Seventy matches. Ten franchises. 58 days of theatre that produced one of the tightest league phases in the Indian Premier League’s eighteen-year history.
When the dust finally settled on Sunday evening, three teams – Royal Challengers Bengaluru, Gujarat Titans, and Sunrisers Hyderabad – had finished on identical totals of eighteen points. A fourth, Rajasthan Royals, scraped through on sixteen with a victory over the Mumbai Indians on the final matchday. Net Run Rate, that most unforgiving of tiebreakers, separated them into seedings. The architecture of what follows is now set: RCB against GT in Qualifier 1 at Dharamsala on May 26, SRH against RR in the Eliminator at Mullanpur on May 27, with the final scheduled for May 31 at the Narendra Modi Stadium in Ahmedabad.
The question, of course, is who wins it. Consensus opinion rarely satisfies in a format as volatile as T20 cricket. To answer it with any rigour, this analysis draws on the WPA Impact Index – a proprietary player valuation model built by the author – applied to all seventy league-stage matches, producing ball-level win probability metrics, batting and bowling impact scores, and a composite team performance rating across every phase of play. What the numbers reveal is a contest in which each finalist brings a structurally distinct identity, and in which one side’s advantages look, on balance, substantially more durable in knockout conditions.
The case for Gujarat Titans
Begin with bowling, because in high-stakes T20 cricket, bowling wins tournaments. On that singular axis, the Gujarat Titans are not merely the best side in this playoff field – they are categorically superior to the other three combined. Their attack claimed 102 wickets across the league phase, eighteen more than any other playoff side. Their economy of 9.16 runs per over is the leanest of the four. Their bowling dot-ball percentage of 39.8% – nearly nine percentage points clear of the next team – speaks to an ability to build pressure in sustained passages that no opposition batting lineup can simply plunder through.
The architecture of that attack is what makes it genuinely threatening. Kagiso Rabada leads with 24 wickets at an economy of 9.19 and a dot-ball rate of 44%. Mohammed Siraj, operating with the new ball and at the death, took 17 wickets while conceding at 8.59 and producing a dot-ball percentage of 46%. Rashid Khan provided 19 wickets of wrist-spin control at 8.72. Three bowlers, three disciplines, all operating above the line that separates competence from excellence. Against that, Shubman Gill (616 runs at 161.7 SR), Sai Sudarshan (581 runs), and Jos Buttler (469 runs) ensure the batting is not merely decorative.
The WPA model assigns GT a tournament-win probability of 36% – the highest of the four. Their record against fellow playoff opponents reinforces the case: a win over SRH by 82 runs, over RR by 77 runs, and a second-leg win over RCB. In the second half of the season, Gujarat played three matches against their playoff rivals and won all three. Their average WPA team impact score across the final five league matches was 979 – the highest of any side in the group.
The threat from Sunrisers Hyderabad
No team in IPL 2026 was more compelling to watch. SRH finished the league phase with the highest composite WPA score of all ten franchises (10,620), driven by a batting lineup of almost surreal destructiveness. Abhishek Sharma operated at a strike rate of 206.23 across the season. Ishan Kishan contributed 569 runs at 178. Heinrich Klaasen, the most prolific impact batter by the model’s raw scoring metric, accumulated 606 runs while managing his team’s middle phase with rare authority. In the powerplay, SRH scored at 11.02 runs per over, second only to Rajasthan Royals among the four playoff sides.
The structural flaw, however, is not well hidden. SRH’s bowling economy of 9.90 is the worst of the playoff field. They claimed only 84 wickets in the league phase, the fewest of the four sides. Ten of their fourteen matches were spent batting first, which means their record as chasers is a sample of just four games. When they faced the side most likely to test their batting, the Gujarat Titans, they were beaten by 82 runs. That single data point looms large. The WPA model gives them 27%, which accounts for their devastating batting ceiling while discounting a bowling attack that may not hold under sustained knockout pressure.
The defending champions’ dilemma
RCB earned the top seeding and with it a direct path to the final from Qualifier 1. Their trump card is Bhuvneshwar Kumar, who produced the most economical bowling season of any frontline seamer in the playoff field: 24 wickets at 8.07 runs per over, a dot-ball percentage of 37.6%, and a wicket-taking record (13.75 balls per wicket) that no equivalent operator across the four sides can match. Their death-over batting, at 11.38 runs per over, is the best of any playoff side in the tournament, a function of a lower order that consistently executed when the pressure peaked.
The problem is the broader context. RCB’s last five WPA scores read 472, 778, 519, 992, 474. This is not the form profile of a tournament favourite; it is the oscillation pattern of a side that plays brilliantly one match in two. Their bowling beyond Bhuvneshwar is mediocre by playoff standards. Their final league game was a 55-run loss to SRH. The WPA model places them at 24%, close enough to the frame to be genuinely dangerous, but undermined by a structural reliance on individual brilliance rather than systemic depth.
The Rajasthan gamble
Rajasthan Royals must win three consecutive knockout games to lift the trophy. On the aggregate data, that is the longest route to the title, and it begins against an SRH side that beat them twice in the league – once by 57 runs, once by five wickets.
Their case rests on two extraordinary individual performers. Vaibhav Sooryavanshi, at a season strike rate of 232, is the most explosively disruptive powerplay batter in the tournament – a player capable of reducing an opposition’s best-laid plans to rubble inside six overs. Jofra Archer (21 wickets, 8.77 economy, 44.9% dot balls) is a quality seam bowler with the ability to generate wickets in phases that matter most. The problem is depth. Their overall bowling economy of 9.79 is the second-worst among the four, and their 0-2 record against GT – including that 77-run defeat in Match 52- represents a significant structural vulnerability should they advance to meet Shubman Gill’s side. The WPA model assigns them 13%: winnable, but only if the variance falls almost entirely their way.
What the data says about the final
The most likely final, on current probability, is a Gujarat Titans versus Sunrisers Hyderabad meeting – the two highest-performing sides across the league phase. In that contest, GT’s bowling attack carries a material advantage over SRH’s. The Eliminator matchup between SRH and RR is the most unpredictable fixture of the entire playoff sequence: one explosive batting lineup against another, with thin bowling on both sides. That is precisely the kind of match that produces the outlier result tournament narratives thrive on.
But the numbers, for now, point clearly. Gujarat Titans walk into this playoff as the most complete side, the best-equipped bowling attack, and the in-form team of the back half of the season. The trophy is theirs to lose.
Methodology note
The WPA Impact Index is a proprietary player performance model designed and built exclusively by the author. It assigns ball-level win probability deltas to every delivery across a match, then attributes those deltas to individual batters, bowlers, and fielders based on their role in each outcome. The model produces batting, bowling, and fielding impact scores that are aggregated at the player and team levels across matches. Manual performance ratings – applied by the author to each match – supplement the algorithmic output to account for contextual contributions that ball-level data alone does not fully capture. Captaincy ratings are assessed separately. All figures in this piece are sourced from this model, applied to ball-by-ball data from all seventy IPL 2026 league-stage matches.
Win probability estimates presented in this article are derived from a statistical model applied to historical in-season performance data. They represent probabilistic assessments, not predictions. Cricket, particularly in knockout formats, is subject to variance that no model can fully account for. Injury, conditions, toss outcomes, and individual match-day performance can materially alter results. The figures should be read as analytical starting points, not as forecasts of specific outcomes.