MCPS Top-20 University Admissions (2020–2024): Blair vs Churchill vs Wootton vs Richard Montgomery

Executive summary

  • This post compiles estimated top-20 U.S. university admission counts (U.S. News “Top 20”) for four Montgomery County (MD) public high schools—Montgomery Blair (MBHS), Winston Churchill (CHS), Thomas S. Wootton (WHS), and Richard Montgomery (RMHS)—across graduating classes 2020–2024.
  • The underlying data is drawn from MCPS “College Bound” / acceptance reporting and Bethesda Magazine’s annual self-reported admissions charts; the figures should be treated as directional, not audit-grade.
  • Across many schools in the top-20 list, the largest absolute admit counts in this compilation cluster around Cornell, UC Berkeley, and UCLA.
  • Churchill shows a notably high figure for UPenn (~42; ~1.45% of grads) in the compiled table; RM appears strong on Brown (~28; ~1.0%) and Columbia / UChicago (~11; ~0.4%); Blair appears strong on STEM-heavy destinations (e.g., MIT ~18; Caltech ~11).

What the numbers do (and do not) mean

Important caveats (these matter more than the rank-ordering):

  1. Self-reported / school-reported admissions are incomplete. The Bethesda Magazine charts are based on self-reporting and are not fully verifiable by school officials.
  2. Counts are “offers,” not enrollments. A student admitted to multiple schools may appear multiple times across rows.
  3. Denominators are estimated. Percentages are derived from approximate class sizes (e.g., Blair ~750, Churchill ~580, Wootton ~530, RM ~550 per class), so the percent-of-graduates figures are estimates.
  4. Top-20 definition changes over time. The table follows a “Top 20” framing, but rankings shift by year; interpret as a “selective national university set,” not a fixed taxonomy.

Compiled table: estimated admits by university (2020–2024)

Top 20 University Montgomery Blair (admitted; % grads) Winston Churchill (admitted; % grads) Thomas Wootton (admitted; % grads) Richard Montgomery (admitted; % grads)
Princeton University ~10 (0.3%) ~6 (0.2%) ~3 (0.1%) ~5 (0.2%)
Harvard University ~6 (0.16%) ~3 (0.10%) ~2 (0.08%) ~4 (0.15%)
Columbia University ~7 (0.2%) ~4 (0.1%) ~6 (0.2%) ~11 (0.4%)
Massachusetts Inst. of Tech (MIT) ~18 (0.5%) ~5 (0.2%) ~5 (0.2%) ~4 (0.15%)
Yale University ~10 (0.3%) ~5 (0.2%) ~1 (0.04%) ~9 (0.3%)
Stanford University ~5 (0.13%) ~0 (0%) ~1 (0.04%) ~8 (0.3%)
University of Chicago ~7 (0.2%) ~7 (0.2%) ~6 (0.2%) ~11 (0.4%)
University of Pennsylvania ~13 (0.35%) ~42 (1.45%) ~7 (0.26%) ~22 (0.8%)
California Institute of Tech (Caltech) ~11 (0.3%) ~1 (0.03%) ~2 (0.08%) ~1 (0.04%)
Johns Hopkins University ~27 (0.7%) ~13 (0.45%) ~8 (0.3%) ~14 (0.5%)
Northwestern University ~23 (0.6%) ~30 (1.0%) ~5 (0.2%) ~10 (0.4%)
Duke University ~8 (0.2%) ~15 (0.5%) ~6 (0.2%) ~12 (0.45%)
Brown University ~23 (0.6%) ~30 (1.0%) ~2 (0.08%) ~28 (1.0%)
Dartmouth College ~5 (0.13%) ~2 (0.07%) ~7 (0.3%) ~8 (0.3%)
Vanderbilt University ~6 (0.16%) ~0 (0%) ~2 (0.08%) ~7 (0.25%)
Cornell University ~47 (1.3%) ~47 (1.6%) ~30 (1.1%) ~42 (1.5%)
Rice University ~8 (0.2%) ~1 (0.03%) ~2 (0.08%) ~5 (0.2%)
University of Notre Dame ~4 (0.1%) ~5 (0.17%) ~2 (0.08%) ~3 (0.1%)
University of California, Berkeley ~52 (1.4%) ~43 (1.5%) ~17 (0.6%) ~32 (1.2%)
University of California, Los Angeles (UCLA) ~38 (1.0%) ~48 (1.7%) ~20 (0.75%) ~18 (0.65%)

A few patterns worth noticing (without over-interpreting)

1) “Big three” by count: Cornell, UC Berkeley, UCLA

Across all four schools, Cornell / UC Berkeley / UCLA appear to drive a large share of the admits. This is consistent with a mix of factors: larger class sizes at those institutions (vs. ultra-small private cohorts), strong STEM pipelines, and (for UC schools) a large national applicant pool where admits can still be numerous even when the admit rate is low.

2) Different “peaks” by school

Even with all the caveats, a few rows jump out:

  • Churchill → UPenn (~42) and UCLA (~48) are unusually high in this compilation.
  • Richard Montgomery → Brown (~28), UPenn (~22), and Columbia / UChicago (~11 each) look particularly strong.
  • Blair → MIT (~18) and Caltech (~11) stand out relative to the others, aligning with Blair’s well-known magnet/STEM signal.

3) Percentages are useful mainly for “order of magnitude”

The percent-of-graduates column helps normalize (a bit) across different class sizes, but because both the numerator (self-reported offers) and denominator (approx class size) have noise, it’s best read as: “roughly around one-in-a-hundred,” “a few per thousand,” etc.

How to use this (practically)

If you’re a parent/student trying to reason about outcomes:

  • Treat this as a contextual signal, not a promise.
  • Compare program fit (magnet vs comprehensive), student distribution, and counseling resources, not only the top-20 line items.
  • Remember: admissions outcomes are driven by individual applicant strength + cohort context + institutional priorities; school-level aggregates can mask huge within-school variance.

References