thepossum, on 2020-November-29, 16:53, said:
I think part of the problem is that everyone seems to be arguing different things for starters
I reckon you could definitely challenge "more likely than not", even use of "likely" in the wording but maybe "marginally more likely" (in the sense of, there exists an epsilon > 0 etc) is more appropriate - thats the forumlation I've been working on - and I have needed to simplify to two suits and two hands or a very simplified gaame to demonstrate the obvious symmetry.
Also, are we talking similarity or identicality in shape etc Obviously with 4 hands and 4 suits identical shape is an option - is it that specific?
Does there need to be the same number of suits as hands? Is the number of cards in each suit irrelevent (eg 4 hand 4 suit 5 card game etc)
I'm sure similar (in the broader sense) is more likely the more extreme (or freaky - Pavlicek) your hand is
But come to think of it, its obviously more likely for identical shapes
I will go even further and claim that the chance that everyone has the same shape hand as yours increases too
- in fact this is the most simple and obvious case
(Restrict it to ordered hands to simplify 1,2,3,4 . There is only one comintaion where all 4 hands have an identical shape to any particular shape etc. (5-4-0-1, 1-5-4-0, 0-1-5-4,4-0-1-5), once you have that shape the probablity everyone else has that shape has gone up - because you have many fewer deals to divide by) But this very simple case is not the interesting one. The more interesting one is whether overall freakiness has gone up in the other hands too but that seems obvious
It reminds me a bit of the first time I heard the weather forecaster discussing the SOI and the forecast for rainfall. The forecast was 50% chance of above median rainfall. I used to think that was strange and obvious until I heard at other times that it was on 30% chance of above median rainfall etc
Oh, and getting back to the obsession with shuffling I maintain the distributions are not affected by shuffling at all
But while I find shuffling theory rather tedious (I dipped into a paper for a few seconds) - one thing that fascinates me about packs of cards developing character over time is whether they can every be restored to being interesting packs after (entropy???) has increased so much they have become boring - I wonder looking at the world at the moment if shuffling theory and entropy and irreversible processes apply to the world at the moment - and that sadly it is irreversible. No chance of it every being fun or interesting again
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Since I have the data, I spent a little while putting together a program that determines these basic statistics for each of the 39 generic hand shapes (4=3=3=3 to 13=0=0=0):
(1) The numbers & proportions of deals that have 1, 2, 3, 4 and no hands of that shape;
and, analysing the sets of deals that contain the shape in question,
(2) The percentages (and numbers) of such deals that have 2 or more hands of that shape, and
(3) The percentages (and numbers) of such deals categorised by the longest suit in one (or more) of the other hands.
Results for the two shapes (7=2=2=2 & 4=3=3=3) you originally cited are:
Total deals: 53,644,737,765,488,792,839,237,440,000
Target shape: 4=3=3=3
18,904,824,864,906,126,262,212,096,000 deals with 1, 2, 3 or 4 of target:
1: 15,538,600,726,161,191,018,436,096,000 = 82.19384 % of all such deals;
2: 3,078,920,993,459,221,886,976,000,000 = 16.28643 %
3: 237,337,380,888,197,990,400,000,000 = 1.25543 %
4: 49,965,764,397,515,366,400,000,000 = 0.26430 %
Length of longest suit(s) in the other 3 hands:
4: 1,341,268,488,045,803,116,800,000,000 = 7.09485 %
5: 9,389,152,279,359,996,649,015,910,400 = 49.66538 %
6: 6,567,757,945,719,475,336,421,990,400 = 34.74117 %
7: 1,457,245,678,194,666,886,759,833,600 = 7.70833 %
8: 143,135,495,072,392,782,498,739,200 = 0.75714 %
9: 6,173,444,779,468,438,792,320,000 = 0.03266 %
10: 91,533,734,323,051,923,302,400 = 0.00048 %
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Target shape: 7=2=2=2
1,091,600,331,330,190,676,219,596,800 deals with 1, 2, 3 or 4 of target:
1: 1,082,537,511,172,874,920,302,796,800 = 99.16977 % of all such deals;
2: 9,049,166,455,516,001,717,760,000 = 0.82898 %
3: 0 = 0.00000 %
4: 13,653,701,799,754,199,040,000 = 0.00125 %
Length of longest suit(s) in the other 3 hands:
4: 34,329,156,878,471,495,040,000,000 = 3.14485 %
5: 465,402,880,409,496,995,338,813,440 = 42.63492 %
6: 444,973,635,287,073,217,557,872,640 = 40.76342 %
7: 126,372,546,662,990,136,932,966,400 = 11.57681 %
8: 19,117,164,834,880,139,265,638,400 = 1.75130 %
9: 1,360,547,526,432,774,670,848,000 = 0.12464 %
10: 43,938,720,545,275,887,851,520 = 0.00403 %
11: 461,010,300,641,525,606,400 = 0.00004 %
For example:
(1) of all the 1.9x10^28 deals with a 4=3=3=3 hand only 16.28643% have 2 such; for 7=2=2=2 the proportion is 0.82898%.
(2) of all the deals with a 4=3=3=3 hand the longest suit in one of the other hands is 7 cards in 7.70833% of such deals; for 7=2=2=2 the proportion is marginally higher at 11.57681%. Whilst there are differences, if you plot the bar graph of the percentages for each length the patterns of the distribution for each shape are markedly similar.
The data does not support any sort of "symmetry" law. Of course, this is what one would expect: one is concerned with the ways in which the 39 cards not in the "target" hand are distributed between the other 3 hands: in the 4=3=3=3 case those are 9 cards of one suit and 10 of each of the others; for 7=2=2=2 it's 6 of one and 11 of each of the others. Just as 5 cards in two hands tend to split 3-2, so n cards over 3 hands tend to distribute relatively evenly; it's the same effect. It's not surprising that finding 7+ in one of those hands is relatively uncommon.
Edit: Tables put into 'code' format.