Japan’s San-en NeoPhoenix is using generative AI to analyze game data, surface overlooked patterns and support strategy development while keeping final decisions in human hands.
By The Grey Ghost
WHAT’S HAPPENING
Japanese professional basketball team San-en NeoPhoenix has developed an AI-assisted analysis system with Amazon Web Services to help evaluate game performance and prepare strategy.
The system organizes large amounts of game data, identifies patterns linked to successful plays and then uses generative AI to produce reports for the team’s human analysts.
By allowing AI to handle part of the initial data review, analysts can spend more time on video analysis, testing ideas and preparing for upcoming games.
WHY IT MATTERS
The system shows how generative AI can move beyond basic statistics and become part of a professional sports analysis workflow.
San-en NeoPhoenix can customize the AI through prompts so that it focuses on information the team considers important. That allows the system to better reflect the club’s tactical priorities and the way its analysts evaluate games.
Team analyst Kimura said the technology has also surfaced aspects of gameplay that might otherwise have received less attention.
WHO BENEFITS
Coaches and analysts gain another source of information when reviewing opponents, lineups and strategy.
Players may benefit from more detailed preparation and analysis of game situations.
Sports organizations can use AI to process large amounts of performance data without requiring analysts to manually organize every data point first.
Data and cloud providers gain another practical use case for generative AI inside professional sports.
WHO LOSES
Traditional analysis methods that rely heavily on manual data organization could become less efficient compared with AI-assisted workflows.
Teams without access to strong data infrastructure or enough historical information may also have difficulty getting the same value from similar systems.
San-en NeoPhoenix found that the AI was less useful early on when less game data was available.
WHAT HAPPENS NEXT
The team plans to expand the system beyond game strategy.
Potential future uses include broader data management across Japan’s B.League, player injury prevention and giving the AI more information about opposing coaches and their tactical philosophies.
The larger question is not whether AI replaces coaches.
For San-en NeoPhoenix, the current model is collaborative: AI organizes information, identifies possibilities and suggests alternatives, while human analysts and coaches decide what actually matters on the court.
