If you created a March Madness bracket this month, you likely encountered a common question with each collegiate matchup: What provides one squad with an advantage over another? Is it a team’s performance throughout the regular season? Or the synergy among its players? Perhaps it’s the expertise of its coaching staff or the excitement surrounding a leading scorer.
All these elements contribute to a team’s prospects of moving forward. However, according to a recent study conducted by MIT researchers, there’s one figure who consistently enhances their team’s performance: the data analyst.
The latest research, which was published this month in the Journal of Sports Economics, assesses the impact of basketball analytics investment on team success. The authors focused specifically on professional basketball and evaluated the investment in data analytics for each NBA team against the team’s win record over 12 seasons. They discovered that teams that employed more analytics personnel and allocated more resources to data analysis generally achieved greater success on the court.
The size of the analytics department had a positive and statistically relevant effect on team victories, even when considering other variables such as a team’s payroll, the experience and synergy among its players, the stability of its coaching staff, and player injuries throughout each season. Despite all these factors, the researchers observed that the strength of a team’s data analytics team, so to speak, was a reliable predictor of the team’s victories.
Furthermore, they quantified the value of basketball analytics based on their influence on team wins. They determined that for every four-fifths of a data analyst, a team secures one additional win in a season. Interestingly, a team can also achieve an additional win by increasing its roster salary by $9.6 million. One interpretation is that the contribution of one data analyst equates to at least $9 million.
“I’m not aware of any analyst being compensated $9 million,” remarks study author Henry Wang, a graduate student at the MIT Sports Lab. “There remains a disparity between how players are valued and how analytics are valued.”
While the focus of the study is on professional basketball, the researchers suggest that the findings are pertinent beyond the NBA. They theorize that collegiate teams utilizing data analytics may gain an advantage over those that do not. (Take note, March Madness enthusiasts.) The same likely applies to sports overall, as well as in any competitive environment.
“This research not only resonates in sports but beyond, raising the question: What is the concrete impact of big data analytics?” states co-author Arnab Sarker PhD ’25, a recent doctoral graduate from MIT’s Institute for Data, Systems and Society (IDSS). “Sports serve as an ideal, controlled environment for analytics. However, we are also curious about the extent to which we can observe 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 dividends
Throughout the sports industry, the number and scope of data analysts have increased in recent years. The significance of sports analytics in leveraging data and statistics to enhance team performance gained attention in 2011 with the release of the movie “Moneyball,” adapted from the 2003 book “Moneyball: The Art of Winning an Unfair Game” by Michael Lewis, who detailed the 2002 Oakland Athletics and general manager Billy Beane’s employment of baseball analytics to win matches against more affluent Major League Baseball franchises.
Since then, data analysis has broadened its reach into various other sports, aiming to utilize the diverse and fast-paced sources of data, measurements, and statistics presently available. In basketball, analysts can wear multiple hats, employing data, for instance, to optimize a player’s health and minimize injury risk, and to predict a player’s performance for draft decisions, free agency acquisitions, and contract discussions.
A data analyst’s efforts can also affect in-game strategy. A notable example: Over the past decade, NBA teams have strategically shifted to shooting longer-range three-pointers, after Philadelphia 76ers President of Basketball Operations Daryl Morey SM ’00 concluded that statistically, increased three-point attempts lead to more victories. Currently, each of the 30 NBA teams employs at least one basketball analytics staff member. Yet, even though a data analyst’s role is entirely data-driven, there is a lack of substantial data regarding the impact of analysts themselves.
“Teams and leagues are investing millions of dollars in adopting analytical tools without a clear understanding of return-on-investment,” Wang observes.
Quantitative impact
The MIT researchers aimed to measure the effect of NBA team analysts specifically on winning games. To achieve this, they consulted major sports data sources such as ESPN.com and NBAstuffer.com, a website that compiles extensive statistics on NBA games and team data, including analytics personnel, which the website’s operators gather from publicly accessible information, such as official team press releases and staff directories, as well as LinkedIn and X profiles, and news and industry reports.
For their study, Wang and his colleagues collected data on each of the 30 NBA teams, spanning from 2009 to 2023, starting from 2009, the year NBAstuffer.com began tracking team information. For every team in each season during this timeframe, the researchers noted an “analyst headcount,” indicating the number of basketball operations analytics staff engaged by a team. They defined an analyst to include data analysts, software engineers, sports scientists, research directors, and other technical roles, but also personnel who may not be officially recognized as analysts but are notably active in the basketball analytics community. Generally, they found that in 2009, a mere 10 data analysts were working throughout the NBA. By 2023, that figure had expanded to 132, with some teams hiring more analysts than others.
“Our objective is to assess a team’s commitment to basketball analytics,” Wang clarifies. “The optimal measure would involve each team reporting exactly how much they invest annually in their R&D and data infrastructure and analysts. However, they are unlikely to disclose that information. Therefore, headcount serves as the next best indication.”
In addition to the analytics headcount, the researchers also gathered data on other factors influencing wins, such as roster salary (Does a higher-paid team win more games?), roster experience (Does a team with more veterans win more games?), coaching consistency (Did a new coach alter a team’s win record?), and season injuries (How did a team’s injuries affect its victories?). The researchers also accounted for “road back-to-backs,” or the frequency of consecutive away games played by a team (Does the fatigue from continuous travel impact wins?).
The researchers utilized all this data in a “two-way fixed effects” model to estimate the respective contribution of each variable to the number of additional games a team can win in a season.
“The model assesses all these influences, allowing us to see, for instance, the tradeoff between analyst and roster salary when affecting total wins,” Wang explains.
The discovery that teams with a larger analytics headcount generally win more games was not entirely unexpected.
“We are still at a stage where the role of the analyst is underestimated,” Wang states. “There is likely a sweet spot concerning headcount and victories. You cannot employ 100 analysts and anticipate an 82-and-0 season. However, many teams are still operating below that optimal point, and the competitive advantage offered by analytics has yet to be fully realized.