Analytics In Action: Illinois Tech hoopsters get an assist from data science program
With Illinois Tech’s men’s basketball team on a roll this season, one assist is coming from the university’s Master of Data Science program.
The Division III basketball team went from winning no games in 2013, the year before head coach Todd Kelly came on board, to winning two games in 2014, four in 2015, to achieving a record of 19-5 as of Feb. 21. Their Massey rating has gone from around 375-400 to 117. On Jan. 4, they cracked the 100-point mark for the first time since 2007. And for the first time in program history, the Illinois Tech men’s basketball team has qualified for the U.S. Collegiate Athletic Association’s National Championships.
Although he says athlete talent is the No. 1 predictor of success, Kelly attributes about 20 percent to 25 percent of the turnaround to analytics from students in Illinois Tech’s Master of Data Science program.
“They provided two key kinds of information. First was the players’ adjusted plus/minus,” Kelly says. “Plus/minus basically calculates how many points a team has gained or lost during that player’s time on the court. Secondly, they provided me with my best five-man lineups for specific situations: defense, three-point shooting, rebounding and late game.”
Pulling in Data Science Help
The data science program teaches students how to explore data using high-level mathematics, statistics and computer science. Coach Kelly approached program directors in March 2016 for help. Students Denis Bajic and Larry Layne took on the project as their practicum in summer 2016, with data science program director Shlomo Argamon.
The three worked closely with Kelly throughout the summer, delivering final results in August 2016. They provided Kelly with each player’s plus/minus, PER (player efficiency rating) and what the best lineups would be for specific situations. They also calculated each player’s offensive and defensive win shares, adjusted PER, game simulations, best five-man lineups for a variety of situations and several other useful metrics.
Bajic, who played basketball in junior high and high school and who follows the Illinois Tech team, says, “Advanced analytics can provide a significant advantage when incorporated into a game plan. Seeing that many Division III teams either didn’t take regular statistics, or didn’t utilize data to their advantage, we thought we could gain valuable insights not only on our team, but on other teams, which would help the coaches plan strategies more effectively.
“We looked at all the teams Illinois Tech played in the 2015-2016 season and scraped their season statistics, along with IIT’s statistics, to get a feel for what other teams were doing better. We utilized all these statistics to build new analytics based on what’s currently used in the NBA (player efficiency ratings, win shares, adjusted plus/minus, etc.).”
“We then looked at these newly generated statistics for the Illinois Tech players and, after comparing them to performances against other teams, gained insights on how the program could improve. This knowledge translated into coming up with many different lineups that would be deployed as the situation called for, as well as tweaks the team could make (e.g., defensive scheme alterations). We used Python for scraping and analysis and Tableau for visualization.”
Going the extra mile
Layne led construction of the simulations, as well as working on the plus/minus calculations.
“The idea for these simulations was to see if the team was playing to expectation, and if not what areas they could improve,” Balic adds. “We also provided him with a dashboard of all the analytics we developed so he could easily make his decisions. I watch the sport daily and have been keeping tabs on the Illinois Tech team this season, and it’s great to hear of the success that they’re having.”
“We intended for the simulations to use Denis’ top line-up calculations,” Layne notes. “We would try different line-ups with different teams in the simulator in order to determine if certain players or play styles would work better against different opponent teams or styles. Unfortunately, we only reached the proof of concept stage by the end of the practicum.”
“At the end of the day, our players make it work,” Kelly says. “They’re the ones that make it happen. But the data science students gave us invaluable help and allowed us to leverage one of the things that makes IIT unique – it’s a STEM school strong in quantitative problem-solving. Most Division III schools are liberal arts schools and don’t have a data science and analytics master’s program.
“I plan to do this again next year,” Kelly continues. “In the meantime, I encourage everyone to come out and see this team!”
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