wimdledont wrote:

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 Yes, but live rankings, here...

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Yes, my reply was too sparsely explained. I was just re-clarifying what the actual current positions were, and filling in the associated names and numbers, as, by design, they were not on the chart of GB places I posted earlier.
I figure if one wants to know which player occupies each GB rank, then Steven's table has alll the information one could possibly require. This was trying to look at relative placements regardless of player. If #10 improves, whether by the imcumbent improving, or someone from a lower place surging up, so we improve overall? etc.

Despite Maia's superb efforts, our numbers took a hit last week.

The stuff about number of sets and ranking differential, referenced in the Glasgow thread, will have to wait until tomorrow, as I need to export it to another program.
Apologies to the zero people who were eagerly anticipating it.

From the Glasgow thread: Oaklands' suggestion about likelihood of larger ranking differentials to end in 2 sets
Not really GB womens tennis related, though it uses the entire 2017 womens data, so I'll put most of it in a spoiler to save even more of my worthless type I error spam here.

Here though is the 'simple' version - data is for every single pro match played by a GB woman in calendar year 2017:

Rankings with smaller differentials - up or down - do have a slightly increased chance to got to a deciding set.
Rankings with the very largest differentials are very likely to be straight sets.

And for those interested in bad charts, the other bit with a whole lot of bad nonsense and some other ways of looking at the question is under the spoiler

Spoiler

Here is the raw data for every match. The patterns are hard to spot, hence the chart outsode the spoiler that then groups things into bins.
Any matches including UNR players are excluded here, as the nominal ranking for them is ususally 999, and that skews the axes.
NB: Interesting side note: it's easy to see the corridor of absence for our players being ranked 400-500 through the year.

But, we have a lot of UNR players, that play against a lot of UNR plyers - we lose far too much data.
So, I set UNR as WR 1300, and included all matches, as below:

The UNR, being the maximum value permitted in the data set, naturally form one boundary of the population.
Great. This shows us nothing really, so lets look at it a different way.

What effect are those UNR players having on matches? Group them up and look at the numbers:
OK, that's more definite: Matches with UNR players have dramatically fewer sets on average

Here's the number of matches for each category in the chart above

OK, so differential has some effect, the number if UNR players has some effect, what about 'quality of the players'? Here's an attetmpt to look at that:

Well, there's more, but as I'm talking to myself here already, I'll spare any more

 

Not sure when Mandy will get back into the fray - should be a shake-up   Nice charts,

With two GB hard court $25K to compare, here are some recent field comparisons:

Will see next week, and in subsequent weeks, whether the softness this week is entirely due to the Fed Cup, or not.
No real excuse for the Burnie $60K though, unless people were considering travel times for this week in various Fed Cup locations.