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Watch Me Playing, I Am a Professional a first Study on Video Game Live Streaming

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Watch Me Playing, I Am a Professional a first Study on Video Game Live Streaming Watch me playing, I am a professional A first study on video game live streaming M. Kaytoue1, A. Silva1, L. Cerf1, W. Meira Jr.1, C. Ra¨ıssi2 1 2 Belo Horizonte { Brazil Nancy { France Mining Social Network Dynamics @ WWW 2012 Lyon (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 Watch me playing, I am a professional 2 / 38 N Events and tournaments Watch me playing, I am a professional 3 / 38 N Social TV Watch me playing, I am a professional 4 / 38 N Contribution Starting 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 Watch me playing, I am a professional 5 / 38 N Outline 1 A first characterization of the E-Sport community 2 Predicting stream popularity 3 Ranking streamers 4 Conclusion and perspectives Watch me playing, I am a professional 6 / 38 N 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 description date The date of crawling of the tuple login Unique identifier of a user/streamer game The game or topic of the stream description A text description of the stream count The number of viewers/spectators watching the stream at a given time Watch me playing, I am a professional 7 / 38 N A first characterization of the E-Sport community 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 27,120,337 Length streamed 215.3 years Length watched 9,622.4 years Watch me playing, I am a professional 8 / 38 N A first characterization of the E-Sport community Views along the weeks (When?) 70000 1600 viewers streamers 60000 1400 50000 1200 40000 1000 avg nb of viewers 30000 800 avg nb of streamers 20000 600 10000 400 Sun Mon Tue Wed Thu Fri Sat Sun Watch me playing, I am a professional 9 / 38 N A first characterization of the E-Sport community Geographic distribution (Where?) Watch me playing, I am a professional 10 / 38 N A first characterization of the E-Sport community Top 20 most popular games (What?) Game Audience Release StarCraft II 35.05% July 2010 Heroes of Newerth 8.89% May 2010 League of Legends 8.19% Oct. 2009 World of Warcraft 6.24% Nov. 2004 Call of Duty: BO 3.88% Nov. 2010 Street fighter 4 3.26% Apr. 2010 Star Wars (TOR) 2.98% Dec 2011 The Elder Scrolls 2.36% Nov. 2011 MineCraft 2.03% Nov. 2011 Rage 1.98% Oct. 2011 Marvel vs. Capcom 3 1.67% Feb. 2011 Dota 2 (beta) 1.55% Sep. 2011 Battlefield 3 1.39% Oct. 2011 Warcraft III 1.22% July 2002 Halo: Reach 1.20% Sept. 2010 Mario Kart 7 1.18% Dec. 2011 Dark Souls 1.10% Oct. 2011 Zelda SS 1.05% Nov 2011 Gears of War 3 0.93% Sept. 2011 Counter-Strike S 0.89 % Nov. 2004 Others 12.95% Watch me playing, I am a professional 11 / 38 N A first characterization of the E-Sport community Local game popularity (What?) Heroes of Newerth Zelda World of Warcraft Warcraft III The Elder Scrolls Mario’s Street Fighter Star Wars Starcraft II Rage MineCraft Marvel vs. Capcom League of Legends Halo Counter-Strike Gears of War Dota Dark Souls % of daily audience Call of Duty Battlefield Time (days) Watch me playing, I am a professional 12 / 38 N A first characterization of the E-Sport community Major E-Sport events (What?) 70000 60000 50000 40000 30000 20000 Oct. 11 Nov. 11 Dec. 11 Jan. 12 IEM N-Y Home Story Cup MLG Orlando NASL S2 Finals IGN Pro League NE League S2 Grand Finals DreamHack Winter 12 hours for charity Blizzard Cup #views Watch me playing, I am a professional 13 / 38 N A first characterization of the E-Sport community Stream and Streamer characteristics 1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.3 0.4 cumulative (%) 0.2 cumulative (%) 0.3 0.1 0.2 0 0.1 100 101 102 103 104 105 100 101 102 103 104 105 106 duration (min) duration (min) (a) Stream (b) Streamer Duration of streams and aggregate duration of streamers 1 1 0.8 0.8 0.6 0.6 0.4 0.4 agregate views 0.2 agregate views 0.2 0 0 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 stream rank streamer rank (c) Stream (d) Streamer Stream and streamer audience Watch me playing, I am a professional 14 / 38 N 1 A first characterization of the E-Sport community 2 Predicting stream popularity 3 Ranking streamers 4 Conclusion and perspectives Predicting stream popularity Motivation Current Twitch recommendation strategy New and interesting streams may take too long (or even never) to become visible Watch me playing, I am a professional 16 / 38 N Predicting stream popularity Motivation 1 0.9 0.8 0.7 0.6 Streaming sessions 0.5 have a highly skewed popularity distribution, 0.4 0.3 short duration,cumulative (%) and slow popularity evolution. 0.2 0.1 0 100 101 102 103 104 105 0.3 duration (min) average session for the top-100 streamers 0.25 0.2 0.15 1 0.1 0.8 0.05 proportion of the overall maximal popularity 0 0.6 0 2 4 6 8 10 12 14 16 hours since the beginning of a session 0.4 (e)agregate views (f) (g) 0.2 0 Stream popularity, duration and popularity evolution 0 10 20 30 40 50 60 70 80 90 100 stream rank Watch me playing, I am a professional 17 / 38 N Predicting stream popularity Idea Predicting popularity using initial popularity records 105 105 104 104 103 103 102 102 101 101 popularity after 1 hour popularity after 1 hour 100 100 100 101 102 103 104 105 100 101 102 103 104 105 popularity after ti minutes popularity after ti minutes (h) ti = 5 min. (i) ti = 30 min. Correlation between stream popularity after ti minutes and 1 hour Watch me playing, I am a professional 18 / 38 N Predicting stream popularity Correlation Varying ti Correlation between popularity after ti minutes and 1 hour 1 14 0.9 13 12 0.8 11 0.7 correlation 10 0.6 corr. 9 mean squared error ε 0.5 8 5 10 15 20 25 30 ti (min) Watch me playing, I am a professional 19 / 38 N Predicting stream popularity Prediction Model Model log(pop(tf )) = β0 + β1 log(pop(ti )) + Predicted vs. actual (based on popularity after ti minutes) 105 105 104 104 103 103 102 102 101 101 0 0 actual popularity after 1 hour 10 actual popularity after 1 hour 10 100 101 102 103 104 105 100 101 102 103 104 105 predicted popularity after 1 hour predicted popularity after 1 hour (j) ti = 5 min. (k) ti = 30 min. Watch me playing, I am a professional 20 / 38 N Predicting stream popularity MSE Varying ti MSE for different values of ti (minutes) 1 14 0.9 13 12 0.8 11 0.7 correlation 10 0.6 corr. 9 mean squared error ε 0.5 8 5 10 15 20 25 30 ti (min) Watch me playing, I am a professional 21 / 38 N 1 A first characterization of the E-Sport community 2 Predicting stream popularity 3 Ranking streamers 4 Conclusion and perspectives 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? Watch me playing, I am a professional 23 / 38 N 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 are not comparable with Mill.Stephano. crawl time Oct. 29 16:30 Oct. 29 16:35 Nov. 10 19:00 EG.IdrA 1950 6350 1020 Mill.Stephano 4450 3680- WhiteRa 935 2301 4535 Watch me playing, I am a professional 24 / 38 N 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 voters and apply a Condorcet method that is known to be good for ranking: Maximum Majority Voting.
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