Invention Summary:
In today's dynamic world, assembling effective teams is crucial for success in various fields. By analyzing past performance, personality traits, and skill sets, predictive models can provide insights into the potential synergy or conflicts that may arise among team members. This prediction is invaluable as it enables organizations to make informed decisions during the team formation process, ensuring a harmonious and productive work environment. With the growing emphasis on collaboration and teamwork, leveraging predictive modeling can enhance efficiency and facilitate the achievement of shared goals in this rapidly evolving world.
Researchers at Rutgers University have identified a method for predicting the impact of adding new players to a team using a statistical process named “Inverse Optimal Transport” (IOT) which formalizes the problem of finding an optimal coupling between probability measures given a cost matrix. This method facilitates prediction of future team performance when comprised of individuals who have not previously worked together.
Advantages:
- Simulations performed validating theoretical results and validating predictions on real‐world NBA performance.
- Able to facilitate prediction of future team performance when comprised of individuals who have not worked together previously.
Market Applications:
The proposed approach has a wide applicability including predicting team performance in:
- Sports like basketball and soccer
- Team members in an industrial setting
- Resume/candidate matching, and targeted advertising
Intellectual Property & Development Status: Patent filed: WO 2023/114197. Available for licensing and/or research collaboration. For any business development and other collaborative partnerships contact marketingbd@research.rutgers.edu
