Reddit reviews Who's #1?: The Science of Rating and Ranking

I just had a look at datdota, they actually let you download CSV files for each page! That's a good start.

I actually get my data from several community and stats sites and the team names differ somewhat between them. This is easily solved by making a look up table for <Name on Site1> -> <Name on Site2> if you do it for the 30 biggest teams that's most of the work. I just get my program to tell me if it's found one it doesn't have in the table, then I just update it manually (I got a few a day for a week then about 1 a week since). I did this because I found that sometimes one site would miss games!

You can use EV, I think that would save people a lot of time. I actually show the Kelly Criterion bet % which is a bit more complicated, if you want to use that you should adjust it to make it more conservative like I have done so it doesn't over estimate your model's edge.

If you read the original thread I have some information on how my model works and what data it takes into account. If you have any questions about it you can ask me but I won't tell you everything haha.

You could read some books on rating and ranking algorithms, one that I read that inspired my dota model is called Who's #1?. I can recommend some more general books on prediction if you're interested.

You could also look into ELO ratings systems, the first generation of my model was ELO that modeled teams. While a good start, if you do any testing you'll find that rating teams isn't all that accurate because players change teams all the time.

My model models players which is far, far more complex but is significantly more accurate.

I've been starting to read this book . Very helpful and very thorough explanation about how many other different polls are done.

Check this book out http://www.amazon.com/Whos-The-Science-Rating-Ranking/dp/0691154228

Goes over different ranking methods people use to rank sports teams. It will go over the initial method Massey used which he has since tinkered with but it will get you started.

This is not really a python question, you might get a better response from /r/statistics or /r/datascience

Ranking things is actually quite difficult since there's really no "correct" answer. This book is a quite interesting "pop" book on the subject.

I'm an applied mathematician, not a statistician, but I'll take a crack at this. If you had variability info (standard deviation) that would be peachy because you could use confidence intervals. Unfortunately, we can't always get [the data that] we want.

So, we conjure a standard deviation for ourselves. This is not a great solution, but we can estimate s approx range / 4. (note, this is a terrible rule of thumb. But we take what we can get). So s approx 5 / 4 = 1.25.

Now that we have this, we can built a confidence interval. If some of your ratings numbers are small (like, less than n = 30), use a t-distribution confidence interval.

Thus, your "true" rating is the lower bound of the confidence interval:

weighted = avg - t_dof * 1.25 / sqrt(n)

Where dof is n-1, n is the number of ratings you have for a particular item, and t is the t-value for the desired width of the confidence interval and dof on a t-distribution with mean 0 and standard deviation 1.

You can get the t-value using scipy.stats.t.ppf using

from scipy.stats.t import ppf

t_dof = ppf(0.975, dof)

For your typical 95% confidence interval.