How we score schools

SweetSpot doesn't rank California public schools. It scores them — and then lets you decide what counts. Here's how.

Data from CDE SARC, cycles 22-23, 23-24, 24-25. Last refreshed 2026-05-15.

How we score

Every California public school is scored against 9dimensions, drawn directly from the state's annual School Accountability Report Card (SARC) filings. For each dimension, we map the school's raw number (test proficiency, suspension rate, teacher credentialing %, etc.) to a 0–10 score using absolute anchor tables — never against its peers. A school's fit score is the weighted average of those dimension scores, with weights you control.

Move a slider on the priorities panel, and every school's fit score re-computes in real time. Nothing about the score depends on rank, curve, or which other schools happen to be near it.

Where the data comes from

Every number on this site comes from the California Department of Education's SARC files, published annually. We don't scrape, infer, or generate — we map columns from CDE's official tables to the dimensions we score on. The 3-cycle report covers school years 22-23 through 24-25.

Source URLs and per-metric file lineage are documented in the appendix below.

Three things we deliberately don't do

No peer-relative ranking.: A school's score doesn't depend on which county you pulled it up in. A 50%-disadvantaged school in Marin and one in Imperial get the same score on the same numbers. We picked the anchor tables once, and they apply statewide.
No rolling averages.: Every score reflects the most recent cycle's data, full stop. We don't smooth bad years into trailing averages — that hides what actually happened.
No hidden algorithm.: The full anchor tables, direction-flipping logic, and metric lineage are below. No black box. No “proprietary blend”.

Read the appendix →

Appendix

For journalists, skeptics, and anyone who wants to verify a number.

Anchor tables

Each dimension's raw → 0–10 mapping. Percentile cutoffs are computed once at build time from the full statewide distribution.

High math proficiency

Higher is better

Column: SMATH_Y1

Percentile	Raw value	Score
5th	1	0
50th	29	5
95th	75	10

Values between the 5th and 95th percentile are linearly interpolated. Outside that range, scores clamp at 0 or 10.

High reading proficiency

Higher is better

Column: SELA_Y1

Percentile	Raw value	Score
5th	11	0
50th	41	5
95th	80	10

Values between the 5th and 95th percentile are linearly interpolated. Outside that range, scores clamp at 0 or 10.

Strong science scores

Higher is better

Column: SSCI_Y1

Percentile	Raw value	Score
5th	4.41	0
50th	24.24	5
95th	67.07	10

Values between the 5th and 95th percentile are linearly interpolated. Outside that range, scores clamp at 0 or 10.

Small class sizes

Lower is better

Column: AVG_SIZE

Percentile	Raw value	Score
5th	13.12	10
50th	23.62	5
95th	30	0

Values between the 5th and 95th percentile are linearly interpolated. Outside that range, scores clamp at 0 or 10.

Low suspension rates

Lower is better

Column: SSUSP_Y1

Percentile	Raw value	Score
5th	0	10
50th	1.52	5
95th	13.04	0

Values between the 5th and 95th percentile are linearly interpolated. Outside that range, scores clamp at 0 or 10.

Strong attendance

Lower is better

Column: RALL

Percentile	Raw value	Score
5th	4.3	10
50th	18.9	5
95th	65.9	0

Values between the 5th and 95th percentile are linearly interpolated. Outside that range, scores clamp at 0 or 10.

Fully credentialed teachers

Higher is better

Column: SPFUL_Y1

Percentile	Raw value	Score
5th	38.17	0
50th	88	5
95th	100	10

Values between the 5th and 95th percentile are linearly interpolated. Outside that range, scores clamp at 0 or 10.

Less administrative overhead

Higher is better

Column: PTCHSAL

Percentile	Raw value	Score
5th	22.74	0
50th	28.87	5
95th	35.34	10

Values between the 5th and 95th percentile are linearly interpolated. Outside that range, scores clamp at 0 or 10.

High graduation rate

Higher is better

Column: SGR_Y1

Percentile	Raw value	Score
5th	27.28	0
50th	90.5	5
95th	99.51	10

Values between the 5th and 95th percentile are linearly interpolated. Outside that range, scores clamp at 0 or 10.

Data lineage

Metric	SARC file	Available in
Math proficiency (% met/exceeded)	caall.xlsx · CAASPP math	22-23, 23-24, 24-25
Reading proficiency (% met/exceeded)	caall.xlsx · CAASPP ELA	22-23, 23-24, 24-25
Science proficiency (% met/exceeded)	caallsci.xlsx · CAST	22-23, 23-24, 24-25
Average class size	acselm.xlsx + acssec.xlsx · class size	22-23, 23-24, 24-25
Suspension rate (%)	susexp.xlsx · suspensions	22-23, 23-24, 24-25
Chronic absenteeism rate (%)	chronic.xlsx · chronic absenteeism	22-23, 23-24, 24-25
Fully credentialed teachers (%)	teacherprep.xlsx · fully credentialed	22-23, 23-24, 24-25
Teacher pay share of total compensation (%)	salary.xlsx · teacher pay share	22-23, 23-24, 24-25
4-year graduation rate (%)	cohort.xlsx · 4-year graduation	22-23, 23-24, 24-25

Known limitations

Small-sample noise.Schools with fewer than 50 students often have suppressed or volatile values for state-tested metrics. We don't drop them — we render whatever CDE published — but treat single-year movements at small schools with skepticism.
School closures.Schools listed in one cycle's SARC may not appear in the next. We don't backfill — the movers leaderboard only includes schools present in both 23/24 and 24/25.
Demographic-target reversal. Some dimensions (PERDI, PEREL, PERSD) score based on whether a school matches a parent-selected demographic target, not whether the value is high or low. See scoring_tables.py for the precise reversal logic.
Why some metrics start at 23/24.CDE occasionally renames or restructures columns between cycles. When a metric's column isn't present (or isn't comparable) across all three cycles, the chart starts at the first cycle where it stabilizes. The Available in column above shows the actual span.