The Domestic Cricket Data Crisis: Where There Is No Ball-Tracking, That Is Where Real Analysis Lives

Look at a Ranji Trophy scorecard. The innings detail is complete — runs, balls, fours, sixes, wickets. But which ball landed on what line and length, with what intent the batter played a shot, where the fielder was standing — none of those questions will be answered by that sheet. This is the gap that stands between domestic cricket and franchise cricket, and this gap determines how deeply we can understand the game.

Working as a data analyst at a Mumbai digital sports startup, I first learned that having data and using data are two different things. When I built my first xG model for the 2026-18 Indian Super League, the biggest challenge was not the math of the model — the challenge was that very few people were even collecting football data. In a cricket-first market like India, football data was dismissed as 'niche'. But the weekly xG tables did not stop, and within a few months that same column became the outlet's most-read football feature. The lesson was clear: rigorous metrics can attract readers even in a cricket-first market, provided you present them honestly.

Now apply that same lesson to cricket. IPL broadcast money can buy the level of tracking where every ball's speed, revolutions, swing and batter's footwork is extracted from multiple cameras. The same Ranji Trophy — the real pipeline of Indian cricket — is often deprived of that data infrastructure. This does not mean analysis is impossible in domestic cricket. It means the method of analysis has to change. When you do not have tracking, you build proxies — estimated indicators — and clearly label them as 'proxies'. This honesty is the foundation of the work done in places with the least data.

Take a concrete example. Suppose a fast bowler takes 40 wickets in a Ranji season. Looking at the scorecard, you can say he was the best bowler of the season. But without tracking, you cannot tell what kind of wickets those were — did he trouble batters with the ball, or did they lose their wickets playing bad shots. In my experience this distinction is decisive. I have seen bowlers' numbers where a large share of their wickets came from risky shots by chasing teams, while their record against the top order was ordinary. The scorecard hides all of this. So whenever I analyse domestic numbers, I first write what the sample is, how far its interpretation goes, and which question will remain unanswered by this data.

This is why the absence of data is itself a finding, not an excuse. A league without tracking also tells you which questions its ecosystem never bothered to ask. Women's cricket is the freshest example. The WPL has been running since 2026, and its historical record is extremely thin compared to men's cricket. When you have only three or four seasons of data, you cannot talk about long-term trends. But you can certainly say what this sample proves and what it does not. This clarity is necessary, because ending a debate by saying 'the sample is small' is the easiest excuse — and the biggest trap too. The right way is to state which question this sample can answer, and which test will settle it definitively.

With this thinking, I return to the 2026 World Cup. As a data consultant for Star Sports, I calculated France's PPDA at 14.3 in the group stage — the highest among contenders — and predicted they would concede very few chances. France became champions and conceded only six goals in the entire tournament. At the time a TV commentator dismissed my analysis on air as 'overcomplicating the game'. But my Twitter thread went viral and reached ESPN. This experience reminds me every time that metrics do not tell you who will win; they tell you where a team chooses to breathe. PPDA never told me France would win — it told me where France released pressure and where it built it. That is the difference between outcome worship and decision-architecture analysis.

Applying the same logic to cricket, look at the IPL's 'Impact Player' rule. This rule is not just an extra player — it allows teams to buy matchups. A team can use the Impact Player as a sixth bowler to target the part of the opposition's batting order that is weak against spin. If you only look at runs, you will not see this decision. You have to look at auction strategy — which team opened its purse for which matchup, who changed the net run rate arithmetic. This is where data tells the real story.

But a caution is necessary here. Never fill the gap between correlation and causation. Suppose the 2026 ISL season, played behind closed doors in Goa. I analysed the distance covered per team. I found home teams' average distance dropped from 108 km to 104 km, and home win percentage fell from 45% to 32%. I wrote that home advantage was psychological, not tactical. Coaches initially dismissed it, but the data held. But if I had said 'empty stadiums eliminated home advantage', I would be making correlation into causation. The truth is that the drop in distance could have been due to many reasons — weather, schedule, or team composition. So I kept my claim limited: home teams' performance changed in empty stadiums, and this change points to psychological factors. This is a signal, not a theory.

Now the most important question: are we measuring the data the model has, or the game that actually is? This is the biggest risk of my profession. Because IPL broadcast data is clean and available, franchise cricket gets more coverage, while Ranji and women's cricket stay thin. Data availability quietly sets the agenda, and every analyst is drawn to the cleanest dataset in the room. There is only one way to avoid this: tag every claim with its provenance. If the evidence comes from a broadcast feed, write it clearly. And keep a fixed share of work for the fixtures where no one is tracking. This is not just ethics, but a question of analytical quality.

Similarly, dismissing the TV commentary box and regional-language coverage as 'noise' is another trap. My whole career is the argument that unmeasured opinion is the problem — but that does not mean every non-numeric voice should be put in the same category. Regional commentary is doing more to democratise the game than any board program. The right way is to treat commentary as a data source — log the claims, interview the voices, and check whether the eye test predicted something the model missed. 'You only had to watch him in the nets' — that is not evidence. But 'he bowled on the same line for 40 minutes in the nets' — that is an observation that can be counted.

Boards and broadcasters also fall within the same scrutiny as a bowling model. BCCI's money sets the global calendar and also the domestic data infrastructure. IPL's broadcast wealth produces the tracking that the Ranji Trophy never receives. This is not just a difference in resources — it determines which player will be understood deeply and who will simply be counted in the scorecard. If we want to take domestic cricket seriously, we have to ask with the same seriousness why its data infrastructure is not being invested in. This question is not of teams, but of institutions.

One more thing that surprises me again and again: milestone worship. Countdowns to fifty-over centuries, retirement tributes, viewing a match situation through the lens of star worship — this is the coverage reflex that treats a personal milestone as the story. It is the exact opposite of my method: outcome worship, where I want decision analysis. Similarly, a single-number prophecy like 'this XI will win the World Cup' is also against my method. A single composite rating, a model output without confidence intervals — this is false precision. I am a risk-adjusted builder, and I would rather publish a range and a failure case than a clean headline.

So the next time you look at a Ranji Trophy scorecard, pause at the one line that is not there — the ball's line, the batter's intent, the fielder's position. That absence will tell you which game we chose to measure and which we did not. And if you read that absence not as an excuse but as a question, you will get closer to the truth of domestic cricket that matters more than any broadcast graphic.

Will we see the same tracking for Ranji matches next season that we see in the IPL? If not, then at least let us start stating what we cannot see.