Following the publication of early trends for the 2026 West Bengal Assembly Elections by the Election Commission of India and ADR, the impact of the much debated Special Intensive Revision (SIR) on the electoral landscape of West Bengal has become much more apparent nationwide.
Data provided by the Election Commission of India (ECI) and independent SIR impact analysis suggest a seismic shift in the state’s traditional vote banks, particularly across the border districts and the critical Murshidabad belt.
Early trends: BJP surges, TMC trails
According to current trends, the Bharatiya Janata Party (BJP) has taken a commanding lead in 157 seats, crossing the majority mark of 148. The Trinamool Congress (TMC), meanwhile, is trailing significantly, leading in only 53 seats. This represents a sharp departure from the 2021 results, a change analysts attribute largely to the implementation of the SIR process.
While early ECI trends at 10:49 AM showed the BJP leading in 92 seats and AITC (TMC) leading in 52 (out of 147 trending seats), broader impact assessments suggest a final shift of 157 seats for the BJP compared to just 53 for the TMC. The diverging fortunes in Bengal highlight the unique impact of administrative interventions on electoral outcomes.
How did SIR impact BJP and TMC vote bank
A pivotal factor in these numbers is the impact of voter exclusions. The SIR process has excluded approximately 27 lakh voters from the electoral rolls, citing ‘logical discrepancies’ and other verification factors. (Where voters may have moved or died.)
While broader estimates from SIR impact reports suggest a total of 91 lakh voters were removed across the state’s 294 constituencies, the 27-lakh figure represents a core demographic shift that has directly affected the competitive margins of the ruling TMC.
Murshidabad: Worst hit district from SIR
Murshidabad, a district with a 67% Muslim population and 22 assembly seats, has emerged as the highest-impact zone under the SIR. Approximately 4.5 lakh voters were deleted in this district alone, the heaviest per-capita deletion in the state.
As per independent analysts, the high number of deletion of voters in Murshidabad has affected the number of votes coming from the sizable Muslim community in the region that previously served as a reliable votebank for Mamata Banerjee led TMC. There are an estimated 41-54 Muslim-decisive seats at stake where this consolidation remains the primary counter-force to the SIR-driven shift.
In a development described as the most dramatic demographic shift post-SIR, the Jangipur constituency has reportedly flipped from a Muslim-majority to a Hindu-majority electorate.
Mahua and Matua Belts: The Border Factor
The impact is equally pronounced in the Mahua (Matua) and border belts, where the SIR, CAA, and immigration narratives converge. Classified as a district that was extremely significantly impacted by the much debated SIR exercise, this district saw roughly 3.8 lakh deletions.
In North 24 Parganas and Nadia, the Matua community (frequently associated with Mahua-belt politics in the state’s tribal and forest fringes) remains the decisive factor. The SIR Impact Analysis categorizes the Matua-heavy regions as an high impact zone, particularly due to the intersection of SIR and CAA (Citizenship Amendment Act) narratives.
As per independent analysts, the post-deletion data indicates a visible rise in the Hindu vote share in border constituencies such as Chopra, Islampur, Goalpokhar, and Farakka. Analysts from News18 a suggest that while deletions have reduced the absolute voter base, they have simultaneously spiked turnout among the remaining electorate in these belts, particularly among communities seeking to affirm their citizenship status.
| District | Deletions | Seats | SIR Level | Muslim % | Muslim Decisive | Matua/Rajb. | Border | Worst-hit Seat |
|---|---|---|---|---|---|---|---|---|
| Murshidabad | ~4.5L | 22 | V HIGH | 67% | YES | — | Partial | Jangipur (flipped) |
| N 24 Parganas | ~3.5L | 33 | V HIGH | Mixed | Partial | Matua | Yes | Baduria / Basirhat |
| Nadia | ~3.0L | 17 | V HIGH | ~45% | Partial | Matua Core | Partial | Ranaghat (78% del.) |
| Malda | ~2.5L | 12 | V HIGH | 51% | YES | — | Partial | Mothabari (37K) |
| S 24 Parganas | ~2.5L | 31 | V HIGH | ~35% | Partial | — | No | Metiaburuz (25K) |
| Uttar Dinajpur | ~2.0L | 9 | V HIGH | 50% | YES | — | Yes | Goalpokhar (30K) |
| Birbhum | ~1.8L | 11 | HIGH | ~40% | Partial | — | No | Murarai / Nalhati |
| Cooch Behar | ~1.5L | 9 | HIGH | ~25% | No | Rajbongshi | Yes | Sitalkuchi (20K) |
| Kolkata | ~1.3L | 11 | HIGH | ~25% | No | — | No | Bhabanipur (47K) |
| Howrah | ~1.2L | 16 | HIGH | ~25% | No | — | No | Uluberia Purba |
| Dakshin Dinajpur | ~0.7L | 6 | MED | ~32% | No | — | Yes | Harirampur |
| Purba Bardhaman | ~0.6L | 16 | MED | ~22% | No | — | No | Ketugram |
| Alipurduar | ~0.4L | 5 | MED | ~14% | No | — | Yes | Kumargram |
| Jalpaiguri | ~0.4L | 7 | MED | ~21% | No | Rajbongshi | No | Rajganj |
| Paschim Bardhaman | ~0.4L | 9 | MED | ~22% | No | — | No | Asansol Uttar |
| Hooghly | ~0.3L | 18 | LOW | ~14% | No | — | No | Singur |
| Purba Medinipur | ~0.2L | 16 | LOW | ~11% | No | — | No | Nandigram |
| Paschim Medinipur | ~0.2L | 15 | LOW | ~10% | No | — | No | Kharagpur |
| Darjeeling | ~0.2L | 6 | LOW | ~11% | No | — | Yes | Siliguri |
| Bankura | ~0.1L | 12 | LOW | ~9% | No | — | No | — |
| Purulia | ~0.1L | 9 | LOW | ~9% | No | — | No | — |
| Jhargram | ~0.04L | 4 | LOW | ~7% | No | — | No | — |
| Kalimpong | ~0.03L | 1 | LOW | ~5% | No | — | Yes | — |