The lines and lengths are trying to tell us something
Taking a closer at line-length combinations used against different batters to see if there's more than what meets the eye
Matchups across all forms of cricket are predominant. They take different forms, and are incorporated within gameday strategy differently, but the thought process behind a bowling line-up is to bowl deliveries least suitable to a batter’s playing style. Their strengths. In some cases, their idiosyncrasies.
For the uninitiated, a matchup refers to a head-to-head record that highlights how a batter performs against specific types of bowling or situations. For instance, it could detail a batter's performance against spinners (e.g., leg-break, off-spin, or slow left-arm) or pacers. It might also analyze how they fare against faster-than-average deliveries or their scoring rate in the powerplay against various bowlers. The possibilities for matchups are vast, offering numerous ways to assess performance.
Some broad matchups are widely recognized by everyone. For example, right-hand batters are slow to strike against SLAs, left-hand batters aren’t able to hit right-arm off-break all too well, etc. Data around batter-specific matchups is now readily available. For example, Rishabh Pant finds it hard to score against right-arm express quicks (averaging 19 striking at 130), Virat Kohli is extremely cautious batting against SLAs and OBs, striking at 110 and 111 against them respectively.
Some batters may not dominate every bowling style, but they consistently perform decently and deliver sizeable returns against most types of bowlers. To understand how to effectively challenge these players, we can analyze specific combinations of line and length that bowlers use against them. By delving deeper into these patterns, we can identify the precise deliveries that are most effective in restricting their scoring, taking their wickets more efficiently, or achieving both objectives simultaneously.
The success percentage of the most commonly used line-length combinations in T20 matches across various phases of an innings is shown above. This percentage indicates how often each line-length combination results in a wicket. Unsurprisingly, the yorker on the stumps has the highest success rate, almost twice that of the short ball drifting down the leg side, at 2nd. However, simply reviewing these combinations doesn’t provide much insight. It’s more useful to plot these success percentages against the cost of each line-length combination for both spin and pace bowlers.
Side note: For any upcoming analysis, we will not be considering on-the-stump yorkers for either spinners or pacers.
The similarities and differences here are equally intriguing. Good-length deliveries, regardless of the type, offer comparable chances of success for both spin and pace bowlers. Deliveries pitched between good length and short, drifting down the leg side, are the least effective for both styles, although they are nearly twice as successful for pacers compared to spinners. On the other hand, a good-length delivery wide outside off-stump is slightly more effective for spinners and also proves to be less expensive. Conversely, short-pitched deliveries on the stumps are twice as likely to result in a wicket for pacers compared to spinners and are also significantly less costly as a bowling option.
Let us now take a closer look at some of the titans of the game to see if there is more than meets the eye.
Andre Russell, since 2019, has struck 2,005 runs at a SR of 180 and average of 27.5. Pretty decent numbers, given his entry points and what is often required of him. These numbers translate to him giving 27 runs off every 15 balls he faces before losing a wicket. More than decent.
If we further split these numbers by the bowling kind (right-arm or left-arm pace), we can unearth deltas in this seemingly one-sided matchup to discover his worst performing matchups. Against left-arm medium and right-arm fast, Russell averages 20 RpW striking at less than 160. Focusing on right-arm fast, against which he’s gotten out 19 times for 390 runs at a SR of 157. One might look at this and choose to default to right-arm fast against the giant, but it’s pertinent to look at the lines and lengths he’s fallen victim to, to understand how this match-up can be used against him in the most effective manner.
The success % indicates the proportion of balls bowled at a given line-length that yielded a wicket. As you can see, for all line-length combinations for which at least 10 balls were bowled, Russell’s found himself to be out of answers for balls pitched outside the off stump bowled short. For all other kinds of deliveries pitched outside off, he’s struck 50 runs at 200 SR, 83 at 166 and 40 at 160 for full, short of good length, and good length respectively. In the limited number of outside off stump short balls bowled to him. From the limited occurrences on hands, pitching him short and outside off-stump can be inferred to be a good strategy, but it hasn’t been used well enough.
Depending on the stage the innings is in, relative bowling strategies can be adapted to limit batters. In chases with a relatively bigger cushion of runs, bowling sides may choose to deploy bowling strategies with a higher success %. Similarly, with a modest target at hand, bowling teams can limit the abilities of the opposition batters by bowling line-length combinations they’re the most conservative against.
Suryakumar Yadav is an absolute beast in T20 cricket. Although in a lean patch right now, he is potentially the only cricketer that will go down as an all-time great because of his brilliance in only one format, the 20 over game. He, like most Indian batters, struggles a bit against SLA, but still fares better than most of his contemporaries. He’s conservative against the straight-on SLAOs, bowled at the stumps from a good length. As the bowler drifts his line away from the stumps, he finds himself to have more room, and his striking ability improves as the ball gets wider or fuller.
On the other hand, his numbers against leg-break bowlers paint a prettier picture. He strikes at 150 at an average of 46 RpW. For all batters with a minimum of 500 runs against leg-break bowling, only Nicolas Pooran has scored runs more quickly and at a higher average than him.
While the ball lined up on the stumps pitched at a good length from a SLAO bowler sets his striking ability back, he’s more proactive against a similarly pitched delivery coming from a leg-break bowler (52 avg, 148 SR). It will be cruel to call it a weakness, but he is relatively tamer against balls that are pitched outside the off-stump on a good length by a leg-spinner
He strikes at 121 against balls pitched on a good length outside off-stump, compared to 149 for deliveries of a similar length but targeting the stumps. Additionally, he loses his wicket at almost the same rate relative to the runs scored in both scenarios. While not an overwhelmingly effective matchup, this is a strategy that teams should consider using against him.
Some line-length combination matchups are easier to unearth, with just a little bit of digging. Heinrich Klaasen is one of the greatest T20 bats in the world right now. The man has an unmatched ability against spin, one of the most lethal hitters in the death overs, and fares well against pace bowling of all kinds as well (1,538 runs at a SR of 154 and an average of 29.5 RpW). For the 933 balls against pace that we have line-length information for, we can look at the matchups against major line-length combination to see what has helped oppositions limit his ungodly ability.
Klaasen demolishes absolutely everything that’s full and quick, and pounces on the short ball when there’s width offered as well. Keeping it pitched on the stumps at a good length or slightly shorter than that keeps him honest and puts him in a position where he’s both conservative and also vulnerable to losing his wickets.
Of the 11 wickets that have come from bowling him outside the off stump on a good length, 6 of them were taken by right-arm quicks.
If I were to templatize the structure of a T20 innings for a top-order batter (batting positions 1-3), since they, on average, play a larger proportion of the innings than the rest of the batting order, one would go about actively rotating the strike in the powerplay and hitting balls freely over the 30-yard circle on account of restricted fielders on the boundary line, then going comparatively slower in the middle phase where they anchor the innings, and finally unleashing in the death overs.
In a similar fashion, kinds of bowling can also be templatized, and much simply at that. Typical 20 overs in a T20 innings comprise the first 6 overs bowled in an 80/20 split between pace and spin, followed by a 60/40 split between spin and pace between overs 7-11, a 50/50 split in overs 12-15 and again an 80/20 split for the final 5 overs. This fashion has replicated itself almost too rigidly since 2019 (the data undertaken in this study), although teams are now starting to get shrewder with their bowling attacks, playing right into the conditions at hand and throwing the typical bowling attack template out the window (case in point: the SA20 game between Paarl Royals and Pretoria Capitals where PR bowled all 20 overs with spin on a slow, gripping Paarl wicket, the first such instance in the history of the format).
What can be helpful at this stage is to look at how well batters progress playing different kinds of bowling as they pace up the innings in a 20-over game. For this, I’ll take a sample of 25 batters (the highest run-scorers in the powerplay since 2019) and observe how their striking and dismissal rate changes from the Powerplay (overs 1-6) and death (overs 16-20).
Several things jump out the minute you look at this graph. Batters like Finn Allen and Will Jacks are, unsurprisingly, at the top-left corner, striking really quickly in the Powerplay while being dispensable with their wicket. A very high proportion of the 25 batters are concentrated in the area with the average ranging from 25-35 and the SR between 120 and 160. Faf bests Kohli in both the average RpD and the SR while Warner is much of an accumulator.
KL Rahul would have stood out as an obvious exception if it wasn’t for Rizwan who averages almost 70 RpD and strikes at only 123.
What was fascinating to me was that you can draw almost draw an imperfect curve to exhibit the different kinds of openers that I’d like to call, for the lack of a better word, optimal.
They could either be batters that lose their wickets early on, but not before they’ve scored a decent amount of runs at a hefty SR, often enough for the middle order bats to take things forward and set-up a good total
Or they could be batters that accumulate runs without losing their wicket, but they aren’t too precarious about their wicket, like KL and Rizwan. They understand the importance of sticking around, but not at the cost of playing too slow, which in turn would put pressure on the rest of the batting order
You have Finn Allen and Warner on the extremes here, with batters like Jacks and Faf trying to strike a balance somewhere between the anchoring and aggression.
Let us now look at how these batters have fared against pace bowling in the death overs, when they’ve had time to settle in. Quick caveat: keeping a filter of at least 150 runs in the final 5 overs has seen batters like Banton, Will Jacks, and Paul Sterling not make it to the data table.
What’s observable here is that most batters hover around the 200-odd strike rate region with a wide range of averages on show. Salt loses his wicket after every 26 runs while the same is close to 38 for Kohli. Guptill stands out as an anomaly here with his SR close to 240, but that’s come off the back of 78 deliveries, as opposed to 442 by Kohli.
What can be derived from here is that once batters do get their eye in, they’re better suited to strike at the rates best only matched by your dedicated finishers. However, the ability to stay not out through the death overs while continuing to score varies greatly across batters.
Similar tests can be conducted to analyze how batters perform against different combinations of lines and lengths predominantly bowled in specific phases of an innings, in order to identify their deltas and determine how they can be limited based on the line-length combinations that trouble them the most.
Our hypothesis on the importance of precision in line-length combinations is further validated when we evaluate bowlers based on the proportion of effectively defensive deliveries they bowl. The data clearly indicate that a higher percentage of deliveries pitched on a good length outside the off-stump strongly correlates with a bowler’s economy rate. This trend holds consistently across both spin and pace bowlers, with only a few expected outliers.
This analysis considers bowlers who have bowled over 1,000 deliveries between 2019 and October 2024, with available line-length data. The dataset includes 40 spinners and 74 pacers, evaluated based on the percentage of deliveries bowled outside the off-stump on a good length.
Two standout outliers emerge—Sunil Narine among spinners and Jasprit Bumrah among pacers—both regarded as among the most effective bowlers in the format’s history. Interestingly, they each rank second highest in their respective categories for the percentage of deliveries bowled in this area while also maintaining the lowest economy rates within their groups. Bhuvaneshwar Kumar bowls substantially more balls in that area while still leaking ~7.5 RPO while both Fazalhaq Farooqi and Jofra Archer bowl close to the median proportion of good-length balls while having some of the lowest economies, indicating that their bowling arsenal comprises other line-length combinations that assist them in keeping their bowling tight. For example, with balls swinging in (for Fazalhaq) and hit-the-deck stock deliveries (for Archer).
A freaky coincidence was to see both Sikander Raza and Varun Chakravarthy having bowled exactly 25.80% of their deliveries in that region going at exactly 7.29 RPO each over the sample data.
The trend lines for both spinners and pacers are nearly parallel, though pacers tend to have a higher economy rate on average, likely due to their frequent role in the death overs.
Line-length combinations provide a strong directional framework for understanding how to limit a batter’s scoring, but incorporating additional granular metrics—such as ball speed, release point, variation in pace upon pitching, and ball-tracking data on impact—would enhance precision. These factors could uncover deeper insights into why certain deliveries trouble specific batters more than others, leading to more effective bowling strategies.