IPL 2026

IPL 2026 Dismissal Fingerprints

How does each top bowler take wickets, and which dismissal mode is each top batter most vulnerable to? Bowler-credited dismissal-mode distributions across 73 IPL 2026 matches — caught, bowled, lbw, stumped.

Dataset: 2026-10-18Published: CC-BY-4.0 · Cite freely

Summary

Two bowlers can take the same number of wickets in completely different ways. One edges batters to the slips; another knocks the stumps over. That how is a fingerprint — it tells you what a bowler actually does to a batter, and which batters are built to resist it. This report decomposes every bowler-credited dismissal in IPL 2026 into its mode (caught, bowled, lbw, stumped, hit-wicket) and reports, for the busiest bowlers and most-dismissed batters, the distribution that defines their season. Run-outs are excluded from bowler fingerprints (they are not bowler-credited) but retained for batter vulnerability.

Scope

Dimension Value
Competition IPL 2026
Matches 73 (both innings; 1 of 74 abandoned, no ball data)
Dismissals classified 835 bowler-credited + run-outs, from score.name on each wicket delivery
Bowler fingerprint floor ≥12 bowler-credited wickets
Batter vulnerability floor ≥8 dismissals (all modes)
Dataset version 2026-10-18

The baseline: IPL 2026 is a caught league

Across all 835 bowler-credited dismissals in IPL 2026, the mode split was:

Mode Share
Caught 81.0%
Bowled 13.8%
LBW 3.6%
Stumped 1.4%
Hit wicket 0.2%

Four of every five wickets are catches. This is the reference line every fingerprint below is read against — a bowler at 90% caught is an edge-and-aerial bowler; one at 28% bowled is beating batters through the gate far more often than the league norm.

Bowler fingerprints

Busiest bowlers (≥12 credited wickets), by dismissal mode:

Bowler Wkts Caught Bowled LBW Stumped
Kagiso Rabada 29 86.2% 10.3% 3.4% —
Bhuvneshwar Kumar 28 78.6% 17.9% 3.6% —
Jofra Archer 25 72.0% 24.0% 4.0% —
Anshul Kamboj 21 81.0% 19.0% — —
Rashid Khan 21 57.1% 28.6% 9.5% 4.8%
Eshan Malinga 20 90.0% 10.0% — —
Mohammed Siraj 19 89.5% 10.5% — —
Rasikh Salam Dar 19 73.7% 26.3% — —
Kartik Tyagi 18 94.4% 5.6% — —
Sunil Narine 17 76.5% 23.5% — —
Prasidh Krishna 16 93.8% 6.3% — —
Prince Yadav 16 68.8% 31.3% — —
AM Ghazanfar 15 60.0% 20.0% 13.3% 6.7%

What stands out:

Batter vulnerability

Most-dismissed batters (≥8 dismissals), by how they got out:

Batter Outs Caught Bowled LBW Other
Vaibhav Suryavanshi 16 87.5% 6.3% 6.3% —
Sai Sudharsan 16 81.3% 6.3% — hit-wkt 12.5%
Shubman Gill 16 81.3% 6.3% 6.3% stumped 6.3%
Travis Head 15 86.7% 13.3% — —
Ishan Kishan 15 80.0% 13.3% — run-out 6.7%
Abhishek Sharma 14 92.9% 7.1% — —
Devdutt Padikkal 14 78.6% 21.4% — —
Dhruv Jurel 14 50.0% 28.6% 14.3% stumped 7.1%
Jos Buttler 14 64.3% 21.4% 7.1% stumped 7.1%
Nicholas Pooran 14 64.3% 28.6% — run-out 7.1%
Yashasvi Jaiswal 14 92.9% 7.1% — —

What stands out:

Why this matters

Dismissal mode is the missing half of a matchup. A bowler who takes 86% of his wickets caught (Rabada) against a batter who is 50% bowled (Jurel) is a different contest than the raw averages suggest — one attacks the edge, the other is beaten through the gate. These fingerprints feed directly into the head-to-head and player-profile surfaces: when the data says "dismissed by X," this report says how.

Provenance

Source: CricketStudio aggregation of IPL 2026 ball-by-ball data. Dismissal mode classified from the score.name field on each wicket delivery (Catch Out → caught, Clean Bowled → bowled, LBW OUT → lbw, Stumped, Run Out, Hit Wicket). Bowler fingerprints count bowler-credited dismissals only (run-outs excluded); batter vulnerability counts all dismissals. Sample floors disclosed above. Window: IPL 2026 (73 captured matches, season complete). Computed 2026-10-18 UTC.