Methodology & Definitions

Data coverage at a glance

This is real, sourced data — not a demo. Current coverage:

California today; the enrollment and peer-comparison methods are geography-neutral and built to extend to other states.

A one-of-a-kind AI-native analytics platform — powered by expert analysis

Most school-data tools hand you a dashboard and leave the analysis to you. This one is fundamentally different: it's an AI-native data analytics platform — but what makes it one of a kind isn't AI on its own. It's the expert human analysis working in tandem with, and powering, the AI models.

Every interpretive judgment embedded in this product was designed by Test Prep Gurus' team of admissions and enrollment experts — the same advisors who have spent years helping families navigate UC outcomes and helping schools sustain their enrollment. The "what this means" bullets, the demand-vs-retention diagnoses, the market-share reframing for shrinking-county schools, the GPA over-performance signal, the multi-axis context against state / county / peer / elite tiers, the peer-similarity engine, the curation rules that filter mis-merged records and small-sample noise — all of it is the codified judgment of human experts.

AI is the engine that takes those expert judgments and runs them across every California high school, every year, simultaneously. It doesn't replace human expertise. It scales it.

The result: every school profile reads as if it had been hand-analyzed by an admissions counselor — because, in the analytical sense, it was. The data sources are public; the interpretive layer is what makes this product one of a kind.

What that means in practice:

• Expert interpretation, automatically applied. Every school profile generates plain-English narrative explaining its UC trajectory, market-share dynamics, GPA over/under-performance, and demand-and-retention diagnosis — produced by analytical rules our experts wrote, run by AI across the whole state.
• Signal over noise. Curation rules and anomaly guards (also expert-designed) filter out mis-merged records, virtual charters, and small-sample distortions so headline numbers reflect real performance. You see what's true and comparable, not whatever the raw download contained.
• Context, not just rates. Every metric is benchmarked against peer-similar schools, the county, and statewide percentiles. A 56% UC Reach gets a "Top 10%" badge with the cutoff visible; a −3% enrollment trend gets a "winning relative share" reframe when the county is shrinking faster.
• Primary-source data, no reputation surveys. Every figure ties back to UCOP, CDE, NCES, IRS 990, IPEDS, OpenAlex, or Opportunity Insights — cited and dated. The numbers are auditable; the human + AI interpretation on top is the value-add.

What is UC Reach?

UC Reach is the flagship metric of this tool. It answers a single question: what share of a school's graduating class actually earns admission to one of the six most competitive UC campuses?

"Top 6" means UC Berkeley, UCLA, UC San Diego, UC Santa Barbara, UC Irvine, and UC Davis. UC Santa Cruz, UC Riverside, and UC Merced are excluded because their substantially higher admit rates would obscure the signal at the campuses that drive most application stress.

Why it can exceed 100%: admits are summed across the six campuses, so a student admitted to several UCs counts at each. A UC Reach above 100% isn't an error — it marks an exceptionally high-performing senior class winning admission at multiple competitive campuses.

Unlike admit rate — which measures success only among those who applied — UC Reach captures the full pipeline from the graduating class to UC admission. A school with a 90% admit rate but only 5 applicants has a profoundly different profile than one with a 60% admit rate and 200 applicants.

Why Admit Rate Can Be Misleading

Consider two hypothetical schools, each with 200 seniors:

• School A: 10 applicants, 9 admitted → 90% admit rate, Reach of only 4.5%
• School B: 120 applicants, 72 admitted → 60% admit rate, Reach of 36%

School A looks more selective on admit rate alone. School B is actually delivering UC access to a far larger share of its students. Both metrics are valid — but admit rate without Reach tells an incomplete story.

This tool surfaces both so you can see the full picture.

All Metrics Defined

UC Application Reach

How much of the senior class enters the UC application pipeline at the most competitive campuses. A school with high Application Reach but low Admit Reach may have preparation or strategy gaps. A school with low Application Reach may have counseling or awareness gaps.

Why values can exceed 100%: applications are counted at each campus, so a senior applying to all 6 UCs adds 6 to the numerator. A figure above 100% just means the average senior applied to more than one UC — the norm at UC-active high schools.

UC Reach (= UC Admit Reach)

The flagship metric. The share of the graduating class earning admission to one of the six most competitive UC campuses.

What "above 100%" means: when a school generates more than 100 top-6 UC admits per 100 seniors, the typical strong student is being admitted at multiple selective UCs. Above 100% is a strength signal — and a rare one: fewer than 1% of California high schools clear it. (Mechanically: admits are summed across the six campuses, so a student admitted at UCLA + UCSD + UCI contributes to the numerator at each.)

UC Enrollment Reach

What fraction of the senior class ultimately enrolls at one of the six most competitive UCs. A gap between Admit Reach and Enrollment Reach may signal cost issues, competition from privates, or geographic factors.

UC Admit Rate

Traditional admit rate, restricted to the six most competitive campuses. Useful for understanding the competitiveness of the applicant pool — but always read alongside Application Reach.

UC Yield Rate

How many admitted students choose to enroll at the competitive UCs. Low yield can indicate strong competition from private colleges, out-of-state schools, or other factors.

Selective Reach (UCSD, UCSB, UCI, UCD)

UC Reach narrowed to the four "selective" campuses below Berkeley/UCLA. Uses campus-level totals where unique-student data isn't yet available, so students admitted to multiple of these campuses are counted at each.

Elite Reach (UCB + UCLA)

The highest-selectivity signal. Same double-count caveat as Selective Reach — a student admitted to both UCB and UCLA is counted twice when unique-student data isn't available.

Campus-Level vs. Unique Student Data

The University of California publishes two types of admissions data:

• Campus-level counts — published for each of the nine UC campuses individually. One student admitted to both UCLA and UC San Diego is counted twice (once in each campus's count).
• Unique systemwide counts — each student is counted once regardless of how many campuses admitted them. When this data is available, it is used for UC Reach.

For schools where UCOP has not published unique-student totals, this tool falls back to summing the per-campus admits across the top 6 — which means students admitted to more than one of the six campuses are counted at each. UC Reach values above 100% for these schools indicate strong students winning admission at multiple competitive UCs (and the value reflects that strength), not a data error.

When UCOP publishes official unique-student data for a given year, these values tighten automatically — the platform re-derives them on the next data refresh.

Why only the top 6 UCs?

UC Santa Cruz, UC Riverside, and UC Merced admit at substantially higher rates than the other six campuses. Including them in the Reach calculation would inflate every school's number and dilute the signal at the campuses families and counselors actually focus on. By restricting to the six most competitive campuses, UC Reach is comparable across schools and meaningful as a measure of competitive UC access.

We plan to add UCSC, UCR, and UCM as a separate "All 9 UCs" view in a future update so both perspectives are available.

Senior Class Size

All Reach metrics require a denominator: how many students were in the graduating class. This tool uses California Department of Education (CDE) grade 12 enrollment data. Key notes:

• CDE enrollment data is published by school year, which may lag the UC admissions year by one cycle.
• Grade 12 enrollment includes all 12th-grade students, not just those who graduated. Some schools have significant dropout rates that affect the denominator.
• When official CDE data is unavailable, the tool displays "senior class size estimate" and marks all Reach metrics accordingly.

Data Limitations & Caveats

Year-to-Year Volatility

Single-year comparisons between schools of different sizes can be misleading. A school with 50 seniors will show large percentage swings from one year to the next with small absolute changes in admit counts. We recommend:

• Using the 5-year trend chart on each school's profile page.
• Treating schools with fewer than 100 seniors especially cautiously.
• Comparing schools across multiple years before drawing conclusions about trajectories.

Enrollment trends & the 3-year projection

Enrollment metrics use grade-by-grade counts by school and year. A school's trend is the change from its first to its most recent year on file. The 3-year projection extrapolates the annualized rate observed over that span — it is an extrapolation of the recent trajectory, not a forecast of any school's plans, and it omits one-off shocks. The "tuition revenue at risk" figure simply multiplies projected enrollment change by a tuition figure you set.

"Most similar nearby schools"

Each school's comparison set is the schools that most resemble it, blending:

• Type — a hard filter: private schools are compared to private schools, public/charter to public/charter (families compare like with like).
• Geographic proximity — straight-line distance.
• Enrollment size — schools of similar scale.
• Religious orientation (private schools only) — Catholic, other-religious, or nonsectarian/secular.

We take the nearest same-type schools and re-rank them by a weighted blend of these factors, returning the closest matches. Religious orientation comes from the NCES Private School Universe Survey; where a private school could not be matched, that one factor is simply skipped.

Which schools are included

Rankings, district aggregates, and peer comparisons exclude nonclassroom-based / virtual charter schools. Because they enroll students from across the state, they aren't comparable to a local brick-and-mortar high school and would distort local leaderboards. They remain searchable and keep their own (clearly labeled) profile pages.

Data Sources

UC admissions outcomes: University of California Office of the President (UCOP), "Admissions by Source School" (UC Information Center). Campus-level applicants, admits, and enrollees by sending high school, 2018–2025. universityofcalifornia.edu.

Enrollment & senior class size: California Department of Education (CDE) enrollment data for public schools, and the CDE Private School Affidavit for private schools. cde.ca.gov.

Private-school religious orientation: U.S. Department of Education, National Center for Education Statistics — Private School Universe Survey (PSS). nces.ed.gov/surveys/pss.

Data is refreshed as agencies publish new cycles; figures may lag the current school year by one cycle.

What This Tool Cannot Tell You

• An individual student's chances of admission to any UC campus.
• Whether specific students from a school were admitted.
• How demographic subgroups within a school perform (data is school-level).
• The quality of education, campus life, or post-graduation outcomes.
• How a school compares to the state average without statewide data loaded.

For a deeper, context-rich analysis of your school's enrollment trajectory and the outcomes that drive family choice, consider an Enrollment Trend Audit.