{"id":25770,"date":"2025-03-28T17:13:36","date_gmt":"2025-03-28T11:28:36","guid":{"rendered":"https:\/\/www.revoscience.com\/en\/?p=25770"},"modified":"2025-03-28T17:16:27","modified_gmt":"2025-03-28T11:31:27","slug":"basketball-analytics-investment-is-key-to-nba-wins-and-other-successes","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/basketball-analytics-investment-is-key-to-nba-wins-and-other-successes\/","title":{"rendered":"Basketball analytics investment is key to NBA wins and other successes"},"content":{"rendered":"\n

Investment in analytics may also benefit college teams and fields beyond sports, a new study shows.<\/em><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n

Jennifer Chu<\/p><\/div><\/div>\n\n\n

CAMBRIDGE, Mass. — 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\u2019s record through the regular season? Or the chemistry among its players? Maybe it\u2019s the experience of its coaching staff or the buzz around a top scorer. <\/p>\n\n\n\n

All of these factors play some role in a team\u2019s chance to advance. But according to a new study<\/a> by MIT researchers, there\u2019s one member who consistently boosts their team\u2019s performance: the data analyst. <\/p>\n\n\n\n

The new study<\/a>, which was published this month in the Journal of Sports Economics,<\/em> quantifies the influence of basketball analytics investment on team performance. The study\u2019s authors looked in particular at professional basketball and compared the  investment in data analytics on each NBA team with the team\u2019s 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. <\/p>\n\n\n\n

Analytics department headcount had a positive and statistically significant effect on team wins even when accounting for other factors such as a team\u2019s 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\u2019s data analytics bench, so to speak, was a consistent predictor of the team\u2019s wins. <\/p>\n\n\n\n

What\u2019s more, they were able to quantify basketball analytics\u2019 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\u2019s impact is worth at least $9 million. <\/p>\n\n\n\n

\u201cI don\u2019t know of any analyst who\u2019s being paid $9 million,\u201d says study author Henry Wang, a graduate student in the MIT Sports Lab. \u201cThere is still a gap between how the player is being valued and how the analytics are being valued.”<\/p>\n\n\n\n

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\u2019t. (Take note, March Madness fans.) And the same likely goes for sports in general, along with any competitive field. <\/p>\n\n\n\n

\u201cThis paper hits nicely not just in sports but beyond, with this question of: What is the tangible impact of big data analytics?\u201d says co-author Arnab Sarker PhD \u201925, a recent doctoral graduate of MIT\u2019s Institute for Data, Systems and Society (IDSS). \u201cSports are a really nice, controlled place for analytics. But we\u2019re also curious to what extent we can see these effects in general organizational performance.\u201d<\/p>\n\n\n\n

The study is also co-authored by Anette \u201cPeko\u201d Hosoi, the Pappalardo Professor of Mechanical Engineering at MIT. <\/p>\n\n\n\n

Data return<\/strong><\/p>\n\n\n\n

Across the sports world, data analysts have grown in number and scope over the years. Sports analytics\u2019 role in using data and stats to improve team performance was popularized in 2011 with the movie \u201cMoneyball,\u201d based on the 2003 book \u201cMoneyball: The Art of Winning an Unfair Game\u201d by Michael Lewis, who chronicled the 2002 Oakland Athletics and general manager Billy Beane\u2019s use of baseball analytics to win games against wealthier Major League Baseball teams. <\/p>\n\n\n\n

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\u2019s health and minimize injury risk, and to predict a player\u2019s performance to inform draft selection, free agency acquisition, and contract negotiations. <\/p>\n\n\n\n

A data analyst\u2019s 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 \u201900 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\u2019s job is entirely based on data, there is not much data on the impact of analysts themselves. <\/p>\n\n\n\n

\u201cTeams and leagues are spending millions of dollars on embracing analytical tools without a real sense of return-on-investment,\u201d Wang notes. <\/p>\n\n\n\n

Numbers value<\/strong><\/p>\n\n\n\n

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\u2019s 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.<\/p>\n\n\n\n

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 \u201canalyst headcount,\u201d 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\u2019t 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. <\/p>\n\n\n\n

\u201cWhat we\u2019re trying to measure is a team\u2019s level of investment in basketball analytics,\u201d Wang explains. \u201cThe 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\u2019re not going to do that. So headcount is the next best thing.\u201d<\/p>\n\n\n\n

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\u2019s win record?) and season injuries (How did a team\u2019s injuries affect its wins?). The researchers also noted \u201croad back-to-backs,\u201d or the number of times a team had to play consecutive away games (Does the wear and tear of constant travel impact wins?). <\/p>\n\n\n\n

The researchers plugged all this data into a \u201ctwo-way fixed effects\u201d model to estimate the relative effect that each variable has on the number of additional games a team can win in a season. <\/p>\n\n\n\n

\u201cThe model learns all these effects, so we can see, for instance, the tradeoff between analyst and roster salary when contributing to win total,\u201d Wang explains.<\/p>\n\n\n\n

Their finding that teams with a higher analytics headcount tended to win more games wasn\u2019t entirely surprising.<\/p>\n\n\n\n

\u201cWe\u2019re still at a point where the analyst is undervalued,\u201d Wang says. \u201cThere probably is a sweet spot, in terms of headcount and wins. You can\u2019t 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.\u201d <\/p>\n","protected":false},"excerpt":{"rendered":"

Investment in analytics may also benefit college teams and fields beyond sports, a new study shows.<\/p>\n","protected":false},"author":2,"featured_media":25773,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17,30],"tags":[],"class_list":["post-25770","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research","category-entertainment"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0.jpg",1100,733,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0-200x200.jpg",200,200,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0-600x400.jpg",600,400,true],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0-768x512.jpg",750,500,true],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0-675x450.jpg",675,450,true],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0.jpg",1100,733,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0.jpg",1100,733,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0.jpg",1100,733,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0-870x570.jpg",870,570,true],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0-600x733.jpg",600,733,true],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0-600x600.jpg",600,600,true],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0-760x490.jpg",760,490,true],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0-550x360.jpg",550,360,true],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0-95x65.jpg",95,65,true],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0.jpg",640,426,false],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0.jpg",96,64,false],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2025\/03\/MIT-NBA-Analysts-01_0.jpg",150,100,false]},"author_info":{"info":["Jennifer Chu"]},"category_info":"Research<\/a> Entertainment<\/a>","tag_info":"Entertainment","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/25770","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=25770"}],"version-history":[{"count":2,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/25770\/revisions"}],"predecessor-version":[{"id":25774,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/25770\/revisions\/25774"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/25773"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=25770"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=25770"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=25770"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}