Vol. I · № 1

The Readymedygo Almanac

2027 · QLD Intake

A Monte Carlo concordance ⁂ Fifty thousand simulated ballots ⁂ Compiled for Searle & Keir

☞ A page set down by The Great Monty soothsayer, statistician, friend to the bewildered intern-to-be

A reading of the lot, in three parts

Where Will You
Be Next Year?

“Tell Monty your desires. He will gaze into the future and tell you where you might end up for internship next year, to one decimal place.”

— The Great Monty, Vol. I
Part I

Your rank order

Drag to reorder, or use the arrows. Rank one is your top choice.

Setting the type…

Part II

The field of applicants

Edit first-preference counts to redraw the field.

☞ Load a field, by Monty's hand, or adjust the numbers below manually.

Hospital Cap. Applicants Ratio
Part III

Cast the lots

When the order is set and the field looks right, ask Monty to draw the ballot fifty thousand times.

☞ Select how displaced applicants choose their backup hospitals.

Monty draws the ballot fifty thousand times. Takes a moment.

Monty waits at the table. The deck is set.

A reader's note

On the method, its limits, and where to read further.

Three short essays for anyone consulting the almanac.

About this page

I built this to make the ballot feel legible. It shows how the system works and why outcomes can be unintuitive: you can do everything right and still land your 11th preference. That is bad luck, but it is part of the machinery.

It also makes the strategy clear. Either pick an undersubscribed hospital as your first choice to guarantee the posting, or list your true preferences in strict order from most wanted to least wanted. Monty is a Monte Carlo simulation underneath the theatre: place everyone at first preference, reassign those bumped from oversubscribed hospitals, weight the bumps by the wider field's taste, and repeat fifty thousand times.

Disclaimer

These figures are not predictions. They are conditional probabilities under a simplified model of the ballot. The real allocation rules include factors this simulator does not fully represent: special circumstances, rural training pathways, bonded positions, and the precise displacement order. Treat the numbers as a feel for the shape of the odds, not a forecast.

Past ballots are an imperfect guide to future ones. The lot, when cast in earnest, may fall otherwise.

Further reading

For the authoritative process, consult the current Queensland Health Junior Doctor Recruitment guide and the Medical Intern Campaign documentation. They set out eligibility, joint application rules, the special-circumstances pathway, and the official order of allocation.

Always defer to the current guide over anything you read here.

Monty's working

Assumptions & formulae, shown plainly.

What the soothsayer is, and is not, doing behind the curtain.

Assumptions of the model
  1. Every applicant is placed initially at their stated first preference. Lower preferences are not consulted unless that applicant is later bumped.
  2. When a hospital is oversubscribed, Monty picks one such hospital uniformly at random and resolves it; the order of resolution affects who lands where.
  3. Within that hospital, the applicants drawn out to make capacity are chosen uniformly at random from those present, with the user's party as an indivisible unit in joint mode.
  4. Displaced applicants who are not the user choose a new hospital with probability proportional to their fallback weight (based on the selected fallback model), and only among hospitals that still have room. The "Combo" model uses the square root of the first-preference count to flatten demand, and multiplies it by 3.0 if the hospital is in the same geographic region as the one they were bumped from.
  5. The user's party, if bumped, walks down the user's own ranked list and takes the first hospital with capacity for the whole party. If none remain in the ranked list, Monty places them at any hospital with room; failing that, the final preference is over-filled.
  6. Joint mode treats the user's pair as a single unit (size 2). Solo mode treats them as a single applicant (size 1). No other joint applications in the wider field are modelled explicitly.
  7. Special-circumstances pathways, rural-training streams, bonded positions and the real ballot's precise priority order are not modelled.
  8. The simulation runs for fifty thousand trials. Each trial halts after at most five hundred displacement steps to guard against pathological loops.
The formulae, as Monty draws them
Probability your party is drawn out P(drawn) = 1 − ∏j=0e−1 ( n − k − j ) / ( n − j ) where n is the current applicant count at the hospital, e is the excess over capacity, and k is your party size (1 solo, 2 joint). The product is the chance nobody in your party is drawn across e sequential picks without replacement; one minus that is the chance at least one of you is.
Destination for a displaced (non-user) applicant P(dest = h) ∝ wh  ·  𝟙[ countsh < caph ] where wh is hospital h's calculated fallback weight based on the chosen mode (e.g. ah for Raw, or √(ah) · 3.0 for Combo if in the same region), and the indicator is one only if h still has room and is not the hospital just resolved.
Your reported probability of landing at hospital h P(land = h) ≈ ( trials ending at h ) / N with N = 50,000. The Monte Carlo estimate's standard error at p ≈ 0.5 is roughly √(p(1−p)/N) ≈ 0.22%, so single-percentage-point shifts are within Monty's drawing tremor. Read tenths with caution.