Demo mode — 15 sample IM programs. Tune your stats below to see how the math changes for you.
Match plan · sample
Updates as you type

Internal Medicine

Specialty
Internal Medicine
Other specialties coming soon · tier: accessible
Home state:
Stratum: 250-259
P(match somewhere) · across the 15 sample programs
99%
8 signaled · 7 unsigned
Mean interviews: 8.3
Signal lift
+0pp
Next step
Now swap these sample programs for the ones you're applying to.
Add your real programs in Signal Strategy — your Match Plan auto-builds.
Strategy read · using your stratum

Strong portfolio for a Step 254 US MD applicant.

Your stratum’s invite rates are applied to the 15 sample programs. Tier mix below is well-balanced for IM. The model says you’d most likely match at Temple University Hospital.

Portfolio shape
Tier mix · uses your stratum’s invite rates against the 15 sample programs.
Tier mix — 15 programs
Reach 1Match 14Likely anyway 0Out of reach 0
Match Day forecast
10,000 simulated cycles using your stratum’s invite rates.
Match destination distribution
Temple University HospitalPA
33.3%
University of MarylandMD
28.1%
University of Cincinnati Medical Center/College of MedicineOHsignal
26.4%
Ohio State University HospitalOHsignal
4.9%
Rush University Medical CenterIL
3.9%
No match
1.1%
You’ve tuned the stratum. Now pick the programs.

Trade these 15 samples for your actual list.

Sign in, pick the programs you’re applying to in Signal Strategy. Your Match Plan auto-builds with the numbers you saw above — but personalized to your portfolio.

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How the math works

P(invite at p) uses empirical Bayes shrinkage on real applicant-level data — counting how often signaled vs unsignaled applicants in your stratum got interviewed at this program across recent cycles.

P(match somewhere) = 10,000 Monte Carlo simulations of the NRMP algorithm using your portfolio’s rank order. Caps at the specialty’s tier-aware ceiling to account for unmodeled risk.

Probabilities are directional, not deterministic.