Kabaddi Enters the Data Age

Pro Kabaddi League is now in its twelfth season. Viewership has grown every year. Sponsorship revenues have tripled in five years. And yet, until recently, the data infrastructure underpinning the sport looked more like a domestic cricket tournament in the early 2000s than a modern professional league.

That is changing fast.

What Makes Kabaddi Hard to Measure

Unlike cricket, where discrete events — balls, wickets, runs — map naturally to a data schema, kabaddi is continuous. A raid lasts somewhere between three and thirty seconds. Contact events are simultaneous and overlapping. Defining what counts as a 'meaningful tackle' versus incidental contact requires judgment calls that don't exist in ball-sport analytics.

"The first challenge in kabaddi analytics is agreeing on what you're measuring. The second challenge is measuring it fast enough to be useful."

— Kadamba Data Team

Our approach has been to build a tagging schema from first principles — developed in partnership with coaching staff who know what questions they actually need answered. The result is a system that captures raid outcomes, tackle types, bonus points, and do-or-die situations in real time.

Early Findings

The patterns that emerge from even one season of detailed kabaddi data are striking. Do-or-die raids — where a raider must score or be out — account for a disproportionate share of momentum shifts. The best teams in the league convert do-or-die situations at nearly double the rate of the bottom four.

Tackle efficiency — the ratio of successful tackles to total tackle attempts — correlates more strongly with league position than any offensive metric. It's a pattern that mirrors what defensive analytics found in basketball a decade ago.

What's Next

Player tracking — using computer vision rather than GPS given the contact nature of the sport — is the next frontier. Several franchises have expressed interest in systems that can map a raider's movement patterns and predict escape routes. We're building it.