1. Loras College Men’s Basketball
Application of Data Analytics and Visualization in NCAA Division III Men’s Basketball
Thomas Rhomberg
Loras College Men’s Basketball

2. Lineup Optimization Measuring shot quality Analyzing lineup efficiency
Criteria used to measure each shot:
Determine likelihood of success
Optimize shot selection
Analyzing lineup efficiency
Compare lineup plus minus data
Measure positive and negative possessions
Ultimately use the information to build highest quality lineups
Shot on 3 point line or closer
Play run correctly or reversal before shot
Shot is uncontested
One project that we worked on had to do with optimizing lineups. Our goal with this project was optimizing scoring ability by putting players on the court that will have the most impact on scoring, and creating high-quality scoring opportunities for others 1

3. Lineup Visualizations
Average shot quality
Lineup plus minus
Shot chart shows where higher shot quality comes from
Basic zones help players and coaches understand where each player shoots best/worst
Lineup +/- Chart shows the +/- of each lineup in top 25 of both that and minutes, used to drill down to the players that play best with each other 2

4. Results & Future Improvements
Field goal percentage increases significantly as shot quality rises
Future Improvements
Strategic data collection
Analyze shot quality of set plays
Measure impact of player on teammates
FG% and Shot Quality
Avg. shot quality w/ 2 starters on
1.31
Avg. shot quality w/ 3 starters on
1.59
Higher shot quality directly leads to higher fg%
More starters typically lead to better shots
Strategic data collection will help us gather the most information without wasting our limited time
We hope to measure the quality of shots we get from each play we run
I am working to write a algorithm that takes into account the impact that a player has on others while they are on the court and look to measure the expected points they can add by being on the court 4

5. Questions? 5