I’m excited to share my latest article where I explore leveraging LLMs for competitive advantage in rugby. Having worked closely with a Rugby staff on optimizing team performance with data, I wanted to see how using the latest and greatest features of top AI providers can help augment tactical insights.
Key points:
Multi-Model Strategy: By asking identical tactical questions to several LLMs, we gathered diverse insights from different sources on team performance, strengths, weaknesses, and strategic opportunities.
Battlecards: Our approach feeds into a web app that produces battlecards, helping coaches quickly assess opponents and fine-tune strategies.
I’d love to hear your thoughts on the scalability and robustness of using multi-model AI strategies in sports analytics and how similar methods could benefit other competitive domains.
I loved this idea ! I decided to develop a cli that reproduces these commands here for those interested: https://github.com/sahbic/todo-cli
It can synchronize tasks on github, and I can use a telegram bot to list and add tasks to the list from my mobile phone.
Key points:
Multi-Model Strategy: By asking identical tactical questions to several LLMs, we gathered diverse insights from different sources on team performance, strengths, weaknesses, and strategic opportunities.
Battlecards: Our approach feeds into a web app that produces battlecards, helping coaches quickly assess opponents and fine-tune strategies.
I’d love to hear your thoughts on the scalability and robustness of using multi-model AI strategies in sports analytics and how similar methods could benefit other competitive domains.