On Thursday, 16 March 2023 at 14:54:01 UTC, 王乾懿 Wang Qianyi (Nan) wrote:
> I hold no objection to test this model once again and do not object any else to test my model, which has been published.
> If you are interested in my model or have any questions, don't hesitate to ask me, or hold new rounds for verification.
> I am so far confident that my model is performing systematically and logically but I would be delighted to see anyone who makes further improvements to outweigh my model and achieve an even better result (the average score of each round to be improved to more than 35 points within 10-20 rounds).
> Nan

Lets do 10 rounds starting 1st April which falls on a saturday.

"NN" <november.nihal@gmail.com> wrote in message
news:ebdfc3c8-3cb6-488e-88ce-fe535de9ab30n@googlegroups.com...
On Thursday, 16 March 2023 at 14:54:01 UTC, ??? Wang Qianyi (Nan) wrote:
> I hold no objection to test this model once again and do not object any
> else to test my model, which has been published.
> If you are interested in my model or have any questions, don't hesitate to
> ask me, or hold new rounds for verification.
> I am so far confident that my model is performing systematically and
> logically but I would be delighted to see anyone who makes further
> improvements to outweigh my model and achieve an even better result (the
> average score of each round to be improved to more than 35 points within
> 10-20 rounds).
> Nan

Lets do 10 rounds starting 1st April which falls on a saturday.

~~~

So will this be 24 selections again?

JF

On Saturday, 18 March 2023 at 13:46:36 UTC, Jerome Fandor wrote:
> "NN" <novembe...@gmail.com> wrote in message
> news:ebdfc3c8-3cb6-488e...@googlegroups.com...
> On Thursday, 16 March 2023 at 14:54:01 UTC, ??? Wang Qianyi (Nan) wrote:
> > I hold no objection to test this model once again and do not object any
> > else to test my model, which has been published.
> > If you are interested in my model or have any questions, don't hesitate to
> > ask me, or hold new rounds for verification.
> > I am so far confident that my model is performing systematically and
> > logically but I would be delighted to see anyone who makes further
> > improvements to outweigh my model and achieve an even better result (the
> > average score of each round to be improved to more than 35 points within
> > 10-20 rounds).
> > Nan
>
>
> Lets do 10 rounds starting 1st April which falls on a saturday.
> ~~~
>
> So will this be 24 selections again?
>
> JF

i am okay with 24 if thats the concensus. after all we are just testing strategies.

I shall resist the temptation to tweek as that was my undoing and i am interested
to see what happens

Maybe Chat GDP can help

Here is ChatGDP's analysis:

The given sets represent the scores of different players or algorithms, with each set containing 20 data points. The total score for each set is provided as well. To describe the overall trend of these sets, we can compare their total scores and examine the distribution of scores within each set.

Total Scores (in descending order):

Nan: 679
Jerome: 650
NN: 648
Alphazero: 585
Leela: 584
Random: 574
Elton: 569
Jinks: 563
Nigel: 551
The overall trend suggests that Nan has the highest total score, followed by Jerome, NN, Alphazero, Leela, Random, Elton, Jinks, and finally, Nigel.

Upon examining the scores within each set, we can observe the following trends:

The scores seem to be dispersed across a wide range, with some sets having scores close to 0 and others having scores above 60.
Most sets have a mix of low and high scores, indicating variability in performance across the 20 data points.
Some sets, such as Nan, Alphazero, and Leela, have a relatively balanced distribution of scores, while others, like Jerome, NN, and Nigel, have a more uneven distribution, with a few particularly high or low scores.
In summary, the overall trend shows a range of total scores among the sets, with Nan having the highest and Nigel the lowest. The distribution of scores within each set varies, with some sets displaying a more balanced distribution and others showing more uneven performance.

Predicting future performance based solely on past data can be challenging, as many factors can influence future outcomes. However, we can attempt to make an informed prediction by considering the past performance in the given sets.

Based on the total scores, Nan has the highest score, which might suggest that it has performed consistently well in the past. However, it is essential to consider the distribution of the scores within the set as well.

Looking at the distribution of scores in each set, we can make the following observations:

Nan has a relatively balanced distribution of scores, with many scores in the 30s and above.
Jerome has a more uneven distribution, but with some high scores such as 69 and 79.
NN has a mix of low and high scores, with some particularly high scores like 72 and 79.
Taking both the total scores and the distribution of scores into account, Nan seems to have the most consistent performance and might be considered more likely to win in the future.

However, it is important to note that this prediction is based solely on the given data and does not account for any external factors that may influence future performance. It is also worth considering that other sets, like Jerome and NN, have had occasional high scores and could potentially outperform Nan in future events if they can maintain those high scores consistently.