Slides about Whole History Rating

when the hundred thousand games played question came you can read Firepluk on drdein's forhead

quote: frog compatible

I admit I lolled :D

Jasper
There currently is a python package but I think it's rather ugly. I'll probably do a C++ implementation at some point. The problem with both the python and my own implementations in Java and C# is that they're based off of an existing ruby whr library. One that is as badly designed as the language it's written in. It stores entire matrices when it only needs to store the diagonal. I've fixed it up a bit for the Zero-K implementation, but I still don't like it.

Also the Zero-K implementation is currently the only one supporting FFA and Teams, since that wasn't part of the original paper.

dyth68 this can effectively be tuned by adjusting the omega^2. Higher values there will give larger ratings (changes) and thus more pronounced win chances. Me increasing that constant is also why we have 3000+ elo now. In the end I try to find a compromise that works well for most game modes.

Very good presentation! Directly using natural ratings makes the formulas much simpler to get. Just the logistic distribution is harder to see if you're not used to those formulas.

I'm not so sure if dyth68's concern is considered by omega². If omega was sufficiently small to make ratings not overshoot and enough playing time without skill changes passed, the rating deviation over players should converge independently of omega. Of course the assumptions of no overshoot and enough time without skill changes do not hold. Therefore, omega has some influence, but it's not proportional.

It's good to see the formulas for omega_t and omega_g. What is g in the formula for omega_t²? The number of games the player played on that day or in total?

It seems that we are currently using very big omegas. It was interesting to see that this has a better log score. But maybe there is a local maximum of log score at smaller omega. To which extent is the better log score due to bigger omega only caused by faster changes for new players or by making it depend on game number rather than only time? And I'm wondering to which extent the human and frog changes worsen log score and balancing.

Thanks for bumping, I've overlooked this thread when it was created.

Yes of course it is calculated before knowing the outcome.

One thing I didn't mention is that I tried tuning the w^2 not just for maximum predictiveness, but also good modeling. So giving WHR all battles in advance and then trying to predict all battles with the precalculated rating history. A high w^2 is not always optimal there, depending on the dataset. This was mostly to verify that the w^2 not only leads to good predictiveness in future games, but also past games.