When professional sports teams use big data and analytics, their objective is to improve player performance and win more games.
Analytics is the science of looking for patterns in data to make more informed decisions.
National Football League teams rely heavily on data as well. For instance, the Philadelphia Eagles used analytics for everything from in-game strategy to roster management as the team ultimately went on to win Super Bowl LII – its first Super Bowl victory in franchise history – in 2018.
That’s why, as a computer science researcher and educator, when I use big data and analytics to help the men’s and women’s basketball teams at Johnson C. Smith University, where I teach, my objective is much broader than just figuring out how players can score more points and win more games
But colleges and universities are coming up short when it comes to preparing students to take these jobs.
White students earned 39,492 – or 55.2% – of bachelor’s degrees in computer and information sciences in the 2016-2017 school year.
Asian Americans represent 5.6% of the U.S. population. For these students, using data in sports is about more than winning games
The problem is even more dire for women of color in computer science.
Broadening participation through university programs is just one way to narrow the gap. And by teaching the use of analytics in sports, it’s a way to get students to see computing as much more than just programming or fixing computers, as important as those tasks may be.
Sensors and shots
Coaches and players use this data to improve how the team performs throughout the season.
We have analyzed shot attempts, makes and misses, and have discussed how this data could inform players shot choices.
Beyond the court
The DATA Bulls project has implications that go beyond my campus.