Wow, I wrote this article ages ago, didn't expect to see it posted here today.
I just want to clarify the point of the article:
Why would you fit a curve to the data when you can just use the actual data?
That's the point of the article.
We're in the age of big data, we should use it to make better win rate predictions. Elo's exponential curve is fine, it's approximately right, it's just now we can have databases of millions of games and we can just do better. Elo was invented before the big data age and it is limited by that.
That's all I'm saying.
I shouldn't have included all the other stuff in the article, it just distracts from the point.
Thanks for writing the article and sharing your work with the world, I really enjoyed it! I think the central point you make is very interesting.
I'd be interested to know what fit you used for the red "line of best fit", why not a straight line? My main question here is do you actually expect a player ~210 points above another to win _less_ than if they were only ~190 points above? (the first dip in the red graph)
I just want to clarify the point of the article:
Why would you fit a curve to the data when you can just use the actual data?
That's the point of the article.
We're in the age of big data, we should use it to make better win rate predictions. Elo's exponential curve is fine, it's approximately right, it's just now we can have databases of millions of games and we can just do better. Elo was invented before the big data age and it is limited by that.
That's all I'm saying.
I shouldn't have included all the other stuff in the article, it just distracts from the point.