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Statistics for Game Players: A Practical Overview
A plain guide to the statistics ideas that matter most for game results: averages, spread, sample size, uncertainty, updating beliefs and spotting weak claims.
Math Toolkit
Variance, distributions and inference applied to games.
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A plain guide to the statistics ideas that matter most for game results: averages, spread, sample size, uncertainty, updating beliefs and spotting weak claims.
Learn how Bayes' theorem updates a belief after new evidence, using simple card and dice examples with every number worked out by hand, step by step.
Understand conditional probability through revealed cards and dice: how knowing one fact changes the chance of another, with each calculation shown.
See how wide the uncertainty is around an observed win rate, why 60 wins in 100 games is a range rather than a fact, and how sample size narrows it.
Learn why unusually good or bad results tend to be followed by more typical ones, how that differs from the gambler's fallacy, and how to spot it in game data.
Learn how selective numbers in ads, forums and screenshots mislead, and which simple questions help you test a gaming claim before you believe it.
The law of large numbers says averages settle over many trials, but it does not even out streaks. See the coin and dice examples and the common misreadings.
Work through the Monty Hall puzzle step by step: why switching wins two times in three under the standard rules, and the intuition behind it.
See how sums of many small random events, such as dice or coin flips, form the bell curve, and what the 68-95 rule tells you about typical ranges.
Use one die, two dice and ten dice to see how variance and standard deviation measure spread, and why two games with the same average can feel very different.
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