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Given a contest between two opponents where it is unknown who is favored, how many games must be plays to reach a given level of confidence in who is favored?

For example, Player A and Player B can play a game and determine a winner. There is a certain probability of Player A winning, which we would have a pretty good idea of if they played 1,000,000 games. They start playing. Player A wins three games and Player B wins one. The actual win % of Player A is 75%, but the game could easily be a coin flip and we're just seeing variance. How many games would take to have 90% confident that the overall win % approaches a single number?

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The answer to this is really not quite suited to math stack exchange. In a nutshell, it's "go look up confidence intervals." This is an introduction to the topic.

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  • $\begingroup$ Thanks! I will look into this. $\endgroup$ Commented Apr 7, 2016 at 12:57
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    $\begingroup$ This seems to be what I'm looking for. Here's another resource on confidence intervals a friend shared with me: onlinestatbook.com/2/estimation/mean.html $\endgroup$ Commented Apr 7, 2016 at 19:14
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Depends on what the actual win probability $p$ is.

Since your question is simply binary, a game with two outcomes, you can use the approach below.

A version of the Chernoff bound gives a useful estimate, assume $p\geq 1/2,$ then let $X_i=1$ if game $i$ is a win. Let $X=X_1+\cdots+X_n$ the number of wins in $n$ games, and let $\hat{p}=X/n,$ be the empirical win percentage. Then we have,e.g., $$ Pr[X>np+x]=Pr[\hat{p}>p+(x/n)]\leq \exp\left(-\frac{x^2}{2 n p(1-p)}\right) $$ where $\epsilon=x/n$ can be chosen. See the Wikipedia on Chernoff bounds for more details/versions of these bounds given what you know and whether the bound you want is additive or multiplicative, or even two sided. As $p$ approaches zero or one, convergence is even quicker, since the product $p(1-p)$ is approaches zero. Thus you can put in $p=1/2$ for a conservative approach.

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  • $\begingroup$ Thank you! I don't understand yet but I will do some reading to learn more about Chernoff bounds. $\endgroup$ Commented Apr 7, 2016 at 12:58

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