If you filled out a March Madness bracket this month, you probably faced the same question with each college match-up: What gives one team an edge over another? Is it a team’s record through the regular season? Or the chemistry among its players? Maybe it’s the experience of its coaching staff or the buzz around a top scorer.
All of these factors play some role in a team’s chance to advance. But according to a new study by MIT researchers, there’s one member who consistently boosts their team’s performance: the data analyst.
The new study, which was published this month in the Journal of Sports Economics, quantifies the influence of basketball analytics investment on team performance. The study’s authors looked in particular at professional basketball and compared the investment in data analytics on each NBA team with the team’s record of wins over 12 seasons. They found that indeed, teams that hired more analytics staff, and invested more in data analysis in general, tended to win more games.
Analytics department headcount had a positive and statistically significant effect on team wins even when accounting for other factors such as a team’s roster salary, the experience and chemistry among its players, the consistency of its coaching staff, and player injuries through each season. Even with all of these influences, the researchers found that the depth of a team’s data analytics bench, so to speak, was a consistent predictor of the team’s wins.
What’s more, they were able to quantify basketball analytics’ value, based on their impact on team wins. They found that for every four-fifths of one data analyst, a team gains one additional win in a season. Interestingly, a team can also gain one additional win by increasing its roster salary by $9.6 million. One way to read this is that one data analyst’s impact is worth at least $9 million.
“I don’t know of any analyst who’s being paid $9 million,” says study author Henry Wang, a graduate student in the MIT Sports Lab. “There is still a gap between how the player is being valued and how the analytics are being valued."
While the study focuses on professional basketball, the researchers say the findings are relevant beyond the NBA. They speculate that college teams that make use of data analytics may have an edge over those who don’t. (Take note, March Madness fans.) And the same likely goes for sports in general, along with any competitive field.
“This paper hits nicely not just in sports but beyond, with this question of: What is the tangible impact of big data analytics?” says co-author Arnab Sarker PhD ’25, a recent doctoral graduate of MIT’s Institute for Data, Systems and Society (IDSS). “Sports are a really nice, controlled place for analytics. But we’re also curious to what extent we can see these effects in general organizational performance.”
The study is also co-authored by Anette “Peko” Hosoi, the Pappalardo Professor of Mechanical Engineering at MIT.
Data return
Across the sports world, data analysts have grown in number and scope over the years. Sports analytics’ role in using data and stats to improve team performance was popularized in 2011 with the movie “Moneyball,” based on the 2003 book “Moneyball: The Art of Winning an Unfair Game” by Michael Lewis, who chronicled the 2002 Oakland Athletics and general manager Billy Beane’s use of baseball analytics to win games against wealthier Major League Baseball teams.
Since then, data analysis has expanded to many other sports, in an effort to make use of the varied and fast-paced sources of data, measurements, and statistics available today. In basketball, analysts can take on many roles, using data, for instance, to optimize a player’s health and minimize injury risk, and to predict a player’s performance to inform draft selection, free agency acquisition, and contract negotiations.
A data analyst’s work can also influence in-game strategy. Case in point: Over the last decade, NBA teams have strategically chosen to shift to shooting longer-range three-pointers, since Philadelphia 76ers President of Basketball Operations Daryl Morey SM ’00 determined that statistically, shooting more three-pointers wins more games. Today, each of the 30 NBA teams employs at least one basketball analytics staffer. And yet, while a data analyst’s job is entirely based on data, there is not much data on the impact of analysts themselves.
“Teams and leagues are spending millions of dollars on embracing analytical tools without a real sense of return-on-investment,” Wang notes.
Numbers value
The MIT researchers aimed in their new study to quantify the influence of NBA team analysts, specifically on winning games. To do so, they looked to major sources of sports data such as ESPN.com, and NBAstuffer.com, a website that hosts a huge amount of stats on NBA games and team stats, including hired basketball analytics staff, that the website’s managers compile based on publicly available data, such as from official team press releases and staff directories, as well as LinkedIn and X profiles, and news and industry reports.
For their new study, Wang and his colleagues gathered data on each of the 30 NBA teams, over a period from 2009 to 2023, 2009 being the year that NBAstuffer.com started compiling team data. For every team in each season during this period, the researchers recorded an “analyst headcount,” meaning the number of basketball operations analytics staff employed by a team. They considered an analyst to be data analysts, software engineers, sports scientists, directors of research, and other technical positions by title, but also staff members who aren’t formally analysts but may be known to be particularly active in the basketball analytics community. In general, they found that in 2009, a total of 10 data analysts were working across the NBA. In 2023, that number ballooned to 132, with some teams employing more analysts than others.
“What we’re trying to measure is a team’s level of investment in basketball analytics,” Wang explains. “The best measure would be if every team told us exactly how much money they spent every year on their R&D and data infrastructure and analysts. But they’re not going to do that. So headcount is the next best thing.”
In addition to analytics headcount, the researchers also compiled data on other win-influencing variables, such as roster salary (Does a higher-paid team win more games?), roster experience (Does a team with more veterans win more games?), consistent coaching (Did a new coach shake up a team’s win record?) and season injuries (How did a team’s injuries affect its wins?). The researchers also noted “road back-to-backs,” or the number of times a team had to play consecutive away games (Does the wear and tear of constant travel impact wins?).
The researchers plugged all this data into a “two-way fixed effects” model to estimate the relative effect that each variable has on the number of additional games a team can win in a season.
“The model learns all these effects, so we can see, for instance, the tradeoff between analyst and roster salary when contributing to win total,” Wang explains.
Their finding that teams with a higher analytics headcount tended to win more games wasn’t entirely surprising.
“We’re still at a point where the analyst is undervalued,” Wang says. “There probably is a sweet spot, in terms of headcount and wins. You can’t hire 100 analysts and expect to go in 82-and-0 next season. But right now a lot of teams are still below that sweet spot, and this competitive advantage that analytics offers has yet to be fully harvested.”
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Written by Jennifer Chu, MIT News
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