Newtonian physics describes the world through a handful of simple dimensions: length, breadth, depth, and time. Modern physics goes much further. String theory, for instance, posits a universe with many more hidden dimensions, introduced to explain phenomena the simpler framework cannot. Cricket, at least in mainstream conversation, still lives in its Newtonian phase, stubbornly explaining batters using only a few familiar dimensions.

The dimensions cricket typically uses to classify batters revolve around two simple axes: batting position and strike rate. We speak of top-order batters and finishers, and within the top order, of aggressors and anchors. Yet such crude categories capture only a thin slice of a batter’s craft, leaving the rest to anecdote and folklore. In his prime (not the current version missing an entire knee), MS Dhoni was the archetypal finisher who preferred to take an innings deep, while Andre Russell often seemed intent on hitting two sixes off the same ball. But what of the less mythologised cricketers? Even for someone with a long career like David Miller, it is not entirely clear what story the streets tell about him (except of course, for the 38 ball hundred).

In this piece, we try to measure several dimensions of batting that are otherwise left to memory, commentary, and the occasional bard who sings of matches long past.

This piece is about anchoring, and the many forms it can take. Traditionally, an anchor is imagined as a top-order batter averaging above 35 at a strike rate of roughly 130 (basically Virat Kohli). We go beyond just the top-order trope by broadening the definition of an anchor to any batter who holds the innings together. MS Dhoni is just as much an anchor when he does the same job from the rear end of an innings.

Baseline: Anchoring and the Art of Staying In — Net Effective Balls (NEB)

We measure a batter’s ability to steady an innings through the average number of balls they face per innings. But this metric immediately runs into a problem: it mechanically favours openers over batters who arrive three wickets down, perhaps late in the twelfth over. To compare batters fairly, we therefore adjust for the point at which they enter. Specifically, we estimate how many balls a batter would be expected to face given their entry point. We define net raw balls as the difference between the balls a batter actually faces and this expectation.

But not all balls are created equal. Surviving a delivery from Varun Chakravarthy is very different from seeing off one from Ravindra Jadeja. The former has a knack for forcing wickets in seemingly impossible situations, while the latter is famous for spells of 4–0–28–0 that quietly squeeze an innings without necessarily threatening the batter’s survival.

To account for this, we assign each ball a weight — its effective ball value — based on the bowler, the conditions, and the phase of the innings. Crucially, we normalize the length of an innings using net effective balls (NEB), rather than simply normalizing by net raw balls faced.

Notably, we do not account for the number of wickets fallen when a batter arrives at the crease. One could argue that, even for a fixed entry point, the expected length of an innings should depend on the state of the game: a batter walking in during the twelfth over at two down is more likely to attack than one arriving at five down. We choose not to adjust for this. The purpose of the metric is precisely to capture how a batter responds to the situation in front of him. If he attacks because the side is well placed, that makes the innings less anchor-like; if he consolidates, that too is part of the same story. Both innings are therefore evaluated against a common baseline, rather than being benchmarked separately by wickets fallen.

We use NEB as our baseline to explore several dimensions of anchoring.

Part I: The Three Basic Dimensions

We begin with three basic dimensions that shape a batter’s legacy: their role (the situations in which they come out to bat), their ability to bat long (captured by NEB), and how quick they score runs.

Dimension 1: Longevity — NEB

We begin with the batters who have the highest career NEB.. The list is still dominated by openers and top-order batters. Part of this is mechanical: they have more opportunity to accumulate balls above expectation, especially on days when they bat deep into the innings. There is also a selection effect, with stronger batters typically pushed up the order. That said, the list still features a healthy number of middle- and lower-order players with positive career NEB — Klaasen, Dhoni, Russell and Miller among them.

We almost named this statistic the KL Rahul Career Metric (cf. Kevin Pietersen’s tweet about watching paint dry) except that Sai Sudharsan currently edges him out. Sudharsan sits at a career NEB of 10.16, compared to Rahul’s 8.18. The leaderboard itself is an eclectic mix — Sudharsan, Simmons, and Hussey, with Dhoni, Gayle, and Warner not far behind. Together, they span very different batting profiles — in consistency, strike rates, entry points, and scoring patterns — the next dimensions of anchoring that we explore.

Badrinath’s 2011 season is an interesting anomaly in the year-wise leaderboards. He is the only middle-order batter (with at least seven innings) to top the NEB charts; everyone else is an opener. The list also features some repeat offenders: Kohli (2016 and 2024), Rahul (2020 and 2021), Warner (2017 and 2019), and, somewhat surprisingly, Lendl Simmons (2014 and 2015). At the very top still sits Shaun Marsh’s 2008 season, which continues to hold the highest NEB recorded across all eighteen IPL seasons — a staggering 20.76. More remarkable still is that he did it in just 11 games. For all our reservations about the Orange Cap as a concept, this is one of those rare seasons where we are genuinely impressed by the winner.

Dimension 2: Speed — True Strike Rate

Suresh Raina spent almost his entire IPL career playing on the rank-turners of Chennai. CSK built their side around the belief that no team could score more than 140 on the vast stretches of the MA Chidambaram Stadium, and did so on the back of Raina’s ability to take on spin in the middle overs. The spinners would then mop up the chase. Despite spending eleven years on such surfaces — the kind that would give Quentin Tarantino ideas for particularly violent scenes involving batters — Raina still finished with a strike rate of 136.76.

Kohli, on the other hand — perhaps for his pious deeds in a previous life — has spent his entire IPL career at the Chinnaswamy. Even at his peak, in 2015 and 2017, he finished those seasons with strike rates below 131. His career strike rate stands at 132.86, lifted in part by the ballistic new avatar of the last two years. But the gap between Kohli and Raina is not just the 3.9 runs per hundred balls suggested by the raw numbers.

The True Strike Rate (TSR) — our thanks to Varun Alvakonda and the CommBox/Good Areas team for letting us use this metric — adjusts for the expected scoring rate given the era, conditions, and match situation. In other words, a finisher playing all his cricket in Chennai in 2016 is judged differently from an opener playing in Mumbai in 2026. On this measure, Raina boasts a TSR of 11.45, compared to Kohli’s 2.79.

The batter with the highest TSR — and you should probably stop watching cricket if you did not see this coming — is Virender Sehwag, at 45.67. In 2011, he was a fairly typical opening batter in terms of NEB, facing around 21 effective balls per innings. But what he did with those balls was outrageous: he scored 64 runs per hundred balls faster than par. sixty-four. Our focus, however, is on anchors. Holding an innings together is difficult enough without also trying to score at Sehwag’s pace. KL Rahul, for instance, stands out among the more conventional anchors: his TSR sits comfortably above 5, an impressive number for someone whose primary job is simply to stay at the crease.

But when it comes to anchors, one man towers above the rest: Christopher Henry Gayle. For someone who faces 2.88 effective balls per innings above expectation, his TSR sits at a staggering 31.24. He was not easy to dislodge, and an absolute migraine while he lasted. His 2011 season might be the best in IPL history, combining an NEB above 6 with a TSR of 76.38! Gayle, alongside Warner, de Villiers, Klaasen, SKY and Buttler, represents what Shayan Khan and Jarrod Kimber call the ultimate anchors — batters who have mastered the art of taking risks without getting dismissed. Gill and Kohli over the last two seasons have been impressive, posting TSRs of 9.03 and 6.45 respectively, but they remain some distance away from the absurd numbers produced by the others on that list.

Dhoni, as mentioned earlier, delays taking risks for as long as possible. His TSR reflects this, sitting almost exactly at par at –0.11. David Miller’s career follows a similar pattern. His TSR of 0.27 suggests scoring roughly at the expected rate, while his NEB of 2.66 indicates that he often lasted longer than the situation demanded. Pollard represents a different breed of finisher. He does not anchor the innings in the same way — his NEB of 1.44 is noticeably lower — but compensates by scoring far more aggressively, with a TSR of 7.56.

Dimension 3: Context — Performance by Entry Point

A batter’s mean NEB and TSR tell us how far above or below par they operate, but not when their innings begins. To understand the role a batter actually plays, we must view NEB alongside their entry points — and, as we will see, their strike rates. MS Dhoni provides a simple case study. The table below shows how often he walks out to bat in different phases of an innings, along with the corresponding NEB values.

As expected, the heart of Dhoni’s career lies in entries between overs 10–12 (55 innings at an NEB of 6.05), 13–15 (65 innings at 3.53), and 16–18 (52 innings at 1.99). This profile firmly establishes him as a finisher-anchor — to borrow Jarrod Kimber’s term.

A more non-obvious case is Rishabh Pant. Over the course of his IPL career, he has batted almost everywhere — from opening the innings to coming in as low as number seven. While the current consensus is that he is best suited to a blistering top-order role, his optimal deployment remains unclear. Looking at his NEB by entry point provides a first step toward answering that question.

Pant maintains a positive NEB even when arriving after the tenth over, but is far more effective as an anchor when batting earlier. His NEB peaks between the fourth and sixth overs, with a slight dip between the seventh and ninth — a strong case for him at one drop.

To fully understand how he should be deployed, however, we also need to look at his strike rates by entry point. His TSR is a blistering 26.86 when he comes in within the first three overs, dropping to 12.24 when he arrives in the latter half of the powerplay, and tapering off further until around the 13th over — where it shoots back up to roughly 30. It is worth noting, though, that most of his standout innings from these later entry points date back to 2019.

The data would therefore nudge you towards using him as an aggressive option when he walks in to open, or as an ultimate anchor when he arrives between overs 4 and 6. But this is also where the limits of data begin to show — questions of comfort against the new ball and other technical considerations matter just as much before making such micro-decisions.

Putting It Together: A Rough Map of Anchors

Before moving on to more refined metrics, we first bring these dimensions together to sketch a rough classification of anchors. Among batters with a positive NEB, the NEB–TSR space breaks into a handful of clusters, which we group into six broad categories.

Ultimate anchors: The batters in this category have disrupted the game in different ways. SKY and Klaasen are prime examples. Klaasen’s straight pull has practically changed the lengths spinners bowl in T20 cricket. It is less clear whether SKY’s sweeps and scoops are replicable by more mortal batters.

Safehouses: Under the conventional definition of an anchor — someone who bats through the entire innings — these are the best in the business. The usual names appear here: Sudarshan, Hussey, Kohli, Gill, and Tilak. Dhoni’s NEB is lower than the other safehouses, but the threshold for finishers is adjusted to account for the structural disadvantage faced by lower-order batters.

Plus anchors: These batters fit the conventional definition of an anchor, but score far more briskly. Gaikwad is the poster boy for this category, with an almost effortless ability to dismantle spin — just ask Rashid Khan. They are not quite at the level of the ultimate anchors. Pant sits right on the boundary of this group — one more standout season and he might well be promoted to an ultimate anchor.

Itchy hands: These batters do not stand out as anchors if judged purely by NEB — but that misses the point. They are extraordinary hitters who also happen to face more balls than expected. Raina is a perfect, and often misunderstood, example. His fanbase gave us one of cricket’s enduring misnomers — Mr. Consistent. In reality, his superpower lay in disrupting the typically docile middle overs: stepping out, sweeping, and most importantly, unlocking a repeatable loft over extra-cover.

More generally, these are batters who get restless after a couple of dot balls. Think Russell, Pooran, and Bairstow. Pollard belongs to this group too — his numbers are similar, but his approach was more measured and calculated.

Conventional anchors: Gambhir, Dhawan, and Steve Smith fall into this bucket — batters who resemble safehouses or plus anchors, but fall just short on NEB or TSR. David Miller and Hardik Pandya belong here too. A slightly better Miller might have qualified as a safehouse; a slightly better Pandya might have moved into itchy-hands.

Are they really anchors? These are batters whose role is not immediately clear from the numbers. Perhaps they have simply been used all over the batting order, leaving them with not-so-sexy numbers. The biggest tragedy here is that Rohit Sharma falls into this zone. One could argue that his IPL career splits into two parts — one where he tried to be a plus anchor, and another where he tried to be Sehwag. Maybe the two phases together pushed him into this category. Of course, there is also the harsher counterargument: that he never quite lit up the IPL. Many players in this zone share a similar sense of statistical confusion.

Dinesh Karthik is another example. He has batted almost everywhere from No. 3 to No. 8 — part middle-over shepherd, part last-over pace basher. His numbers are a soup of all the roles he has played in the IPL.

Part II: Beyond the Basics

From this point onward, we focus primarily on batters with a positive NEB and move beyond the averages of Part I. Two players can arrive at the same NEB in very different ways: one might produce the occasional outrageous innings surrounded by a string of failures (Venky Iyer), while another might quietly assemble a run of consistently above-par outings without ever producing a headline performance (Sudarshan).

Some batters also appear to specialize by innings — a first-innings specialist (Klaasen) versus a second-innings specialist (Salt). And then there are players who seem to outlast everyone around them, occupying the crease far longer than their teammates — Rahul, of course, needs no introduction here.

Dimension 4: Consistency — The Pessimist’s Prediction

Sai Sudarshan seems to get automatic starts. In his last 30 innings, he has produced an NEB of around 10 — roughly his career average — a staggering 19 times. Across 40 IPL appearances, he has recorded an NEB of 5 or more in 25 innings. He just does not fail. Contrast this with Gayle. Despite a strong career NEB of 2.88, he still finished with a negative NEB in more than 37% of his innings. Compared to Sudarshan, Gayle is far less reliable at getting started, but far more destructive once he does.

To distinguish the Sudarshans of the world from the Gayles, we turn to what we call the pessimist’s prediction — separating metronomes from chaos agents.

The obvious way to measure consistency is the standard deviation of a batter’s NEB. But this alone does not tell the full story. Hussey’s standard deviation is 16.44, compared to Mitch Marsh’s 14.76 — yet no one in their right mind would put the two in the same conversation on consistency.

The nuance lies in their means. Hussey averages 7.06 effective balls per innings; Marsh, just 1.08. Hussey can afford a larger spread and still be called consistent: even a drop of seven balls leaves him around par. For Marsh, the same drop tells a very different story — it is far more likely that he played a 35 off 14 than holding the innings together.

We capture this interaction through what we call the pessimist’s prediction (PsP) — mean NEB minus one standard deviation. If NEB were normally distributed, a batter would exceed this benchmark roughly 84% of the time. In effect, it is what a pessimistic fan would expect from the next innings.

The pessimist’s prediction almost feels designed to showcase Sai Sudarshan — and he does not disappoint. He has the highest average NEB in IPL history (10.16), with a standard deviation of 13.56, giving him a PsP of −3.3 (the highest among batters to have faced 400 effective balls). In other words, he operates far above par not just on average, but with striking regularity. This is exactly what you would want from a metronome: even when things go wrong, he still drags the innings towards par.

Tilak Varma appears near the top of the chart too, with a staggeringly low standard deviation of just 0.9. His TSR, surprisingly, is negative — so his consistency comes at the cost of scoring rate. Yet that kind of reliability pairs well with volatile batters like Abhishek, Samson, and Dube, and likely played a role in his selection in an otherwise high-variance Indian T20 side this World Cup.

At the other extreme, consistency is not always desirable. For batters averaging close to zero NEB, consistency simply means failing in roughly the same way every match. You would much rather they win you two games in a season, even if they are invisible (or worse, visible for the wrong reasons) for the rest. As the mean NEB drifts towards zero, variance becomes a virtue rather than a vice. In those cases, you would rather have a chaos agent than a metronome.

Mitch Marsh is the textbook chaos agent. His PsP sits at −13.47, driven by a standard deviation of 14.76 against a mean NEB of just 1.08. I can only dream of the day Marsh and Sudarshan open the batting for the same team.

Rahane is a more subtle case. He is far from the ever-consistent accumulator one might imagine him to be — his standard deviation sits at 15.12. Before CSK rehabilitated his career, he was something else entirely — slow, yes, but not especially reliable.

This metric is dominated by middle- and lower-order batters — for good reason. For such players, both mean NEB and standard deviation tend to be small. A lower-order batter simply does not face enough balls, and even a golden duck does not push him far below par. As a result, the metric (correctly) predicts that most innings look similar in terms of balls faced.

It therefore makes more sense to stratify batters by entry point when discussing their PsP. Against this backdrop, it is particularly striking that Sudarshan and Tilak Varma appear near the top of the list despite batting consistently in the top-order.

Compared to batters who face their first ball within the first three overs, the two Rahuls stand out for their consistency. Both Rahul Dravid and KL Rahul have a PsP below −9. In other words, they are almost guaranteed to face at least 12 balls every innings (an opener is expected to face roughly 24).

As we move deeper into the innings, roles begin to diverge. Badrinath, used by CSK as a pinch-blocker, plays his part to perfection by staying in when sent in during the latter half of the powerplay. Steve Smith, a chaos agent in the powerplay, becomes far more reliable in the middle overs, with a PsP of −1.89. Towards the back end, Dhoni and Pollard stay true to his reputation — the finisher who gets the job done. So do Miller and Rinku Singh.

Andre Russell, however, is more nuanced than we give him credit for. When he arrives between overs 10 and 12, his PsP is −5.25 — not far from Dhoni’s −3.48. But as he is pushed further down the order, his PsP drops sharply, reflecting his increasing volatility.

The volatile end of the spectrum is far more fun. PsP helps explain why teams are so bullish on Venky Iyer. He faces less than a ball above par per innings, and his strike rate is not quite elite for someone without a strong NEB. Yet when Iyer switches into beast mode, the ceiling is enormous. His PsP of −13.40 is among the lowest for batters with this entry point. Reinforcing our decision to group him with the itchy hands, Raina falls squarely in the Venky Iyer zone of volatility too — albeit with a stronger NEB and TSR.

Interestingly, the ultimate anchors do not feature near the top of this list — with the possible exception of AB de Villiers when he walks in between overs 13 and 15. As a sanity check, we compare the CDFs of Warner’s and Buttler’s NEB distributions with that of an average batter who arrives within the first three overs and has a positive NEB. The curves almost track each other, suggesting similar dispersion. Their standard deviations — and by extension their PsP — are fairly middle of the road.

Dimension 5: First Innings vs Second

Phil Salt has a TSR of 26.9, and it barely changes between innings — 23.84 in the first, 28.92 in the second. There is a certain brutality in the way he bats; beyond a point, as a bowler, all you can do is surrender and hope he goes out hit wicket.

But the length of his innings could not be more different. When chasing, he has an NEB of 5.49 — almost Kohli-esque. When setting a target, he struggles to stay in, with an NEB of −7.41 (the largest gap for any batter in IPL history across innings). When chasing, he does not stop until the game is truly dead and buried. Batting first, even if you are hit for a couple of sixes, you still expect him to sky one.

Heinrich Klaasen is the anti–Phil Salt. He has an NEB of 5.63 in the first innings and just 1.84 in the second. His TSR tells the same story: 36.89 in the first, compared to a far more modest 8.61 in the second. Curiously, another elite spin-basher shows the same pattern. Rajat Patidar faces 4.07 balls above par in the first innings, but 2.34 fewer in the second, with his TSR dropping sharply as well.

Kohli lies almost exactly in the middle of the first vs second innings continuum. He is well known for his ability to boss run-chases, yet his NEB is surprisingly similar across the two innings — around 5.7 in both, with his TSR only slightly better in the second (5.06 compared to 0.92 in the first). This suggests he is equally effective in both situations.

But the chase-master tag can still be justified. He has roughly twice as many not outs when chasing as when setting a target, and while NEB counts balls faced per innings, it does not capture how often he sees the job through. Of the 27 run chases where he has remained not out, RCB have won 23. Perhaps we are trying a little too hard to bend the numbers to fit the Kohli chasing lore — he is, after all, just as good on either side of the innings break.

Measuring the difference in TSR across the two innings reveals some sharp preferences. Among batters with at least 40 innings, Pat Cummins stands out the most in favour of chasing: his TSR in the second innings is 35.53, compared to −29.66 in the first. Tim David and Rinku Singh follow a similar pattern, thriving far more when the target is known. At the other end, Klaasen’s numbers reinforce what we have already seen — he is built for setting the game up, striking at 36.89 above par in the first innings and only 8.61 in the second. His South African teammate de Villiers shows a similar tilt, suggesting that this preference for the first innings might not be entirely incidental.

There are also batters who sit closer to neutral. Shreyas Iyer has one of the smallest gaps in TSR between the two innings among players with a reasonable sample size. Hardik Pandya and Pant fall into a similar bucket, and, as mentioned earlier, so does Kohli

Dimension 6: The Lone Wolf

Despite having a TSR comfortably above five, we all know KL Rahul has the ability to bat much quicker. KL Rahul knows that KL Rahul has the ability to bat quicker. Yet one explanation he often offers for throttling the innings is that nobody else can hold things together in case he gets out. Fair enough. He did spend a large part of his career at PBKS, a franchise whose playing XI often felt like a game of ringa-ringa roses. On those grounds, perhaps his crimes against TRP can be forgiven. But that raises a natural question: can we quantify how instrumental he actually was in holding the innings together?

To formalise this, we define marginal effective balls (MEB) as follows. For a batter in a given innings, MEB is the difference between his effective balls faced and the highest effective-balls tally recorded by any other batter from his team in that innings. If he faces the most effective balls, his MEB is positive and equals the gap to the second-highest tally. Otherwise, it is negative and equals the gap to the top batter. In this sense, MEB captures a batter’s marginal contribution to holding the innings together: the more effective balls others have already absorbed, the smaller his incremental role, and hence the lower his MEB.

The MEB metric is designed to reward safehouses and conventional anchors, and the names near the top of the leaderboard are largely what you would expect. In no particular order, the list features the usual suspects: Simmons, Conway, Warner, Hussey, Kohli and company. Lending some credence to KL Rahul’s long-running complaint about a lack of batting support, he appears third on the list.

The more striking name on the leaderboard, however, is Sai Sudarshan. For most of his career, he has batted alongside Shubman Gill, who himself has piled on runs, posting NEBs of over 12, 3 and 7 between 2023 and 2025. Last season, Sudarshan even shared the crease with an extremely adamant Jos Buttler, who recorded an NEB of 6.41. Despite featuring in such a heavyweight top three, Sudarshan still ends up with an MEB of only −6 — yet another piece of evidence that he has been the immovable rock of the IPL.

Judged season by season, Shaun Marsh’s 2008 campaign stands alone, with an MEB of +11.25. To put that in perspective, Sangakkara was the only other KXIP batter that season to play at least seven innings and finish with a positive NEB — and even he managed only 2.63, compared to Marsh’s 20.7 (still the highest single-season NEB in IPL history). What makes this even more remarkable is that MEB is not a metric one expects to finish above zero. To do so, a batter has to outlast every teammate, game after game, by enough to offset the negative points from the occasional failure. Marsh did this to an almost comical degree. In only 10 of the 18 IPL seasons has any batter with at least seven innings even managed to finish with a positive MEB.

The closest modern approximations to Marsh’s one-man carry act are Warner in 2019 (4.61), Buttler in 2024 (4.05), and Kohli in 2024 (3.55). Even Kohli’s monstrous 2016 season was softened by the presence of de Villiers and Gayle, which dragged his MEB down to 2.38. Marsh, by contrast, was not merely the best batter in his side. He was the side. Returning to the protagonist who got us down this road, Rahul topped the MEB charts in back-to-back seasons — 2020 (−0.05) and 2021 (3.07) — at the height of the strike-rate debate around him.

These six dimensions only begin to sketch the shapes and sizes anchors come in. There are still many layers left unexplored. Some batters slip into cruise control after a blistering start (without working out the numbers yet, Buttler?), while others begin cautiously and accelerate into something far more dangerous (obviously, Gayle). Some live off the automatic single (Kohli); others go dot, dot, dot before a boundary (Simmons). And then there are matchups — batters who feast on spin (Klaasen, Dube, Pooran) and those who prefer pace on the ball (Lynn, David, Brook). This is just for anchors. Bashers might need a few dimensions of their own too.

Even with just the six dimensions from this chapter, we can already get deep into the anatomy of a batter’s career. Take Raina. A nostalgic CSK fan would remember him very differently from what the numbers suggest — and in this case, nostalgia is simply off. A better description would be Mr. Itchy Hands: highly volatile (PsP of −11.9), equally effective across both innings (a gap of just 1.39 in NEB), and, crucially, operating alongside a strong support cast (MEB of −17.84). If this chapter does anything, it is to show that anchoring is far too rich to be squeezed into a couple of dimensions. In Chapter 2, we take this string theory of anchoring a step further.