Player Performance Category
Winner: Team: Dagger 3
Their app enables real-time player selection based on game situations, showcasing advanced predictive model decision-making.
Team Members: Jake Graff, Quinn Robnett, and Nick Rovelli.
Player Injury Category
Winner: Team: iHoopInsight
Their advanced machine learning model defines an injury risk score for each player, which offers the potential of improved player injury prevention.
Team Members: Shashank Guda, Rithika Gurram, Vishnu Charugundla, and Varshin Bhaskaran.
This year’s competition had over 30 teams from across Syracuse University participating. Both undergraduate and graduate students from the iSchool, A&S, Falk, Whitman, and ECS came together, showcasing exceptional creativity, innovation, and hard work.
Let’s hear from some of the winners:
From team iHoopInsight, Shashank Guda, the team leader, is pursuing a master’s in Applied Data Science. His focus lies in artificial intelligence, large language models (LLMs), and machine learning. Members of this winning team also include, Rithika Gurram (master’s student in Applied Data Science), Vishnu Charugundla (master’s student in Applied Data Science), and Varshin Bhaskaran (master’s student in Applied Data Science).
Q: How did you assemble the team?
Guda: I’ve known Vishnu for a long time, so working with him was really easy since we understand each other well. Rithika and I have two classes together, and I’ve always admired her insights, so I knew she’d be a great fit. Finally, I brought in Varshin because of his experience in Python, which added a lot of value to the team. We worked really well together.
Q: What were some of your main takeaways from the experience?
Guda: Though I have a basic understanding of basketball, I had to dive deeper into the mechanics of the game to make our analysis meaningful. This experience helped me realize how important subject matter experts are in bridging the gap between technical insights and real-world applications.
Q: What interested you about this competition?
Gurram: The competition intrigued me because it provided an opportunity to work with real-world sports data and apply machine learning techniques to predict injury risks—an area where analytics can significantly improve player safety and performance. It was also exciting to know that our work could have a direct impact on the basketball program, offering a unique chance to bridge data science with sports.
Q: Any other interesting parts of the competition, or surprises?
Gurram: One of the most interesting aspects of the competition was discovering the strong correlations between muscle imbalances, particularly the hamstring-to-quad ratio, and the likelihood of injury. We were surprised to see that guards had the highest injury risk, with their performance metrics and muscle imbalances significantly affecting their injury probabilities. Another surprise came from the training data itself: although certain performance metrics like distance and speed showed weak correlations with injury risk, the injury types, such as tendonitis and strains, were more common in guards and forwards. This insight helped us focus our prevention strategies on specific player positions with tailored recommendations.
Q: How did you fit into the team?
Charugundla: I started with analyzing the data andx then frequent meetings with the team to share the insights on every stage. I contributed to the data cleaning, exploratory data analysis (EDA), and modeling phases, but I also worked closely with the team to ensure everything came together smoothly. It was a great team effort, and I really appreciated the collaboration throughout the project.
Q: How do you hope your project impacts the basketball program?
Charugundla: I hope our injury prediction project can make a real difference in how the basketball program approaches player health and performance. By identifying risk factors like muscle imbalances and giving coaches and trainers position-specific insights, we’re hoping they can implement more targeted injury prevention strategies. Our predictive model could help staff spot high-risk players early on and intervene before injuries happen. In the long run, I’d love for our work to contribute to keeping players healthy and on the court, leading to better team performance and success.
Q: What interested you about this competition?
Bhaskaran: The opportunity to work on real data and potentially impact the basketball program was incredibly exciting. Combining my technical expertise with my passion for sports felt like the perfect challenge to showcase the power of analytics.
Q: What were some of your main takeaways from the experience?
Bhaskaran: The experience reinforced the importance of storytelling with data. Clean data and well-structured visuals can help bridge the gap between raw numbers and actionable insights. Additionally, working collaboratively in a team sharpened my communication and problem-solving skills.
Nick Rovelli, has received his bachelor’s in Sports Analytics, and is now pursuing a master’s degree in Applied Data Science.
Q: What interested you about this competition?
Rovelli: I like basketball and thought this could be a good opportunity to use some skills I have learned throughout the years and potentially win a prize, and with my partners I knew we could be in contention.
Q: How did you fit into the team?
Rovelli: We all knew basketball decently well going into this, but basketball was nobody’s forte. Knowing what a coach would want and how to deliver insight to them in real-time is really important as I have learned through my experience as a data analyst for the men’s soccer team.
Q: How do you hope your project impacts the basketball program?
Rovelli: I believe this research will help the program work in late game and quick shot situations where data will be able to quickly be relayed from analyst to coaches to help the coach make more informed decisions. In addition, this could help in recruiting and potentially elevate certain players to give us an edge over opponents.