Chris Fant wrote:
I first thought I would keep my ideas secret until the Asmterdam
tournament, but now that I have submitted my paper, I cannot wait to
share it. So, here it is:
http://remi.coulom.free.fr/Amsterdam2007/
Comments and questions are very welcome.
I'd like to propose a potential direction of further research. In
your paper, you acknowledge that the strong assumption that each
feature's Elo can be added to form the feature team Elo may not be
correct all the time.
The Stern/Herbrich/Graepel method did not need to make this assumption
because for them a feature team was it's own first class feature
(leading to exponential growth of the number of features). You could
evaluate the degree to which each feature violates additive-Elo
assumption by distributing that feature to all the other features and
retesting the prediction rate.
For example, instead of having features {Pass, Capture, Extension},
you would evaluate the Pass feature additive-Elo assumption by testing
with features {Capture, Extension, Pass-Capture, Pass-Extension}.
This is a bad example, because Pass and Capture are mutually exclusive,
and so are Pass and Extension, but I understand the idea, and I think it
is very good. In fact, I had planned to add it to the conclusion of the
paper. That is the reason why I wrote that "relevance of the model"
section. But it was 1 AM when I finished the paper, and I had been
writing it since early in the morning, so I decided I wouldn't start
explaining something complicated, sorry.
Another (less important) problem I forgot to mention in the paper,
regarding the validity of the model, is the hypothesis of independence.
When training from consecutive moves of the same game, it may not be
reasonable to consider that all the positions are independent. Sampling
one or two random position in every game might be better.
Thanks for your remarks,
Rémi
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