Category Archives: Stats Theory

Stats Theory

Significant Statistics: A Basic Guide to Informing Your Opinions with Oracle’s Elixir

League of Legends esports as we know it turned the corner this year on its first decade of existence. As the scene matures, historical datasets will grow and analysts will streamline their methods. In the long run, statistics should become more an integral part of how the community discusses and understands the game. My hope is that any LoL esports fan who reads this, no matter their math/statistical background, should have an improved picture of how they can use stats on this handy site to help inform their perceptions of player and team performance.

A Long Time Coming: Statistics in Traditional Sports

The year was 1966. Davey Johnson, then-second baseman for the Baltimore Orioles, had a disagreement with his manager Earl Weaver. The subject: Johnson had written a computer program called “Optimizing the Orioles Lineup” to try and convince Weaver that, statistically, he should bat second. Continue reading Significant Statistics: A Basic Guide to Informing Your Opinions with Oracle’s Elixir →

Stats Theory

Early-Game Rating 2.0

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Oracle’s Elixir’s Early-Game Rating is getting a makeover! Beginning with Worlds 2019, teams’ ratings will be calculated using the new system.

Jump to charts showing the relative value of gold and dragons.

Early games always seem to be more explosive at international events, and the 2019 meta has been heavily early-game focused, so I expect EGR to be a very informative metric throughout the tournament.

What is not changing about EGR and MLR?

Fundamentally, the definition of Early-Game Rating isn’t changing: Continue reading Early-Game Rating 2.0 →

Stats Theory

What are the Odds? Modeling Win Probability in League of Legends

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My personal Holy Grail of League of Legends statistics has always been an accurate, theoretically sound predictor of in-game win probability. Some of my earliest explorations in advanced LoL stats came in the form of win probability modeling, and I’ve always kept a close eye on the attempts others were making in that space, but up until now I haven’t had the necessary data structure, resources, or time to put together my own model.

I’m now excited to share an early look at the beta version of my win probability model.

Scroll down to see an example of the model in action!

I’m not going to go into technical details aside from saying the core model is a logistic regression—most of the details will remain proprietary—but in a moment I’ll share an example of how the model interpreted one of the games from the LCS 2019 Summer Finals.

I knew the time was right to start talking about this model publicly after I spent part of the LCS Finals sitting with Tyler “FionnOnFire” Erzberger and “field testing” the model’s predictions. Every so often, I would plug game state numbers from the current game into a calculator and ask Fionn to make a prediction about which team was favoured to win, and at what percentage. Time after time, the calculator landed within 5 percentage points of Fionn’s estimate. That outperformed even my own expectations, and I think it says something about Fionn’s understanding of LoL, too! (The model’s precision and accuracy are also pretty good, but since the model is still a work in progress I’m going to keep those metrics to myself.)

My model is not only built on sound statistical foundations and a comprehensive understanding of the underlying data, it also effectively captures the nuances of pro LoL with real authenticity to the nature of the game and its complex interrelationships between game variables. I’ve controlled for factors like game time, the different types of elemental drakes, towers, Baron Nashor, Elder Dragon, Inhibitors, and much more, all appropriately reflected based on the ways they influence the game.

When you put it all together and apply it to Game 4 of the LCS Finals between Cloud9 and Team Liquid, one of the most hotly contested games of the series, you get a data visualization like this:


Click for full-size image

Continue reading What are the Odds? Modeling Win Probability in League of Legends →