Reveal patterns and trends in physiological and performance data from basketball games and practices to help the analyst, coach, and trainer make proactive decisions.
In the recent years, sports analytics has become a growing field, leveraging real time analytics compared against historical trends to help coaches optimize player performance. (A notable example being the Moneyball book and movie.) We partnered with a collegiate basketball team to visualize their basketball data, which contains combined physiological and performance data.
In an initial interview with our partners, we identified their roles, experience with using data, and their desired goals from collecting the data. The primary users for the visualization are the Analyst and Strengths Coach, who compares trends leading up to a game with game outcome. These users may use the visualization to back up their recommendations with the basketball coach, a secondary user.
There are several sites that collect and share performance data for NBA games, which are manually tracked. We have a unique opportunity to also work with raw physiological data that has been gathered through sensors worn under the players' uniforms.
For each player, our dataset contains the following measures for both game days and practice sessions:
More data, more problems
Because we were dealing with multiple dataset files, measures, and units, there were several data manipulations that were undertaken to organize the data and make it usable to feed into Tableau.
Through this exercise, we were able to make recommendations to our user for how for more effectively track data in the future. In particular, tracking the performance data relative to the continuous clock will make data analysis much easier.
Initial questions and experiments in Tableau
Based on on user interviews and our understanding of the data, we wanted to use visualizations to try to answer some preliminary questions that our user may have about the data.
1. How does the previous seven days of practice affect player performance at a game?
2. Are we overtraining our players that may result in injuries or poorer game performance?
3. How might spikes in heart rate correlate with shot accuracy?
Building an interactive interface in Tableau
Interactivity adds more utility to a visualization, supports exploration, and aids in story telling. The DataBall visualization interface supports several interactions to navigate through multiple views and zoom in on detailed information, including:
Layout and visual refinements
After multiple iterations and usability studies, we found that our visualization was usable, but there were many refinements needed to improve interactivity and performance. In particular, as the lead designer, I made key decisions on:
Recommended Future Work
After a brief detour in d3, we ultimately decided to focus on delivering the final visualizations through the Tableau software. This is due to the nature of the partnership, the skills of our team, ands time-cost effectiveness. However, here is an exploration of how the visualization and interface might look, given enough time, resources, and skills.