💥Types of Cricket Data: From Ball-by-Ball to Player Biometric Stats

Cricket is a game where every ball, every run, and every movement can be turned into valuable data. To understand how Generative AI works in cricket, it’s important to know what kinds of data power these smart systems.

At the most basic level is ball-by-ball data — every delivery’s speed, line, length, bounce, and the outcome: runs, wickets, extras. This live feed forms the backbone of match summaries, scorecards, and commentary.

Then comes player performance data, which includes batting and bowling averages, strike rates, economy rates, and match-by-match records. These stats help fans, coaches, and fantasy team players compare and pick players smartly.

Beyond the basics, modern cricket uses tracking and sensor data. Technologies like Hawk-Eye, Snickometer, and ball trackers record pitch maps, wagon wheels, and shot trajectories. Fielding heatmaps show where a player covers the ground.

The latest frontier is biometric and fitness data. Players now wear GPS trackers and smart wearables that monitor heart rates, fatigue levels, sprint speeds, and even hydration. Coaches use this data to manage workloads, prevent injuries, and design personalized training plans.

All these data types — from the visible scoreboard to invisible sensor streams — feed into AI systems that generate insights, commentary, visuals, and predictions. The richer and more accurate the data, the smarter the AI’s output.

This topic lays the groundwork for readers to appreciate how each layer of cricket data fuels the next generation of GenAI tools that make the game more exciting on and off the field.