league-player-classifier

In professional gaming, team compositions are crafted carefully to ensure that players are of the highest skill level compatible with each other. However, the process by which players are able to work together, in both professional and non-professional gameplay, leaves room for exploration. In this paper, we use game data sourced directly from League of Legends to analyze similarities between ranked gameplay to gain insight on whether archetypes exist in professional and non-professional gaming.

league-player-classifier was created under Riot Games' "Legal Jibber Jabber" policy using assets owned by Riot Games. Riot Games does not endorse or sponsor this project.

Usage

Install all requirements from requirements.txt using pip install requirements.txt.

Functions:

1. To perform a fresh analysis of the data...
2. To run the generation file to pull new data from the Riot API...

Repository Architecture

Directories

/cdragon_en_US

Data pulled directly from CommunityDragon or CDragon, the Riot Developer API's static data. Downloaded 10.25.1, the latest version on 12/18/20.

• summoner.json; utilitzed in data analysis for conversion of numerical spell keys to qualitative data
• item.json; utilitzed in data analysis for conversion of numerical item keys to qualitative data of item name

/data

Data pulled directly from Kaggle, for referencing non-professional games/players to send to the Riot API itself.

/docs

Contains misc. files that were used for brainstorming and more recent submissions, as well as for writing documentation.

/league_pro_matches_data

Data pulled directly from Kaggle. Manually transformed 2019-spring-match-data-OraclesElixir-2019-05-21.xlsx into a .csv for processing on the development side.

Executable Files

Files prefixed with a _pro refer to utility related to professional player data, and those without or with a _challenger refer to non-professional player data.

kmeans.py

Performs the k-means clustering algorithm. Imported into process_data_pro.py and process_data.py.

process_data_pro.py

process_data.py

Analyzes the csv data generated from to riot_to_csv... files below. Performs the following:

• clustering
• reading of input files; csvs
• data transformation (merging of dataframes)
• writes the clustered/elbow figures as shown in the docs
• generates the table values as shown in the docs

To perform a fresh analysis of the data:

1. Ensure that the data in all write_qual... files are populated. By default, they should be populated, but executing the riot_to_csv files wrongly can wipe them.
2. Run python process_data_pro.py or python process_data.py.
3. View the console for data being printed out.

riot_to_csv_pro.py

riot_to_csv.py

Performs call to Riot API to generate csv files.

• riot_to_csv_pro.py correlates to _pro.csv util files
• riot_to_csv.py correlates to _challenger.csv files

To run the generation file: Note: requires a verified League of Legends account! For demo purposes, I am able to only provide a key that lasts for 24h, please reach out in the case of assistance.

1. Log onto account at Riot Games Developer Portal
2. Go to Dashboard, and look for Development API Key section.
3. Generate API key by clicking Regenerate API key. Save this somewhere for the next step.
4. Execute file: python3 riot_to_csv_pro.py or python3 riot_to_csv.py
5. The file will prompt for you to enter your API key - enter it from step 3.
6. The file will run for a very long duration of time. When complete, the previous .csv file in the respective expected location will be overwritten with fresh data.

To shorten the execution time length for demo/testing purposes:

otherwise, may take upwards of 5 hours to finish executing, but no worries - contains rate limiting accomodations so no monitoring needed

riot_to_csv_pro.py:

1. Line 27: Set MAX_MATCHES to a smaller number (ex. 2)

riot_to_csv.py:

1. Line 11: Set PLAYER_LIMIT to smaller number, or alternatively uncomment Line 10.
2. Line 70: Set MAX_MATCHES to a smaller number (ex. 2)

Utility

write_qual_challenger.csv

write_quant_challenger.csv

Contains data on challenger/grandmaster/master players using players referenced from this dataset. Analysis performed completely separate due to lack of data and effort needed to transform data to match Riot API data, because no comprehensive non-pro dataset. Generated from riot_to_csv.py.

Data is separated into two .csv files:

1. Files containing the stub _qual contain qualitative data, so the following features in the scrollable code block below.

summonerName,item0,item1,item2,item3,item4,item5,item6,perk0,perk0Var1,perk0Var2,perk0Var3,perk1,perk1Var1,perk1Var2,perk1Var3,perk2,perk2Var1,perk2Var2,perk2Var3,perk3,perk3Var1,perk3Var2,perk3Var3,perk4,perk4Var1,perk4Var2,perk4Var3,perk5,perk5Var1,perk5Var2,perk5Var3,perkPrimaryStyle,perkSubStyle,statPerk1,statPerk2,championId,spell1Id,spell2Id,role,lane

2. Files containing the stub _quant contain quantitative data, so the following features in the scrollable code block below.

summonerName,win,kills,deaths,assists,largestKillingSpree,largestMultiKill,killingSprees,longestTimeSpentLiving,doubleKills,tripleKills,quadraKills,pentaKills,unrealKills,totalDamageDealt,magicDamageDealt,physicalDamageDealt,trueDamageDealt,largestCriticalStrike,totalDamageDealtToChampions,magicDamageDealtToChampions,physicalDamageDealtToChampions,trueDamageDealtToChampions,totalHeal,totalUnitsHealed,damageSelfMitigated,damageDealtToObjectives,damageDealtToTurrets,visionScore,timeCCingOthers,totalDamageTaken,magicalDamageTaken,physicalDamageTaken,trueDamageTaken,goldEarned,goldSpent,turretKills,inhibitorKills,totalMinionsKilled,neutralMinionsKilled,neutralMinionsKilledTeamJungle,neutralMinionsKilledEnemyJungle,totalTimeCrowdControlDealt,champLevel,visionWardsBoughtInGame,sightWardsBoughtInGame,wardsPlaced,wardsKilled,firstBloodKill,firstBloodAssist,firstTowerKill,firstTowerAssist,firstInhibitorKill,firstInhibitorAssist,creepsPerMinDeltas0-10,creepsPerMinDeltas10-20,creepsPerMinDeltas20-30,xpPerMinDeltas0-10,xpPerMinDeltas10-20,xpPerMinDeltas20-30,goldPerMinDeltas0-10,goldPerMinDeltas10-20,goldPerMinDeltas20-30,xpDiffPerMinDeltas0-10,xpDiffPerMinDeltas10-20,xpDiffPerMinDeltas20-30,damageTakenPerMinsDeltas0-10,damageTakenPerMinsDeltas10-20,damageTakenPerMinsDeltas20-30,damageTakenDiffPerMinsDeltas0-10,damageTakenDiffPerMinsDeltas10-20,damageTakenDiffPerMinsDeltas20-30

write_qual_pro.csv

write_quant_pro.csv

Contains data generated from riot_to_csv_pro.py, rows contain data from Spring 2019 Tournament Matches. See above for naming conventions.

Archive

wip.txt

wip_submission.zip

The above is an archive of my submissions for milestone checkins.

/archive_ipynbs

Previous work that was used before migration to standard .py files, as .ipynb files do not register well with Git.

...

For an extremely rather shoddy explanation of this work written at ungodly hours, please reference the following paper or presentation.