Modern Cricket Analytics: Wagon Wheels, Pitch Maps and the Control Percentage
How professional cricket teams use ball-tracking data, wagon wheels, pitch maps and Control% to scout opponents and plan match strategies.
Professional cricket teams in the 2020s employ dedicated data analysts who use ball-by-ball tracking databases to prepare detailed dossiers on opponent batters and bowlers. The tools available — wagon wheels, pitch maps, Control percentage, wagon zones, wagon-wheel heat maps — have transformed match preparation from impressionistic to evidence-based, while also changing how broadcasters present the game to audiences.
The Wagon Wheel
The wagon wheel is a top-down diagram of a cricket field with lines radiating from the pitch to show where a batter's runs were scored. A batter who hits predominantly through the covers will have a wagon wheel clustered in the off-side arc between point and mid-off; a batter who favours the pull and cut will show clusters in the square leg and backward point zones. Analysts use multi-match wagon wheels to identify scoring patterns and, crucially, gaps — areas where a batter either does not score or scores in very low volumes. These gaps inform fielding positions (plugging them) and bowling strategies (bowling to force a batter into uncomfortable areas).
Pitch Maps
A pitch map plots where a bowler's deliveries land on the pitch. A seam bowler's pitch map shows whether they consistently hit a good length (the most challenging area for batters, approximately 6-8 metres from the batter) or drift full (easier to drive) or short (easier to pull). Analysts study opponent bowlers' pitch maps to predict where deliveries will land and thus where a batter should be positioned at the crease — coming forward to negate full deliveries, going back to pull short ones. They also study own bowlers' maps to identify control problems and adjust run-up or release points. High-speed cameras under the ICC's official ball-tracking contracts can record precise landing coordinates to within a centimetre.
Control Percentage and Its Uses
Control percentage (Control%) measures how often a bowler delivers the ball into their intended target area — essentially a quantification of bowling accuracy. The definition of the control zone varies by team and context, but a common definition requires the ball to land on a good-to-full length on or just outside off stump within a defined width. A high-quality Test seam bowler might achieve 65–75% control; a T20 specialist aiming for the blockhole at death is targeting a narrower zone and might achieve 50–60%. Control% is used by teams to identify fatigue (accuracy dropping across a long spell), identify bowlers under pressure (accuracy falling in the second half of innings), and evaluate bowling performance beyond the raw economy rate.
Frequently asked questions
How much do analytics actually change match-day cricket decisions compared to traditional captain's instinct?
The relationship between data analytics and captain's decision-making in professional cricket is more complex than either the 'data has replaced instinct' or 'captains ignore analysts' narratives suggest — the reality involves a structured pre-game analytical phase that heavily informs options, followed by in-play decisions where real-time conditions, player feel and captain's read of the match still dominate. Pre-game: where analytics dominate: in Test cricket, team meetings before a series are now entirely data-structured. Analysts produce opponent dossiers showing pitch maps for their bowlers (where they land the ball), wagon wheels for their batters (where they score), dismissal patterns (how they got out across their last 30-40 innings) and wagon-zone heatmaps for different match situations. These dossiers directly determine batting orders (putting players who are strong against left-arm spin higher when the pitch will aid it), bowling plans (opening with swing bowling to a batter who has an outside-edge dismissal pattern) and fielding positions at match start. This pre-game analytical work is comprehensive and measurable — it is not guesswork. In-play: where experience and instinct still dominate: the most important in-play decisions — when to take the new ball, when to declare, who to bowl in a decisive over — require real-time assessment of factors that no pre-match analysis fully captures: how tired a bowler looks, how a particular pitch has deteriorated unexpectedly, how a specific match psychological moment is shifting momentum. Data analysts sitting off the field cannot always communicate complex recommendations quickly enough to affect decisions in the 30 seconds between overs. Experienced captains like Ricky Ponting, MS Dhoni and Joe Root described in interviews using analyst input as a starting framework that they then adjusted based on match conditions. The synthesis: elite international cricket teams use a model where analytics set the probabilities (this batter is 40% more likely to get out between overs 15-25 of a Test innings than in the first 15) but the captain and player still exercise the in-play judgment of whether this specific match moment is typical or exceptional. The best teams do both — data-informed preparation, experienced in-play adjustment.