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LoL: The Champion Tier List Challenge

Last week I made a bold claim. I said: “you CAN use Solo Queue data in professional play, you just have to be smart about it!”. In fact, I wrote a whole article explaining how I have landed on this conclusion. As I alluded to at the end of last weeks article: I’m going to predict which Champions I think will be strongest in professional play this patch.

Published on: 16th June, 2022

LoL: Can Solo Queue data be used for Professional play?

Let us start simple. Do solo queue win rates reflect professional play? If a Champion is atop the “Solo Queue Tier List Patch X.X”, will this be reflected in its pro performance...?

Published on: 8th June, 2022

LoL: How Much Does Side Selection Matter, Really?

How much of a game is driven by luck? Who controls the fates, the player or the roll of the dice? Take two traditional games, chess and poker. In the former, each decision is your own and without question the one who played the best shall win the game. In contrast, for poker the luck of the draw can have disastrous consequences even when played to perfection...

Published on: 1st June, 2022

LoL: G2 vs. PSG - The problem with ego-drafting

G2 Esports faced PSG Talon in the Rumble Stage of this years Mid-Season Invitational and there was no doubt in any analysts mind that the LEC super-star team were the solid favourites. That was until the drafting phase. I did not like the G2 draft. I tweeted my thoughts, then bet against the favourites with odds of 5:1.

Published on: 26th May, 2022

LoL: “Snowballatility” and why it matters in esports

Imagine you’ve just loaded into the last game of your Platinum promotional series and your team decides to group together, just in case the enemy invades. You’ve read them like a book. They head into your jungle to meet their demise. You clean ace them, with your Top laner securing a Penta Kill. He returns to base with 1600 Gold to spend. The question is, if you could choose which Champion your Top laner was playing — who would it be...?

Published on: 12th May, 2022

LoL: How Gold Statistics can be used, and misused — An Evaluation of LEC Top Laners (Spring 2022)

Experts in the field tell you that Gold wins games, and so you should pick the player who gets the most. This makes sense to you, but how do we evaluate “Gold”...?

Published on: 5th May, 2022

LoL: Why Performance doesn’t equal Viewers in Esports

It’s dusk on Saturday the 26th of February and the League of Legends European Championships (LEC) is in full swing. Everything is up for grabs. Who makes play-offs? Who secures first seed? Tonight, it’s the eventual 1st place Regular Season team vs. 5th. At the time, a vital one for both. The stage is set and the cameras roll. However, half-way through the game the Twitch viewer numbers peak at a measly 96,000. The lowest of almost any game this season...

Published on: 28th April, 2022

LoL: Player Scouting and Statistics

There are no silver bullet statistics in esports. There isn’t a number that one could point to and say “see, I have proven the point”. This is particularly true for League of Legends and even more so when trying to determine the value of a player...

Published on: 12th March, 2022

LoL: Draft of the Week - Rogue vs. Excel Esports

Each week, with use of our cutting-edge Machine Learning model, we predict the results of the upcoming games using a combination of Team and, more importantly, Draft statistics. From these predictions, we can evaluate which teams won draft the hardest, and more importantly: why. These, are the Drafts of the Week...

Published on: 7th February, 2022

The Power of AI in Esports

My first role as a Data Scientist was in finance, my job was to build predictive models to estimate the monthly P&Ls. Of course, there was some base value in being able to provide these forecasts to the wider teams as it made all sorts of logistics easier to plan for. However, that wasn’t where the real value lied. The true power of the AI wasn’t its ability to predict the outcome of the status-quo, it was the ability to evaluate how best to change it!

Published on: 4th February, 2022

LoL: Draft of the Week — Team Vitality vs. SK Gaming

Each week, with use of our cutting-edge Machine Learning model, we predict the results of the upcoming games using a combination of Team and, more importantly, Draft statistics. From these predictions, we can evaluate which teams won draft the hardest, and more importantly: why. These, are the Drafts of the Week...

Published on: 3rd February, 2022

LoL: Draft of the Week — Team BDS vs. Team Vitality

Each week, with use of our cutting-edge Machine Learning model, we predict the results of the upcoming games using a combination of Team and, more importantly, Draft statistics. From these predictions, we can evaluate which teams won draft the hardest, and more importantly: why. These, are the Drafts of the Week...

Published on: 27th January, 2022

LoL: The Draft Review Model

A Brief Introduction to iTero Gaming iTero Gaming is a UK based start-up which was founded towards the end of 2021, although contributing work has been underway since 2017. It is applying the Latest in Artificial Intelligence (AI) to the Greatest in Esports. What this means is that iTero is building tools & capabilities designed to help take the top esports teams to the next level of performance. One such tool is designed for coaches to more effectively perform “Draft Reviews”…

Published on: 26th January, 2022

LoL: Using "Personality Quizzes" & Nearest Neighbors for Recommendations

This is the final in a 3-part series on recommendation methods, however I would consider this actually to be by far the most introductory of any article I’ve written and therefore would recommend starting here. For the experienced of you, I hope there is still value to be had — but you may want to skip a few introductory sections. I’ll make it clear in the article where these points are...

Published on: 2nd December, 2021

LoL: UMAP and K-Means to Classify Characters — and why it’s useful

Published on: 26th October, 2020

As of today (October 2020) there are 151 “Champions” (playable characters) in the online game, League of Legends, each offering a unique and individual play-style that has made it the single most popular e-sports in the world. Although the variety provides for an engaging competitive environment, it creates complexity for both new players and analysts alike. If every Champion is unique, how is a new player expected to understand the intricacies of every match-up or an analyst meant to summarise the performance of a player?

LoL: Content-Based Champion Recommendations with Dimensionality Reduction

Published on: 19th September, 2020

Today, we’re looking at a different form of recommendation algorithm known as a “Content Based Model”. This technique instead looks to connect items together based on their similarities, i.e. if you’re buying a PS4 sports game produced by EA then here are some other PS4 sports games produced by EA. This technique is favourable when you have no information about user preference, such as when just launching the product...

LoL: Analyzing “Tilt” to Win More Games

Published on: 28th May, 2020

Tilted. A term commonly used among poker players, although it has also been thoroughly adopted by the wider gaming community. Supposedly, it’s origins come from the mechanical pinball machines that would freeze the flippers if the player tried to tilt the machine, sometimes even displaying the warning: “TILT”...

LoL: Graph Networks for Champion Recommendations

Published on: 11th June, 2019

Let us imagine you worked for Riot Games. Your first task is to design a way of generating Champion (playable characters) recommendations in their game, League of Legends. How would you approach the problem?