Analysis of the Impact of Patching on League of Legends Artian Kica Andrew La Manna Lindsay ODonnell Tom Paolillo Mark Claypool In Proceedings of the 2nd International Workshop on Collaboration and Gaming ID: 541878
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Slide1
Nerfs, Buffs and Bugs – Analysis of the Impact of Patching on League of Legends
Artian Kica, Andrew La Manna, Lindsay O’Donnell, Tom Paolillo Mark Claypool
In
Proceedings of the 2nd International Workshop on Collaboration and Gaming (
CoGames
),
Orlando, Florida, USA, October 31 - November 4, 2016Slide2
Introduction
Patching long-standing use in software after release Fix bugs or improve performanceBut modern games use patching for moreAdd content, expanding gameplay
Adjust game balance, changing existing gameplayResearch in patching
Analysis of patches adoption for software security
[8]
, but do not enhance software features/contentAnalysis of patching behavior for open vs. closed source [9], but security focus, not features
2Slide3
League of Legends
3Most popular game in world [1], played by more than 27 million people each day [3]Professional leagues (e-Sports), with $2 million in prizes [3,4] Success at top levels
requires cooperationTeam game: 5 versus 5In game, Players =
Champions
130+ choices 10 picked, 6 banned each gameSlide4
Patching in League of LegendsSince 2009 release, patched over 160 times
Average 1.5 patches/monthTraditional software patches (fix bugs)Modern game patches (release content, adjust balance)Keeps game fresh for playersGame balance affects enjoyment [6]Despite importance, little analysis of
kinds of patches and impact on gameplay
4Slide5
Analysis of League of Legends
3rd Party Site w/Champion Info3rd Party Site w/Patch Info
5
But no correlation of patch data with champion data
must be done
manually
And only latest patch information
no historic data
from prior patchesSlide6
This PaperGoal:
analyze patching in League of Legends (LoL)Classification and categorizationImpact on gameplaySample games from Riot LoL databaseHarvest and classify patch notes
Develop Web site for interactive explorationInvestigate
patching and game data
Disseminate
results (this talk)6Slide7
OutlineIntroduction (
done)Methodology (next)Gather data on LoL games Harvest data on LoL patchesWeb siteAnalysisConclusion7Slide8
Gather Game Data
Access game data provided by Riot Games APIChampions picked, banned, game won/lostCrawl to get ~11,000 unique players player sampleGather game histories for all playersFrom histories,
choose random sample of 450,000 games
game sample
From game sample, compute rates for each champion
Pick rate, Ban rate, Win rate
8Slide9
Harvest Patch Data
LoL Wiki has Riot’s patch notes Human-readable, unstructured textAfter manual inspection:Develop automation to take advantage of common formatting and languageExtract each change as single patch noteDevise taxonomy of single patch notes for categorization and analysis(next slide)
9
http://
leagueoflegends.wikia.com/wiki/Patch
Slide10
Patch Taxonomy
PatchSlide11
Patch Taxonomy
Bug Fix – correct inadvertent mistakes in game softwaree.g., bug fixed where interrupting player action would render champion unable to cast spellsVisual – modify look of game (map and/or
champion)e.g., new visual particles added to spellGameplay
–
affect champions and their interactionsGameplay
Patch
Visual
Bug FixSlide12
Patch Taxonomy
Numeric – quantified modifications to game statistics for championse.g., amount of damage dealt per attackUtility – affect champion’s ability interacts with other game aspectse.g
., added effect to slow opponent hit by spellQuality
of Life
–
affect ease of use of champione.g., visual indicator to better determine where spell hits
Gameplay
Patch
Visual
Numeric
Utility
Quality of Life
Bug FixSlide13
Patch Taxonomy
Buff – increases strength of champion e.g., base armor increased from 19 to 23Nerf – decreases strength of champion e.g., spell radius reduced from 350 to 300Neutral – neither clearly nerf nor buffe.g., base damage changed from 100 at level 1 and 500 at level 3 to 150 at level 1 and 450 at level 3
Gameplay
Patch
Visual
Numeric
Utility
Quality of Life
Buff
Nerf
Neutral
Bug Fix
Buff
Nerf
Neutral
Buff
Nerf
NeutralSlide14
OutlineIntroduction (
done)Methodology (done)Web site (next)Analysis Conclusion14Slide15
Website – LoL Crawler
Explore patch data and game dataBuilt in PHP, nodeJS and C#Select champion to analyzeScreen similar to game screen15
http://lolcrawler.cs.wpi.edu
/
Slide16
Website – LoL Crawler
Display main rates for championWin, Pick, BanToggle on/offLink to patch categorization and notes at bottom16
http://lolcrawler.cs.wpi.edu
/
Slide17
OutlineIntroduction (
done)Methodology (done)Web site (done)Analysis (next)Game data: Pick, Ban,
Win ratesPatch data: Bug,
Visual
,
GameplayCombinationConclusion17Slide18
Game Data - Win, Pick & Ban Rates
18Pick rate skewed, Ban rate really skewed – perceived champion strength variesWin rate
centered around 50 – champions mostly win same amount- Normally distributed? (see next slide)Slide19
Game Data - Win Rate Normality Test
19Follows line well (0.99 correlation) – appears normalBut tails deviate – thinner than would be expected – maybe intentionalSlide20
Patch Data - Bug, Visual & Gameplay
20Bug fixes are not most common – unlike traditional patches
Gameplay changes dominate – modify how game is playedSlide21
Patch Data - Quality, Utility & Numeric
21Numeric changes dominate – maybe easiestQuality changes fewest – maybe most significantSlide22
Patch Data - Neutral, Buff & Nerf
22Buffs and Nerfs equally common – tweak up/down in strengthNeutral
distinctly less so – most adjustments clearly one waySlide23
Combined - Changes vs. Win Rate
23Champions further from 50% patched moreSlide24
Combined – Change Direction vs. Change in Win Rate
24High win rates tend to get nerfs
Low win rates tend to get buffsSlide25
Combined – Change Direction vs. Change in Win Rate
25Web allows for exploration of outliersSlide26
Combined – Change Direction vs. Change in Win Rate
26Urgot (patch
134)8 buffs, but decrease in win rate!
Why?
Pick rate
doubled after patchProbably non-Urgot players tried him out, but still tough to playSlide27
Combined – Change Direction vs. Change in Win Rate
27Gangplank (patch 154)
Nerfs, but increase in win rate?Large re-work of abilities (
neutral
)
Many small nerfsOverall, stronger championSlide28
Combined – Change Direction vs. Change in Win Rate
28Kalista (patch 157)Nerfs
, but increase in win rate?3 nerfs
, all clearly negative
But other champs that counter
Kalista changed/nerfed, making her strongerSlide29
Conclusion29
Traditional software patches fix bugs, while modern game patches change contentAnalysis of League of Legends, most popular game in worldSampled game data (465k games)Harvested
patches (160 patches, 7700 changes)Built Website
http://lolcrawler.cs.wpi.edu
/
Analyzed dataSlide30
Conclusion
RatesBan/pick rates skewedWin rates normal, except for tailsPatchesGameplay (2x day) dominate bug fixes
Nerfs and buffs equalCombinedChamps
win
rates
further from 50% patched most oftenWebsite allows exploration30
Traditional software patches fix bugs, while modern game patches change content
Analysis of League of Legends, most popular game in
world
Sampled
game data (465k games)
Harvested
patches (160
patches,
7700 changes)
Built
Website
http://lolcrawler.cs.wpi.edu/
Analyzed
dataSlide31
Future Work
Other LoL game data e.g., gold, kills/deaths/assistsOther LoL patchese.g., changes to itemsCompare to similar games e.g., Defense of the Ancients 2 (Valve, 2013), Heroes of the Storm (Blizzard, 2015)Compare to other games e.g., first person shooters
31Slide32
Nerfs, Buffs and Bugs – Analysis of the Impact of Patching on League of Legends
Artian Kica, Andrew La Manna, Lindsay O’Donnell, Tom Paolillo Mark Claypool
In
Proceedings of the 2nd International Workshop on Collaboration and Gaming (
CoGames
),
Orlando, Florida, USA, October 31 - November 4, 2016