Pressure Index: What Bangladesh's Middle-Over Dot Clusters Taught Me

Chittagong, the fourteenth over. The spinner released it flat; the batter drove straight, long-on fielded — one run. The next ball was pushed to deep midwicket — one. The third hit the pad, an lbw appeal, not out — dot. The fourth, a top-edged sweep dropping between two fielders — dot. The fifth, a reverse sweep, missed — dot. The sixth, a lofted shot to long-off — two.

My scorebook reads 1, 1, 0, 0, 0, 2. Three dots in six balls, and the commentary box was already saying Bangladesh were losing control. I wrote something different. Four of those six deliveries were bowled on a length where the batter had no swing, only a push. The real information was not in that over; it was in the first ball of the next one. Before the model had a name, I counted chances by hand, and I have never dropped the habit.

I learned the language of pressure measurement from football's PPDA — Root: PPDA and Germany. In 2026, writing about Germany's 0-2 defeat in Russia, I found their PPDA was 6.2, yet they conceded 18 shots and generated only 0.8 xG. Low PPDA means high pressing — that simple reading collapsed that night.

Transplant that reading straight into cricket and you will be wrong. Football's pressure is continuous; cricket's pressure is discontinuous. Pressure here arrives ball by ball, over by over, phase by phase. So my dossier uses three cricket-specific pressure events. Dot-ball cluster: three or more consecutive dots along the same bowler or the same boundary line. Wicket-taking ball: the batter's run-making shot ratio across the two deliveries before the wicket fell. Boundary suppression window: the over span in which boundaries per ball are falling.

I set the baseline against Mirpur's winter pitch, second-innings dew, and the opposition's spin depth. Sitting in my data room in Khulna, I have seen repeatedly that without those three variables, a Mirpur win and an overseas win cannot be weighed on the same scale.

Across the last fourteen innings I counted by hand — seven by Bangladesh, seven by opponents — 3,372 balls in total. Of those, 1,284 were dots: 38.1 percent. But that is the unadjusted figure. Split it by phase and the picture moves: dots in the powerplay 34.6 percent, middle overs (7-15) 42.3 percent, death overs 31.9 percent. The middle overs are the most expensive region, because the ball is old, the spinner has it in hand, and the innings clock is running against the batter.

Now the environment correction. On Mirpur's winter pitch the ball bounces low, and shots must be played without getting under it. Once dew settles, batting in the second innings gets easier because a wet ball kills the spinner's grip. Opposition spin quality is not equal either. After correction the numbers read: 44.8 percent on a dry pitch against strong spin, 36.9 percent in a dew-affected second innings, 38.2 percent on a bouncy overseas surface. In other words, at least six percentage points of that frightening Mirpur dot count is the pitch's receipt, not the batter's bankruptcy.

The real work starts here — the relationship between a cluster and the ball after it. Across the fourteen innings there were 211 deliveries immediately following three consecutive dots. On those release balls, boundaries came off 46 of them, 21.8 percent; wickets fell on 19, 9.0 percent. The ball after three dots is a tossed coin — the reward is large and so is the risk. The problem is that on 147 of those 211 balls, the batter went long, with a success rate of only 23.1 percent. Trying to break dot pressure, he forced the very ball on which two balls of rotation would have been the easier play.

In my notebook, per 100 balls, a few indices read like this. Mushfiqur Rahim is the most reliable middle-over rotation index, because his sweep and cover drive send the ball to opposite sides. Towhid Hridoy attacks the release ball but his success rate swings. Shakib Al Hasan is the best at breaking a dot-after-dot chain, because he forces the spinner to change his line. Jaker Ali is sharp between overs 16 and 20 but weak in rotation between 7 and 15.

The bowling side must be read with the same logic. Mustafizur Rahman's cutter is economical at the death, but a slower cutter gives the batter time, so his dot-to-boundary rate runs high. Taskin Ahmed's wide yorker creates dots, but when the set-up fails the ball drifts to the leg side. Rishad Hossain's googly is superb as a wicket-taking ball, yet when his line shortens the dot cluster breaks by itself.

Add the second layer of environmental correction. In 2026, studying 83 Bundesliga matches in empty stadiums under an adjusted xG model, I built a coefficient that added 0.15 xG to away teams. For cricket, my dew coefficient is now 0.018 runs per ball for the side batting second — roughly nine runs across fifty overs. At Mirpur under lights, the margin of victory is often decided by exactly those nine runs, and we call it luck.

This is where my second warning matters. Is a dot-ball cluster the cause of a collapse, or its symptom?

I split the innings in two. Where no wicket fell in the powerplay, middle-over dots were 33.2 percent. Where one or two wickets fell in the powerplay, they were 46.8 percent. More than thirteen points of difference, with the same human being batting between overs seven and fifteen. The real event happened earlier — the powerplay wicket. The middle-over dot cluster is its shadow, because a new batter must set himself and the opposition spinner then gets an attacking field. An analyst who blames the batter for middle-over dots is punching a shadow.

The eye test is a witness, not a judge; the model keeps the transcript. And behind Mirpur's home record sits roughly six runs of dew — a subsidy we routinely mistake for skill.

There is another trap: the heatmap. We assume a heatmap shows us a batter's role. It shows where the shot went, not why. The same batter hitting the same zone is a rotation machine one day and self-destructive the next; the difference lives in the team plan and the bowler's length map. Heatmaps are the new tea leaves, and I stopped reading tea leaves the day I started counting by hand.

The same applies to selection. We still treat a batting order as a hierarchy. I stopped reading team-change stories the day I learned to read risk profiles — who takes what risk in which phase is the real language.

For the next series I am adding one column to my dossier — release-ball strike rate. A side that breaks a dot cluster does not merely hit bigger; it reads the game between two deliveries. The signal for the next round is simple: if the rotation rate across the first twelve balls after the twelfth over drops below thirty-six, the innings will fold late, dew or no dew.

The question now sits in front of the selectors — a boundary hitter at number five, or a rotation machine? My notebook is still silent. The column is ready.