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ELO as Bitcoin?

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Date Editor Before After
12/19/2017 1:04:32 AMUSrank[I]burp before revert after revert
Before After
1 [quote]That means that your skill will be constantly updated as time passes and people you played against play other matches. The system will know more about them, and in turn, know more about you.[/quote] 1 [quote]That means that your skill will be constantly updated as time passes and people you played against play other matches. The system will know more about them, and in turn, know more about you.[/quote]
2 \n 2 \n
3 It doesn't know more about my previous games when it gains knowledge about other people's games *now*. Or am I misunderstanding that? 3 It doesn't know more about my previous games when it gains knowledge about other people's games *now*. Or am I misunderstanding that?
4 \n 4 \n
5 Say a person starts to play and gets some initial rating ( I didn't read the paper about WHR, so please correct me about how things are!) . That rating will have some large uncertainty, of course. I play against her and our ratings are adjusted. The *adjustment* to my rating should be given a rather large uncertainty, and the new player's rating is also adjusted and her rating uncertainty is lowered. Now let's say I lose against the new player and my rating was decreased a little. The new player now plays a lot of games and her rating increases, and her rating uncertainty decreases. How can this possibly affect my current rating? The new games the player played can't say anything about her skill state in the game that he played against me. There must clearly be a time component in this algorithm, and I assume there is? Like how long the correlation length is. How do you set these constants to sensible values? 5 Say a person starts to play and gets some initial rating ( I didn't read the paper about WHR, so please correct me about how things are!) . That rating will have some large uncertainty, of course. I play against her and our ratings are adjusted. The *adjustment* to my rating should be given a rather large uncertainty, and the new player's rating is also adjusted and her rating uncertainty is lowered. Now let's say I lose against the new player and my rating was decreased a little. The new player now plays a lot of games and her rating increases, and her rating uncertainty decreases. How can this possibly affect my current rating? The new games the player played can't say anything ( much?) about her skill state in the game that she played against me in the past. There must clearly be a time component in this algorithm, and I assume there is? Like how long the correlation length is. How do you set these constants to sensible values?