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

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.

Shortly after joining Esports One as the Head of Esports Data Science, I knew I’d found the right environment and opportunity to make this happen. 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!

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

Analyst Challenge – Contesting Rift Herald 4v5, August 24

August 24, 2019 Challenge

This analyst challenge involved reviewing a run of play from 10:00 to 11:05 of game 1 from the 3rd-place match between Counter Logic Gaming and Clutch Gaming.

The Responses

Several good responses came in from pro analysts, coaches, and players.

Continue reading Analyst Challenge – Contesting Rift Herald 4v5, August 24

Win Conditions: LCS 3rd Place, 2019 Summer

The 3rd-place match between Counter Logic Gaming and Clutch Gaming could go either way, but each team has certain strengths they can focus on and weaknesses they need to protect if they want to come out ahead.

Here are two pivotal interactions that I believe could decide this series, and my personal prediction for the outcome.

CLG Vision Control vs. Clutch Flank Initiations

Last week I called out CLG’s mid and late game vision control as an issue that I’ve seen from them throughout the summer split. If CLG can improve on their vision game, it’ll tip the match much more in their favour, but if Clutch can find the same windows to exploit that Cloud9 found, we could see an upset. Continue reading Win Conditions: LCS 3rd Place, 2019 Summer

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