How to read win rate, pick rate, and sample size
A practical guide to reading League statistics without overvaluing one win-rate number.
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infolol guides
From statistics to in-game choices
Guides written and reviewed by the infolol operator to help you adapt data to sample quality, team composition, and game state.
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Rune and item build selection guide
How to adapt a statistical build to the enemy composition and the state of the game.
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Using build statistics by role
Why top, jungle, mid, ADC, and support should interpret the same statistics differently.
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How to read win-rate confidence intervals
A practical explanation of why the same displayed win rate carries different uncertainty at different sample sizes.
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How a new patch changes statistical interpretation
Why a large sample can stop being current advice after a patch, and how infolol labels aging datasets.
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Scope and bias in the KR Emerald+ sample
What the infolol sample covers, what it misses, and why it should not be generalized to every League player.
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How the infolol tier score is calculated
The formula that combines win rate and pick rate, and why a tier is not an absolute measure of champion power.
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Survivorship bias in completed-item statistics
Why a high win rate for completed core items cannot be treated as proof that buying them caused the wins.
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