Javascript simulation with graphs

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Thank you everyone for the feedback. Have tried to eliminate all sources of bias and increase efficiency. Decks at the desired count were found by walking through the deck and taking them as they come. From 1,000,000 to 100,000,000 hands per action as needed to get the EV margin of error below 0.003, and the difference between the top two actions to a confidence interval of 0.99 (typically a few are too close to call), about 7-10,000,000,000 hands per count. 25-45 minutes per count using an i9-12900H and 82 threads. The graphs look pretty smooth. (As to the previous comment about the RNG, it is JavaScript’s Math.random() in general, and Fisher-Yates for shuffling the deck.)

Here are the i18 and f4 indices that I get, calculating the true count by rounding the decks remaining (first index is rounding to whole deck, second is half deck). They are all the same as or one less than the ones in BJA3. If the indices themselves are rounded (rather than the current practice of flooring), all but a few are the same as in the book.

newIndices0213.png

In case anyone is interested . . . .

[Cacarulo]

Thank you everyone for the feedback. Have tried to eliminate all sources of bias and increase efficiency. Decks at the desired count were found by walking through the deck and taking them as they come. From 1,000,000 to 100,000,000 hands per action as needed to get the EV margin of error below 0.003, and the difference between the top two actions to a confidence interval of 0.99 (typically a few are too close to call), about 7-10,000,000,000 hands per count. 25-45 minutes per count using an i9-12900H and 82 threads. The graphs look pretty smooth. (As to the previous comment about the RNG, it is JavaScript’s Math.random() in general, and Fisher-Yates for shuffling the deck.)

Here are the i18 and f4 indices that I get, calculating the true count by rounding the decks remaining (first index is rounding to whole deck, second is half deck). They are all the same as or one less than the ones in BJA3. If the indices themselves are rounded (rather than the current practice of flooring), all but a few are the same as in the book.

newIndices0213.png

In case anyone is interested . . . .

Your indices are much better now. Just a few small discrepancies (+/- 0.5) but overall they are correct. I'll pass you a table for 4D,S17,DOA,DAS,SPA1,SPL3.

CC Tags (A-T):   -1   1   1   1   1   1   0   0   0  -1

+-----------+-------+-------+--------+--------+--------+
|   Play    |       |       |   CA   |   CA   |   SIM  |
+-----------+-------+-------+--------+--------+--------+
| Insurance | -     | -     |   2.9  |    3   |   2.9  |
| 15vT      | Hard  | Stand |   4.2  |    4   |   4.0  | 4.7
| 16vT      | Hard  | Stand |   0.1  |    0   |   0.0  | 0.4
| 16v9      | Hard  | Stand |   4.5  |    5   |   4.4  | 4.5
| 12v6      | Hard  | Stand |  -1.0  |   -1   |  -1.0  |-1.2
| 12v5      | Hard  | Stand |  -1.5  |   -1   |  -1.5  |-1.5
| 12v4      | Hard  | Stand |   0.1  |    0   |   0.2  | 0.0
| 12v3      | Hard  | Stand |   1.7  |    2   |   1.7  | 1.5
| 13v3      | Hard  | Stand |  -2.2  |   -2   |  -2.2  |-2.4
| 12v2      | Hard  | Stand |   3.4  |    3   |   3.5  | 3.4
| 13v2      | Hard  | Stand |  -0.7  |   -1   |  -0.8  |-0.9
| 10vA      | Hard  | DD    |   3.5  |    4   |   3.3  | 4.0
| 11vA      | Hard  | DD    |   0.8  |    1   |   0.7  | 1.5
| 10vT*     | Hard  | DD    |   3.8  |    4   |   3.7  | 3.7
| 9v7       | Hard  | DD    |   3.4  |    3   |   3.3  | 3.5
| 8v6       | Hard  | DD    |   1.9  |    2   |   1.9  |
| 8v5       | Hard  | DD    |   3.5  |    4   |   3.4  |
| 9v2       | Hard  | DD    |   1.0  |    1   |   0.9  | 1.0
| A8v6      | Soft  | DD    |   0.7  |    1   |   0.6  |
| A8v5      | Soft  | DD    |   1.2  |    1   |   1.2  |
| TTv6      | Split | -     |   4.7  |    5   |   4.6  | 4.8
| TTv5      | Split | -     |   5.0  |    5   |   5.0  | 5.5
| TTv4      | Split | -     |   6.6  |    7   |   6.5  |
| TTv6      | Split | Das   |   4.7  |    5   |   4.6  | 4.8
| TTv5      | Split | Das   |   5.0  |    5   |   5.0  | 5.5
| TTv4      | Split | Das   |   6.6  |    7   |   6.6  |
| 15vA      | LSurr | -     |   1.5  |    2   |   1.4  | 2.0
| 14vT      | LSurr | -     |   3.1  |    3   |   3.1  | 3.4
| 15vT      | LSurr | -     |  -0.3  |    0   |  -0.3  |-0.2
| 15v9      | LSurr | -     |   2.4  |    2   |   2.3  | 2.3
+-----------+-------+-------+--------+--------+--------+

Sincerely,
Cac

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Thank you again, Cac, for the data; it was a big help calibrating the system.

Performance improvements allowed reducing the EV margin of error to 0.001 in about the same amount of run time, 1,000,000 to 10,000,000,000 hands per action as needed, with the difference between the top two actions to a confidence interval of 0.9999. About 36,000,000,000 hands in 107 minutes, 100 threads.

All but two of the indices (16v10 = 1, 9v7 = 3) are the same as the "modern" indices on page 213. The catch-22 indices are also the same.

I calculated the EV difference by summing win rate difference from basic strategy * hand frequency * count frequency * units bet for each count (rather than approximating an integral as in the book). The hand frequency changes with the count. Compared to the book, R2 correlation is 0.926.

insurance 117 115
16vT 53 49
15vT 37 18
T,Tv5 17 24
T,Tv6 17 27
10vT 16 8
12v3 13 17
12v2 11 7
11vA 10 25
9v2 9 8
10vA 7 7
9v7 7 5
16v9 7 1
13v2 7 6
12v4 6 9
12v5 6 4
12v6 4 4
13v3 4 2

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I wanted to be able to do something like the ranked Nifty 50 for other counts such as UBZ2, but the intensive with-and-without SCORE measurements in the Hi-Lo book are beyond me; so I generated risk-averse Hi-Lo indices using graphs of CE instead of EV, which is relatively simple. Index R2 correlation is 0.973; rank corrrelation is 0.840. The top nine are the same, with a few rearrangements. All but two of the top 20 are the same. 10 vs. T is in almost the same position.

The Nifty 50, 4.5/6 H17 DAS Play-All 1-12

hand index CE gain cumulative
16vT 1 0.08069 28.2%
12v3 1 0.03155 39.3%
T,Tv6 5 0.02745 48.9%
T,Tv5 6 0.01735 54.9%
15vT 4 0.0168 60.8%
12v2 3 0.01182 64.9%
9v2 2 0.00844 67.9%
16vA 4 0.00821 70.8%
10vA 3 0.00751 73.4%
11vA 0 0.0074 76.0%
12v4 0 0.00711 78.5%
A,8v5 2 0.00668 80.8%
T,Tv4 7 0.00468 82.4%
8v6 3 0.00418 83.9%
13v2 -1 0.00389 85.3%
9v3 0 0.00362 86.5%
9v7 4 0.00329 87.7%
12v5 -2 0.00313 88.8%
15vA 6 0.00313 89.9%
10vT 5 0.00245 90.7%
10v9 -1 0.00215 91.5%
A,8v4 4 0.00205 92.2%
A,2v5 1 0.00185 92.8%
A,9v6 5 0.00173 93.4%
13v3 -3 0.0017 94.0%
A,8v6 0 0.00159 94.6%
8v5 4 0.00156 95.1%
9,9vA 3 0.00156 95.7%
16v9 5 0.00134 96.2%
9v4 -2 0.0013 96.6%
A,3v4 3 0.00123 97.0%
A,7v2 1 0.00118 97.5%
A,9v5 5 0.00108 97.8%
12v6 -3 0.00081 98.1%
11vT -4 0.00077 98.4%
A,2v4 4 0.00072 98.6%
13v4 -4 0.00069 98.9%
14v2 -4 0.0005 99.1%
T,Tv3 9 0.00048 99.2%
A,8v3 6 0.00046 99.4%
4,4v4 4 0.00037 99.5%
A,9v4 7 0.0003 99.6%
13v5 -5 0.00026 99.7%
16v8 8 0.00021 99.8%
8v4 7 0.00017 99.8%
15v9 8 0.00015 99.9%
14v3 -5 0.00015 100.0%
17vA -5 0.00013 100.0%
16v7 10 1E-05 100.0%