India has now lost 16 consecutive coin tosses across all formats, with the streak extending from January 31, 2025, to October 2, 2025 (the West Indies – India Test match in Ahmedabad that concluded today). Here’s the baffling list by chronology:
| Coin Toss Loss No. | Date | Match | Venue | Captain |
|---|---|---|---|---|
| 1 | Jan 31, 2025 | 4th T20I vs England | Pune | Suryakumar Yadav |
| 2 | Feb 02, 2025 | 5th T20I vs England | Mumbai (Wankhede) | Suryakumar Yadav |
| 3 | Feb 06, 2025 | 1st ODI vs England | Nagpur | Rohit Sharma |
| 4 | Feb 09, 2025 | 2nd ODI vs England | Cuttack | Rohit Sharma |
| 5 | Feb 12, 2025 | 3rd ODI vs England | Ahmedabad | Rohit Sharma |
| 6 | Feb 20, 2025 | ODI vs Bangladesh | Dubai (Champions Trophy) | Rohit Sharma |
| 7 | Feb 23, 2025 | ODI vs Pakistan | Dubai (Champions Trophy) | Rohit Sharma |
| 8 | Mar 02, 2025 | ODI vs New Zealand | Dubai (Champions Trophy) | Rohit Sharma |
| 9 | Mar 04, 2025 | ODI vs Australia | Dubai (Champions Trophy Semi-final) | Rohit Sharma |
| 10 | Mar 09, 2025 | ODI vs New Zealand | Dubai (Champions Trophy Final) | Rohit Sharma |
| 11 | Jun 20, 2025 | 1st Test vs England | Leeds | Shubman Gill |
| 12 | Jul 02, 2025 | 2nd Test vs England | Birmingham | Shubman Gill |
| 13 | Jul 10, 2025 | 3rd Test vs England | Lord’s | Shubman Gill |
| 14 | Jul 23, 2025 | 4th Test vs England | Manchester | Shubman Gill |
| 15 | Jul 31, 2025 | 5th Test vs England | The Oval | Shubman Gill |
| 16 | Oct 02, 2025 | 1st Test vs West Indies | Ahmedabad | Shubman Gill |
In mathematics, probability measures how likely an event is to occur, and it’s always expressed as a number between 0 (will never happen) and 1 (will definitely happen every time). For a standard fair coin toss, the probability of either heads or tails is exactly 0.5 (or 50%). This is because there are two possible and equally likely outcomes: the coin will either flip to heads or tails (not counting the vanishingly small number of times it may fall on its edge, in which case the toss will be repeated until a result is achieved anyway).
Every toss is also independent, which means that the result of one toss will have no impact on the result of any other toss. When events are independent, the probability of several events occurring in succession is the product (multiplication) of their individual probabilities. So, the probability of losing (or winning) two fair tosses in a row is: Probability of 2 losses = 0.5 × 0.5 = 0.25.
The probability of losing (or winning) 3 fair tosses in a row is therefore = 0.5 × 0.5 × 0.5, which is 0.125.
We’ve lost 16 consecutive tosses across formats, geographies, and captains. The probability of winning or losing a fair coin toss is 0.5 or 1/2. Which means the probability of losing 16 consecutive fair coin tosses is… (0.5)16, which equals 1 in 65,536, or ≈0.0000152588%.
Now, it really must be noted that a cricket coin toss is quite different from a simple game of coin toss between two people (though the mathematics remains exactly the same). The Indian skippers were not always the ones tossing the coin, neither were they always the ones calling heads or tails. In cricket, the standard procedure is that the host captain tosses while the visiting captain calls. However, at neutral venues where neither captain is the host, the procedure varies: a neutral party such as a match official or invited dignitary may toss the coin, or one of the captains may be chosen to toss, or tournament regulations may specify the exact protocol. This means India’s losing streak has transcended not just different formats, captains and venues, but also different toss procedures, making it an even weirder demonstration of statistical randomness.
I decided to investigate the mathematics of this absurdity.
0.0000152588%
How rare is a 0.0000152588% chance of any event happening? Well, more people are struck by lightning annually,1 but fewer people are likely to die by meteorite strike2.
Similar things have happened in cricket before- The Netherlands have previously lost 11 consecutive tosses, and and several teams have lost 9 in a row.3 Rohit Sharma himself has lost 12 consecutively (equalling Brian Lara).3
Independence and the Gambler’s Fallacy
The Gambler’s Fallacy is the (mistaken) belief that because India “lost so many times in a row,” they’re “due” for a win, but since each coin toss is independent and past outcomes have absolutely no impact on the next. Each toss remains a 50-50 chance, regardless of what’s happened before.
The Law of Large Numbers and the Nature of Streaks
The Law of Large Numbers states that if an independent act is performed enough times, the outcomes of this independent event (the coin toss in our case) will eventually (that is, in the long term, given a large number of coin tosses) match the predicted probable outcome of that event (that is, 50% of the times the coin will flip heads, and 50% of the times it will flip tails), but this will of course include every coin toss ever, and not restrict itself to India’s male cricket captains.
This simply means that though the average outcome will even out to about 50% wins and losses, streaks such as 16 losses in a row are still possible, just extremely unlikely. Given enough cricket matches played, even “impossible” events are destined to surface from time to time. Cricket tosses represent a relatively small sample size in the grand scheme of probability. Even if we consider all international cricket matches ever played, this would still represent a small enough sample size where unusual streaks can and will occur (to understand this, compare every cricket toss to every coin toss that has ever happened in history).
Information Theory
In Information Theory, the rarer an event is considered, the more surprising it is found to be. This means losing one toss is not surprising since there is a 50% chance of losing any one random fair toss. However, losing 16 tosses in a row must be considered very surprising because it involves the following outcomes:
Lose the first toss (50% probability), then lose the second toss (50% probability), then lose the third toss (50% probability), then lose the fourth toss (50% probability), then lose the fifth toss (50% probability)… then finally lose the 16th toss, also with a fifty percent probability that you could win it or lose it.
Which means that if nothing else, at least my bewilderment at the streak is justified.
Sources:
- What are the chances of being struck by lightning?
- What are the Odds a Meteorite Could Kill You?
- Most consecutive toss losses in ODIs, full list: India extend all-time world record
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