Lol Category

What does this project solve

Lol Category is an AI that solves classifying lol champions to 6 base classes:

• Assassin
• Mage
• Marksman
• Tank
• Bruiser
• Support

Classification is done using clustering algorithms:

• K-Means
• Spectral
• Expectation Maximization

Idea behind the project

Idea for this project was to be able to classify league of legends champions based on their builds and stats. At the time of doing this project (maybe even now), there were only predefined classes for champions. For example, Fizz is classified as an assassin, but there were times when he, or it, was played as a bruiser. This would lead to wrong statistics for users.

Gathering data

There was no existing data set, so data had to be fetched somehow. Luckily, LOL API had everything that was necessary to complete the project. To avoid noise in data (random builds and trolls), only data for challenger and master players was collected from EUW server, because EU > NA. :troll_face:

LOL API doesn't provide matches for a certain league/division, but provides info about players in those leagues. The first step was to get all players in those leagues. The second step was to get their last 20 matches. After playing with the data, it was clear that short matches had a lot of troll builds, which polluted the data, and gave bad results. Matches were filtered to contain only matches above 30 minutes.

477 matches were gathered, which means 4770 examples of champion builds and stats.

The script for gathering the data can be found here Riot announced changing of LOL API, so it could get deprecated.

Data used for classification

• Physical damage dealt (%)
• Magical damage dealt (%)
• Bonus health
• Bonus mana
• Bonus armor
• Bonus magic resistance
• Bonus attack speed (%)

Some stats were not used, because they depended too much on the players performance. Idea was to be able to classify every champion regardless of the players performance.

Some examples of such data:

• CS per minute
• Damage dealt per minute
• Gold per minute

Preparing data

All data needed to be normalized because matches never lasted the same, so people who played in shorter matches would always have worse stats.

Z-Score was used to normalize the data.

Visualization

Data was reduced to 2 dimensions from 7 dimensions (each attribute is one dimension) using PCA (Principal Component Analysis) and visualized in 2D.

Results

K-Means:

• Assassins 1331
• Mage 1092
• Marksman 561
• Tank 355
• Bruiser 543
• Support 888

Spectral:

• Assassins 1128
• Mage 1097
• Marksman 540
• Tank 362
• Bruiser 701
• Support 805
• None 137

Expectation Maximization:

• Assassins 1458
• Mage 1095
• Marksman 484
• Tank 301
• Bruiser 524
• Support 984

Interpretation

You can see in the results how similar classes are close to each other and non similar classes are distant. At the time of classifying this data. Some of the ADCs were building items as assassins, lethality items, which shows how these two classes are close to each other. Also, bruisers are between tanks and assassins, as they build somewhere in between. Mages and assassins and marksman are nowhere close, while supports are between mages and tanks. Which makes sense, because they build half tank, half ap, half regen depending on the champion.

Validation

Silhouette Score was used for validation of clustering algorithms.

Also, for recognizing classes for clusters 10 best and worst examples were taken into account. Best examples are points in a cluster which have minimal distance to cluster center among all points in cluster. Worst examples are points in a cluster which have maximal distance to cluster center among all points in a cluster.

After finding those examples, domain knowledge was used to assign classes to clusters.

Potential Use

Making classifications more reliable. Automatizing making statistics for players.

Improvement

Recognizing multiple classes for champions. Recognizing more detailed classes for champions, see here.

Technology Stack

• Python
• SciKit Learn