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With over 200 attendees and representatives from 15 professional sports teams, this year鈥檚 Carnegie Mellon Sports Analytics Conference (CMSAC) was the largest to date.

Each year, students and industry professionals alike come to 好色先生TV鈥檚 campus to rub elbows and watch demonstrations that highlight the latest sports research from the statistics and data science community.

鈥淚鈥檓 seeing a lot more ability among a broader class of people,鈥� said , creator of ESPN's sports analytics group and a pioneer in the field. 鈥淭he student posters were great, and some of the regular sessions had advanced stuff 鈥� stuff you wouldn鈥檛 have seen in professional sports teams 10 years ago.鈥�

At this year鈥檚 conference, Oliver gave the keynote address, but he was also impressed by how far the field has come and how much interest it now generates.

鈥淚 was a little bit of a misfit in college,鈥� said Oliver, who is the author of 鈥淏asketball Beyond Paper: Insights into the Game's Analytics Revolution.鈥� Lots of people at universities were interested in sports then, too, but virtually none of them were looking at them from the science and tech side of things.

鈥淲hen I realized I could make a living in sports and data analytics, I promised myself that I would make sure to give back to students so that they could learn how to do it, too,鈥� said Oliver.

The Golden Age of Sports Analytics

Today鈥檚 analysts have access to more data than ever before, but also data of a quality that is almost unfathomable for earlier generations.

鈥淟iterally every tenth of a second, the NFL鈥檚 Next Gen Data chips provide information for where every single player is positioned on the field. The direction they鈥檙e moving, the speed they鈥檙e moving,鈥� said Ron Yurko, assistant teaching professor in CMU鈥檚 Department of Statistics & Data Science and director of the Carnegie Mellon Sports Analytics Center. 鈥淚t鈥檚 wild.鈥�

鈥淭he MLB has information about every single swing in Major League Baseball,鈥� said Yurko, who has co-organized CMSAC since 2017. 鈥淚n baseball and basketball, they have what鈥檚 called 鈥榩ose skeletal data,鈥� where we know at every fraction of a second, where is the elbow, the shoulder, the kneecap, and in three-dimensional space.鈥�

Of course, the driving force behind CMSAC is what to do with all of that data.

This year, participant projects showed that data can be used to model the physical limits of athlete output, direct basketball players on when to foul their opponents and guide NFL teams toward better Draft Day decisions. In previous years, CMU鈥檚 Quang Nguyen has used NFL data to develop new metrics for defensive line performance, , as well as to assess how adept wide receivers are at changing their direction.

鈥淭his is like my Super Bowl,鈥� said Nick Schnell, CMO and head of growth for , top sponsor for the conference. 鈥淚t鈥檚 crazy what you can do with all of this data once you have it. You can tell a story.鈥�

As if to illustrate that point, Catharine Ramage, a senior studying statistics and data science and business administration, gave a presentation on how CMU鈥檚 Buggy teams use sports analytics data to shave seconds off their race times. Most attendees did not know what the sport was, of course, so Ramage brought out a full-size buggy and showed videos to better depict the combination of track, luge and Formula 1 racing.

What Ramage did not divulge was her actual data, which was blacked out on the screen, lest any of her competitors鈥� analysts were hiding in the crowd.

鈥淚 don鈥檛 know how many of you are spies,鈥� she said.

Joking aside, the presentation showed that data analytics can enrich and improve sports of all kinds, and in ever more surprising ways.

鈥淥ne of my favorite sports analytics papers was from the world of horse racing, and it found that horses with a left ventricle in their heart that was larger, on average, went on to win more races,鈥� said Ramage. 鈥淚 think we鈥檙e constantly looking for the left ventricle of Buggy racing, if you will.鈥�

Real People, Real Questions

鈥淲e鈥檝e been tracking biometrics since the Greeks held their first Olympics,鈥� said , assistant professor of data science at the University of Virginia, during a talk she gave at the conference about the need to balance innovation with privacy.聽

In Kupperman鈥檚 presentation, she encouraged her colleagues to remember that there are real people behind all of this sports data 鈥� not only athletes, but also coaches, managers and countless other stakeholders 鈥� and that each of them is fighting to keep their jobs. At the end of the day, sports analytics needs to find answers to questions that matter.

鈥淚 really encourage students to always keep digging through the past and keep looking at things that have already been 鈥榮olved鈥�,鈥� said Kupperman. 鈥淭here is a constant need to think differently.鈥�

With so much data on hand, there have also never been more opportunities for students looking to get into the industry.

鈥淚 think if you look across all the leagues, it鈥檚 a lot of 25-year-olds doing a lot of the big, heavy lifting. And that work starts at conferences like these,鈥� said , an alumnus of CMU鈥檚 Electrical and Computer Engineering Department who is vice president of the baseball research development department for the Milwaukee Brewers.

鈥淩ight now, there are probably around 150 people working in the NFL alone. When I started, there were like 12,鈥� said , vice president of product at Teamworks and a data analyst who has previously worked for the Pittsburgh Steelers and Jacksonville Jaguars, among other professional sports teams. 鈥淭he students that are coming in now have abilities that I just couldn鈥檛 have imagined.鈥�

Rebecca Nugent, head of the Department of Statistics & Data Science and Fienberg Professor of Statistics & Data Science, sees an incredibly bright future for researchers and educators in sports analytics.

鈥淭here have always been researchers and students interested in sports analytics, but now that the technology has advanced to provide unprecedented access to data in almost all sports, the pace and quality of work has rapidly accelerated,鈥� said Nugent. 鈥淟iterally a game-changer!鈥�

The advances remind Oliver of an age-old question.

鈥淚n high school, everyone was always asking, 鈥榃hat is all this math good for?鈥欌�� Oliver said. 鈥淣ow, we have a much better answer than we used to.鈥�