The same can costs wildly different money.
Everything below is computed in your browser, straight from the pipeline's own output files. Switch between the latest run and the full accumulated history — no averages were rounded to make a point.
How prices are distributed
Every Monster listing in the country, bucketed by shelf price. A wide, lumpy spread means chains are pricing the same product very differently — a tight single peak would mean the market has settled.
Price range per flavour
For each flavour: the cheapest and priciest shelf in the country, with the middle 50% of listings as the solid bar and the median as the notch. The longer the line, the more it pays to shop around.
Chain × flavour
Median shelf price for every chain/flavour pair we can see. Reading down a column tells you which chain is cheapest for that flavour; reading across a row tells you whether a chain prices its range evenly.
Does location change the price?
Listings grouped into latitude bands, using each store's real coordinates. These are bands of the map, not municipal or district boundaries — the chain price files carry no city name, only a numeric code.
Cheapest shelves
The ten lowest individual listings in the country right now.
Priciest shelves
The ten highest. Same product, same week.
What the archive has collected
Every pipeline run appends to history/ (one file per month) and never overwrites it, so this record only grows. Each bar is one run.
What this data can't tell you yet
Being straight about the limits is the whole point of publishing the method alongside the numbers.