🧩Input Data: Ball-by-Ball Feeds, Video Streams, and Player Profiles

To understand how Generative AI brings cricket alive — from auto-written match summaries to smart highlight reels — you first need to know what it feeds on. The fuel for any cricket GenAI system is input data — raw streams of numbers, text, and visuals flowing in real time.

The most fundamental layer is the ball-by-ball feed. Every delivery is logged with precise details: bowler, batter, ball speed, line, length, spin, outcome, and fielding positions. These live feeds come from scorers, tracking cameras, and on-field sensors. They’re the backbone for instant scorecards, win probability graphs, and real-time commentary generation.

Next is video stream data. High-definition video feeds from multiple camera angles capture the match from every possible perspective — pitch-side cameras, stump cams, drone footage, and slow-motion replays. This visual data is what powers AI systems to clip highlights, detect key moments (like wickets or boundaries), and even create customized reels for different fan interests.

Then there are player profiles and historical stats. Generative AI uses massive databases of players’ past performances, fitness levels, playing styles, and records against specific opponents. This context helps AI generate more meaningful commentary, compare performances, or simulate future matchups.

Put together, this blend of structured stats, dynamic video, and deep player context forms the raw material for GenAI to create commentary, visuals, predictions, and fan content. The better and richer the input data, the more human-like and useful the AI’s output becomes — making it feel like a living, breathing companion that watches the game alongside you.