Understand Beta-Binomial Model for Win/Loss Rating Systems

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SUMMARY

The discussion focuses on the beta-binomial model for win/loss rating systems, emphasizing its use in estimating players' true success rates through a combination of prior beta distribution and observed wins/losses. The formula for estimating the success rate is given as $$\hat{p}=\frac{n_{\text{wins}}+\nu\rho}{n_{\text{games}}+\nu}$$, where $\nu$ and $\rho$ represent prior information. The method is noted for its empirical Bayes approach, utilizing frequentist techniques for parameter estimation. Additionally, discrepancies in sample moment calculations from various sources are highlighted, particularly regarding the first and second sample moments.

PREREQUISITES
  • Understanding of beta-binomial models
  • Familiarity with Bayesian and frequentist statistical methods
  • Knowledge of sample moments and their calculations
  • Basic grasp of probability distributions, particularly beta distributions
NEXT STEPS
  • Explore the derivation and applications of the beta-binomial model in rating systems
  • Learn about Bayesian statistics and empirical Bayes methods
  • Investigate the calculation of sample moments and their significance in statistical analysis
  • Review case studies or examples of win/loss rating systems using the beta-binomial model
USEFUL FOR

Statisticians, data analysts, game developers, and anyone interested in developing or understanding win/loss rating systems based on statistical models.

ParoXsitiC
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I asked a question here: http://math.stackexchange.com/questions/183483/rating-system-incorporating-experience and I'd like to understand how I do the beta-binomial model, but he is taking a bit long to answer so I am hoping to get an answer.


His answer is as follows:


One option is to use something like the [beta-binomial model][1].

The general idea is that players' true *success rates* (or winning frequency) come from an underlying distribution (e.g. a beta distribution). As a player plays more games and you get actual information on wins and losses, the prior information from the beta distribution is combined with the wins/losses information which is expected to follow a binomial distribution (based on the true success rate $p$), and a posterior estimate of the success rate made as
$$\hat{p}=\frac{n_{\text{wins}}+\nu\rho}{n_{\text{games}}+\nu}$$
where the beta distribution essentially has the effect of a prior information equivalent to $\nu$ games with $\rho$ success rate.

The advantage of this method is that for playes with few games, the estimated success rate is *shrinked* towards the population mean; extreme success rates due to highly uncertain success rate estimates for playes with few games are avoided.

Due to the similarity with Bayesian methods, this type of approach is often referred to as empirical Bayes. However, the parameters $\nu$ and $\rho$ used to specify the beta distribution are estimated using traditional frequentist methods (moment or maximum likelihood estimates).

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I looked the wikipedia on it here: http://en.wikipedia.org/wiki/Beta_binomial

and found an example calculating the alpha and beta using sample methods - Given this data set:

Males 0 1 2 3 4 5 6 7 8 9 10 11 12

They came up with the 1st sample moment as : 6.23
2nd sample moment as 42.31

I searched for a while how this calculation was actually done, and finally came to this wikipedia page: http://en.wikipedia.org/wiki/Beta_distribution#Parameter_estimation

Which says the 1st sample moment and 2nd sample moment are just the sample mean and sample variance,

which I found to be 6 and 15.166667 - different than their 6.23 and 42.31.

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I just want find a good rating system that involves win and losses and need a laymen explanation on how it's done. I also asked the question here: http://www.rugatu.com/questions/2845/understanding-statistics-beta-binomial-model but he also failed to follow up and make sure I did it right :\
 
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ParoXsitiC said:
Which says the 1st sample moment and 2nd sample moment are just the sample mean and sample variance,

That article says the variance is the second moment centered about the the mean, so perhaps the m_2 in the other article is the second moment centered about 0 , which is just the mean of the squares of the data. I leave the arithmetic of testing that theory to you!

The Wikipedia article on the beta-binomial is ambiguous about the meaning of m_2 given that the Wikipedia article on moments refers only to "moments about" some value. I'll put a comment on the talk page of the article and see if there is a response.
 

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