SSC CGL 2026 Expected Cutoff 🔥 | Tier 1 Answer Key Analysis | Safe Score & Selection Chances

Summary

The speaker explains how candidates can estimate their chances of qualifying for SSC CGL Tier 1 using ranking-platform data and an approximate normalization method. The main point is to compare an estimated normalized rank with the likely number of candidates called for Tier 2—not to judge chances from raw marks alone.

Suggested method

1. Enter your response-sheet data on a ranking platform. The speaker recommends Rank Mitra, which he believes has a large dataset. More entries may make its estimates more representative, although the platform cannot include every candidate.

2. Consider your shift percentile, not just your raw score. Raw marks can be affected by differences in shift difficulty and normalization. The speaker gives a simplified explanation of the equipercentile method: a candidate’s position within their shift can help estimate their position across the full candidate pool. He notes that the actual process is more involved.

3. Estimate your normalized overall rank. In one example, a general-category candidate has a raw score of 147 and a shift percentile of 86.89—around the top 13.11% of the shift. Applying that percentile to a platform dataset of about 247,000 candidates gives an estimated normalized rank of roughly 32,000–33,000. The estimate may change as more data is added.

4. Compare the rank with the estimated Tier 2 call range. The speaker cites 4,412 unreserved seats and assumes approximately nine candidates may be called per seat. That suggests a call range of around 39,000–40,000 candidates. Since unreserved seats are open to candidates from all categories, general-category candidates should consider their overall rank, not only their category rank.

In the example, the estimated rank of 32,000–33,000 is within that approximate range, leaving a substantial buffer. The speaker considers the candidate likely to clear the cutoff but emphasizes that this is not certain.

1. For reserved-category candidates, check both overall and category rank. A reserved-category candidate may qualify through an unreserved seat if their overall rank is high enough. Otherwise, they can compare their category rank with the estimated call range for that category.

In the SC example, the platform’s normalization changes a score of 123 to about 118. The speaker describes the estimated position as around the 60th percentile and gives an approximate SC category rank of 9,100. Using 1,715 SC seats and the same rough nine-to-one ratio gives an estimated call pool of about 15,435, or a buffer of roughly 6,000 places. The speaker says the same approach can be used for OBC, ST, EWS, and horizontal categories.

Caveats and advice

• The platform dataset does not represent the full candidate population. The speaker estimates that around 13.5 lakh people may have taken the exam and suggests allowing a margin of roughly 1,000–2,000 ranks for candidates missing from the platform data.
• Bonus marks, normalization differences, and unreported candidate data could change the estimates. The actual outcome will only be known once the final result is released.
• The assumed selection ratio is not fixed. The number of candidates called per seat could be higher or lower than nine, and changes in vacancies could also affect the call range.
• These calculations are estimates, not guarantees. Candidates—especially those near the borderline—are advised to keep preparing for Tier 2 rather than rely solely on a prediction.
• The video ends with a brief Testbook promotion, including a coupon code and a Diwali offer.

Speakers and sources featured

• Rishikesh — the speaker and presenter; he identifies himself by name at the end.
• Rank Mitra — the ranking platform cited as a source of candidate-rank and percentile data.
• Shubham Sir (RBE) — referenced for a more detailed explanation of equipercentile normalization; he does not speak in the video.
• Testbook — mentioned in a promotional segment.