditure to get a computer or some data,” a sports analytics expert who’s worked with NBA teams, said. “It’s ridiculous. It’s two different budgets.” (For what it’s worth, one study found that the average price of an MLB win is $1,016,674 in player salary, an NBA win is $1,572,768, and an NFL win is $11,878,369.)

As some teams mature and develop systems to handle routine reporting like data gathering, they may be able to think about building teams to handle some of the other stuff. Those that don’t will find themselves hitting a wall. In the past, teams could get away with having Excel, a computer and a staffer or two. Advances in sports analytics overall mean that groundbreaking work requires increasing talent levels, computing power and time to experiment.

One NHL analyst told us about this dream staff:

“You need a couple different people unless you are a one-man team willing to put in 20-hour days. Just the data handling alone — the NHL is pretty archaic in their data to begin with — is one full-time person. If you wanted to expand from coaching and tactical to GM trades and to the draft/strategic long term, you need one data person, two or three analytical people whose job is formulating and communicating analysis, and then I would have two or three developers working on dashboards and tool sets. If you’re going to give a GM a sheet of paper with a recommendation, he may or may not pay attention. But if you give him a tool that he can play with, then it’s not your idea. It’s his idea.”

A staff of one wouldn’t have the resources to develop and build that tool.

But rather than hire a number of positions, many teams still seek unicorns. Teams want one person who can fill all of those roles and then also have the skills to communicate results to others. This Marlins job posting asks for an intern with scripting ability, database management and statistical proficiency. Those tools sometimes overlap, but an actual combination of all three is rare, and most people with such skills can make a substantial amount of money elsewhere. And keep in mind, baseball is ahead of most other sports. A college student with this background can get paid $7,200 or more a month with housing to work at Microsoft or Facebook, or make $12.50 an hour for the Marlins.

Nothing matters if no one is listening

Finally, analytics can only be effective if the decision makers use what they are given. “There seems to be too much focus on results, and not enough focus on the quality of the process,” one respondent said.

Some analysts expressed concern that teams didn’t pay attention to their work.

“If the GM or president of basketball operations doesn’t want to read the results, it doesn’t really matter how talented your Ph.D. in the basement is,” said Barzilai“You can have organizations that are using analytics well even if they aren’t doing cutting-edge analytics just by relying on what people might think of as fundamental analytics or the stuff that was coming out five years ago or stuff that is public.”

Another added that it’s frustrating: “When the numbers are so overwhelmingly in favor of one decision and it doesn’t happen due to someone’s feelings about public perception or a ‘well, it’s always been this way’ attitude.”

Consider The New York Times’s Fourth Down Bot, a simple formula that tells readers when teams should go for it on fourth down. The bot believes that coaches are too conservative, and would universally benefit from going for it more often. If a coaching staff listened to the bot, they’d benefit in the long term.

Generally, the decision makers in the front office are more receptive than others to input from the analytics staffers. In the NBA, NFL and NHL, at least 50 percent report weekly correspondence with the general manager, while just 20 percent in MLB do. Just 10 percent of the overall sample says they have weekly correspondence with players.

Team officials say they are continually working to refine the processes, to incorporate all the information they receive. “We don’t see ourselves as having an ‘analytics team’ or ‘process of incorporating analytics,’” an assistant general manager of an NHL team said. “We look at the best information we have when making decisions, and everything’s assimilated similarly whether it’s one scout’s eye test or another (or even the same) scout’s or an analyst’s tracking data or historical comparisons that some might call ‘analytics.’”

Some teams are better than others at applying what they learn. According to our respondents, the Spurs (NBA), Maple Leafs (NHL), Dolphins and Browns (NFL) are leading the analytics push in the less-than-quick-to-adapt leagues, with the Browns the most open about their ambitions. Said one: “A team like Toronto is doing it by the book in terms of how analytics should be impacting teams. They are … eating everyone’s lunch. It will pay off in the next two to five years and teams will start to say wow, look at their talent pool. Teams will start copying them.”

Despite analytics’ increasing role and media attention, it’s still early days. There are algorithms to write, data to dissect and knowledge to create. We asked respondents to say what percentage of the most important questions in their sport have been answered. The results, averaged by sport:

MLB – 56 percent

NBA – 38 percent

NHL – 32 percent

NFL – 31 percent

Soccer – 17 percent

Sports analytics developed a great deal in the past few decades, but there’s still plenty more to discover.




  1. Good stuff. Thanks for doing this. Hard to find good info in sports these days. Everything seems to be more cryptic than it should be.

  2. I do agree, that when it comes to analytics it would only be effective when their predictions are heeded by the professionals and the ones who hired them as you’ve mentioned. That seems to be a rough but significant work for sports financing since most of the times analytics would help the teams get the best players as possible if they make use of the information. It’s interesting to learn about all this since my son is really into sports and it got me curious about its behind the scenes works. Thanks!

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