Maytsh wrote:> That can be avoided by changing the method of arranging ranking games as: ...
Well, yes, forcing randomization might work. But on the other hand, players might not like this solution because it doesn't allow them to play with their friends or a preferred configuration. On the other hand again, one can also see this as exactly the kind of thing that should be prevented.
I agree with the latter statement. Playing for rank is like playing for championship where you can't choose your opponents.
If you want to play with your friends play a "friendly" match on which you have whole control (kick/password/invites/blinds etc).
For poker, it could actually be feasible. I feel like we're being a bit too theoretical here, so let's put forward a concrete proposal for this kind of ladder:
* min_games somewhere around 1/5th of the typical number of games in the season period
* games_average=0
(And dropping the requirement to have min_games for getting listed - with games_average = 0 that shouldn't be necessary anymore)
Oh yeah, and obviously replace "wins" by some measure where you get points for second place and beyond, like Bock suggested. Maybe like (1, 3/4, 1/2, 1/4, 0) or (1, 1/2, 1/4, 0, 0). The good thing about the bayesian rating is that we don't have to balance anything here, this can well be asymmetrical. Whatever gives the nicest rating distribution in the end.
Actually, from some SNG games I have participated in, it's more like: first player triples his money, second player doubles it, third player gets his money back, no money for the rest. Since it's already decided that there will be 10 players in ranking games I think we could recommend a: 3, 2, 1, nil, nil, nil, nil, nil, nil, nil) point distribution.
Given the above, the formula that after this discussion we recommend is:
Score = points_won / (games_played + games_min)
Are we good?
Note to developers: For the first months, I would really like to see how this revised formula compares to the initial "bayesian estimate" formula of:
Score = ( points_won + (games_min * score_average ) ) / (games_played + games_min)
> would you agree that this kind of "preserving the score" attitude is diminished?
There's still one minor "preserving problem" with this kind of approach: In case a player got lucky and hit a good run of bad players and/or good cards, he might be compelled to stop playing because he knows that he's got worse chances in the coming games. A player in the first place might stop playing. This is mostly a matter of setting min_games high enough, however.
Having to set games_min high enough (which means changing it every couple of months as the total games played rises) might mean that we cannot easily "delete" it from the formula, thus the original "bayesian estimate" may still have a merit.
At the moment of my calculations, for a total of 1127 games played by 634 players: the median of games_played is "4" with a score_average of "0.16". Excluding the "0/1" players (leaving 474 players), the median is "6" and the score_average is "0.18". Excluding the players "who have played 3 games at most" (leaving 333 players), the median is "9" and the score_average is "0.19". The corresponding
score_median values (for completeness, I am not trying to confuse you

) are "0.07", "0.14" and "0.19" respectively.
> how about Microsoft's TrueSkill algorithm?
I know it, but I feel like it is too hard for players to "get", even compared to ELO. At some point players get the feeling that you're hiding behind a mountain of numbers in order to escape their criticism. I wouldn't want that. But that's just personal

I tend to think that if the calculation algorithm is sound, then any criticism of "unfairness" can be taken care of by stating the facts and perhaps do a "damage control"-kind of discussion.
Note: I have made 4-5 edits to this post since beginning.