How IPL Trade Values Works?

We’re pretty good at measuring performance these days. We’ve built Expected runs, player impact models, win probability added which tell us how much a player contributes on the field. We also know what teams paid for them at the auction.

However, a trade is a bit different. Ex, If Mumbai Indians send a player to Chennai Super Kings, what has Chennai really received?

To value a trade, we need to primarily answer four questions:

1. What is the player worth?

2. What is he worth to this particular team?

3. What can the team realistically recover if he leaves?

4. Is making the trade better than keeping him?

IPL Trade Values answers these questions through five connected components:

Player Valuation → Team Fit → Replacement & Auction → Trade Economics → Counterfactual Simulation.

The framework starts by putting every player on a common scale using Projected WAR (pWAR), then asks how that value changes when the player is placed inside a specific squad. It then models the auction pool and competition for replacements, thus calculates the possible recoverability from the auction, prices the financial side of the transaction, and finally compares the team in two different states: one where the trade happens and one where it doesn’t.

What the model has to solve?

There are four problems to solve before a trade can be priced.

Valuation: What’s a player’s intrinsic worth, isolated from the team context.

Projection: What is he likely to be worth next season and the one after that. This is much harder in a league as short and volatile as the IPL.

Team Fit: A player is never worth the same to every side. A team is basically twelve slots (once you factor in the impact player) of different exposure and importance, which together contribute to a collective Win% and a set of constraints.

Replacement: When you trade a player away you go into an auction with a limited purse, competing against nine other teams for a pool of players that is not known during the time of trades.

I. What is a player worth?

Firstly, we need to put players in the same units, because there are number of roles and each comes with its own context. What I’ve used is runs above replacement, converted into wins.

For every ball, I computed ‘Runs Created’: (Actual runs - what a team would expect from that over-and-wickets state, with the cost of a wicket priced in properly). The batter gains that value and the bowler loses exactly the same amount. Each of those steps has intricacies worth understanding. So, before I move on, let me discuss those first.

A wicket is not worth a fixed number of runs: I fitted run expectancy across the 2023 to 2026 seasons, which gives you the average runs a team still scores from any given position. The value of a wicket is then just the gap between the state before and the state after. At any fixed point in the innings, a wicket costs more when more have already fallen, because you’re pulling the tail closer.

With 120 balls left and nobody out, a wicket costs about 14 runs. In the last five overs it costs around 2.5.

Runs are not worth the same at every ground: Over a season the venues a player happens to visit can shift his numbers by a few percent in either direction. So every ball is adjusted by a venue factor before it counts.

Every ball is also divided by a Leverage Index, so a run in the powerplay and a run at the death are weighed by how much scoring that state normally produces.

How many runs is one additional win?

The answer is about 5.9 runs per match, or 82.5 runs across a 14 game season.

So this means that if you improve your team by six runs a match, all season, you will win one extra game. In the 2023 to 2026 data, about one loss in six was by six runs or fewer.

Now, I had to define first Who exactly is the replacement? I’ve defined “Replacement level” as the standard you’d get from a player who is freely available. What I believe is a franchise watches its players every day in the nets and then decides who to pick. Hence, I sorted every player by the share of his team’s matches he actually played, and performance tracks it almost perfectly in the IPL. Players picked in more than 80% of games strike at 154. Between 60% and 80%, 145. Below 15%, 106.

So, replacement level is basically the standard player freely available. I took the top eight batters and top six bowlers per team by playing time as league-level because they’re replacing someone who would’ve been in their place in their absence, and everyone below that line sets the bar.

Converting Runs to WAR

The final step is a rescaling that sets a league average regular at 1.00. On that scale: replacement is 0, a regular sits near 1.5-2, MVPs are worth around 5 WAR and notably 62 of 318 contracted players are below replacement. Also, I must mention that many players have thin IPL records. So I ran the same engine on T20Is and blended the two by precision.

Using the player’s WAR I can potentially calculate: What should I expect this player to contribute if I put him on the field next season? This is the Projected WAR (pWAR) which will be used in all the simulations going forward.

But it still wasn’t enough to evaluate a trade. Because the way MI sees a player vs what CSK sees in that same player can differ based on their requirements and other intangible constraints. What we just found is the isolated worth of the player, now let’s bring the element of the team to this.

II. What is he worth to this team?

Let's take Dewald Brevis' case. His WAR comes out at 1.12, so now, if we ask what he'd be worth to Mumbai Indians (if traded)? Answer is almost nothing, as MI already have Rickelton and Jacks, both of whom are ahead of him. Bringing Brevis in results in an option for a position that's already covered.

Slots are not equal

I want you to think of a squad as a set of roles and slots rather than simply a collection of players. Different positions receive different amounts of exposure, meaning a potential chance to contribute. An opener faces far more balls than a number eight, while a primary bowler contributes far more deliveries than a seventh bowling option. Therefore, the same improvement in pWAR can have very different contribution in projected Win% depending on where the improvement occurs. This exposure is combined with the estimated importance of each batting position and bowling phase when evaluating the team’s needs.

To solve this, I built a team-selection optimizer. For every player, I mapped the role he can realistically perform. It respects the overseas limit, batting positions and bowling-phase coverage, and finds the best legal XII for each squad.

So, a hole can be: a player leaving, or a slot where the best replacement player you have left is below what that position should deliver (calculated from the league average requirement for each role).

Now after knowing this, the intuitive next step was to find: What the team actually loses when a player leaves and how likely will the team recover that loss in the mini auction.

III. Can you just buy a replacement?

Turns out, it's a bit complicated.

The problem is that the trade window shuts before the release deadline, which shuts before the auction, so at the moment you’re deciding ‘to trade or not to trade’, you don’t know which players will even be available. So, how do I know whether I’ll be having those options in the auction pool which can be brought to solve my team’s vacant slots. And, ideally I don’t want only a player who can fill that slot, but I would hope he’s someone who’s better than the average player of the same role.

To solve this ambiguity, I had to predict the auction pool. I trained a model on the 2023 to 2026 transitions using salary, share of matches played, performance against the average for that specific role, and squad depth in front of the player. The model outputs a probability for every contracted player. I set the cutoff separately for each role and nationality, choosing the probability that reproduces that group’s own historical release rate. That produced my release list.

Hardik Pandya’s release probability lands at 0.78, which is worth noting given the trade talk, because it says Mumbai were probably moving on either way. At the other end, Sooryavanshi sits at 0.004 and Sai Sudharsan at 0.006 meaning nobody is releasing those two.

For the auction itself, I ran it as a sequential thing. First, I asked what kind of player the vacancy in that team actually needs. Each team bids what the player is worth to its remaining holes, and walks away when the price goes past that.

Willingness to pay:

For any player, a team’s ceiling is the improvement he makes to their best legal XII, converted into money. Take KKR’s opening slot. If the player currently filling it projects at 1.85 pWAR and a 2.60 pWAR opener replaces him, the gain is 0.75 pWAR and because an opener slot carries more of a season’s exposure than most, that gain is worth roughly one and a half times what the same upgrade would be at number six.

For converting this calculation in terms of money, I regressed what teams actually paid at the 2023, 2024 and 2026 mini-auctions against what each player was worth going in:

price = −₹1.24cr + ₹1.20cr × pWAR + ₹2.21cr × scarcity + ₹0.27cr × flexibility + ₹4.42cr × capped − ₹1.37cr × overseas

So Kolkata’s ceiling for that opener works out near ₹7 crore, and above that they’re overpaying, for that 0.75 pWAR gain.

But we should also remember that in auctions, most of what a player costs in this market has very little to do with how many wins he is likely to produce.

Which gives you a demand curve:

I categorized the quality of the player from bad to excellent and recorded what each team would pay at each level. Punjab won’t pay a rupee for an opener until quality passes pWAR of the current openers, because their incumbent is already better than that. Kolkata start bidding at 0.18 pWAR because they have a genuine hole.

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Recovery Probability

This gives us the quantity I actually need for trade evaluation: If this player leaves, what is the probability that the team can recover his value through the auction? I separated this into the Probability of filling the vacancy and the Probability of acquiring someone at least as good as the player lost because a team can have a high probability of filling a role while having a much lower probability of replacing the actual quality it lost. That distinction helps us to produce the recovery-value distribution used in the trade evaluation.

IV. Putting a price on Trades

In Football or Baseball, once you buy a player, you can keep him for years. In IPL, due to a 3-year cycle the upcoming 2027 is the last season of the current cycle. After that, the mega auction occurs and teams have to start again. So whoever gets Hardik Pandya is effectively renting him for one season, with an option to retain him at a fixed slab price afterwards: ₹18, 14 and 11 crore for capped players and ₹4 crore for uncapped. Hence, an uncapped Indian who's already good is such a strange asset as he's the only kind of player you can keep cheaply.

Surplus value: (What he’s worth - what he costs) summed over every year you hold him.

Surplus = (market-fair price − salary) + retention option value

The first bracket is the guaranteed 2027 season where market-fair price is:

price = −₹1.24cr + ₹1.20cr × pWAR + ₹2.21cr × scarcity + ₹0.27cr × flexibility + ₹4.42cr × capped − ₹1.37cr × overseas

The second term is 2028 onward, and obviously it only exists if you retain him. So, it’s only worth something when his projected value clears the slab price:

where value_t is his value in the year ‘t’ and slab price is at least ₹11 crore for a capped player and ₹4 crore for an uncapped one.

Opportunity Cost

If I spend ₹10 crore acquiring one player, that ₹10 crore isn’t available for the other vacancies in my squad. So the financial calculation feeds back into the auction model through opportunity cost. So the trade is evaluated using:

(Player value + team fit + replacement value + financial surplus − opportunity cost) rather than simply comparing the two players involved.

V. Does the team actually end up better off?

So now we’ve got something substantial- there’s a value, a price, and a probability of recovery. The last thing left is to actually run the season and see what changes wrt States S1 and S2 as they’re 2 different states of the same team based on the decision they took in the trade.

S1: No trade so the player(s) stays. The team goes into the release process with its original squad, builds its purse, enters the auction and fills whatever vacancies it has.

S2: Trade. The trade happens first which in turn changes the squad, the salaries and the vacancies. The release decisions are then applied to that new state, the purse changes accordingly, and the team enters the auction with a different set of needs.

Season Simulation

Then I simulated the season 2000 times for both states. The simulation produces distributions for Regular-season win percentage, Playoff probability, Title probability, Final squad/XII value, and Asset value.

The trade’s effect is then measured as the difference between the two states: Δ=S2−S1. So, for example: Δ Playoff Probability=P(Playoffs ∣ S2)−P(Playoffs ∣ S1)

Trade Utility

The final trade utility asks a counterfactual question: Is the team better off after making the trade than it would have been if the trade had never happened? That is the final output of the framework.

The trade that already happened

Pant to Delhi, Kuldeep to Lucknow.

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Lucknow gained nine points of playoff probability by giving up their captain. Pant on ₹27 crore was costing them more than he was returning, and shedding that contract freed up a squad they could actually build in the auction. Delhi barely moved either way meaning their playoff probability won't change much by this trade.

Now, I won't bore you more with more technical stuff, here's what you've been waiting for this long to see:

The three versions of the Hardik Pandya trade

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For CSK that’s a spread of four percentage points of title probability and it’s decided entirely by which second name goes to MI. Mhatre alone is worth more than twice what Khaleel is worth in this deal and that only shows up because Mhatre’s surplus value is way more than Khaleel’s and most likely he’ll remain uncapped till the next mega auction. So, CSK fans don’t give up Mhatre. Also, try to push Hardik’s salary down. But the reality is he doesn’t fix what’s actually wrong for them.

Mumbai improves in all three cases, which shows they would be the happier of the 2 sides. Interestingly, MI has more odds of reaching playoffs in the 2nd case than the 3rd which makes sense because they need a player of Khaleel’s attributes way more than that of Brevis.

Finally, if I can measure what a team gives up in a trade, and then show the conditions under which the deal makes sense, I’m closer to answering the question I started with:

What is an IPL trade actually worth?

If you enjoyed taking this journey with me, do give me a follow, share the article so it reaches more people and let me know what you think in the comments.

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Baseball went through exactly that rethink. For years, starting pitchers were expected to work deep into games, and managers usually waited for visible trouble before replacing them. Then the data began showing that hitters became increasingly successful each time they faced the same starter.

Now, instead of asking how long is a starter supposed to pitch?, teams began asking where is he actually most valuable? That small change in thinking eventually changed how pitching resources were used.

I think ODI cricket may have one of those moments sitting right in front of us.

Why overs 31–40 may deserve a rethink after the ICC’s latest rule change.

With the ICC’s new single-ball rule for the final 16 overs, overs 31–40 have become the last free-scoring period in an ODI innings. Although, the 2nd Powerplay comprises overs 11 to 40, with at most 4 fielders allowed outside the circle, the overs 31 to 40 phase has certainly shown some characteristics, which I will explain shortly, that are different than the 20 over phase before them with the same field restrictions.

This window of last 10 overs before the fielding side gets extra fielder outside the circle appears to be a Batting Powerplay, but most teams haven’t adjusted so far. With another ODI World Cup approaching, that could matter.

So, Who Are We Actually Waiting to Attack?

There is a reason I keep coming back to overs 31–40. On paper, there is nothing particularly special about this ten-over block, but look at who is actually bowling it.

Since 2019, the distribution of bowling overs changes quite noticeably once an innings reaches the 31st over. The first and second bowling options, the bowlers captains rely on most, see their involvement drop. Meanwhile, the fourth, fifth and sixth options take on a much bigger share.

The fifth bowling option alone bowls 19.3% of all deliveries in this phase, more than any other bowling option. The sixth option contributes another 16.5%. This is a very different bowling environment from the first 30 overs.

Figure 1. Share of deliveries bowled by each bowling option in men’s ODIs since 2019. The distribution shifts towards the weaker bowling options during overs 31–40, before moving sharply back towards the frontline bowlers at the death.

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I think there is a fairly simple reason for this. A captain has 50 overs to find from his attack. The fifth and sixth options, often an all-rounder or someone in the side who isn’t primarily there for his bowling, have to get their overs in somewhere. Overs 31–40 appear to be where a lot of those overs are being found.

If I group the first four bowling options together and compare them with options five and six, the difference becomes even clearer.

Figure 2. Combined usage of bowling options 1–4 and 5–6 across an ODI innings. Overs 31–40 provide the greatest exposure to the fifth and sixth bowling options.

Options five and six bowl just 24.8% of deliveries in the first 30 overs. During overs 31–40, that jumps to 35.8%. By the final ten, it is back down to 23.2%.

So, for ten overs, more than one in every three balls is coming from the fifth or sixth bowling option.

Now this is where it gets interesting for me.

If this is the phase where batting sides get their greatest access to the secondary bowlers, surely we should see them being attacked?

We don’t.

Figure 3. Combined run rates conceded by the first two and fifth/sixth bowling options. Despite their increased exposure in overs 31–40, the secondary options concede just 5.35 runs per over.

The fifth and sixth bowling options concede at only 5.35 runs per over between overs 31 and 40. For the phase where these bowlers are at their most exposed, with only four fielders allowed outside the circle, that feels remarkably low. Move further into the innings and the same category of bowlers concedes 6.17 per over. In the final five, it shoots up to 8.23.

Teams are clearly willing to take more risk later. Once they get towards the final ten, and especially the final five, everyone knows what is coming. They’ll throw their bats at everything in the final 5.

But why wait? Think about the trade-off.

In overs 31–40, I can potentially attack the fifth or sixth bowling option while there are only four fielders outside the circle.

Or

If I wait until overs 41–50, the fielding side gets another boundary rider.

And if I wait even longer, until the final five, I am increasingly likely to be facing the bowlers the opposition captain has specifically saved for that job. And, if it reverses, which it can due to the new rule, it’ll be even more difficult to hit.

So we seem to have arrived at this slightly strange situation where batting teams are being given perhaps their best combination of bowler access and fielding restrictions, and are still treating the period relatively conservatively. I think there are runs being left here.

So, Who Is Actually Using This Window?

If overs 31–40 really are giving batting teams something extra, then the obvious next question is: who has actually figured it out?

I looked at how the ten teams from the 2023 World Cup have batted in this phase since the start of 2023. And there isn’t one common approach. In fact, the differences are quite revealing.

Figure 4. Overall strike rate against non-boundary strike rate in overs 31–40 since 2023. Bubble size represents batting average.

India, for example, have the highest non-boundary strike rate of the ten teams. England score faster overall, but India keep the innings moving better when the boundary isn’t there. Pakistan aren’t far behind on either measure.

That matters in this phase. I don’t think attacking overs 31–40 simply means hitting more fours and sixes. The better version is being able to push the baseline up even between boundaries.

The risk side tells another story.

Figure 5. Balls per boundary against balls per dismissal in overs 31–40. Moving towards the top-left represents more frequent boundaries while losing fewer wickets.

India are almost on their own here. They find a boundary roughly every 9.6 balls, but lose a wicket only once every 41.6 balls. England find boundaries even more frequently, while Pakistan, New Zealand and South Africa sit in a similar attacking cluster.

But the fourth graph is the one I find most interesting.

Remember what we established in the previous section. This is the phase where the fifth and sixth bowling options are most exposed. So forget overall scoring for a moment. What happens when teams face those two bowlers specifically? Now the separation becomes much clearer.

Figure 6. Strike rate and batting average against the fifth and sixth bowling options during overs 31–40.

Pakistan have probably been the closest to what I am talking about. Against options five and six, they score at a strike rate of 107.6 while averaging 47.

England are at 104.8 and 44.5. India score slightly slower at 101.2, but average an outstanding 51.1. New Zealand are aggressive too, striking at almost 104, although they lose wickets more often.

That top-right part of the graph is where I would want to be. But then look at everyone else. Australia strike at just 91.9 against the fifth and sixth options. South Africa, despite being one of the better overall scoring sides in this phase, are below 100 against them. Sri Lanka are at 93.0. Afghanistan, Netherlands and Bangladesh fall even further away.

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So, Are ODI Teams Attacking Too Late?

Think about ENG vs IND’s Lords’ ODI, IND had made a small comeback with the ball in the 31-40 phase picking wickets and not allowing ENG openers to whack the ball. When the 1st wicket fell, I was surprised to see Joe Root coming out to bat. Better strategy could’ve been sending an enforcing batter before Root, on that day, which might have increased the scoring rate. It felt like ENG left many runs on the field, however due to death bowling weakness Buttler outrageously scored in the final 4 overs. But, this won’t happen everyday. This mistake from them might have actually led to India winning the series.

These aren’t the overs where I would be happy simply getting through at five or five-and-a-half an over. If the fifth bowler has overs left, I want to face those overs now. If the sixth option comes on, I don’t want to wait for over 46 to decide that it is time to attack because by then, the game has changed. There is another fielder outside the circle. The frontline bowlers are coming back. And under the new ball rule, waiting may become even less rewarding. Even among the best teams, I don’t think anyone has completely maxed it out yet.

And there is another benefit which won’t necessarily appear in the run-rate graph.

Suppose I attack your fifth bowler in the 33rd over and your sixth bowler in the 35th. Suddenly, you have a decision to make.

Do you let me continue attacking the matchup?

Or do you bring one of your best bowlers back?

If you choose the second option, I have already achieved something. I have made you spend a resource earlier than you wanted to. An over from your best bowler in the 36th is one fewer over he can bowl when I am trying to launch at the end.

That is why I don’t see overs 31–40 as simply another chunk of the middle overs anymore. I almost see it as a Batting Powerplay hiding in plain sight.

If you made it this far, thanks for reading. If you found this interesting, do share it around.

I write about cricket strategy, analytics and the ideas hiding underneath the game. Subscribe along if that sounds like your thing.

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Between 2021 and 2024, Kohli was still one of the most feared batters in the IPL. Yes, he had faced a slight dip, but the sheer volume of runs he scores every season make him a dependable batter. His average of 40, a strike rate of 130, all of it was still formidable. But somewhere in the rapidly changing nature of the T20s, his method was becoming slightly negative towards his team.

As a batter in T20s, your goal should be to play in a way that maximizes the Win% of your team in all the phases. The issue with Virat was bowlers had quietly been working out his method: Virat Kohli at the start of an innings was very different from Virat Kohli once he’d settled in. He would leave the big shots until he had seen the bowler and found his rhythm.

His 1st 15 balls in every game was a crucial window for the bowlers, a window where bowlers wanted to bowl at him rather than the opposite batter, to minimise the runs lost in the Powerplay. (Win Percentage Added = 2.29 % per match IPL 2021-24 vs 3.56 % in IPL 2025-26)

The strike rate in those first 15 balls of an innings in 2021-24: 130.3, with a dot ball percentage of 42.8%. Nearly one in two balls was a non-scoring delivery. For a player of his calibre, that was a number opposition tried to exploit.

By 2025-26, that same first-15-ball strike rate had become 156.2, with dots down to 34%. A 26-run improvement per hundred balls, from the moment the first delivery is bowled.

What changed? Everything. And it starts with one shot.

The Numbers Are Worth Sitting With First

Before the story, let’s establish the scale of the transformation which Virat completed. Overall, Kohli went from a strike rate of 129.84 and average of 40 in 2021-24 to a strike rate of 153 and average of 52 in 2025-26. Dot balls fell from 35.6% to 30.0%. He went from hitting a boundary every 6 balls to every 5 balls.

Those are meaningful improvements for any batter. But if we closely observe the background of the story, what was happening in his team RCB, and then what was the impact of Virat’s newfound method on the team’s performance, this looks even more important. The biggest takeaway is how Virat has helped them to win the Powerplay battle. 65% of the IPL matches are decided in the Powerplay.

In the first six overs, Kohli’s strike rate went from 134.62 to 156.52. His dot ball percentage in the Powerplay fell from 43.02% to 35.12%. He went from hitting a boundary every 4.71 balls to every 3.83 balls.

This is like a completely different batter, because the stats do suggest that. But averages and strike rates are the result. The story is in what changed underneath them and that is one of the more fascinating reinventions I’ve seen a batter undertake at this stage of a career.

Chapter I: The Plan The Bowlers Had

Let’s go back to 2021-24. Cricket teams were slowly moving the analytical way now. (I think they’re still slow to this day...)

Against Kohli, the map showed something useful: back it up short.

The real plan was subtler. Short of a good length. That irritating back-of-a-length corridor that sits between the drive and the pull. Where you can’t really get under it, and you can’t really get on top of it either. Where you’re either pushing tentatively through mid-off, or cutting behind point, or steering to third man.

In 2021-24, when Kohli faced short-of-good-length deliveries from pace bowlers, he was doing exactly that. His response was scattered: steered (14.2%), cut (12.5%), flicked (12.5%), cover drove (8.2%). The pull — the response that actually punishes this length appeared in just 14.9% of those balls.

The overall strike rate on short-of-good-length pace deliveries: 119.9. The pacers had known where to ball and they used to exploit it pretty well.

Chapter II: The Response

Somewhere between 2024, Virat Kohli started treating short-of-good-length pace as something to attack.

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He was limited to third man and cover region against back of a length deliveries but now, that instead of steering those to third man he’s pulling them to square leg. He used to score 1.5% less than the league average right hander but now scores nearly 4% more than him in square leg region against short of a good length deliveries.

In 2025-26, the pull shot on short-of-good-length pace went from 14.9% to 25.7% of deliveries. The steer that bleeds singles and dots in roughly equal measure fell from 14.2% to 5.7%. Cut shot increased from 12.5% to 18.1%.

Also, he’s pulling with significantly better control: Pull shot control vs pace rose from 61.5% to 68.1%.

What This Did to Right-Arm Fast Bowlers in the Powerplay

This is where the story becomes remarkable as we understand how this contributed to RCB’s batting strength. RCB started to win more in the Powerplay phase due to Virat’s adaptation.

Against right-arm fast bowlers in the Powerplay between 2021 and 2023, Kohli scored at 144.4. Already above the league average of 123.5. The RAA (Runs Above Average) was +27.8.

By 2024-26, that number became 176. The league average in the same conditions moved from 123.5 to 144.3, a 21-point improvement. Kohli’s moved 32 points. His RAA against right-arm fast bowling in the Powerplay jumped from +27.8 to +32.5.

The wagon zones tell the story of how. In 2021-24, his primary scoring region against RF in the Powerplay was Covers (22%).

By 2025-26, covers had dropped to 15%. But Square leg jumped from 9.6% to 12.73%. Mid wicker, Mid On jumped too.

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This is what the pull shot transformation looks like on a wagon wheel. When you start pulling short-of-good-length deliveries with purpose and control, the scoring goes leg-side and square. You also start forcing bowlers to bowl fuller to avoid the pull and when they do, you can drive them all over the field from Cover to Mid on. What was once a back-of-a-length plan is now a back-of-a-length problem for the bowler.

The Domino Effect: Every Length Becomes a Threat

When bowlers can’t bowl back of a length to Kohli, they have to bowl full. And when they bowl full to Virat Kohli, they’re back on his turf. The cover drive, the on drive, the straight drive all were unleashed.

His strike rate against good-length deliveries outside off stump went from 116.3 to 138.0, with the dot ball percentage falling from 39.8% to 33.1%.

The dismissals confirm the power shift. Against Right arm Fast bowlers in the Powerplay in 2021-24, he was dismissed 6 times in 133 balls. In 2025-26, just 2 times in 86 balls. And both of those dismissals — one pull caught at mid-on, one steered catch behind were uncontrolled rather than technical failures.

Chapter III: The First Fifteen, No Longer A Window

In 2021-24, Kohli’s first 15 balls of an innings produced a strike rate of 130.3 with a dot ball percentage of 42.8%. After ball 15, his strike rate was 129.4 which is almost identical. He was batting at roughly the same pace throughout, having spent the first phase settling in. He couldn’t go after it from ball one.

In 2025-26, the first 15 balls produce a strike rate of 156.2 with dots at 34.0%. After ball 15, it’s 136.6, actually lower. But he attacks from ball one now. The pull shot weaponization is part of why: if the fast bowlers come at you in the Powerplay with back-of-a-length plans, you are now equipped to punish them immediately, from the very first delivery. And the SR after the Powerplay isn’t an issue but a necessity, Virat’s attacking job is done after the Powerplay because Virat playing attacking shots in the middle overs, taking risk, isn’t the best solution for RCB, rather it’s the role for the likes of Padikkal, Patidar, Krunal and Jitesh. T20 is a role-playing game. Virat plays the second fiddle and that is important for RCB to win more.

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Chapter IV: The Middle Overs Puzzle

Against spin bowlers in middle overs between 2021 and 2023, Virat Kohli was scoring at a strike rate of 100.0. The league average in that same phase was 123.4. He was 23.4 points below the average IPL batter against spin in the middle overs. He was scoring at a pace that, against the background of how modern T20 batting has evolved, was not ideal for any team he was batting in.

In 2021-23, against spin in the middle overs, Kohli was dismissed 8 times. He was hitting a boundary every 14.2 balls, which was poor.

What specifically troubled him more was Off spin, not many talk about this and yes it is surprising as he’s a Right hander, but Off breaks troubled him.

Against off-spinners in 2021-23, Kohli was scoring at SR of only 82.1. The league average was 121.7. He was sitting 39.5 points below the league average against off-spin. Four dismissals in 56 balls and a dot % of 39.3%.

In the middle overs specifically where off-spinners operate most effectively his SR was 79.1. Against a league average of 118.7. A difference of -39.7 percentage points.

These are ordinary numbers to have against any bowler type at this level. To have them against off-spin, a bowler variation that the right handed batters are usually expected to dominate, tells you everything about the nature of the plan.

The Pitch Maps Tell the Story

The pitchmaps against off-spin across the two eras are almost uncomfortably clear.

In 2021-23, the ball that got Kohli out repeatedly was a very specific delivery: good length, outside off stump.

By 2024-26: zero dismissals against off-spin. The bowlers are still bowling there but you can see the volume of deliveries have have shifted from outside off stump to on the stumps but they’re not getting him out anymore. Also, off spinners are bowling more fuller to him now, and this change is because Virat forced them to do so.

Chapter V: The Slog Sweep - A Shot That Wasn’t There

For Rohit Sharma against left-arm spin, it was the sweep transformed from a minor part of his game to a weapon that accounted for 28% of his runs against Slow Left Arm bowlers. Virat too did something similar.

Against all spin bowlers in 2021-23, the slog sweep contributed 4.1% of Kohli’s runs with a Control rate of 50%, which was of course low.

However, by 2024-26: the slog sweep contributes 17.2% of all his runs against spin. That’s a 4x multiplication in usage and run contribution from a single shot. That shot is why the off-spin plan stopped working.

When we close our eyes imagining Virat playing against off spinner, we can just naturally envision him driving the good length ball towards long off for a single. But now when you bowl there the slog sweep scores off those deliveries and forces the bowler to bowl more fuller, and when they do that they play into the hands of Kohli.

Between 2021-23 and 2024-26, Kohli’s runs in Mid wicket have doubled. His leg-side run percentage against spin in the middle overs (square leg plus mid-wicket combined) went from 37% to 56%. Against a league average that barely moved 29.0% to 29.3%, his leg-side dominance against spin went from +6.3pp above league to +13.7pp above league. The mid wicket which contributed 25.53% of his runs against off spin, now contributes 52.87% which is more than half.

The Bowler-Type Picture: The Only Plan Left

There is one area where Kohli is still below league average: Slow left arm bowlers. In 2021-23 his SR against them was 100.5, league was 123.5. In 2024-26, he’s at 122.3 against a league of 138.2. Below league in both eras, though the deficit narrowed from -23pp to -15.9pp.

The slow left-arm plan still holds some value. However, in RCB this changes. He’s playing with partners like Padikkal, Patidar and sometimes Krunal in the middle overs which either keeps the SLA out of the game or is taken down by his partner. This is a proper partnership.

He’s found the balance that T20 cricket at the highest level requires: don’t lose the wicket in the middle overs and let the Powerplay do the damage. Virat is winning the battles in the Powerplay with such dominance that the game has often already tilted by the time spinners get their extended runs.

Why This Matters

He identified a specific, structural problem in how bowling attacks were setting him up. The back-of-a-length plan and the tentative start. He kind off thought about this from the opposition’s perspective. That realization was enough for him to adapt his batting which now significantly contributes in RCB’s winning machine. He saw how teams were setting up for him, and he changed before they could keep using the same plan. That is the point of the whole piece.

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That lesson, it turns out, applies pretty cleanly to T20 cricket. I know that Cricket and Pokemon are not 2 things which one can associate closely and even if you have no idea what Pokemon is stay with me, it’ll be fun. Maybe playing a bit too much Pokemon Showdown ignited that curiosity to search in the cricket data and look if the types exist. And, before any game, identifying the opponent’s current type and checking historical records against it can help teams make smarter decisions about playing XI, tactics, and approach.

The numbers from thousands of T20 matches tell a surprisingly clean story: certain team profiles beat certain other team profiles at rates that are statistically hard to dismiss. Some matchups are genuinely “super effective.” And just like in Pokemon, a team that looks like a loser on paper can still win, but only if they change the way they play.

Let’s build this up from the beginning.

The Type System: What Kind of Team Are You?

I split teams into four batting types and four bowling types.

Batting Types: A, B, C, D

Powerplay, is the heart of these profiles. But why is the Powerplay so important?

Since 2023, teams scoring more in the Powerplay win 65.89% of their matches.

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Every team gets a batting label based on two things: their run rate in the Powerplay and their run rate in the middle overs and death. Both are measured relative to the league average in that specific competition and year.

The four quadrants that come out of this are:

A: Fast starters who fade. These teams score above the league average in the Powerplay but drop below it through the middle and death overs. They come out swinging, take full advantage of fielding restrictions, and then tend to consolidate.

B: Slow starters who finish strong. Below average in the Powerplay, above average later. These teams tend to be patient early, take time to read the surface and conditions, and then build into the innings. Often their best hitters bat at four, five, six.

C: Strong throughout. Above average in both phases. These teams are hard to plan against because there is no obvious window where the bowling side gets a free phase.

D: Weak throughout. Below average in both phases. This is not necessarily a disaster, as we will see, because a D-type batting team can still win matches if their bowling is elite. But it does mean they are regularly starting with their bowling under pressure.

Bowling Types: A, B, C, D

Here the two measurements are Powerplay bowling strike rate and overall economy rate across the full innings.

A: Powerplay wicket-takers who leak runs. They strike early and often in the Powerplay. They disrupt the top order, but they give away runs across the innings.

B: Economical but not threatening with wickets. They dry up the scoring without creating breakthroughs early.

C: Excellent at both. Low Powerplay strike rate and low economy. The complete bowling attack.

D: Poor at both. Expensive and wicketless. When a team has a D-type bowling attack, they are essentially hoping their batting bails them out every single time.

The Full Type Code

A team’s full type is written as two letters: one for batting, one for bowling. So a team labelled CC (strong batting throughout, wickets and economy in bowling) is the complete package. A team labelled DD (weak batting throughout, expensive and wicketless bowling) is in structural trouble. Everything else sits somewhere between those extremes.

Where IPL 2025 Teams Actually Landed

The Top teams of the last season- RCB: CC PBKS: CA MI: CC GT: AB

Do the Types Actually Predict Anything?

Here is the overall win percentage by type across all matches in the dataset:

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The spread runs from 66.8% down to 19.9% which isn’t noise. When a type is winning nearly 67% of the time across more than 2000 matches, the type is capturing something real about team quality and profile.

But the overall win rate is not the interesting part. The interesting part is what happens in specific matchups.

The Matchup Chart

In Pokemon, type advantages work on a simple scale. An attack is either super effective (does double damage), normal (standard damage), not very effective (half damage), or has no effect at all.

I built an equivalent scale for T20 team matchups.

Here is what the data says for each type.

Some Key Profiles for Better Understanding

CC (Strong batting throughout + Complete bowling: wickets and economy)

The CC type is as close to a legendary Pokemon as this dataset has. Against nine different types, it wins more than 55% of the time. It is super effective against virtually everyone. Only against itself and the other top-tier types like CB (strong batting, economical bowling) and BC (late flourish in batting, complete bowling) it doesn’t have a decisive edge.

The one type that does not just roll over for CC is BC (late flourish, complete bowling). BC gives CC a real contest because the complete bowling attack limits CC’s strong batting enough to keep things competitive. CC still wins the majority, but the margin compresses.

CB (Strong batting throughout + Economical but not a wicket-threat in bowling)

CB profile does not bowl teams out. It starves them instead. And when paired with strong batting in both phases, it wins because the opposition simply cannot score enough runs to matter. I

The catches: CB is not very effective against CA (strong batting, powerplay-wicket bowling) and CC (strong batting, complete bowling). Against teams that can score freely and threaten wickets early, CB’s miser bowling is not threatening enough to take control.

BC (Late Flourish in Batting + Complete bowling: wickets and economy)

BC is the type that wins through bowling. Moderate batting means they will not always post imposing totals, but their bowling is complete enough to defend or restrict effectively.

AC (Fast-start batting + Complete bowling)

AC is arguably the most interesting type in the dataset because it is the classic unpredictable profile that actually comes out ahead overall. Fast-start batting means AC teams go hard in the Powerplay. Complete bowling means they can take wickets and control runs. The combination makes them genuinely dangerous.

AC is not very effective against AB (fast-start batting, economical bowling), BC (late flourish in batting, complete bowling), CB (strong batting, economical bowling), and CC (strong batting, complete bowling). Consistently strong batting types with any bowling control eat into AC’s attacking style.

DA (Weak batting + Powerplay wicket-taking bowling)

DA is perhaps the saddest type in the chart. The bowling can take wickets in the Powerplay, which should theoretically create pressure. But the batting is weak throughout both phases, which means even when the bowling performs, the total being defended is almost never enough. DA is not very effective against almost everything in the dataset. DD (Weak batting + Poor bowling)

DD (Weak batting + Weak bowling)

DD is the base Magikarp of this system. Not very effective against everything, super effective against nothing. 19.9% win rate across 1365 matches.

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The Lesson That Actually Applies Here

Here is the thing about Pokemon that made it more than just a game about hitting stuff: even the worst type matchup was survivable if you played correctly. A Charizard (Fire) against a Blastoise (Water) was not an automatic loss. It was a hard matchup that required a different strategy.

T20 cricket works the same way. The type advantage data tells you the baseline probability. It does not tell you the match is over before it starts.

Here is a beautiful real example from the 2026 IPL.

CSK vs KKR: A DD Type CSK Beats an AD Type KKR by Becoming an AC Type

For the whole of IPL 2026 before the game, CSK had been a DD type. Weak batting throughout all the phases, poor bowling.

KKR for the season were an AD type. Fast-start batting (Powerplay run rate comfortably above average) but poor bowling throughout. The AD type historically beats the DD type pretty comfortably. The data across all of T20 cricket says AD teams win against DD teams 61.8% of the time. A DD team going into that matchup should be worried.

And yet, CSK won.

Look at what CSK actually did on the day. Their match Powerplay run rate was 11.33, and their middle-over run rate was 7.79. Their match bowling strike rate was 18.0 with an economy of 7.65. On the day, CSK’s match type was AC. Fast-start batting (A), complete bowling (C). Wickets and control in the same bowling performance.

AC against AD. That is a matchup where AC wins 54.7% of the time. Completely different probability from the season-level DD vs AD picture.

What this means is that the type system tells you what you need to change. CSK played with a specific plan to attack the Powerplay with the bat and bowl for wickets up front. They changed the Charizard’s moveset. They used moves that were effective against a Water type. And it worked.

This is the analytical gift of the type system.

If you know your type is disadvantaged against your opponent’s type, you know which aspects of your game need to flip on match day. You do not need to be a CC type permanently. You need to play like one for three hours.

The IPL 2026 Matchup Table

Now let’s look at how the 2026 IPL season started through this lens. Each match below shows the two teams’ types for the season, the historical record between those two types, and what actually happened on the day.

What This Is Really About

What the type system does is it gives you a structural picture. It tells you, before a ball is bowled, which team has a profile advantage. It tells you how often that advantage has translated into a result historically. And most importantly, it tells you which dimensions of the game the disadvantaged team needs to change if they want to flip the probability.

If you are a DD type playing a CC type, you are not trying to out-CC them. You are trying to be an AC type for one day. Or a BC type. You are looking at the matchup chart and finding the moveset that gives you a fighting chance. You are doing what every kid with a Charizard eventually figured out: the type you are and the type you play like do not have to be the same thing.

The data across thousands of T20 matches says the types are real. Before any game, identifying the opponent’s current type and checking historical records against it can help teams make smarter decisions about playing XI, tactics, and approach. That combination is what makes this worth knowing.

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His strike rate fell from 176.35 to 115.49. His average dropped from 37.29 to 9.11. His death-overs strike rate, which had been one of his strongest weapons, came down from 197.53 to 117.39.

He has gone from being a genuine finisher to someone who is getting trapped before he can do real damage. The strange part is the numbers do not suggest that he is batting recklessly. They suggest the opposite. Control going up is not what you usually see when a batter is losing his game.

The data says that he is trying to do the right things and still not getting rewarded for them. That is what made me look deeper.

The numbers

That last row says a lot by itself. In IPL 2025, he had at least some innings where he could settle in and build. In 2026, that window has basically vanished. He is getting cut off before he can turn an innings into something meaningful.

So the first question is obvious: what changed?

The real shift came from pace

The batting numbers matter, but the more interesting part was the way pace bowlers started going after him.

In the first 15 deliveries he faced from pace in IPL 2025, 20.2% of the balls were full. His strike rate on those deliveries was 330.

In 2026, that full-ball percentage dropped to 13.6%.

At the same time, good length and short of good length together jumped from 38.4% of balls to 59.1%.

That is a serious tactical adjustment. We can certainly do some statistical tests and arrive on the conclusion that it is not a random change in lengths. It is a deliberate move into the part of the length spectrum where he is being asked to make decisions earlier, defend more often, and work harder for his scoring. In other words, bowlers are not giving him the ball he wants. And that is maybe pointing out to evolution in strategy. The best ball not being a classic good length delivery but something that the batters isn’t expecting or doesn’t want to face.

Against spin, the picture is different. Spinners actually got a little fuller to him in 2026. Full balls rose from 33.3% to 36.4%, and he still scored at 262 on those deliveries. That is not where the issue lies.

The issue is pace. And more specifically, it is pace that has taken away the lengths he used to punish.

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The same shot is no longer bringing the same reward

One number stood out to me more than the rest.

His strike rate on controlled deliveries was 224 in 2025. In 2026, it was 143.4. That means the same kind of shot, when played cleanly, is giving him 36% fewer runs than it did last season.

That happens when the field is already waiting for where he likes to hit it.

Even more telling was the change in his dot-ball percentage on controlled deliveries. In 2025, 22% of those balls were dots. In 2026, it rose to 37.7%.

The scoring zones have narrowed

Once I split the runs by zone, the pattern became even clearer.

Against pace in 2025, Jitesh’s runs were spread across the ground. Mid-wicket, point, fine leg, cover. There was no single area that dominated completely.

In 2026, that changed. Cover became the main scoring zone. It took 30.4% of his pace runs. Point fell from 19.3% to 10.9%. Fine leg also got reduced. So instead of being a batter who could make teams defend all around the ground, he has become one who is being funnelled into a much narrower scoring arc.

That matters because T20 batting is not only about what a batter does well. It is about how many different threats he gives the fielding side. Right now, Jitesh is giving them fewer.

Against spin, he is still okay. The problem is the pace plan.

He is being bowled like a batter who has one main scoring lane

When I compared his pace numbers with the league average for right-handers, the pattern became even harder to ignore.

The average right-hander in IPL 2026 scores 13.6% of his runs against pace in the cover and extra-cover region. Jitesh is at 30.4%. That is a massive difference. Teams are not letting him go straight. They are not allowing him to hit freely through the V. They are loading the off side and making cover the release valve. That is important because once a batter starts leaning on one scoring lane, the fielding side can build around it.

In 2025, he still looked like a batter with multiple options. In 2026, the options have narrowed.

The pitch maps show the plan

The dismissal map tells the story even more sharply.

In 2025, when pace bowled to him, there was no obvious pattern in his dismissals. Different lengths, lines. He was still hard to pin down. Bowlers are clustering in a specific area: good length outside off stump. The field has been set to take away the straight boundary and force the shot into a crowded side of the ground.

The slog has almost disappeared

One of the clearest signs of this shift is the slog.

In IPL 2025, Jitesh played the slog and struck at 244. More importantly, it forced bowlers to think twice about length. If a batter can slog, then the full ball exists as a risk. In 2026, the slog has barely been there at all. Two balls and Zero runs.

And I do not think that is because he stopped wanting to play it. I think it is because pace bowlers stopped offering him the kind of delivery that makes the shot possible. No real full ball, just good length on off stump, where the slog is difficult to execute.

His first ten balls tell you a lot

The first ten balls of his innings are where the shape of the problem becomes obvious.

In 2025, his early scoring had variety. Cover drive, pull, cut, on-drive, several different options. In 2026, the scoring plan is much easier to see.

Cover drive and on-drive now make up more than 60% of his early scoring attempts. He is walking in with one or two visible scoring routes, and the opposition is building around that. The early dot-ball pressure is also much worse. In the first five balls of 2026, the strike rate drops sharply, and several innings are already under threat before he has even had a chance to get through the first layer of the bowling plan.

By the time he gets to balls 11 to 15, too many of those innings have already been cut short.

What is actually missing

This is where I do not think the answer lies in telling him to “bat better.” The control numbers already tell me the technique is not broken. What is missing is variety.

He must bring back the threats that make bowlers hesitate. The straight option has to come back. Even one on-drive a game changes field placements. It pulls someone out from the off side and opens up the rest of the ground.

The slog has to remain a live possibility. The moment the bowler believes it is on, the length changes. That was the threat he had in 2025.

And in the first few balls, he probably needs at least one scoring option that does not fit the pattern teams now expect. What the data shows is that Jitesh is being planned against consistently, and the plan is working because the attack has become predictable.

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There’s a version of Suryakumar Yadav that made cricket look unfair. He was India’s first genuine Modern-day T20 Batter with High AVG and High SR both which was rare. His 360 degree batting style caught the eyes of experts around the world. But, today that same 360 degree style is causing a decline in his performance graph.

Something has shifted. And tonight, with Mumbai Indians facing a must-win against Sunrisers Hyderabad, understanding what that shift actually is matters more than just saying he’s out of form.

The easy story is that SKY has lost touch. But, I tried to find what exactly is wrong with SKY?

The answer is ‘Innovation’

Innovation is the most nuanced skill because it is not about runs alone. It is about options. Something which was hallmark of SKY’s batting excellence.

Innovation here is framed as contextual shot diversity. Instead of looking at runs scored in certain regions or how many unorthodox shots a batter plays, Innovation, if it has to have any meaning in T20 cricket, then it has to be helping the batter to play the field.

For every bowling situation defined by line, length, bowling type, and batter handedness, we observe how scoring shots are distributed across the league. That distribution represents the typical solution space.

We then look at each batter.

Within the same situation, how many different scoring solutions do they actually use? Are they predictable or flexible?

Think of it has a student having a number of ways to solve a math problem, and now depending upon the time he has in his exam, he choses a feasible methodology. In the same manner, the question is the ball itself, and time represents the field placement, so a batter who’ll have more answers to that ball will end up getting a long-term success.

This is measured using an effective number of shots concept derived from entropy. In simple terms, it estimates how many distinct scoring options a batter genuinely uses rather than how many appear once.

This Innovation score has the answer to our query on “Why is SKY unable to perform?”

SKY who used to be that kid in an exam who had multiple answers to each question, today is struggling as his solution space is shrinking. He now doesn’t have the luxury of having multiple methods against every question.

I compared his Innovation in the 2 timeframes, the duration from IPL 2024 tio IPL 2025 and after IPL 2025 to present day.

The result:

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This is a solid result. Reason is simple: His Innovative shots aren’t working the way they used to.

For eg, the sweep which contributed to nearly 35% of runs against spin in 2024-25, does just 22.6 % post IPL 2025.

Also, the control is missing. The control % dropped from 80% to 68%.

But now, his solution space has shrinked. The innovative shots were of value because there was an element of surprise, he used to play conventional shots and then these shots added a differential layer that made him great but now, the primary layer is missing, and the captains too are smart to find ways to restrict his ‘Supla Shot’, Sweeps, Flicks over Fine leg, and so on.

Against spin, his % of runs in the ‘V’ i.e is Mid On + Mid Off has come down from 30% to 22% which is a big drop, and hence, his sweeps, aren’t getting him runs, because they end up being a lone method to solve the question the bowler is asking and since the fields are set for those shots, it’s been difficult for Surya to score.

What he does in 1st 15 balls is very interesting

The % of balls he used to play varieties of sweeps has risen in the 1st 15 balls he faces post IPL 2025, and the % of balls he plays in the region from covers to Mid on has reduced significantly. He’s looking to play those same innovative shots early on and the opposition wants exactly that and because of the reduced solution space, he’s unable to get starts and is getting out early.

The solution is not something very drastic in nature. It’s simply getting back the element of surprise involved in his innovative shots and not making them his primary weapon. His primary layer of extra cover inside out shots, on drives, flicks must come back. Then, over these he should add the differential layer of innovation, and then we’ll get back our SKY again!

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If you’ve followed me for some time, you know I don’t like the use of batting positions in T20s. There has to be an Entry Points Optimized Batting strategy which maximizes the Win% of the team. So let me show you exactly what MI are missing and how they fix it.

First things first. The Powerplay.

MI have a good opening pair of Rohit and Rickelton. There is always a tussle to play de Kock instead of Rickelton, but they are trusting Rickelton and rightly so. But still, this batting pair needs a Powerplay hitter at number 3 when the first wicket falls within the Powerplay.

But why is the Powerplay so important?

Since 2023, teams scoring more in the Powerplay win 65.89% of their matches.

Look at the 2025 IPL Powerplay table.

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Only 4 teams had the R (ratio of Runs Scored/Runs Conceded) above 1, meaning they scored more than they conceded in the Powerplay. 3 of those 4 qualified for the playoffs. The one that didn’t have R>1 but still qualified, the Gujarat Titans, had the best average in the Powerplay and accelerated later into the innings. But this approach which worked in the past isn’t ideal anymore.

Since the last 2 seasons, the game has changed so much that you need a designated hitter in the Powerplay. KKR 2024 had Salt and Narine. RCB 2025 had Salt too.

And MI, they have to add Naman Dhir in the Powerplay. He’s in a great touch and the amount of damage he can do is scary during that time.

MI do have Suryakumar Yadav and Tilak Varma but they need someone who can do that Powerplay impact fearlessly. Look at the T20 data since 2024.

Naman Dhir surpasses both Surya and Tilak in the Powerplay. MI somehow needs this scary impact in the Powerplay which they’re missing right now. Naman can use the field restrictions very well and use that to his advantage. They need this Designated Hitter for the Powerplay which will boost their R.

Even more so because in T20s, the Powerplay is becoming unmissable. Playing it well is so necessary that even though a good Powerplay doesn’t guarantee a win but if you mess up in the Powerplay, whether with the bat or the ball, you’ll be blown away. The game can effectively end in the Powerplay itself.

So, if Rohit and Rickelton have a great first wicket partnership which continues long after the Powerplay, SKY goes out to bat. That’s how the entry points work. But if the wkt falls in the Powerplay, MI should send Naman to boost the Powerplay usage.

MI’s Untapped Middle-Order Spin Hitter

Now, one more role, or rather one more player, that MI are not using well. They’re using Pandya or Tilak at 5. But just look at this.

Since 2024, in T20 cricket, batters at position 5 face one of the highest percentages of spin bowling of any batting position. At positions 1 and 2, around 34% of balls are from spinners. By position 5, that number is 45%. Nearly half the deliveries faced are spin. This means that the player batting in this phase around 10-16 overs must be a remarkable player of spin to maximize the team’s score.

This tells you that the role of a spin hitter is absolutely critical at number 5. Shreyas Iyer played that role for PBKS last season. Patidar did it for RCB. MI do not have any such player in that role right now.

So Sherfane Rutherford walking out ahead of Hardik and Tilak is crucial.

Tilak Varma’s strike rate against spin in the middle overs was 136 between 2023 and 2025. In 2026, in the middle overs, it has dipped to 88.57. On the other hand, since late 2025, Rutherford has completely changed his game against spin. His strike rate has seen a huge jump to 143 in the middle overs against spin, which is well above average and something MI needs desperately.

Look at the wagon charts of Rutherford against spin in 2022-24 and then since 2025.

See the change in the cover and point region increasing by nearly 7% and 9% respectively. Before, he was dependent only on the mid-wicket region with 34% of his runs being scored there.

Now if you dig deeper, the two scoring zone charts show where he plays good length deliveries on the stumps against spin. Earlier he used to target the mid-wicket zone only. Since 2025 he has a strong scoring region in the extra cover region too.

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His relative scoring zone on the same line and length against spin tells the full story. He now scores 10.4% more in the cover region compared to an average left-handed batter.

This makes him an ideal spin hitter for MI. Giving this WI power hitter a license to kill can be a massive plus. Sherfane has the potential to win games on his own.

So where does Tilak go?

Owing to Tilak’s weakness against spin, his role has to be as a pace hitter after 15 overs, finishing the innings with skipper Hardik Pandya. He has the 2nd highest strike rate in the 15 to 20 over phase against pace since 2025, ahead of even Hardik and others. Only Surya is ahead of him. That’s the role. That’s where he belongs right now to provide the finish which ultimately wins you games.

The Optimum Lineup. What it means in numbers.

This optimized lineup will give MI a boost in their Predicted Win% from 58.5% to 61%. I know that looks small. But in a league like the IPL, these are the fine margins which shape matches and possibly entire campaigns.

The players are already there. Naman Dhir for the Powerplay. Rutherford as the spin destroyer in the middle. Tilak and Hardik finishing against pace. Three specific adjustments in one optimized structure.

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After 200 T20s, bowlers usually have you figured out, that could be wth videos or some key inherent patterns. They’ve got many plans. They know your patterns better than you know them yourself.

Rohit Sharma looked at that story, and decided to do something probably nobody has done before.

What’s happened to Rohit between 2021-23 and 2024-26 isn’t just good form. It is one of the most remarkable technical and psychological evolutions you will ever see from a batter at the back end of a legendary career and the numbers make it very clear.

The Obituary That Was Being Written

Cast your mind back to 2021-23. Rohit Sharma was still Rohit Sharma - still dangerous. But there was a plan forming in dressing rooms around the world. A blueprint that captains were quietly sharing.

Left-arm pace. Slow left-arm orthodox. Attack him from round the wicket. Bowl it on stumps. Bowl it good length. Watch him fail.

And you know what? It worked. In 2021-23, against left-arm fast bowlers in the powerplay, Rohit was scoring at 103.02 SR with a RAA (Runs Above Average) of -7.85. That negative number is the important one. It means he was scoring below what an average batter would score against those bowlers. The strategy was working. The plan had legs.

Against slow left-arm orthodox? He was scoring 95 runs off 77 balls. SR of 123. Not at all ideal, also the control issues were there, the dots were piling up, and the bowlers knew exactly where to bowl.

The pitch maps from that era tell the story perfectly. Good length, on-stump line. Four dismissals in that single grid. Bowlers were practically queueing up to target that corridor. When you can dismiss one of the greatest powerplay batters in the world by bowling the same ball repeatedly, you don’t stop. You dig the trenches deeper.

The Decision Most Champions Never Make

Most elite batters, when they sense vulnerability, retreat into their strengths. They score more runs in their favourite zones. They leave the problem areas alone and hope nobody notices.

Why risk your reputation fixing something at 35 when you’ve already done everything?

Rohit Sharma made the other choice. He went to the nets. He studied the data. And he decided - at an age when most batters are thinking about legacy protection - to rebuild his game.

The centrepiece of that rebuild? The sweep shot.

Against spin bowlers in 2021-23, Rohit’s sweep was a minor part (18% of runs) of his arsenal - present, but not dominant. By 2024-26, it accounts for 28.19% of all his runs against spin in the powerplay. That’s a 10% jump. But here’s the number that really matters - the control percentage: 83.33%, which was around 65% before.

You do not play an attacking shot that contributes nearly a third of your runs against quality spin bowling with 83% control by accident. That takes hours in the nets against left-arm spinners, tweaking the trigger movement, adjusting the head position, recalibrating the risk-reward every single time.

And against slow left-arm specifically? The sweep isn’t just a shot anymore. It’s a statement.

The SLA Problem, The Rohit Solution

Let’s zoom in on slow left-arm orthodox bowlers, because this is where the story gets genuinely interesting and wort studying.

Against slow left arm bowlers alone, the sweep shot has given him a huge boost. His SR of 167 is 44 points higher than an avg batter against these kinds of bowlers.

8.2% runs in the square leg region in 2021-23 and that number has skyrocketed to 28% of runs in 2024-26. His reverse sweep and late cuts using the pace of the ball also have increased the runs he scores against these bowlers in third man region.

Now add this: that good-length delivery on the stumps that used to be his weakness? The one where four dismissals were logged in that single grid? Rohit against spin when the ball was bowled on a good length on stump line: He used to play 15% of those balls in the square leg region in 2021-23. During 2024-26 this number has risen to 21.3% which is 8.7% more than an average Right-handed batter against spin on that very line and length.

The 2 key variations of left arm orthodox are both taken to the cleaners now by him. His performance against those 2 types of deliveries has risen significantly.

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In 2021-23, when Rohit faced left-arm spin deliveries, he scored 106 runs off 100 balls. Strike rate of 106. Dot ball percentage of 40%. Balls per boundary of 8.33. These are the numbers of a batter being controlled, being bottled up, being made to work for everything.

By 2024-26: 70 runs off 38 balls. Strike rate of 184.21. Dot ball percentage collapsed to 15.79%. Balls per boundary down to 3.45.

He went from one boundary every 8 balls to one boundary every 3 and a half balls. He went from scoring at 106 to scoring at 184. The kind of transformation he had has forced the SLA bowlers to do something different against him.

So today, when you hear people talk about Axar Patel vs Rohit Sharma as a potential match-defining battle in -they’re right to build it up. Axar has dismissed Rohit three times.

But he’s playing a different Rohit now.

The numbers show the outcome. The human story is something else entirely. They don’t show what it costs, emotionally, to decide that you are not finished yet. That you will not go quietly. That the bowlers do not get to write your story.

Why This Makes the Hitman Immortal

Cricket history is littered with great batters who peaked and declined in predictable arcs. We celebrate them, file them away, and move on.

What we are witnessing with Rohit Sharma is rarer. Genuinely, historically rare. A champion who got better by confronting what he was bad at.

Not at 28. At 36 and 37, with a legacy already secured, he chose the harder path. He chose reinvention over protection.

The sweep percentage against spin going from negligible to 28% of runs. The control percentage climbing to 83%. A strike rate against slow left-arm deliveries that has gone from 106 to 184. Against left-arm pace from 103 to 137 in the Powerplay. The on-stump good-length delivery that used to be his nemesis now being flicked to square leg 8.7% more often than the average batter.

They’re evidence of a mindset that refuses to accept the ceiling that experience and age are supposed to impose.

Rohit Sharma decided to become someone new. That’s why this phase of Rohit’s career might be his most impressive.

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Now, the update.

Because here is the thing nobody tells you about pre-season predictions. You can build the most robust model in the world, validate it across years, across different leagues, account for every squad combination, and then two days before the tournament starts, a fast bowler rolls his hamstring, and a spinner fails a fitness test, and half of your playing XII assumptions go out the window.

That is cricket. And that is also why I ran the model again today, the 27th of March, one day before IPL 2026 begins. This is the most current picture I can give you.

What changed. And why it matters.

Lockie Ferguson is out for the till the halfway stage for PBKS.

Ferguson was a significant part of why PBKS ranked so highly in the model. His wicket-taking ability in the Middle overs is something very few IPL bowlers can match. With him missing the early phase of the season, PBKS’s bowling attack loses a dimension it cannot easily replace internally, but still, they’re a very good side and have decent backup options, so still they remain firmly in Top 2.

The result: PBKS drops from the top spot. MI moves to number one.

Hasaranga’s availability for LSG is genuinely uncertain.

This is the bigger blow. Hasaranga was the key overseas bowling pick that pushed LSG to third in the original forecast. A hamstring injury from the T20 World Cup, a fitness clearance pending from Sri Lanka Cricket, no NOC yet. He’s gonna miss a significant chunk of the season. And LSG do not have a ready backup who matches what he brings in terms of wicket-taking impact in the middle overs. Shahbaz is not a like for like replacement, as Hasaranga was expected to be the wicket taking spinner who they’ll use use in the middle phase with Digvesh, and this combo along with their batting explosives and Indian pace battery (Fully fit) was giving them high chances of qualifications. But, with Hasaranga unavailable for a major part, their predicted Win% had dropped.

LSG drops from 3rd to 5th. Delhi Capitals, who were always just a fraction behind LSG in the model, step into the top 4.

CSK’s decision to open with Ruturaj and Samson rather than Ayush Mhatre.

This one is pure team combination. The model had CSK’s best powerplay outcome running through Mhatre at the top, given the lack of death-hitting depth in the squad. If you cannot score big in the death, you have to maximise the powerplay. Mhatre does that. Samson opening instead changes the mathematical optimum. CSK’s predicted win percentage dips slightly as a result.

This is exactly what makes pre-season prediction so difficult. It is not just injuries. It is that IPL teams make decisions that are far from what the data would suggest. You think you know the probable XII, then someone decides to move a player up the order or drop a specialist because of a gut call, and suddenly your entire model is working off a different foundation.

What the model says right now, as of 27th March 2026:

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You would’ve noticed that there are 3 clusters in the Table of Predicted WIn%. The 1st 3 MI, PBKS and RCB remain the three strongest squads in the competition. That much has not changed. The gap between them and the rest is still meaningful. They’re having highest confidence to be in the Top 4.

Then, in the 2nd cluster, 4 teams are within 4% points, as the competition for the 4th spot is cut-throat.

In the 3rd cluster, SRH’s number dips slightly since Cummins is expected to take a bit more than halfway (And, when he returns this assumes SRH keeps playing Livingstone instead of playing both Malinga and Cummins which is not ideal.)

CSK and KKR remain the teams the model predicts to have a really tough season ahead. Unless someone has an extraordinary run in the tournament, or an uncapped player, who has little to no data has a season of his lifetime they’ll face a lot of difficulties to reach the top 4.

An honest note on what this model is and is not.

The model assumes a probable XII for each team as of today. That is the best information available right now. But IPL teams are not obligated to be logical and they often are not. A player could be rested unexpectedly. Someone could be moved to a completely different role. A new overseas signing could land at the last minute.

None of that can be forecasted. And so, these numbers carry an asterisk that never fully goes away until the first ball of the season is bowled.

What the model is good at is reading squad construction and structural strength. Who is likely to create win probability, in what phase, against what bowling type, at what venue.

Tomorrow it all begins.

The model has done what it can. Now cricket gets to do what it does.

If you are genuinely interested in how this analysis works under the hood, the full methodology piece is here. It covers ICR (Integrated Contextual Ratings). This will help you understand the basis of the model.

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Let us see how this goes.

When you watch an IPL match and a batter hits a six in the 18th over of a chase, do you actually know how much that shot was worth? Not emotionally. Not aesthetically. In terms of what it did to your team’s chance of winning that specific game?

Most cricket analysis does not answer that question. It counts the six. It adds it to a strike rate. It moves on.

That is the problem I set out to fix.

First, Let Me Earn Your Trust

I am not going to ask you to believe a 2026 prediction from a model you have never heard of.

So let me go back to 2025 first.

Before IPL 2025 started, before a single ball was bowled, I ran my model on the squads as they stood and generated a points table forecast.

My predicted top four: MI, RCB, GT, KKR.

The actual top four: PBKS, RCB, GT, MI.

Three of four correct. Champions RCB right. GT right. MI right.

Think about what that means for a moment. In a ten-team tournament where the difference between 4th and 5th place is razor-thin, the model correctly identified three of the four teams that made the playoffs - before the season began.

That is not luck. That is signal.

The Miss Matters As Much As The Hits

I got PBKS wrong. And honestly, that miss helped more than a clean perfect call would have. Why? Because it showed me exactly where the model needed to improve. And I want to talk about that honestly because I think it is the most important part of this whole piece.

A good model should not just look impressive after the season ends. It should teach you something when it misses. That is where the real work begins.

Cricket data is noisy. Very noisy. And if you work in cricket long enough, you know this already.

One breakout player, one role change, one tactical shift, one injury, and the surface numbers start to wobble. Few players from PBKS had almost no prior IPL history for the model to work with. But even in that noise, my model was still catching the small patterns that matter in T20 cricket. It is not just looking at runs and wickets. It is picking up micropatterns in impact, role, usage, and match context. The newer version now has robust ‘League Translations’ to build on cross-league performance. That is the part I trust.

What Is Team Effective ICR?

Before the 2026 table, let me explain what is actually powering this forecast.

ICR — Integrated Contextual Ratings.

It does not ask how many runs you scored. It asks how much you shifted your team’s probability of winning.

Every delivery in a T20 match has a win probability attached to it. Based on score, wickets, overs remaining, target, match context. When you hit a boundary, probability shifts. When you get out, it shifts the other way. When you take a wicket at a critical moment, it shifts significantly.

ICR measures exactly how much each player moves that needle and then normalises it against what a typical player of the same role, in the same phase, in the same league would have done.

The output is not a raw number. It is a contextual judgment. How good were you, relative to the specific situation you were asked to perform in?

If you haven’t read my article on ICR, please go through this:

This will help you understand the basis of the model.

Team Effective ICR takes this one level further. It weights each player’s ICR by their actual usage and builds a team score.

The 2026 Forecast

The model has been updated with squad compositions as of today reflecting actual player availability for IPL 2026.

Here is what it is saying:

1. Punjab Kings - Predicted Win% 59.88

Squad largely intact. Roles clearly defined. The model that underestimated them heading into 2025 is now giving them full credit and then some. Players are no longer unknown quantities. The model can see them now.

If PBKS do not finish top two in 2026, the most likely reason will be injury to a key player. The squad strength is genuinely there.

2. Mumbai Indians - Predicted Win% 58.55

Two consecutive years of being rated second in this model. That kind of consistency means something.

MI’s batting depth shows up clearly in ICR - multiple players in the top quartile of their positional groups. The bowling is the variable. A fully fit, fully motivated Burmah, Chahar and Hardik change their ceiling significantly.

ICR cannot measure fitness or motivation. That is the caveat. The squad quality is not in question.

3. Lucknow Super Giants - Predicted Win% 56.56

This is the bold call. LSG finished 7th in 2025. The model has them jumping to 3rd in 2026.

What is driving it? Their auction. They’ve used the mini auction and trades and most importantly their home venue in a better way.

4. Royal Challengers Bengaluru - Predicted Win% 55.02

The batting order has genuine depth and variety. The bowling is competent without being dominant.

RCB drop slightly from their 2025 prediction but remain firmly top four.

5, 6, 7 - Delhi Capitals, Gujarat Titans, Sunrisers Hyderabad

Win% range: 51 to 53, which is very, very close.

I am going to be honest here. These three teams are genuinely indistinguishable in the model. Two percentage points separate them. Any of them finishing anywhere from 4th to 8th is entirely consistent with their ICR scores. And, in a game like T20, this can happen.

8. Rajasthan Royals - Predicted Win% 49.40

Correctly placed in the bottom half in both our 2025 prediction and the 2025 actual table. The model sees the same structural weaknesses carrying into 2026.

9. Kolkata Knight Riders - Predicted Win% 46.46

The 2024 champions. Second bottom in this forecast.

This might be the most debated prediction in this piece and I want to address it directly. The 2026 model has looked harder at what happened to their squad since the title and the replacements for their departed players do not match the ICR ratings of what they lost.

10. Chennai Super Kings - Predicted Win% 46.23

Issues in the batting core and role overlap. Limited genuine pace in the bowling attack. Structural over-reliance on senior players whose ICR trend lines are moving in one direction.

What This Model Cannot Do

Before you take this to your fantasy league or your team meeting, be clear about the limitations.

It cannot predict injuries. A Bumrah injury changes MI’s outlook entirely.

These are not reasons to dismiss the forecast.

The model tells you about squad construction and structural strength. What happens on the 22 yards is still cricket.

Why I Am Building This

I am not a team. I am not a company. I am one person who became obsessed with a question that cricket analytics has not answered properly yet.

“Why do we keep measuring cricket performance with numbers that do not measure what actually wins matches?”

DeepCrease is my attempt to build something better.

If you are a coach, an analyst, a team management professional, or someone who makes decisions about cricket and you are interested in what this kind of analysis can actually do for you - get in touch.

The season starts next week.

Let us see if the model was right.

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For weeks teams dominate, plans work perfectly, and then suddenly the last match arrives and everything becomes about two or three very small battles.

Tonight in Ahmedabad, India vs New Zealand will probably be decided by a handful of such matchups. I spent the last few hours digging into the numbers and a few patterns started appearing. Some are obvious. Some are a bit surprising.

And one of them made me think of a very simple idea.

If Finn flicks, India wins.

Let me explain in this article.

The first pressure point: Abhishek Sharma vs off spin

One thing that stood out while looking at Abhishek Sharma’s numbers against off spin is how much the game around him has changed.

Between 2023–25, Abhishek was extremely comfortable against off spinners.

Strike rate: 162.5
Average: 36.5

But in 2026, things look very different.

Strike rate drops to 140.7 and dismissals increase sharply.

The interesting part is not just the runs.

It’s how bowlers are bowling to him now.

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Line changes

Length changes

Teams have clearly pushed him wider outside off stump and slightly shorter.

Why?

Because his strongest scoring area against spin used to be through cover.

Earlier, 38% of his runs vs off spin came in the cover region. Now it is down to 24%.

Fielders in deep extra cover especially in the powerplay have forced him to search for runs in leg side. And that is where things get tricky. Abhishek is not a natural sweeper.

Since 2024:

• Cover drive runs: 21.28%

• Sweep runs: 3.15%

So when bowlers deny him the cover region, he has to manufacture something else. That has led to a few dismissals. This is something New Zealand will definitely try.

Against Pacers too, USA had a fielder in Deep Cover for Abhishek in Powerplay, which was a great move. Over 26% of his runs in Powerplay are in this region against pace with 4 dismissals

The new-ball problem for India

India’s openers also have a tough challenge ahead. Both have had difficulties against Matt Henry and Lockie Ferguson.

Abhishek vs Henry

Balls: 4
Runs: 9
Dismissalss: 1

Abhishek vs Ferguson

Balls: 4
Runs: 1
Dismissals 1

Sanju Samson vs Henry

Balls: 10
Runs: 15
Dismissals: 2

Samson vs Ferguson

Balls: 3
Runs: 1
Dismissals: 1

Small samples, yes. But enough to show the matchup is uncomfortable. Sanju Samson also has another problem.

He has been dismissed six times against inswing from right-arm pacers.

And Henry’s biggest weapon is exactly that. So India’s start with the bat will be far from easy.

Why New Zealand might use Ferguson early

Looking at Ferguson’s powerplay numbers since 2024:

Balls: 451
Runs conceded: 523
Run rate: 6.95
Wickets: 32
Bowling strike rate: 14.1 even better than Henry who has Bowl SR around 19.

That is excellent powerplay bowling.

Henry has similar control with a run rate around 7.16.

So the idea of New Zealand using Ferguson early instead of holding him back actually makes a lot of sense especially for bowling Short Deliveries to Samson early on. He’s got both Indian openers once before.

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Santner in the middle overs

Then comes the quiet assassin. Mitchell Santner. He is not flashy, but he is extremely smart.

Since 2021, bowling over the wicket to left-handers:

Strike rate against him: 131

He does not allow runs to flow easily. And that matters because India’s middle order has multiple left-handers. So Santner’s middle-over spell could become a key phase in the match.

Why the New Zealand openers matter so much

Now let’s go back to the title.

Why did I say If Finn flicks, India wins?

Because several both NZ openers especially Finn score heavily with that shot but with exceedingly high false shot %.

Here are some flick shot numbers.

These players love scoring through the leg side. But there is a catch.

Both Cover Drives and Flicks have been double-edged sword for him.

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There are 2 clear clusters of caught dismissals inside the circle, 1. in Midwicket region via playing flick and 2. In Cover region via Cover Drive.

Bumrah needs just a subtle change in pace to induce flick/cover drive which is mistimed in the air.

I looked at false shot percentages against different lines for Seifert.

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Wide lines create the most mistakes. And that leads to an interesting tactical idea.

Axar Patel’s small tactical tweak

Tim Seifert also struggles against slow left-arm orthodox spin.

Balls: 153
Runs: 142
Strike rate: 107
Dismissals: 4

He usually scores strongly through the V region. So one possible plan for India could be slightly unconventional. Instead of attacking the stumps constantly, Axar Patel could bowl wider lines to him. That invites the cut shot.

And Seifert has already been dismissed twice trying to play that shot against that line.

Sometimes an unorthodox adjustment can change the entire contest.

Finals are decided by details

This match is not about who is the better team overall. Both sides are strong. Both have match winners. But finals rarely follow the script.

Sometimes it is just about one small pattern. A batter forced into a shot he does not love. A bowler introduced one over earlier than expected. A field placed slightly differently.

Tonight in Ahmedabad, a few such details might decide the World Cup. And one of them might just be this.

If Finn Allen gets to flick freely, New Zealand will be dangerous.

But if India can push him and the other openers away from that comfort zone… The final might start tilting very quickly.

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What I checked, and why it matters

I pulled together every Varun’s T20 appearance since 2024, then separated his innings into two simple groups. Group A are matches where, after Varun’s first over, the score was 10 runs or less. Group B are matches where the opposition scored more than 10 in his first over.

I then asked a handful of direct questions:

does his economy change? do batsmen play different shots? does his preferred line and length shift? do dismissals happen in a different pattern?

The answers are crisp, and they point at one central idea: giving away runs early forces Varun into a different game, and that different game is worse for him. He basically isn’t Varun anymore, but becomes something very different.

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• When Varun’s first over is 10 runs or less, his economy sits around 7.17, strike rate is about 10.15 (balls per wicket 10.15), and he averages roughly 12.1 per wicket.
• When his first over concedes more than 10, his economy jumps to about 10.02, bowling average to 25.8, and his wickets per match fall. In plain terms, he becomes far less effective.

What actually shifts: line and length
• Line: after a hit first over, Varun’s balls move more into the pads. The share of “on the stumps” increases by about 7.8 percentage points. Outside off decreases by roughly 7.3 points.
• Length: the share of short of a good length and yorker increase slightly, while good length proportion falls a bit. That suggests less consistency in the middle length that Varun usually prefers.
• The practical result: his “golden channel” i.e., good length pitched outside off stump gets rarer. Balls per golden channel go from 3.09 to 4.02, meaning batsmen see more balls outside his ideal corridor when he has been punished early.

These changes are not random. Chi square tests for statistical significance on line and length distributions produce p-values in the 1e-5 to 1e-4 range. That is a statistical red flag that the bowling pattern shifts meaningfully after an expensive first over.

How the batsmen adapt, and why this hurts Varun

• Aggressive shots go up. On-drive and flick share increase when he was hammered early. Defensive shots like drop. Batsmen go hunting, not defending.
• For right handers in particular, the increase in balls into the stumps moves scoring into the mid on region. Mid on share rises to 25% from 17% in this condition. That is exactly the region Varun wants to avoid; it is scoring heaven for on drives and flicks.

• Dismissal patterns change too. Caught percentages rise and bowled reduces slightly.

In other words, when Varun errs in line, batters eat him with straighter hits rather than being beaten.

Variation profile: googly vs leg break

• When games go wrong early, Varun throws a higher share of leg break attempts and seems to lean on the flick-away idea. The economy of his leg break balls rises in those matches, and the googly economy also looks worse. In short, the variations are not buying him escape routes; they are sometimes making the situation worse.
• My read: Varun is trying to fight back by turning the ball away from the stump line which is difficult and especially considering Varun is not a big turner of the ball, when precision is off, the tactic backfires.

When Varun starts the match under pressure, he stops bowling the channel that makes him dangerous. He drifts in, attempts contrarian variations, and the result is higher run rates and fewer wickets. It is not that he loses ability. It is that his profile changes from “subtle, consistent spin” into “hit-or-miss variety”, and modern T20 batters can exploit that.

Players like Varun live on subtlety. His core asset is that he gets batsmen to buy a small uncertainty, and that uncertainty turns into a mistake. But modern T20 often punishes subtlety with brute aggression, especially early on. The key for Varun is not to out-fox batsmen every ball. It is to remain predictable in his best channel while keeping the occasional surprise using variations. That is the paradox of success for craft spinners today: be both simple and rare.

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Drop a comment telling me what facet of the game you want me to deep dive into next. If you disagree, brilliant. Tell me your thoughts. That is how we have better cricket discussions.

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Both teams bat very deep. Both can outscore good bowling on most wickets. That means the battle will be won not by fireworks, but by control. In that sense the Powerplay is not just a phase. It is a decision point. If bowlers do not take early wickets, this game will tilt into a slog where individual hitting power decides everything. That is risky for India because their Powerplay numbers have slipped.

Quick reality check for the Powerplay
• 2024 Powerplay for India: about 2 wickets on average, economy 7.1.
• Since 2025 this has dropped to roughly 1 wicket, economy 8.4.
That small slide matters. Against England’s top three this could be the match.

Arshdeep Singh is a bigger story than people realise.

The raw numbers are blunt. Before Asia Cup 2025, Arshdeep was delivering at elite levels. After Asia Cup, the profile changed dramatically. Compare the two periods:
• Wickets: 53 previously, 10 since.
• Economy: 7.96 then, 9.07 now.
• Average: 23.3 then, 36.3 now.
• Strike rate: 17.5 then, 24 now.

The change in his ball mix matters.

His inswing to right handers fell from about 20% to 13%. His off cutter usage jumped from 19% to 32%.

That is not a neutral technical tweak. That is a change that directly affects matchups. Why should we care? Because Jos Buttler, even when not at his best, is notably weaker against inswing early in his innings.

In the Powerplay, Buttler’s strike rate against inswing sits around 110 and he has been dismissed at a frightening rate in that specific matchup. Translation: if India can bring the old, sharper Arshdeep to the new ball contest, they can expose a real vulnerability.

Bumrah in the second over. Seriously.

Numbers for the first two overs tell their own story.

Jasprit Bumrah still stands out: strike rate 16.8, economy 5.57, average 15.6.

Practical move: bowl Bumrah in the second over. Run the plan: new ball, early pressure, then give Axar a hand over. White ball doesn’t do much after a couple of overs so this move gives him the best chance to use the breeze at Wankhede. Giving Hardik the 2nd over against teams like WI make sense because both their openers don’t attack from the word go but here, it’s not the case. Phil Salt, especially starts from the ball 1.

Why this order? England’s top three- Buttler, Salt and Brook are all right handers. The angle, the skillset, and a second over Bumrah position give India the best chance of snagging that early scalp which would change the match.

Axar’s role is more crucial than headlines suggest

Six of England’s top eight are right handers. That hands Axar Patel an enormous tactical window. Against Brook, Axar’s previous left-arm angles to off stump have beaten Brook’s rhythm; Brook’s scoring rate drops when balls are pitched on or near the stumps compared to balls outside off.

Axar has never got him out so far and that’s because his line is still very outside off stump which he needs to correct.

Bowl stump to stump, force Brook into uncomfortable mechanics, and watch the scoreboard reflect it.

Varun Chakravarthy too can be decisive. He has dismissed Brook three times at a mean of around 15. The googly is a shape to use not to fear.

Openers and the Archer variable

Sanju Samson looked brilliant in the last game, confident and timing clean. He has a known vulnerability: short pitch. Jofra Archer has him three times in 2025 on short deliveries, and Samson’s dismissals include two pull shots against short balls.

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Against Zimbabwe, the same pattern emerged.

Surprisingly, Hope didn’t use Shamar who’s got that pace which can trouble Sanju and even when he bowled to him, he bowled fuller deliveries which is Sanju’s strength and not a single short delivery.

That is a clear blueprint for England. In contrast Abhishek Sharma has handled Archer well. If India can protect Samson early, or better yet use Abhishek to blunt Archer, the opening battle becomes far less dangerous.

Will Jacks and Adil Rashid the left-hand problem and the length contest

Will Jacks presents a left-handed threat with both bat and off spin. Beyond him, Adil Rashid defines this tournament for England. Almost half of Rashid’s deliveries in this World Cup are good length balls.

That is a predictable footprint and one India can attack. Nepal and Italy showed two different winning approaches:

Nepal attacked square of the wicket, Italy attacked straight. The lesson is simple. If you want to neutralise Rashid, you must score off his length balls in the right regions early. Do that and his 24 balls stop being a threat.

Middle overs X factor: Dube or Hardik?

Here is the subtle shift that could lift India to the final. In the 11 to 16 over window, the data gives Dube the edge over Hardik on almost every metric.

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If a right hander like Suryakumar or Samson is already at the crease, giving Dube the first entry between overs 9 and 15 can unlock a higher scoring tempo than a default Hardik entry. This is not criticism of Hardik. This is role clarity. We are asking the team to play people in the spots where they increase Team Winning Probability the most.

Put together, the plan looks simple
• Use Bumrah early. Create the hint of swing.
• Let Arshdeep rediscover an inswing rhythm to hit Buttler’s weakness.
• Use Axar stump to stump against Brook, and bring Varun with googlies ready.
• Manage Samson carefully against Archer. Use Abhishek to absorb heat.
• Attack Rashid’s length in the “proper regions” quickly.
• Slide Dube into that 9 to 15 over entry when the match situation fits.

If India stitches these small moves together, India’s chances of reaching the final in Ahmedabad on March 8 rise sharply. Matches are rarely won on a single metric. They are won on a sequence of correct, context aware choices. This is chess not kaboom.

Tell me what you think
Which tactical move would you bet on? Bumrah in the 2nd over? Axar being aggressive stump to stump against Brook? Promote Dube in the middle? Leave your take in the comments.

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The scoreboard did not look special. It felt like India would somehow crawl to 155 or 160 in the remaining overs. Shivam Dube was talking to himself.

“My job is not to play a big innings. My job is to bring the game back to life.”

At that moment, he was not just a batter. He was speaking the language of T20 cricket.

India eventually scored 176. The highest total ever in a T20 World Cup final. Dube made 27 off 16 balls at a strike rate of 168.75. Among Indian batters, his strike rate was the highest, minimum 10 runs. Yet on the scorecard, that innings does not stand out. With time, it is forgotten.

But that is not the fans’ fault.

The real question is this. Even though T20 belongs to the same family as Test cricket, are they really the same?

In Tests, the scorecard is a mirror of the story.
In T20, that does not always apply.

In T20, when matters more than how much. T20 is teaching us a new language. The only question is, are we ready to listen?

In the early days of T20, everything felt unclear. No one knew what to expect. No one knew how to build a team. If you look at the squads from the first IPL season, it becomes obvious. There was no clear structure, no defined roles. Just big international names. Many of them were not suited for the format at all. Slowly, teams across the world understood that this game is completely different. The focus shifted from fast bowlers to spinners because across phases their economy rates were lower.

In Tests, the basic idea is simple. You cannot win without taking 20 wickets. So in the beginning, even T20 teams focused heavily on wickets. But soon it became clear that something was different.

In Tests, the value of a wicket is roughly the same in any over.
In T20, the value of a wicket drops sharply.

A wicket in the first over is worth of around 12 runs.
A wicket in the final over is worth just 2.

Because of this, bowlers started focusing more on controlling runs than on chasing wickets. Mystery spinners became extremely valuable. Bowlers who could take the ball away from both right and left handers. Every player’s role became sharper. For example, as spinners grew in importance, so did spin hitters such as Shivam Dube. In the future, their value will only rise.

Both batters and bowlers evolved, but in different ways. Batters started using parts of the field that once seemed impossible. They stopped playing the ball and started playing the field. That change transformed bowling as well. Today, the best ball is not the one taught in traditional coaching manuals. It is the ball the batter does not expect or does not want.

Some franchises decoded their home conditions brilliantly. That is how T20 dynasties like Mumbai Indians and Chennai Super Kings were built. In the last T20 World Cup, India selected four spinners with different skill sets keeping Caribbean conditions in mind. They benefited from it fully.

At first, teams focused on reducing dot balls and rotating strike. But in T20, that thinking was fundamentally flawed. IPL 2024 shattered that belief when SRH chased 166 against LSG in just 58 balls. After the match, KL Rahul said, “I am lost for words.” For teams that had not understood the importance of boundary hitting, that was a painful lesson. Today, LSG’s top three attack from the very first ball.

For a bowler, it does not matter whether he gets a wicket. If he has not conceded a boundary, that itself is a win. We can sense all these changes while watching the match. But when we try to talk about them, the right words do not come. The scoreboard does not tell the whole story. T20 keeps reminding us of that.

That is why T20 demands a new perspective. A new vocabulary. A new language. Only then can we truly enjoy this format.

T20 initially inherited players, tactics and techniques from Test and ODI cricket. But in terms of strategy, the game evolved rapidly. The problem is that the language did not evolve with it.

The old vocabulary struggles to express the subtle shifts and decisive moments within a T20 match. Cricket’s language is excellent at describing individual performances.

But T20 is the most team focused format.

We need to identify and name the subtle elements and strategies at the heart of a T20 match. Only then can we understand and truly see them.

And this is not just theory.

Sometimes, in one match, in one moment, T20 speaks its own language right there on the field.

Let us return to Barbados once again.

Look at that scorecard carefully. 27 runs off 16 balls. The numbers do not say much. But ask a different question.

“How much did those 16 balls shift the future of the match?”

The table below shows how much each Indian batter increased the team’s Win Probability in the final.

When you look at that table, one thing becomes clear. Right after Axar Patel, who stands second on that list?

That is when you understand something. Some innings do not look big on the scorecard. But they bring a slipping match back to life. That is T20.

It does not always teach you to look at big numbers. It teaches you to recognize moments. That is why some T20 innings appear small statistically, but are enormous in the story of the match. Next time you watch a T20 game, maybe you will notice something different. Maybe you will remember that T20 is speaking its own language. If you understand that language, not just the match but the drama, the joy and the beauty will feel new.

Now the only question left is this. Are we ready to listen?

If this piece made you look at T20 differently, tell me what moment changed the game for you.

Not the biggest score. Not the loudest innings.
The moment that quietly shifted everything.

Drop it in the comments.

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If you believe T20 needs a new vocabulary, share this with someone who still judges matches only by the scorecard.

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And if you enjoy deep dives into how modern cricket is evolving, subscribe. I write about the game beneath the numbers. The patterns behind the noise. The language we are still learning to speak.

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Let’s build that language together.

Enough is said about the Skill Scale metric developed by CricViz for the World Cup. Creating such a unified metric is not at all easy, and there will always be some edge cases which might be difficult to understand at first glance. The friction comes from the lack of visibility into how those numbers are formed. When the logic stays hidden, interpretation becomes subjective.

That raises a more interesting possibility.

If a Skill Scale is going to shape how players are discussed, can it be built in a way where every number has an understandable cricket meaning?

That question is what led to this work.

Instead of speculating about the broadcast model, my aim is to create a version where the pathway from data to number is clear. A framework where the rating is not just something you see, but something you can trace, question, and understand.

In short, a Skill Scale designed to be transparent by default. Because right now, the broadcast numbers look analytical but feel mysterious. That confusion is what this work starts from. Not to criticise the idea, but to build a Skill Scale where every number has a visible cricket meaning. So, let’s see can we come up with our skill scale?

This project started from a simple belief.

If we are going to describe skills, every skill must come from a behaviour on the field that we can explain.

That is what the DeepCrease Skill Score tries to do.

Not a black box rating. A set of interpretable skill metrics that answer real cricket questions.

Three skills sit at the core right now.

Acceleration.
Power.
Innovation.

(I have skipped control because that is intuitive, and one more issue is for accurate measurement we need a detailed explanation of bat and ball contact in the data.)

Each one is built from a different cricket idea.

Acceleration is about impact speed, not final strike rate, this difference is crucial in understanding what we’re trying to do. A batter can finish with a high strike rate but still take too long to influence the game. He’ll earn many fantasy points due to boundaries and SR, but that SR at a low acceleration would not be enough to take his team home. In T20, timing of impact matters as much as magnitude.

So the first step was to define a reference point for meaningful impact.

Instead of picking an arbitrary strike rate, the model looks at winning innings. Across matches (I’ve used T20I ball by ball data since 2021), we compute the strike rate that winning teams actually achieve. This becomes a dynamic benchmark for what winning batting looks like in that dataset.

Now the question becomes behavioural.

How quickly does a batter reach that level and stay there, because sustaining a high SR is important.?

For every innings, cumulative strike rate is tracked ball by ball. Reaching the benchmark once is not enough. The batter must sustain it for a short window of 5 balls. That prevents random spikes from being treated as acceleration.

This produces a very intuitive quantity: Balls required to reach winning impact.

Batters who consistently get there faster rate higher. Batters who rarely reach it or reach it late rate lower. The final Acceleration rating blends three signals. Speed to the benchmark, frequency of reaching it, and sample size stability.

Acceleration stops being a narrative label. It becomes a measurable timing skill.

Power starts from a different cricket truth. Power is not just sixes. It is meaningful six hitting.

Counting sixes alone rewards volume without context. Strike rate alone hides scoring composition. A batter who scores quickly through fours is different from one who bends the game with sixes.

The Power metric combines three ideas.

Six frequency captures how often a batter hits sixes.
Six share captures how much of their scoring comes from sixes as compared to fours.
Volume ensures sustained hitters separate from small sample outliers.

When these interact, the metric identifies players who repeatedly create large scoring events and rely on them as a core scoring method. Converting this into a percentile rating produces a scale that aligns closely with how we intuitively recognise power hitters. Power becomes structural, not highlight driven.

Innovation is the most nuanced skill because it is not about runs alone. It is about options. Now, this needed the most creativity in thinking, and it’s something which can have different interpretation for different individuals.

Innovation here is framed as contextual shot diversity. Instead of looking at runs scored in certain regions or how many unorthodox shots a batter plays, Innovation, if it has to have any meaning in T20 cricket, then it has to be helping the batter to play the field.

For every bowling situation defined by line, length, bowling type, and batter handedness, we observe how scoring shots are distributed across the league. That distribution represents the typical solution space.

We then look at each batter.

Within the same situation, how many different scoring solutions do they actually use? Are they predictable or flexible?

Think of it has a student having a number of ways to solve a math problem, and now depending upon the time he has in his exam, he choses a feasible methodology. In the same manner, the question is the ball itself, and time represents the field placement, so a batter who’ll have more answers to that ball will end up getting a long-term success.

This is measured using an effective number of shots concept derived from entropy. In simple terms, it estimates how many distinct scoring options a batter genuinely uses rather than how many appear once.

Exposure weighting is critical. Situations faced more often should matter more than rare ones. Light smoothing prevents small samples from exaggerating creativity.

The result is an Innovation score that captures adaptability rather than aesthetics.

A scoop is not innovative because it looks cool. It is innovative when it expands the solution space relative to what most players do in that context.

Once the three skill dimensions are computed, they are converted into percentile style ratings to form the DeepCrease Skill Score. The goal is readability without losing meaning.

Each number answers a question.

1. How quickly does this batter reach winning impact?

2. How strongly do they generate large scoring events?

3. How wide is their scoring solution space across contexts?

That clarity matters more than the exact formula.

Because the real purpose of this work is not ranking players. It is changing how we talk about skill. The bowler metrics are fascinating too, especially the Threat which I’ll work out soon.

When metrics are interpretable, disagreement becomes productive. Debates become valuable because the assumptions are visible.

That is the philosophy behind DeepCrease.

Context first. Behaviour first. Transparency first.

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I’ll shortly put all the batter’s Skill Scores on the DeepCrease Website

https://omkarthecricketest-deepcrease.hf.space/

Skill scores should not feel mysterious. They should feel like compressed cricket understanding.

And ideally, when a number moves, you should be able to imagine what changed on the field.

Finally, As and Nathan Leamon had beautifully explained that the biggest contribution which data can make in Cricket is not towards a team or a player, but it’s explaining this game in a different, and better perspective, this is my small attempt to do so.

If you’re interested in T20 Player Evaluation, then check out my article on Integrated Contextual Rating, which elegantly solves the issue in volatility of various roles in T20 Cricket which differ significantly from the longer forms. This explained 70.47% of the variance in the Win% of teams in IPL 2025. Check it out here:

Since 2025, that number has jumped to 162.24.
That is not just improvement but that’s actually a structural change in how he is scoring. It’s 42 runs per 100 balls higher than the average batter against spin over the same period.

The question isn’t whether he has improved.
The question is what changed?

The straight-drive signal

One of the clearest indicators is his increased use of the straight drive.
Since 2025, Hetmyer has expanded that option by more than three percentage points against spin.

That might sound small, but shot-distribution shifts of even 2–3% are meaningful at the elite level. They usually signal intent, not randomness.

The straight drive does two things against spin:

• It removes the bowler’s margin for error in length.

• It forces captains to protect straight boundaries, which immediately stretches square regions.

Hetmyer adding that option changes the geometry of the field and how the captains will now use the 5 boundary riders permitted.

The real battleground: back-of-a-length spin

The biggest transformation appears on good-length and short-of-good-length deliveries.

Historically, that was a containment zone.
Pre-2025, Hetmyer struck at roughly 120 on back-of-a-length balls against spin.

Since 2025, that figure has risen to 161.

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That is not incremental improvement but that is the removal of a weakness.

When a batter starts scoring at 160+ in what used to be a neutral or defensive zone, bowlers lose one of their safest plans and a strategy which will work well against an average middle order hitter fails miserably against Hetmyer.

Shot selection, not just execution

The shift is also directional.

Earlier, Hetmyer’s default scoring pattern on back off a length deliveries leaned heavily toward the mid-wicket region.
It was predictable enough for teams to set fields around that tendency.

Now the access points are broader.

He is increasingly targeting:

• Mid-on

• Cover

• Straight regions

The plots show, for a given length, which regions of the field he prefers to score in.

This matters because bowlers rely on expectation.
If a batter expands scoring angles without sacrificing strike rate, field setting becomes reactive rather than proactive.

Now, this change allows him multiple options to score on a same delivery and thus he can play the field instead of playing the line and length which used to happen before.

Why this makes him difficult to contain

Spin bowling against Hetmyer is no longer about surviving his strengths but it’s about managing his adaptability.

When:

• Straight play increases,

• Neutral lengths stop being safe,

• And scoring directions widen,

captains run out of clean field templates.

Even good deliveries require protection.

The bigger takeaway

What we’re seeing is not a short hot streak.
It looks like a deliberate adjustment in method, one that specifically targets how spin attacks him through length control.

Hetmyer hasn’t just become more aggressive against spin.
He has become harder to plan for.

And in T20 cricket, unpredictability is often the difference between a dangerous batter and a decisive one.

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I need to tell you something that might annoy you.

Most of what you see online as “cricket analysis”... isn’t analysis.

It’s highlights with numbers attached.

Averages without context. Strike rates divorced from conditions. Wagon wheels that look pretty but explain nothing.

They tell you what happened.
They almost never tell you why.
And they definitely don’t tell you when it will happen again.

But then I started asking different questions. The kind teams ask. The kind that don’t have easy answers on Cricinfo.

And that’s when everything changed.

The Question That Started Everything

Teams don’t ask:

“Who scored runs?”

They ask:

• Where were the runs scored?

• When did they come?

• Against what kind of bowling?

• Under which phase and match state?

• And why does this pattern repeat?

These are smart people with massive budgets. But the tools don’t exist. At least not publicly. So I decided to build one.

That gap is exactly what DeepCrease is built to solve.

A few key features:

1. Strength–Weakness Maps by Real Context

Every batter and bowler can be analysed by:

• Line × Length

• Phase

• Bowling type (pace / spin, sub-types included)

• Matchups (batter vs bowler, along with role vs role)

This means:

• a “weakness” in Powerplay may vanish in Middle overs

• a safe zone vs spin may become a risk zone vs pace

• context flips the narrative and the data shows it

One thing I didn’t expect while building this:

How often “weaknesses” disappear when you change phase + bowler type.

Cricket is a context game.
We’ve just been measuring it wrong.

2. Shot Likelihood Heatmaps (Individual + Relative)

This is where it gets serious.

For any line–length–phase–bowling type combination, DeepCrease shows:

• Individual Scoring Zones
  → Where this batter actually plays the ball

• Relative Scoring Zones
  → Where the batter accesses zones more or less than an average batter

This isn’t a wagon wheel for aesthetics.

It answers:

“What does this batter do differently from the baseline?”

That’s gold for:

• opponent prep

• bowling plans

• field placement

• matchup targeting

Do Subscribe if you are finding value in this work:

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3. Phase-wise Control %, Boundary Intent & Risk Profiles

Instead of generic strike rates, the platform shows:

• Control %

• Boundary intent by zone

• Risk profiles that change by phase

  

4. Matchups That Actually Change With Context

If you want to see:

Where Abhishek Sharma has scored against Shaheen Afridi — and how that changes by phase

You can.

And more importantly:

• you can see where the matchup flips

• where the risk zones emerge

• where the “obvious” narrative breaks

  

5.Batters’ Caught Dismissals

Where exactly a player has been caught while playing his shots in each phase, tells you where to set the field to trap him.
USA had a fielder in Deep Cover for Abhishek in Powerplay, which was a great move. Over 26% of his runs in Powerplay are in this region against pace with 3 dismissals

6. Multi-Tournament, Global Coverage

All of this works across 10+ global tournaments, including:

• IPL

• T20Is

• and multiple franchise leagues

Same logic.
Same framework.
Different contexts.

In simple words

👉 DeepCrease doesn’t tell you what happened
👉 It tells you why it happened
👉 And when it’s likely to happen again

That’s the difference between content and decision-making.

Why make this public?

Because this kind of analysis usually lives inside teams.

Very few teams even do this properly.
Almost none of it is visible publicly.

I decided to change that- at least for now.

🔗 The platform is live:

https://omkarthecricketest-deepcrease.hf.space/

If this feels too detailed- you’re probably the right audience.

What’s next?

This is Version 1.

The scary part isn’t what this does today.

It’s what this kind of analysis unlocks next.

If you’re reading this and you’re:

• a coach preparing opponents

• an analyst tired of surface-level metrics

• a player who wants patterns, not vibes

• or a team / organisation looking for an edge

My DMs are open.

Happy to walk through how this can actually be used for prep and decisions.

Cricket deserves better analysis.
This is my attempt to build it.

Do let me know your views in the comments:

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Why I worked on the phase to 7–11?

While most analysts look at Overs 7-15 as a monolith, the data suggests we are looking at two entirely different phases.

From over 12 onwards, the “Death-over” mindset starts creeping in. The 7-11 window is where the game behaves strikingly different.

The Statistical Jump: Risk is the New Reward

Comparing T20I data from 2017-2021 to 2022-2025 the evolution is clear. The Strike Rate in this phase has jumped from 119.58 to 124.35.

More importantly, Boundary run share in the 7-11 phase has soared from 47% to 52.5% Batters aren’t just looking for gaps anymore; they are looking for the ropes earlier than ever.

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The Death of the “Safe” Top Order

One of the most telling trends is who is facing the balls.

• The Trend: The Top 3 are facing a smaller percentage of balls in this phase, while positions 4 through 7 have seen a significant spike.

• The Tactical Why: Teams are building massive batting depth, giving the middle-order a “cushion” to take high-risk options against spin. Spinners have less economy in all 4 phases of a T20 inning. In future too, teams will be increasingly bowling more overs of spin to restrict the run scoring and as a result, the role of specialist 'Spin Hitter' increases like the role Shivam Dube, Rajat Patidar play for their team.

The Bowling Matrix: Spin vs. Pace

While batters have figured out Pace (SR against pace jumped from 126 to 133), Spinners remain the primary decelerators.

• The Leg-Break (LB) Struggle: With teams stacking Left-Handed Batters (LHBs), standard Leg-Break bowlers without a lethal googly are being targeted.

• The Mystery Spinner (LBG) Edge: This is where the “False Shot” data gets interesting. Mystery spinners have seen a +5.33% increase in False Shots produced because they can turn the ball away from both RHBs and LHBs.

• Off-Spin’s Lethality: Not only are Off-spinners keeping it tight, but their Bowling Strike Rate has plummeted from 32.9 to 26.4. They aren’t just containing; they are the new wicket-takers.

• The Chinaman Asset: Bowlers like Kuldeep Yadav remain essential. Even as batting aggression increases, Chinaman bowlers have maintained an Economy of 6.8 and a lethal Strike Rate of 19.1. In a world of left-handed dominance, the ball turning away from the LHB is the ultimate gold mine.

  

The False Shot Paradox

Interestingly, despite the increase in SR, the False Shot % has risen across all batting positions.

Batters are accepting a higher probability of failure in exchange for a higher scoring rate. For a fielding coach, this is a massive signal: the 7-11 phase is no longer about ‘containment’, it is a primary wicket-taking opportunity.

"The 'quiet period' is dead. Between overs 7 and 11, T20 cricket has become a game of high-variance chaos where the only crime is standing still. We used to call this the 'accumulation' phase; now, it’s just the 'destruction' phase with a different name. The safety net is gone. The batting depth is terrifying. Welcome to the new T20 trench.

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But there is a glitch in the matrix.

While Litchfield dominates most matchups, a specific tactical shift has emerged as her Kryptonite. It’s counter-intuitive, statistically devastating, as the numbers tell a fascinating story. Against off‑spinners, the difference between Over the Wicket (OTW) and Round the Wicket (RTW) isn’t just marginal, but it’s seismic.

The Tale of Two Angles: OTW vs. RTW

The data reveals a staggering contrast in how Litchfield handles off-spin depending on the bowler’s angle. Conventional wisdom suggests lefties are more vulnerable to the Round the Wicket (RTW) angle where the ball spins away. However, Litchfield’s reality is the exact opposite.

When off-spinners come Over the Wicket (OTW), her numbers crater:

• Average: It drops from a massive 91.33 against RTW to a modest 34 against OTW.

• Efficiency: Her Balls Per Dismissal (BPD) falls from 55.3 down to a frantic 20.3.

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Stifling the “Big Three” Shots

Why does this angle work so well? Because it effectively boarded up Litchfield’s favourite scoring windows.

Against the around-the-wicket angle, Litchfield thrives on three signature moves: the Reverse Sweep, the Off Drive, and the Cover Drive. These are her most productive shots, allowing her to manipulate the field at will.

However, the Over the Wicket angle creates a line of delivery that makes these shots incredibly risky. The data shows a significant drop in the percentage of these shots played when the bowler comes OTW. By attacking the stumps from that angle, bowlers aren’t just trying to hit the wickets; they are physically preventing her from playing the shots that make her elite.

The Pressure Valve: Pulling into a Trap

When a batter’s primary scoring options are taken away, they inevitably look for a pressure valve, a way to force the run rate back up.

For Litchfield, this has meant resorting to the Pull, the On Drive, and the Slog Sweep. While she plays these shots significantly more often against the OTW angle, they haven’t been her saviors. In fact, she has been dismissed once playing each of these alternative strokes while facing the over-the-wicket attack.

It’s the classic tactical trap: take away a player’s strengths, and they will eventually beat themselves trying to find a replacement.

The danger zone is specifically identified as length balls on the stump line, where she registers a high 32% False Shot rate. This isn’t just a slump; it’s a tactical bottleneck. These False Shots have resulted in 3 dismissals in this (line, length) combination.

Litchfield’s False Shot % vs Off spinners over the wicket (T20s since 2024)

The Shreyanka Patil Blueprint

We saw this play out in real-time just yesterday. Shreyanka Patil, arguably one of the smartest operators in the powerplay and middle overs, exploited this exact blueprint. By coming Over the Wicket and targeting that stump-line length, she forced Litchfield out of her comfort ultimately claiming her wicket.

This marked Litchfield’s 7th dismissal to this specific angle since 2024, proving that the secret is well and truly out.

Is This the 2026 Blueprint?

As we move deeper into the 2026 season, the question isn’t whether teams will use this tactic but it’s how Litchfield will evolve to beat it. Can she find a way to reopen the ‘V’ in the mid-off region where she currently struggles to score against this angle? Or will off-spinners continue to treat Over the Wicket as the universal Stop button for one of Australia’s brightest stars?

What do you think? Should Litchfield change her way, or is this just a phase that a player of her quality will naturally overcome?

Drop a comment below, and if you found this tactical breakdown useful, share it with your favorite cricket nerd!

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The headlines say Ellyse Perry is missing, the fans say they’ll miss their star. But the data says something much more clinical: RCB just lost their structural insurance policy.

Using my Integrated Contextual Rating (ICR) framework—a model that explains ~70% of win variance in T20 — Using ball-by-ball Monte-Carlo simulations across the last two WPL seasons, I measured how team win probability changes when a player is removed and replaced by an average team batter in each phase and simulates outcomes. That’s why the gap is this large.

Here is why Perry isn’t just a player; she’s a statistical outlier that keeps the RCB machine from collapsing.

1. The 8.2% “Fragility Gap”

Most fans look at runs; we look at Win Probability Delta. When Perry is in the XI, RCB’s win probability sits at 48.8%. Without her, it plummets to 40.5%.

In an 8-game sprint of a league, that 8.2% gap is the difference between a direct final entry and watching the playoffs from the couch. To put it in perspective, that is roughly one full win per season attributed to a single human being.

2. The “Mumbai Machine” vs. The “Superstar Ecosystem”

The most fascinating insight comes when you compare RCB’s dependency to their rivals, the Mumbai Indians.

Mumbai has their own titans—Nat Sciver-Brunt and Hayley Matthews. Our simulations show they swing MI’s win probability by ~7% each. However, there is a fundamental difference in roster construction:

• MI’s Continuity Model: Their win% drop is distributed. If Nat Sciver-Brunt fails, the “structural pillars” of Matthews or Kerr catch the weight. They are built for survivability.

• RCB’s Vertical Model: RCB was built to be led. Perry doesn’t just score runs; she normalizes the “Venue Index,” performing at 1.2x the league average regardless of whether she’s on a rank-turner or a highway.

When you remove the lead pillar from a vertical model, the redistribution of impact often leads to "leverage blindness"—where the replacement players are forced into roles that exceed their tactical threshold.

If you’re a Performance Director, this is the number that keeps you awake. Without Perry to bridge the gap in the batting, RCB has to find a way to recover a 8.2% relative drop in total season win expectancy.

Tonight’s opener against MI isn’t just a match; it’s the first test of whether a “revamped” RCB squad can redistribute the Perry Tax, or if the machine is too fragile to function without its core.

Are you a coach or franchise owner looking to identify your roster’s "Single Point of Failure"? My DMs are open for a deep dive into Win% Delta and Dependency Thresholds for the 2026 season. Let’s look at the game the scoreboard can't see.

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