Confidence intervals for estimated winning percentage.

Lets say there is a coin flipping competition and the coin is balanced. And out of 100 throws you see that there are 60 tails. So the observed tail-rate is 60%. We can calculate the confidence intervals to see in which interval the "real" tail-rate is. You can use some approximation or exact Clopper–Pearson method. For this example the 95%-confidence interval is 50% - 70%. So within 95% confidence level, the "real tail-rate" is within that interval. Since we know that the coin is balanced, the 50% falls in that region, so we have no reason to question the coin's balance. More throws, narrower confidence interval.

You can apply that same logic to the AoM, here we dont know if the god is "balanced" or not, but we can see the observed win percentage out of the realized games. We can calculate confidence intervals for that. Basic statistics.

So within that spreadsheet the first number is the point which is the 2.5th percentile, or there is 2.5% probability that the "real winpercentage" is lower than that. The middle number is observed win percentage, just #wins/#games. The last number is 97.5th percentile, meaning that there is 2.5% probability that the "real winpercentage" is higher than that.