Louisville decided to team up with data engineering and data science experts from PMsquare, an IBM Gold Business Partner that specializes in business analytics solutions.
“This was an exciting project for us to partner on with the University of Louisville,” says Dustin Adkison, Managing Partner of PMsquare. “In most industries, it can be hard to truly visualize the impact of your efforts, but with this project, we knew we were doing more than just helping a basketball team win more games. We were helping athletes stay healthy, and that changes lives.”
Paul Jones adds: “The PMsquare team not only provided technical expertise, they also helped us develop our theories about injury prevention and find ways to make the data actionable. In particular, Erik Hoggard and Eric Dolley deserve a lot of credit for the dedication and innovative ideas they brought to the project.”
The PMsquare team helped to define the project around three focus areas: automation (to streamline the data collection process), investigation (to find ways to model data and predict injuries), and visualization (to help coaches understand the results and put them into practice).
The first step was to find a Cardinals team to act as a proof of concept for the new approach. Paul Jones explains: “Basketball is our most popular sport at Louisville, and Coach Jeff Walz and our women’s basketball staff are fully committed to any tools that will help us with managing player health, wellness and performance over the course of the lengthy college season—so that team was the perfect candidate. We already had a rich history of using technology with the team, and we knew that if we could create a winning framework for women’s basketball, other sports would follow suit.”
Erik Hoggard from PMsquare tackled the data collection challenge by building an automation layer based on Python scripts, which is affectionately known as the “Louisville Scraper”. Instead of manually collecting data from the Catapult and Polar wearable devices, and then going through a long process of uploading and downloading data from various web services, the Scraper acts as an intelligent process automation tool, minimizing the need for human input.
Next, PMsquare brought in data scientists to investigate the causes of injuries. By building a predictive model in IBM SPSS® Modeler, the team confirmed what its coaches had long suspected. Most injuries aren’t sudden freak accidents; they result from a longer-term aggregation of fatigue, stress and other factors. And as a result, they can be avoided.
“Our SPSS model showed that injuries are not just about what happens in training on the day—they are related to the stress and fatigue that build up over time,” explains Paul Jones. “In our case, the period of 27 days before the injury occurred seemed to be the best predictor of whether a player would get hurt. This really backs up our philosophy of looking at our athletes’ experience as a whole, instead of focusing on individual practice sessions.”
The project also raised some interesting insights about when injuries happen: Thursdays and Fridays were the days with the highest incidence.
“We need to do some further investigation, but it’s possible that there’s a weekly cycle at work here,” says Teena Murray. “Most players only have one non-practice day per week. So, it’s possible that by the end of each week, they’re becoming fatigued and the risk of injury increases.”
The investigation phase of the project was eye-opening for Louisville’s analysts—but to make a real difference to the way teams practice, they needed a way to bring the results to life, and convince the players and coaches. Eric Dolley from PMsquare used IBM Cognos® Analytics to build a set of intuitive dashboards that highlight the most critical insights at a glance.
“Coaches’ time is very valuable, so the information we deliver has to be something they can grasp within 10 seconds,” says Paul Jones. “The Cognos dashboards that get the key points across instantly, so there’s no need to interpret tables of figures to understand how fatigued an athlete is, or how likely they are to get injured.”