Category Archives: Stats Theory

Are Mountain Drakes Overrated? Statistical analysis of the value and priority of dragon types

Ever since Riot introduced the concept of randomly-spawning elemental drakes, there has been consistent debate around the relative value of each dragon type. Consensus dictates that infernal and mountain drakes are the most valuable, with cloud and ocean as also-rans.

Currently, the trend of opinion among many experts–somewhat unexpectedly–is that mountain drake is the most useful dragon, above even the ever-popular infernal.

Curiously, the data does not bear out this opinion.

Based on statistical analysis of 2017’s games to date, the value of mountain drakes is much lower than popular perception. Inferno drake is statistically the most valuable by far, while cloud and ocean drakes continue to be underrated. Continue reading Are Mountain Drakes Overrated? Statistical analysis of the value and priority of dragon types

Introducing “Lane Efficiency”

Lane efficiency is a new statistic that measures how well teams manage minion waves throughout the game, both in terms of maximizing their own farming and preventing their opponents from farming. It is reported as a percentage, with values above 50% showing strong performance and below showing weakness.

Full details are below, including the formula, important context for interpretation, and reasons why I chose this approach instead of some alternatives. To see the lane efficiency statistic in action for the NA LCS and EU LCS, head to the findings article. Continue reading Introducing “Lane Efficiency”

Jungler slash lines improve measurement of early-game effectiveness

TL;DR Because of changes to the jungle as part of Season 7, I am proposing a new way of measuring junglers’ early-game effectiveness.

Key Findings

Changes to the jungle for the 2017 season have created some loss of meaning in one of the oldest statistics used for professional play: CS Difference at X minutes. The addition of more small creeps in the raptor and krug camps has created wider variance in the value of a single creep score (CS), leading to inflated CS gaps for junglers without any real difference in the gold or experience gaps being generated.

These changes have some implications for how we should report on junglers’ early-game head-to-heads. Continue reading Jungler slash lines improve measurement of early-game effectiveness