> For the complete documentation index, see [llms.txt](https://seer-2.gitbook.io/seer/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://seer-2.gitbook.io/seer/prediction-market-basics/scalar-markets.md).

# Scalar Markets

It is also possible to make markets to predict some metrics. This works by determining a \[min,max] range and creating DOWN and UP tokens.

For example we could make a market « What will be the inflation in the eurozone in 2024 ? » with a range of \[0,10] %. In this case we would have some « sDAI if Inflation DOWN » and « sDAI if Inflation UP » tokens.

If the value returned by the oracle is the minimum of the range or lower, DOWN tokens redeem for the underlying. If the value is the maximum of the range or higher, UP tokens redeem for the underlying.

If the value is within the interval, DOWN and UP tokens each redeem partially:

* UP tokens redeem for (value - min)/(max - min)
* DOWN tokens redeem for (max - value)/(max - min).

For example, if the inflation is negative (i.e. deflation), « sDAI if Inflation DOWN » tokens redeem for 1 sDAI each. If the inflation is of 2%, « sDAI if Inflation DOWN » tokens redeem for 0.8 sDAI each and « sDAI if Inflation UP » tokens redeem for 0.2 sDAI each.


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