SportWise by Rolling Insights provides users with no-code access to sports statistics and other data to enable users to make more informed Fantasy and Betting decisions. Users select from a directory of data including historical and present season stats, Fantasy League data, and projections to create a personalized view of the data, which updates daily. Rolling Insights is looking to grow the directory of data with a variety of additional data sources such as projection engines or advanced analysis that update daily related to team and player performance.

We would like to collaborate with students to apply the latest artificial intelligence (AI) and machine learning (ML) techniques to our existing dataset. Students will develop an AI / ML model related to projecting player and team performance, or findings other insights in the data so that it can be added to the data directory in SportWise.

This will involve several different steps for the students, including:

• Conducting background research on our existing product and sport industry projection requirements.
• Analyzing our current dataset.
• Researching the latest AI / ML techniques and how they could be applied to our data.
• Developing an AI / ML model that provides unique outcomes or insights into our data.
• Providing multiple solutions that can be applied to solve the same problem.

Examples of Problems we might like to solve:

• Historical and Projected Match Up performance (Team A vs Team B)
• Historical and Projected Player performance for select key stats
• Historical and Projected team performance with player combinations (Team A with Player A and B, versus Team B with Player C and D)
• Home vs Away historical performance & projections
• Others, we're open to suggestions!

By the end of the project, students should demonstrate:

• Understanding of the available dataset
• Understanding of the latest AI / ML techniques
• Identification of ways in which AI / ML can be applied to our company

Bonus steps would include:

• Providing multiple versions of potential models

Final deliverables should include

• A final report on the dataset, the problem solved, methodologies and approaches, outcomes and results, and recommended next steps.
• Source materials such as code and workbooks.

Students will connect directly with us for mentorship throughout the project. We will be able to provide answers to questions such as:

• Our current products and applications of AI / ML
• The current data set and guidance in navigating it
• Input on choices, problems or anything else the students might encounter.