LOL Tinder

Long story short about how and why I designed a tool for League of Legends players

Agnese Ragucci
Sep 11, 2017 · 5 min read

Juicy introduction

In the last year, I spent most of my playing hours fighting Murk Wolves and Raptors in the dangerous jungle of Runeterra.

I prefer to stay in the big slice of players (62.5 million League of Legends users) who don’t care about the competitive and slippery ladder leading to the diamond rank and beyond.

Second bronze to the right and straight on till morning [source: Skill Capped]

Despite being a real noob, I love this game. Its mechanics, lore, and design fascinate me and activate all my prosumer senses.

For this reason, in April 2016, I tried to take part in the Riot Games API Challenge. However, two obstacles prevent me from reaching my goal: the first, short time and bad programming skills; the second, finding Italy among the nations that couldn’t take part in the contest.

This year, after refining my jungler and developer skills, I finally managed to give the world the terrific tool I designed on that occasion, LOL Tinder.

Brief

Although I was no longer following the contest rules, I decided to refer anyway to the original brief:

Submit a piece of software that utilizes champion mastery data which excels primarily in one of the following categories: Entertainment, Usability / Practicality, Creativity / Originality.

The target is the one I know better (because I’m still part of it). I’m talking about all those newbies who want to play all these new champs but have very little knowledge of them. Hence the question that led the project:

If the champion mastery data provides a picture of the user’s playing habits, how can I use this information to suggest new champs to play?

Case studies

Suggesting new content based on user’s choices is a widespread practice. It addresses the user, lowering the degree of uncertainty and improving the overall quality of the experience.

In 2015, Spotify released the Discovery Weekly service. It is a weekly playlist automatically created evaluating user’s musical tastes, last week songs, and similar users’ playlists. Furthermore, the Suggested songs section at the end of every playlist enhances the experience.

Netflix offers a similar service with an estimation of compatibility based on the user watching-history.

First thing first, we’ll create a champion archetype based on the top mastery champions’ features. Later, we’ll define the affinity among the archetype and all the League of Legends champions.

Champion archetype

The data examined by the algorithm are mainly in-game stats, such as Role (Assassin, Fighter, Mage, Marksman, Support, Tank), Difficulty, Attack, Defense, and Magic.

In this first release, the tool refers to the old Overview system. With the new launcher, this section has been modified and enriched. New descriptions, directly related to the game mechanics and actions (i.e. crowd control, utility, etc.), are clearer and more accessible.

I look forward to this data being available through APIs so that I can update the tool to a less old fashioned and D&D-like type of narrative.

Other data examined by the algorithm includes gender and race. To simplify the data races were grouped in clusters: Beast, Human, Mecha, Monster, Spirit, Void-born, Yordle.

Choosing to give equal importance to in-game stats and aesthetic factors made some people doubtful about the project. However, I found it important not to label as secondary the influence that the aesthetics of the champions may still have on target’s choices and gaming experiences.

The weighted average of the top 10 mastery champs features gives us the archetype.

Affinity to the player

The similarity of a champ with the archetype is, therefore, the affinity of that champ to the player.

The application recommends ten champions (different from the top 10 mastery champs) with the highest affinity percent.

Launcher implementation

LOL Tinder can easily integrate the League of Legends launcher.

A dedicated section of the store can show a selection of champions that fit player’s style and tastes.

The player can find a compact and not invasive version of the affinity information inside the champion’s overview.

Conclusions

In the future, I would like to add more variables to the algorithm (i.e. Win rate) to provide more accurate suggestions and affinity rates.

If I had a personal team of developers, I’d also like to make the suggestions influenced by the experiences, styles, and tastes of the community instead of solely by the individual.

Puoi leggere questo articolo anche in Italiano!

LOL Tinder isn’t endorsed by Riot Games and doesn’t reflect the views or opinions of Riot Games or anyone officially involved in producing or managing League of Legends. League of Legends and Riot Games are trademarks or registered trademarks of Riot Games, Inc. League of Legends © Riot Games, Inc.

Agnese Ragucci

Written by

Interaction Designer, Newbie Developer, Game Addicted, Proud Nerd, Workaholic.

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