RRB CMA CBT 2 CUTT OFF PREDICATION

RRB CMA

@RRBCMACONTENT

CEN 05/2025 · CBT Stage 1 · Chemical & Metallurgical Assistant

RRB JE CMA CBT-2
Predicted Cut-Off 2025–26

Zone-Wise Raw Score Thresholds · Based on 52,437 Candidate Dataset

Predicted scores — NOT official. Keep preparing with full confidence!

Chandigarh

76.74

UR Cutoff

Bengaluru

75.75

UR Cutoff

Kolkata

84.62

UR Cutoff

Bilaspur

79.60

UR Cutoff

Chennai

84.42

UR Cutoff

CATEGORY-WISE CUTOFF MATRIX

★ = Must clear UR (no reserved vacancy)

RRB Zone	UR	OBC	EWS	SC	ST
Chandigarh	76.74	76.74	70.47	64.68	76.74
Bengaluru	75.75	74.48	69.06	66.50	59.58
Kolkata	84.62	81.92	80.67	78.00	69.00
Bilaspur	79.60	74.69	72.00	67.00	63.00
Chennai	84.42	84.42	84.42	84.42	84.42

Extreme (80+)

High (75–79)

Moderate (70–74)

Lower Tier (<70)

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t.me/RRBCMACONTENT  ·  Predicted cutoffs based on statistical analysis · Not official RRB data

Deep Dive · How We Predicted the Cutoffs

The Science Behind
Every Number

A full breakdown of the statistical methodology, normalization model, and category migration logic used to derive these predictions.

STEP 01 · DATA SOURCE

52,437 Candidates. One Dataset.

All predictions are grounded in the RankMitra cumulative frequency dataset covering 52,437 CMA candidates who appeared in RRB JE CBT-1 (Feb 19–25, 2026). The data is structured as a cumulative frequency distribution — how many candidates scored ≥85, ≥80, ≥79... all the way down. This dataset is the foundation of every number you see in the cutoff table.

STEP 02 · VACANCY MULTIPLIER

The 1:15 Shortlisting Rule

RRB mandates that 15 candidates are shortlisted per vacancy for CBT-2. So if a zone has 10 UR vacancies, the top 10 × 15 = 150 candidates advance. This multiplier is applied category-wise independently:

Example — Chandigarh:
UR: 13 vacancies → select top 195 candidates
SC: 7 vacancies → select top 105 candidates
EWS: 4 vacancies → select top 60 candidates
OBC/ST: 0 vacancies → must clear UR

STEP 03 · INTERPOLATION FORMULA

Pinpointing the Exact Decimal Cutoff

Because candidates are rarely distributed exactly on whole-number boundaries, we use linear interpolation between adjacent mark intervals. The formula:

Cutoff = M_upper − [ (R_target − R_upper) / (R_lower − R_upper) ] × (M_upper − M_lower)

Where: M_upper / M_lower = mark boundaries · R_target = required rank · R_upper / R_lower = cumulative candidates at each boundary.

Example — Chandigarh UR:
188 candidates scored ≥77, 215 scored ≥76 (density = 27).
Target rank = 195. Deficit from 77-mark boundary = 195 − 188 = 7.
Drop = 7 ÷ 27 = 0.259 → Cutoff = 77 − 0.259 = 76.74

STEP 04 · CATEGORY MIGRATION

The UR Squeeze Effect

The UR list is pure merit — it absorbs top scorers from ALL categories. OBC/SC/ST candidates who breach the UR cutoff are moved into UR seats, freeing their reserved quota for others just below them. This cascading logic drives the reserved cutoffs:

High OBC
Scorer

→

Fills UR Seat

→

OBC Quota
Reserved for Next

In Bengaluru, 60.25 OBC candidates captured UR seats, pushing the OBC quota down to rank 75.25 — resulting in a cutoff of just 74.48 (only 1.27 below UR). In Kolkata, this "UR Squeeze" means 45–65% of UR seats go to OBC candidates.

STEP 05 · NORMALIZATION

Shift Difficulty Variance (±8.28 Marks)

CBT-1 was conducted across 7 shifts (Feb 19–25, 2026). Difficulty varied drastically. RRB normalizes using percentile interpolation against a base shift. The raw cutoffs predicted here are mean values — actual normalized scores vary by shift:

Shift	Date	Mean Score
Shift 1	25 Feb 2026 (S1)	48.25 — Toughest
Shift 2	20 Feb 2026 (S2)	49.63
Shift 3	20 Feb 2026 (S3)	51.14
Shift 4	19 Feb 2026 (S4)	51.86
Shift 5	19 Feb 2026 (S5)	53.54
Shift 6	20 Feb 2026 (S6)	56.46
Shift 7	19 Feb 2026 (S7)	56.53 — Easiest

If UR cutoff is 75.00: Toughest shift candidate needs only ~70–71 raw; easiest shift candidate may need ~78–79 raw to match the same normalized standing.

STEP 06 · ZONE-WISE INTELLIGENCE

Why Each Zone Differs

KOLKATA (84.62 UR)

CLW + SER combined vacancies still can't absorb the hyper-dense eastern talent pool. Premier legacy institutions drive score inflation. 80–85 bracket has 298 candidates in 5 marks.

CHENNAI (84.42 UR)

Only 1 UR vacancy at ICF. No reserved category seats. All categories must clear the same 84.42 threshold — reservation provides zero structural advantage here.

CHANDIGARH (76.74 UR)

Zero OBC + ST vacancies. All OBC/ST aspirants forced into UR competition. Their high scores artificially inflate the UR cutoff and negate their reservation benefit.

BILASPUR (79.60 UR)

Most balanced distribution among all zones. Classic bell-curve. Smooth gradient from UR (79.60) to ST (63.00) with no artificial clustering or bottlenecks.

⚠ IMPORTANT DISCLAIMER

These are predicted raw score cutoffs derived from statistical modeling of candidate performance data. They are NOT official RRB results. Final cutoffs will be based on normalized percentile scores computed by RRB's proprietary system. Use these figures as a preparedness benchmark only. Do not feel demotivated — focus entirely on mastering CBT-2 technical subjects.

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Predictions by t.me/RRBCMACONTENT  ·  Based on 52,437 candidate dataset · Statistical model, not official RRB data