## Understanding and Pushing the Limits of the Elo Rating Algorithm

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Daniel Shawul
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### Re: Understanding and Pushing the Limits of the Elo Rating Algorithm

Michel
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### Re: Understanding and Pushing the Limits of the Elo Rating Algorithm

Daniel Shawul wrote:
Tue Oct 15, 2019 8:14 pm
Let me try again: Paired comparison with ties
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 k-elo 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(1-w)) where w is a function of the elo difference). But this is a triviality.
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towforce
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### 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
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Fulvio
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### Re: Understanding and Pushing the Limits of the Elo Rating Algorithm

Michel wrote:
Tue Oct 15, 2019 8:30 am
https://arxiv.org/abs/1910.06081

Nothing that wasn't already known unfortunately.
tl;dr

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?

Dann Corbit
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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.
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