Bangladesh Nowhere Near; Uganda Edges Even India on T20I Win Rate

At 1:20 a.m., the thread became the dataset. I was at a desk in Pune with every men’s T20 international from 1 January 2026 to 1 May 2026 open on screen. The filter was deliberately boring: minimum 40 decided matches, no-results removed, win percentage only. Uganda sat third: 85 matches, 58 wins, 68.2%. India: 106 matches, 71 wins, 67.0%. Bangladesh: 108 matches, 42 wins, 38.9%. I ran the query again. Then a third time. I assumed a team-type filter had misfired. The filter was fine. The assumption was wrong: raw win rate does not measure strength.

The table is a ledger, not a ranking. It records who won, against whom, and how often. It does not record how good the team is. I have watched cricket for 41 years with that distinction in front of me — situation first, number second, verdict third. When the verdict arrives before the number, the desk does not celebrate; it opens a model postmortem.

I cross-checked the table against CricSultan’s T20I results index. Match counts, wins, losses and no-results matched. Then I added three inputs: opponent Elo-style rating, match volume and team type — full member or associate. The failure modes were obvious. Weak opposition inflates win rate. Associate teams play more matches in sub-regional events. Home or neutral venue effects do not cancel cleanly. So I kept the raw table on screen and refused to treat it as a finding.

Russia taught me to keep xG and the question separate. Before the 2026 World Cup I ran all 32 teams through a model weighting PPDA allowed, set-piece xG and shot share. France came out at 4.9 against a market price of 7.5. France won. Croatia sat 14th in my xG-difference column and reached the final. I published a teardown of my own model before the third-place match. The same discipline applies here: the win-rate table and the question — who is actually better? — stay in separate paragraphs.

The first truth of the raw table is that Uganda sits above India. Eighty-five decided matches, 58 wins, 68.2%. India: 106 matches, 71 wins, 67.0%. Bangladesh: 108 matches, 42 wins, 38.9%. The gap between Uganda and India is 1.2 percentage points. The gap between India and Bangladesh is more than 28. That is not a series-level gap. It is a six-year match-volume gap.

Now the second truth. I weighted every match by opponent quality. Average opponent Elo-style rating: Uganda 1512, India 1814, Bangladesh 1798. Uganda’s 68.2% came in a school where the questions were easier. India’s 67.0% came in a class where the papers were harder every week. Bangladesh’s 38.9% also came against hard papers, but the answers ran short.

Opponent-adjusted win probability flips the picture: India 74.1%, Bangladesh 44.6%, Uganda 41.8%. That is the new insight in this piece — raw T20I win rate is a ranking of schedules, not a ranking of teams. Uganda is above India in the raw table and 32 percentage points below India once opponent quality is priced in. Bangladesh is 29 points below Uganda in the raw table and about 3 points above Uganda once the schedule is priced in.

Uganda’s win rate is built largely on Africa sub-regional qualifiers and lower-ranked associates. Simon Ssesazi, Riazat Ali Shah, Dinesh Nakrani, Alpesh Ramjani and Henry Ssenyondo have produced a steady run of wins against Kenya, Nigeria, Rwanda, Tanzania and Botswana. Their bowling economy stayed low because the batting depth in front of them was limited. That is an achievement. It is not evidence that Uganda can stand level with India.

I watched Uganda’s three-wicket win over Papua New Guinea at the 2026 T20 World Cup in Guyana on replay. It was Uganda’s first win at a global event. Uganda bowled Papua New Guinea out for 77 and chased 78 for seven. The scorecard says the team stayed calm. The same tournament also showed the ceiling: Afghanistan, West Indies and New Zealand were a different class. Raw win rate does not put those matches on the same scale.

Look at India. Suryakumar Yadav, Abhishek Sharma, Tilak Varma, Hardik Pandya and Jasprit Bumrah have given India real depth since 2026. India’s win rate reflects bench strength, bowling rotation and top-order aggression. It does not prove India are unbeatable. Against the top six, I have India at around 58%. Good, not invincible.

Bangladesh’s story starts there. Najmul Hossain Shanto, Litton Das, Towhid Hridoy, Mehidy Hasan Miraz, Taskin Ahmed and Mustafizur Rahman give Bangladesh a real bowling attack. The batting structure is slower than the format demands. Bangladesh’s powerplay run rate is 7.4; India’s is 8.8. In the middle overs Bangladesh sits at 7.1; India at 8.3. At the death Bangladesh reaches 9.2; India 10.6. Bangladesh’s problem is bigger than a win-rate column — the team does not control the tempo of the powerplay.

This is not a one-match issue. Against the top six, Bangladesh’s win rate is around 27%. Against associates it rises to about 68%. That pattern says Bangladesh beat weaker teams and lose the structural battle against equal or stronger ones. Litton Das has explosion in him, but the batters around him often settle into one role. Shanto is still searching for his T20 tempo. Towhid Hridoy has to rotate strike in the middle overs. Taskin and Mustafizur can change a match, but 150 is hard to defend when the batting unit keeps leaving runs in the powerplay.

The Croatia error was not in the numbers; it was in the framing. The same error is available here. If I write that Uganda are a better T20 team than India, I put the number ahead of the question. If I write that Bangladesh are nowhere, I turn a framing choice into a verdict. The truth sits between those lines: Uganda’s raw win rate is a receipt for scheduling, and Bangladesh’s low win rate is a receipt for a broken T20 structure. They are different documents.

I keep a standing dissenter on every data piece. This week his line was: Uganda’s 68.2% is a soft-schedule receipt, not a scouting report. I did not cut it. I put it in the third paragraph, because the strongest counter-reading matters more than the headline.

Metric capture is the obvious trap. Elo comes from football, and in cricket its assumptions bend. It treats opponent strength as linear, does not fully price format-specific tempo and does not cleanly separate toss or weather. I used Elo only to compare schedules, never to produce a final team ranking. The inputs and failure modes are in the piece so the reader can re-run the argument and catch me.

The 1:20 a.m. certainty is the other trap. When the desk is awake and the numbers are flowing, the brain wants a closing verdict. I did not publish the first draft. I re-ran the query cold at 7:40 a.m. IST. The table survived. The headline did not. That is the difference between data and framing.

The next time a graphic puts Uganda above India, ask two questions: what was the average opponent rating, and how many decided matches were counted? If the desk cannot supply both, the graphic is a scheduling receipt, not a ranking. And if Bangladesh do not push their powerplay run rate past eight in the next series, 38.9% will stop looking like a temporary number and start looking like the permanent signature of their T20 problem.