Understanding and Pushing the Limits of the Elo Rating Algorithm
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Understanding and Pushing the Limits of the Elo Rating Algorithm
Ideas=science. Simplification=engineering.
Without ideas there is nothing to simplify.
Without ideas there is nothing to simplify.

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Re: Understanding and Pushing the Limits of the Elo Rating Algorithm
How did they miss our work Paired Comparisons with Ties ?
We compared three draw models and found out Davidson fits chess data best  which they reframed as their own Kelo if I am not mistaken.
Maybe I should alert the authors to our paper and makes them spell out what their unique contribution.
We compared three draw models and found out Davidson fits chess data best  which they reframed as their own Kelo if I am not mistaken.
Maybe I should alert the authors to our paper and makes them spell out what their unique contribution.

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Re: Understanding and Pushing the Limits of the Elo Rating Algorithm
"The requested page could not be found."Daniel Shawul wrote: ↑Tue Oct 15, 2019 2:39 pmHow did they miss our work Paired Comparisons with Ties ?
We compared three draw models and found out Davidson fits chess data best  which they reframed as their own Kelo if I am not mistaken.
Maybe I should alert the authors to our paper and makes them spell out what their unique contribution.

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Re: Understanding and Pushing the Limits of the Elo Rating Algorithm
You should probably contact them so that they can at least refer to your work.
But I think there is essentially nothing in the paper. As you say kelo seems to Davidson.
The only thing that could be considered slightly original is that they identify the draw model corresponding to the pseudo likelihood approach to the elo model with draws counting as the average of a win and a loss (i.e. the contribution of a draw to the log (pseudo) likelihood is counted as (1/2)log(w(1w)) where w is a function of the elo difference). But this is a triviality.
Ideas=science. Simplification=engineering.
Without ideas there is nothing to simplify.
Without ideas there is nothing to simplify.
Re: Understanding and Pushing the Limits of the Elo Rating Algorithm
Just my opinion, but it seems to me that:
1. optimal chess is a drawn game
2. therefore, at a certain level of skill, all games will be drawn
3. therefore, any rating system that doesn't allow for this is fundamentally flawed
1. optimal chess is a drawn game
2. therefore, at a certain level of skill, all games will be drawn
3. therefore, any rating system that doesn't allow for this is fundamentally flawed
Extraordinary experiences become normal, and personal dramas become boring, once you perceive the brain’s prankster at work—and then you can really relax  link
Re: Understanding and Pushing the Limits of the Elo Rating Algorithm
tl;drMichel wrote: ↑Tue Oct 15, 2019 8:30 amhttps://arxiv.org/abs/1910.06081
Nothing that wasn't already known unfortunately.
Based on the ELO difference, the current algorithm gives the following probability of winning:
+0 > 50%
+100 > 64%
+200 > 76%
+300 > 85%
+400 > 91%
What are values of the improved algorithm?

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Re: Understanding and Pushing the Limits of the Elo Rating Algorithm
A while ago, Kaggle had a contest for improved game outcome prediction algorithms.
Uri did pretty well in the contest, if I remember correctly.
Uri did pretty well in the contest, if I remember correctly.
Taking ideas is not a vice, it is a virtue. We have another word for this. It is called learning.
But sharing ideas is an even greater virtue. We have another word for this. It is called teaching.
But sharing ideas is an even greater virtue. We have another word for this. It is called teaching.