Picture two batters walking off the pitch, both with 40 runs off 24 balls.
asserted
batters → picture → balls
One scored them in a chase, where his team needed 110 off 60 balls with six wickets remaining, which means a high pressure environment, and aggressive intent were required.
asserted
environment → score → balls
The other scored the 40 runs when only 70 were needed off 72 balls, with eight wickets in hand — no urgency, less pressure and things more in control.
asserted
70 → score → control
Statistics treat both innings identically.
asserted
Statistics → treat → innings
However, any cricket fan watching from Mumbai to Lahore to Sydney knows that they were not.
asserted
they → watch → Sydney
Numbers lie, or at least they hide something.
asserted
they → lie → something
This gap between what the numbers say and what actually happened on the field isn’t just becoming annoying, it’s glaring in the context of T20s.
asserted
it → say → T20s
It is also the starting point for a new analytical framework called Contextual Batting Intelligence, or CBI, developed by me.
asserted
It → call → me
Strike rate alone was never built to answer the question that actually matters to T20 captains, coaches and selectors: did the batter make and execute the right decision for that specific moment?
asserted
batter → build → moment
Contextual Batting Intelligence attempts to give that answer…
A DIFFERENT QUESTION ALTOGETHER
asserted
Intelligence → attempt → answer
Most cricket analytics still lean heavily on strike rate, which is a metric that measures runs scored per hundred balls faced.
asserted
that → lean → balls
It’s useful and simple, but it’s also blind to context.
asserted
it → ’ → context
A batter smashing 20 off 10 balls in a hopeless chase looks identical, statistically, to one doing the same in a tight finish.
asserted
batter → smash → finish
My framework argues that strike rate alone was never built to answer the question that actually matters to captains, coaches and selectors: did the batter make and execute the right decision for that specific moment?
asserted
batter → argue → moment
A recent widely debated instance of this emerged during the Indian Premier League (IPL) 2025, when the former India batter and commentator Sanjay Manjrekar (who always catches the limelight for his controversial remarks) decided to drop Virat Kohli from his personal top-10 list of IPL batters.
He argued that “T20 cricket is as much about strike rate as runs” and, hence, picked players with higher strike rates over Kohli’s mammoth run tally.
Sanjay’s remarks triggered a heated backlash.
asserted
remarks → debate → backlash
One camp strongly advocated that strike rate taken alone ignores the situations those runs were scored in.
asserted
runs → advocate → situations
Others said that strike rate matters the most in T20 cricket.
asserted
rate → say → cricket
This is the debate CBI aims to resolve.
asserted
CBI → aim → ?
Rather than ranking batters by strike rate or the total runs alone, the model would assess whether Kohli’s tempo in a given innings matched what his team actually required at that very moment.
asserted
team → rank → moment
This would validate a “slower” knock that was, contextually, the ideal one.
asserted
that → validate → knock
This is something a single number like strike rate or final score can never fully explain.
asserted
number → explain → rate
CBI doesn’t discard strike rate; in fact, it recontextualises it.
asserted
it → discard → it
Instead of judging an innings purely on the pace, CBI evaluates every ball as a decision made under a particular set of conditions.
asserted
CBI → judge → conditions
The model assesses how many wickets are left, how many balls remain, the gap between the required run rate and the current run rate, which phase of the innings it is, and the quality of the opposition bowling attack.
asserted
it → assess → attack
TURNING CRICKET CONTEXT INTO MATH
CBI’s goal is to turn what sounds subjective — “this was the right shot for the situation” — into a sequence of measurable mathematical steps.
asserted
this → turn → steps
For every ball of the game, the model first defines the state of the match: the inning’s phase, the wickets lost, the legal balls remaining, the current run rate and, in a chase, the required run rate.
asserted
model → define → chase
All of these factors are taken into account.
asserted
All → take → account
These variables form the current state vector, as that provides the model with a numerical overview of what the batter is facing at that exact moment.
asserted
batter → form → moment
The framework then estimates how costly it could be to lose a wicket in that instance in the match.
uncertain
it → estimate → match
Its risk coefficient, (s), increases as resources become scarce.
asserted
resources → increase → ?
During the second innings, the model adapts; it bridges the gap between the required and current run rate, meaning the same wicket will carry a different cost when a team needs 40 runs from 30 balls than when it needs 40 from 60.
asserted
it → adapt → 60
This is exactly how it is supposed to be according to cricket experts as well.
uncertain
it → suppose → experts
That risk is then combined with the expected outcome of each broad batting action.
asserted
risk → combine → action
In this case, essentially three possibilities are accounted for: playing a dot ball, rotating the strike, or attacking for a boundary.
asserted
possibilities → account → boundary
So, CBI’s core utility equation comes out to be:
U(a|s) = (b,t) × E[runs | s,a] (s) × P(out | s,a)
Here, ‘U’ stands for the contextual utility of choosing action ‘a’ in match state ‘s’, ‘’ is the quality of opposition (with ‘b’ for the ranking of the bowler and ‘t’ for the opposing team), ‘E’ for the expected runs, and ‘P’ for the probability of dismissal.
asserted
U → come → dismissal
We could simply say that the CBI weighs expected scoring value against the risk of dismissal, evaluating whether a risky or defensive shot is worth the potential payoff.
uncertain
shot → say → dismissal
Another variable, omega (), adjusts with the quality of the opposition.
asserted
variable → adjust → opposition
The model then uses a Boltzmann probability function to translate these utility values into the relative likelihood of each action being appropriate in the given context.
asserted
action → use → context
After that, it finally takes a mean of those delivery-level probabilities to produce a player’s CBI Index.
asserted
it → take → Index
CBI AND NUMBERS
To test the idea, I ran CBI against delivery-level data from T20 World Cups played between 2016 and 2024.
asserted
I → test → 2016
…and 20 more, not listed.
The Aporia
