A Bayesian view of cricket’s player of series monsters

Imagine this:

• Sometimes it rains, sometimes it doesn’t.
• You notice that the ground is wet.

Now you ask: “What is the chance that it rained, given that the ground is wet?”

That’s exactly the kind of question Bayes’ Theorem answers.

Think of Bayes’ Theorem as a smart way of changing your mind when new information appears. In life, we start with a belief based on past experience.
Then something new happens. Instead of ignoring it, we usually update what we believe. It’s a part of Probability Theory that helps you combine old information you already have, with new information you have just received.

This is the formula (don’t panic): $P(A∣B) = [P(B∣A)×P(A)]​ ÷ P(B)$¹²

It looks mad, doesn’t it? It took me months to be able to remember the Bayes formula, and it took cricket to help me learn it finally. But first, an explanation of what we have above:

In the formula,

• A = the thing you care about (Example: It rained). This is your starting belief before you see new evidence. It could be anything, such as, it’s dry season so it won’t rain today.
• P(A) is the probability of the starting belief.
• B = the evidence you see. (Example: The ground is wet). This is new information.
• P(B) is the probability of the new information happening.
• the “|” sign in the formula means “given” so P(A|B) will be read as Probability of A given B, meaning that the probability that A is still true given that the new information B is now known (“Now that I see the ground is wet, how likely is it that it rained?”), and P(B|A) is the probability that B is true given that we know that A happened (“If it did rain, how likely is the ground to be wet?”).

Now let’s take some help from cricket WITH MADE UP NUMBERS:

• Let’s say India wins 70% of all cricket matches. This is P(A), where A = India wins 70% of all cricket matches, okay?
• Now imagine Virat Kohli makes a century in 40% of the matches he plays. This is P(B), where B is Virat’s imaginary (I haven’t checked) century strike rate.
• P(A|B) is the probability that India won a match given that Virat hit a century. Let’s keep this at 80%.^{Yes I’m a fan, how did you guess?}
• Now the new information is that India has won a match. So given that we now know that India has won a match, what is the probability that Virat hit a century?

So, now,

• P(India winning a match for any reason) = 70% = 0.7
• P(Virat’s century in a winning or losing cause) = 40% = 0.4
• P(India winning given that Virat has hit a century) = 80% = 0.8
• So, if we know India has won, what is the probability that Virat hit a century?

P(Virat’s Century given that India has won) = [P(Virat’s century in a winning or losing cause) × P(India winning given that Virat has hit a century)] / P(India winning a match for any reason)

or P(Virat Century|India Win) = [P(Century) × P(India Win| Virat Century)] / P(India Win)

P(Virat century∣India wins)= (0.8×0.4​) / 0.70 ≈ 0.457 = 45.7%

I know this is all new and complex for many readers (it took me lots of effort and a Virat-inspired intervention to learn this too), so take your time to read it again if you need to, as many times as might help.

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Player of Series Monsters
At this point I want you to know that Cricinfo doesn’t have a list of women cricketers in decreasing order of player of series awards like they do for the men. There’s also a paucity of tabulated data available for women’s cricket generally. So I’m concentrating only on the men. The list of men is clearly documented, as mentioned:

Name	PoS Awards (Tests, ODIs, T20Is)
Virat Kohli (India)	22
Sachin Tendulkar (India)	20
Shakib Al Hasan (Bangladesh)	17
Jaques Kallis (South Africa)	15
David Warner (Australia)	13
Sanath Jayasuriya (Sri Lanka)	13

Of these, I got Perplexity AI to do some data finding and number crunching for me for Virat, Sachin, and Shakib for ODIs.

Bayes USING REAL NUMBERS
When the team won, how often was this player the reason?

Player	Team	Definition of W	Definition of C	P(W) base win%3	P(C) frequency of centuries	P(W | C) centuries in wins4
Kohli	India (ODI)	India win when Kohli in XI	Kohli scores ODI century	0.616	~0.18 (1 per 5.65 inns)	~0.83 (44 of 53 hundreds)
Tendulkar	India (ODI)	India win when Tendulkar in XI	Tendulkar scores ODI century	0.505	~0.11 (1 per 9.22 inns)	~0.67 (33 of 49 hundreds)
Shakib5	Bangladesh (ODI)	Bangladesh win (overall ODI record)	Shakib scores ODI century (bat)	~0.36	~0.03 (7 in 234 inns)	~0.77 (7 of first 9 tons)

Here’s the Bayes calculation:

Player	Team	P(W) base win%	P(C)	P(W | C)	P(C | W) calculated	Interpretation
Kohli	India (ODI)	0.616	0.18	0.83	0.24 (24%)	~24% of India ODI wins with him include a Kohli hundred
Tendulkar	India (ODI)	0.505	0.11	0.67	0.14 (14%)	~14% of India ODI wins with him include a Tendulkar hundred
Shakib	Bangladesh (ODI)	0.36	0.03	0.77	0.07 (7%)	~7% of all Bangladesh ODI wins include a Shakib hundred

What this means

• Virat Kohli in a strong India: One in every four ODI wins arrives with a Kohli century inside it. He does not just bat well; he bats well in a machine that is already built to win. His centuries are the accelerant on a fire that’s already burning. When India wins, there’s a strong chance he is the one who decided the margin, the pace, the emotional tone of the chase.
• Sachin Tendulkar in a medium India: One in every seven wins contains a Tendulkar century. He played across eras—through the ’90s when Indian cricket was still finding its feet, through the 2000s when it became a force. His centuries had to do more heavy lifting because the team around him was less consistently dominant. The win probability bump he created had to be steeper, had to arrive at moments when India could genuinely lose without him.
• Shakib Al Hasan in a historically weaker Bangladesh: One in every fourteen overall Bangladesh ODI wins includes a Shakib century—but here’s the insight: when he does score a hundred, Bangladesh almost never lose that game (6 of 7). On a much thinner winning base, his performances are load‑bearing. He is not the beneficiary of team strength; he is the architect of team possibility.

Shakib is kind of amazing in this that 6 of his 7 centuries have come in wins, and it got me curious about how many 50+ scores have these gents made in wins, but that data is not available in a clean Bayes format.

Kohli and Tendulkar sit on mountains of 50+ scores in ODIs – over a hundred each when you add fifties to centuries.⁸ Where they differ is in what happens after fifty.⁹ Kohli’s conversion rate from 50 to 100 in ODIs is significantly higher than Sachin’s. Once he’s crossed fifty, he tends to keep going, especially in chases. Part of that is temperament – an almost obsessive refusal to give away his wicket once set – but a big part is structural: India in his era often had deeper batting, was better at chasing (or he was better at chasing anyway), and capable partnerships.

Tendulkar’s 50+ scores, by contrast, sit in a very different ecosystem. He played long stretches of his career in teams that were less stable, so his fifties often had to be the innings and the platform at the same time. The conversion to hundreds is lower not because the intent wasn’t there, but because the conditional environment around him – partners, match situations, opposition attacks – made it much harder to keep going at the same rate. Yet even as “just” fifties, those scores were repeatedly the spine that held up India’s innings.

Bangladesh’s baseline ODI win percentage is far lower than India’s. That means:

• A Shakib 50 – even without going on to a hundred – does outsized work.
• His 50+ scores in tournaments like the 2019 World Cup (where he reeled off one high‑impact innings after another) are not just “good knocks”; they are the narrow ledges on which Bangladesh’s entire chase or defence balances.

And because he does this as an all‑rounder, a fifty for Shakib often comes with 10 overs of spin as well, and Bangladesh tend to look competitive almost exactly on the days Shakib has a good outing.

So much of cricket is about context, and this post reinforced that for me. Virat Kohli doesn’t just score centuries; he does so in a system that consistently wins, amplifying his influence. Sachin Tendulkar carried innings for teams that sometimes struggled, meaning his 50+ scores were often the backbone of a win rather than just the flourish. And Shakib Al Hasan? In a team with fewer wins overall, his big performances don’t ride on a strong machine — they create the machine.

Sources

1. Bayes Theorem – Formula, Statement, Proof | Cuemath
2. Bayes’s Theorem for Conditional Probability | GeeksforGeeks
3. Bangladesh ODI matches team results summary | ESPNcricinfo
4. Virat Kohli vs Sachin Tendulkar: The real GOAT of ODIs, statistical analysis settles the debate | Hindustan Times
5. Shakib Al Hasan Centuries | Cricket.one
6. Kohli vs Tendulkar: A comparison of their 49 ODI hundreds | ESPNcricinfo
7. Virat Kohli vs Sachin Tendulkar: The real GOAT of ODIs, statistical analysis settles the debate | Hindustan Times
8. Most Fifties in ODI: From Sachin Tendulkar to Quinton de Kock | MyKhel
9. Most fifties in career in ODIs – Batting records | ESPNcricinfo