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Contact SupportThe GametimeAI Engine drives our next-level analytics tools. Employing machine learning and AI models, the engine identifies plays, formations, or play characteristics which drive success in different situations. Our next-level analytics tools then take these outputs and feed them back to coaches when they matter most to support decision making. The engine is designed to feed off both team-specific data as well as aggregated data across all users. As GametimeAI grows, so do the models, generating even better insights as more data is ingested.
Our insights tool utilizes the AI-driven GametimeAI Engine to access your data and our aggregate dataset to help you identify patterns live in-game. This goes a step beyond Gamecast, identifying which formations, plays, or play characteristics have been successful (or unsuccessful) in different situations, and flagging these specifically for you to so that you can adjust in real-time.
Playcall, on the other hand, uses the same mechanisms to go a step further – providing you live, situational playcall recommendations when they matter most.
The GametimeAI Engine sits behind Insights and Playcall, crunching your live data against a growing national database to reveal patterns no human staff could track mid-game. While Gamecast shows “what happened,” the Engine explains “why it’s happening” and tells you where to focus.
Insights keeps the feedback to the coach simple – just the information you need to aid decision making without the noise:
Likewise, Playcall is even more straightforward – generating the top playcall recommendations based upon plays, formations, and attributes demonstrated to deliver results in your current situation.