Watch me playing, I am a professional · 2012. 4. 25. · Watch me playing, I am a professional A rst study on video game live streaming M. Kaytoue 1, A. Silva , L. Cerf , W. Meira

Watch me playing, I am a professionalA first study on video game live streaming

M. Kaytoue1, A. Silva1, L. Cerf1, W. Meira Jr.1, C. Raıssi2

1 2

Belo Horizonte – Brazil Nancy – France

Mining Social Network Dynamics @ WWW 2012Lyon (France) - 16 April, 2012.

Electronic Sports

Watching E-Sport on internet: a new entertainment?

Just like traditional sport but with video games

Professional commentators, sponsors, tournaments, etc.

Professional gamers streaming their games over internet

Spectators prefer to watch rather than playing themselves

A new Web community is growing

Widely using Web media such as FaceBook, Twitter, etc. and...

Live video game streaming platform gaining in popularity

Very active, important frequency of events

2 / 38Watch me playing, I am a professional

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Events and tournaments


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Social TV


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ContributionStarting from Twitch.tv audience data

From September 29th, 2011 to January 09th, 2012

Every five minutes, get tuples of active streams(date, login, game, description, count, ...)

We propose a first characterization of this community

Quantitatively: audience, content length, etc.

Qualitatively: What games? Where? etc.

Early prediction of the audience

Ranking most popular professional gamers

Findings

Important for E-Sport actors – With nice perspectives of research


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Outline

1 A first characterization of the E-Sport community

2 Predicting stream popularity

3 Ranking streamers

4 Conclusion and perspectives


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A first characterization of the E-Sport community

Twitch data acquisition and description

Data

From September 29th, 2011 to January 09th, 2012

Every five minutes, get all of active streams and their audience

More than 24 millions of tuples

Cleaning: missing values, removing illegal streams (1.54%), etc.

field descriptiondate The date of crawling of the tuplelogin Unique identifier of a user/streamergame The game or topic of the stream

description A text description of the streamcount The number of viewers/spectators

watching the stream at a given time


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Dataset Summary

Period of analysis Sept 29, 11 - Jan 9, 12#timestamps 28,292 (832 missing)

#logins 129,332#games 17,749#tuples 24,018,644

#illegal tuples 369,470 (1.54%)#sessions 1,175,589

#views 27,120,337Length streamed 215.3 yearsLength watched 9,622.4 years


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Views along the weeks (When?)

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viewersstreamers


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Geographic distribution (Where?)


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Top 20 most popular games (What?)

Game Audience ReleaseStarCraft II 35.05% July 2010

Heroes of Newerth 8.89% May 2010League of Legends 8.19% Oct. 2009World of Warcraft 6.24% Nov. 2004Call of Duty: BO 3.88% Nov. 2010Street fighter 4 3.26% Apr. 2010

Star Wars (TOR) 2.98% Dec 2011The Elder Scrolls 2.36% Nov. 2011

MineCraft 2.03% Nov. 2011Rage 1.98% Oct. 2011

Marvel vs. Capcom 3 1.67% Feb. 2011Dota 2 (beta) 1.55% Sep. 2011Battlefield 3 1.39% Oct. 2011Warcraft III 1.22% July 2002Halo: Reach 1.20% Sept. 2010Mario Kart 7 1.18% Dec. 2011Dark Souls 1.10% Oct. 2011Zelda SS 1.05% Nov 2011

Gears of War 3 0.93% Sept. 2011Counter-Strike S 0.89 % Nov. 2004

Others 12.95%


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Local game popularity (What?)%

of daily

audie

nce

Time (days)

BattlefieldCall of DutyDark SoulsDotaGears of WarCounter-StrikeHaloLeague of LegendsMarvel vs. CapcomMineCraftRageStarcraft IIStar WarsStreet FighterMario’sThe Elder ScrollsWarcraft IIIWorld of WarcraftZeldaHeroes of Newerth


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Major E-Sport events (What?)

20000

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70000

Oct. 11 Nov. 11 Dec. 11 Jan. 12

IEM N-YMLG Orlando

IGN Pro LeagueDreamHack Winter

Blizzard Cup

Home Story CupNASL S2 Finals

NE League S2 Grand Finals12 hours for charity

#views


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Stream and Streamer characteristics

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(d) StreamerStream and streamer audience


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3 Ranking streamers


Predicting stream popularity

MotivationCurrent Twitch recommendation strategy

New and interesting streams may take too long (or even never)to become visible


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Motivation

Streaming sessions have a highly skewed popularity distribution,short duration, and slow popularity evolution.

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hours since the beginning of a session

average session for the top-100 streamers

(g)

Stream popularity, duration and popularity evolution


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Idea

Predicting popularity using initial popularity records

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popularity after ti minutes

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Correlation between stream popularity after ti minutes and 1 hour


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Correlation Varying ti

Correlation between popularity after ti minutes and 1 hour

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Prediction Model

Model

log(pop(tf )) = β0 + β1 log(pop(ti )) + ε

Predicted vs. actual (based on popularity after ti minutes)

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predicted popularity after 1 hour

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MSE Varying ti

MSE for different values of ti (minutes)

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3 Ranking streamers


Ranking streamers

Why rank streamers?

Interesting for

Spectators: Who to watch?

Sponsors: Who to support?

Teams: Who to recruit?

Gamers: Is my rival doing better?

Game editors: Is my game more popular than my concurrents?


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Ranking streamers

Comparing two streamers

Audience depends of other streams active at the same time

Comparison of two streamers when they broadcast together

Example

On Nov. 10 19:00, WhiteRa is preferred to EG.IdrA. They arenot comparable with Mill.Stephano.

crawl time Oct. 29 16:30 Oct. 29 16:35 Nov. 10 19:00EG.IdrA 1950 6350 1020

Mill.Stephano 4450 3680 -WhiteRa 935 2301 4535


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Ranking streamers

Challenge

Difficulty

Raw audience is not a good measure of popularity because of:

daily/weekly variations of the number of viewers and sessions;

variations of the number of viewers along a session.

Idea for aggregating the preferences

Consider the streamers as candidates, the crawl points as votersand apply a Condorcet method that is known to be good forranking: Maximum Majority Voting.


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Ranking streamers

Ranking the pairs of streamers

Three criteria with the following precedence:

c1 How often the first streamer is preferred to thesecond;

c2 How often they have the exact same popularity;

c3 How often they broadcast at the same time.

c1 c2 c3

(EG.IdrA,WhiteRa) 0.9615 0 156(EG.IdrA,Mill.Stephano) 0.9 0 20(WhiteRa,Mill.Stephano) 0.7829 0 175


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Ranking streamers

Building an acyclic directed graph

Until all ranked pairs are processed:

1 Add all tied pairs as edges;

2 For every newly added edge, decide the existence of a cycleinvolving it;

3 Remove those involved in a cycle;

4 Go to 1.


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Ranking streamers

Resulting graph


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Ranking streamers

Results with Top-100 streamers

Focusing on eight StarCraft II players

Web poll (# votes) Simple ranking (pos.) CondorcetWhiteRa (11,112) EG.IdrA (20) EG.IdrA

Mill.Stephano (9,192) WhiteRa (21) Mill.StephanoEG.IdrA (6,746) Liquid’Ret (31) EG.HuKEG.HuK (5,050) EG.HuK (32) WhiteRa

Liquid‘HerO (2,160) Mill.Stephano (33) Liquid‘HerOLiquid’Sheth (846) Liquid‘HerO (53) QxG.SaSe

QxG.SaSe (833) Liquid’Sheth (72) Liquid’ShethLiquid’Ret (684) QxG.SaSe (91) Liquid’Ret


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3 Ranking streamers


Conclusion and perspectives

Conclusion

Characterization of a new Web community

Gathered around social TV (Twitch.tv)

Quantitative and qualitative characterization

Popular tournaments and releases translate into audience

Early prediction of future audience of a stream

Ranking popular players via a Condorcet method

A particular interest

For the actors of this community (spectators, pro-gamers,sponsors, game publishers, etc.)

For the research community (social network, data-mining, socialsciences, etc.)


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Going further into the characterization

A community per se

accommodated with Web technologies,

intensively using Web media like Facebook, Twitter,YouTube,

and very active,

making it an interesting study case for researchers.

Further work

A better characterization, including other media/data

Formally define entities, relations, dimensions, etc


N


Examples

Propagation

Data: Facebook and Twitter streaming announcements

Question: How does it propagate into audience?

Network dynamics & Popularity

Data: List of IRC users logged in and watching a stream

Question: are spectators structured into (evolving)sub-communities?

Question: Can we translate spectator moving from a stream toanother into popularity?


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ExamplesPopularity: a point of view, depends on several factors

Data: Twitch audience, chat session (sentiment analysis)

Data: Forum fan-club, e.g. TeamLiquid.net

Data: Official season ranking

Data: Records of ladder games, e.g. A won against B on day C

Question: How/can “Skylines” determine best players?

Question: Can we early predict rising/dying stars?

Personal recommendation

Data: Twitch data

Question: How to recommend an interesting and unknownstream for a spectator?


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ExamplesFacebook, tweets, IRC events, etc.

NLP, sentiment analysis (each game has a specific vocabulary)

Graph-mining, network analysis


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Examples

Artificial Intelligence

Abstracting (very!) noisy series of events, without knowing thegame state that remains to be approximated


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Examples


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Thank you!All datasets used for this article are available

http://homepages.dcc.ufmg.br/~kaytoue/

Other datasets

[email protected]


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http://homepages.dcc.ufmg.br/~kaytoue/

[email protected]

Documents

Watch me playing, I am a professional · 2012. 4. 25. · Watch me playing, I am a professional A rst study on video game live streaming M. Kaytoue 1, A. Silva , L. Cerf , W. Meira