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Nerfs, Buffs and Bugs –
Nerfs, Buffs and Bugs –

Nerfs, Buffs and Bugs – - PowerPoint Presentation

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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 Download Presentation

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Presentation on theme: "Nerfs, Buffs and Bugs –"— Presentation transcript

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