simpler Stockfleths variants

This variant on the stockfleths move is (very close to) the one developed by Dan Kehn on that he calls the Stockfleths for dummies.  His original description is located at

There is also a video there or directly at

the idea is again as per usual that your goal is to start out with an evenly distributed poertafilter basket as much as possible before ever touching the coffee.  It is surprising how hard it is to overcome a lopsided distribution with any strategy, with the possible exception of the WDT.  

This is a huge advantage in my book for the ridgeless baskets that can be removed from the portafilter and easily slid back in at the last minute.

You can then hold the basket under the grinder and rotate it as the coffee comes out making sure that the coffee falls fairly evenly throughout the basket.

No matter how carefully you grind into the basket it is unlikely to be quite perfect (although on some of the giant grinders this is a little less important since they are more forgiving.  

Now once you have the dose you want in the basket (the number of grams you desire) the goal is to stretch your finger across the basket and rotate the basket in a circle so that your finger sweeps the bed of the coffee into a stunningly even looking distribution.

If your dose is close to the top of the basket this is easy to do. If you want to updose and fit extra in the basket it helps to tap the basket on the counter once or twice to settle the grinds a little lower.  

If you want to down dose you cannot run your finger across the top of the basket as it will not contact the coffee so I like to flex my finger so that it bends slightly below the basket rim to still get a radially symmetrical distribution.  

This strategy works fairly well.  It takes longer than some of the other distribution strategies (perhaps 45 seconds instead of 15 seconds), but is fairly neat and will fix small distribution problems.  I have found it fails if the basket starts out too asymmetrical since it makes it look perfect, but it is not equally dense everywhere so the perfect distribution is an illusion, but it obviously beats leaving the lopsided distribution as is.

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