IBM Japan Innovates MotoGP Rider Development with AI

• Building an analytical platform to support data-driven discovery and development of rider candidates using IBM watsonx and IBM Bob

• IBM Japan also signs a sponsorship agreement as the official AI partner of the Honda HRC Castrol team

IBM Japan, Ltd. (hereafter, IBM Japan) announced today that Honda Racing Corporation (hereafter, HRC) will begin building an AI-based analytical platform in collaboration with IBM Japan to advance the process of discovering, analyzing, and developing MotoGP rider candidates. Furthermore, IBM Japan has signed a sponsorship agreement as the official AI partner of the Honda HRC Castrol team, supporting the team in both technological and operational aspects.

In motorsports, including MotoGP, various factors such as rider skill, machine characteristics, and race conditions influence a team's competitiveness. Previously, HRC relied on experienced specialists to discover and evaluate rider candidates. This initiative aims to elevate the quality of evaluation and development by visualizing insights that were previously difficult to capture solely through human judgment, through the collaboration of these specialists' insights and IBM's AI.

The analytical platform to be built will leverage IBM Japan's AI technology and expertise to realize rider characteristic analysis, visualization of strengths and weaknesses, and report generation AI for development on IBM Cloud. Specifically, in addition to considering the use of external data, including public MotoGP data, machine learning models required for rider evaluation will be built on the integrated AI development studio, IBM watsonx.ai. The AI governance platform, IBM watsonx.governance, will enhance governance aspects such as transparency, bias monitoring, and explainability. Furthermore, for data analysis, IBM's AI agent-driven development partner for enterprises, "IBM Bob," will be utilized. By automating the entire process required for data analysis, from data processing to creating analysis parameters, building machine learning models, and generating result summaries, through conversational interaction, it will enable rapid and high-quality system development.

This initiative will evolve the rider selection and development process into a new data-driven evaluation approach where expert experience and AI analytical capabilities mutually complement each other. This will provide deeper understanding of candidates...