"Electronic-sport" (E-Sport) is now established as a new entertainment genre. More and more players enjoy streaming their games, which attract even more viewers. In fact, in a recent social study, casual players were found to prefer watching professional gamers rather than playing the game themselves. Within this context, advertising provides a significant source of revenue to the professional players, the casters (displaying other people's games) and the game streaming platforms. For this paper, we crawled, during more than 100 days, the most popular among such specialized platforms: Twitch.tv. Thanks to these gigabytes of data, we propose a first characterization of a new Web community, and we show, among other results, that the number of viewers of a streaming session evolves in a predictable way, that audience peaks of a game are explainable and that a Condorcet method can be used to sensibly rank the streamers by popularity. Last but not least, we hope that this paper will bring to light the study of E-Sport and its growing community. They indeed deserve the attention of industrial partners (for the large amount of money involved) and researchers (for interesting problems in social network dynamics, personalized recommendation, sentiment analysis, etc.).

1. 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.

2. 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 / 38
Watch me playing, I am a professional

3. Events and tournaments
3 / 38
Watch me playing, I am a professional

4. Social TV
4 / 38
Watch me playing, I am a professional

5. 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
5 / 38
Watch me playing, I am a professional

6. Outline
1 A first characterization of the E-Sport community
2 Predicting stream popularity
3 Ranking streamers
4 Conclusion and perspectives
6 / 38
Watch me playing, I am a professional

7. 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
7 / 38
Watch me playing, I am a professional

8. 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
8 / 38
Watch me playing, I am a professional

9. A first characterization of the E-Sport community
Views along the weeks (When?)
10000
20000
30000
40000
50000
60000
70000
Sun Mon Tue Wed Thu Fri Sat Sun
400
600
800
1000
1200
1400
1600
avgnbofviewers
avgnbofstreamers
viewers
streamers
9 / 38
Watch me playing, I am a professional

10. A first characterization of the E-Sport community
Geographic distribution (Where?)
10 / 38
Watch me playing, I am a professional

11. 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%
11 / 38
Watch me playing, I am a professional

12. A first characterization of the E-Sport community
Local game popularity (What?)
%ofdailyaudience
Time (days)
Battlefield
Call of Duty
Dark Souls
Dota
Gears of War
Counter-Strike
Halo
League of Legends
Marvel vs. Capcom
MineCraft
Rage
Starcraft II
Star Wars
Street Fighter
Mario’s
The Elder Scrolls
Warcraft III
World of Warcraft
Zelda
Heroes of Newerth
12 / 38
Watch me playing, I am a professional

13. A first characterization of the E-Sport community
Major E-Sport events (What?)
20000
30000
40000
50000
60000
70000
Oct. 11 Nov. 11 Dec. 11 Jan. 12
IEM N-Y
MLG Orlando
IGN Pro League
DreamHack Winter
Blizzard Cup
Home Story Cup
NASL S2 Finals
NE League S2 Grand Finals
12 hours for charity
#views
13 / 38
Watch me playing, I am a professional

14. A first characterization of the E-Sport community
Stream and Streamer characteristics
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
100
101
102
103
104
105
cumulative(%)
duration (min)
(a) Stream
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
100
101
102
103
104
105
106
cumulative(%)
duration (min)
(b) Streamer
Duration of streams and aggregate duration of streamers
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60 70 80 90 100
agregateviews
stream rank
(c) Stream
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60 70 80 90 100
agregateviews
streamer rank
(d) Streamer
Stream and streamer audience
14 / 38
Watch me playing, I am a professional

15. 1 A first characterization of the E-Sport community
2 Predicting stream popularity
3 Ranking streamers
4 Conclusion and perspectives

16. Predicting stream popularity
Motivation
Current Twitch recommendation strategy
New and interesting streams may take too long (or even never)
to become visible
16 / 38
Watch me playing, I am a professional

17. Predicting stream popularity
Motivation
Streaming sessions have a highly skewed popularity distribution,
short duration, and slow popularity evolution.
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60 70 80 90 100
agregateviews
stream rank
(e)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
100
101
102
103
104
105
cumulative(%)
duration (min)
(f)
0
0.05
0.1
0.15
0.2
0.25
0.3
0 2 4 6 8 10 12 14 16
proportionoftheoverallmaximalpopularity
hours since the beginning of a session
average session for the top-100 streamers
(g)
Stream popularity, duration and popularity evolution
17 / 38
Watch me playing, I am a professional

18. Predicting stream popularity
Idea
Predicting popularity using initial popularity records
10
0
101
102
103
104
105
100
101
102
103
104
105
popularityafter1hour
popularity after ti minutes
(h) ti = 5 min.
10
0
101
102
103
104
105
100
101
102
103
104
105
popularityafter1hour
popularity after ti minutes
(i) ti = 30 min.
Correlation between stream popularity after ti minutes and 1 hour
18 / 38
Watch me playing, I am a professional

19. Predicting stream popularity
Correlation Varying ti
Correlation between popularity after ti minutes and 1 hour
0.5
0.6
0.7
0.8
0.9
1
5 10 15 20 25 30
8
9
10
11
12
13
14
correlation
meansquarederror
ti (min)
corr.
ε
19 / 38
Watch me playing, I am a professional

20. Predicting stream popularity
Prediction Model
Model
log(pop(tf )) = β0 + β1 log(pop(ti )) +
Predicted vs. actual (based on popularity after ti minutes)
100
101
10
2
103
10
4
105
100
101
102
103
104
105
actualpopularityafter1hour
predicted popularity after 1 hour
(j) ti = 5 min.
100
101
10
2
103
10
4
105
100
101
102
103
104
105
actualpopularityafter1hour
predicted popularity after 1 hour
(k) ti = 30 min.
20 / 38
Watch me playing, I am a professional

21. Predicting stream popularity
MSE Varying ti
MSE for different values of ti (minutes)
0.5
0.6
0.7
0.8
0.9
1
5 10 15 20 25 30
8
9
10
11
12
13
14
correlation
meansquarederror
ti (min)
corr.
ε
21 / 38
Watch me playing, I am a professional

22. 1 A first characterization of the E-Sport community
2 Predicting stream popularity
3 Ranking streamers
4 Conclusion and perspectives

23. 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?
23 / 38
Watch me playing, I am a professional

24. 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
24 / 38
Watch me playing, I am a professional

25. 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.
25 / 38
Watch me playing, I am a professional

26. Ranking streamers
Ranking the pairs of streamers
Three criteria with the following precedence:
c1 How often the first streamer is preferred to the
second;
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
26 / 38
Watch me playing, I am a professional

27. 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 cycle
involving it;
3 Remove those involved in a cycle;
4 Go to 1.
27 / 38
Watch me playing, I am a professional

28. Ranking streamers
Resulting graph
28 / 38
Watch me playing, I am a professional

29. Ranking streamers
Results with Top-100 streamers
Focusing on eight StarCraft II players
Web poll (# votes) Simple ranking (pos.) Condorcet
WhiteRa (11,112) EG.IdrA (20) EG.IdrA
Mill.Stephano (9,192) WhiteRa (21) Mill.Stephano
EG.IdrA (6,746) Liquid’Ret (31) EG.HuK
EG.HuK (5,050) EG.HuK (32) WhiteRa
Liquid‘HerO (2,160) Mill.Stephano (33) Liquid‘HerO
Liquid’Sheth (846) Liquid‘HerO (53) QxG.SaSe
QxG.SaSe (833) Liquid’Sheth (72) Liquid’Sheth
Liquid’Ret (684) QxG.SaSe (91) Liquid’Ret
29 / 38
Watch me playing, I am a professional

30. 1 A first characterization of the E-Sport community
2 Predicting stream popularity
3 Ranking streamers
4 Conclusion and perspectives

31. 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, social
sciences, etc.)
31 / 38
Watch me playing, I am a professional

32. Conclusion and perspectives
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
32 / 38
Watch me playing, I am a professional

33. Conclusion and perspectives
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 to
another into popularity?
33 / 38
Watch me playing, I am a professional

34. Conclusion and perspectives
Examples
Popularity: 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 unknown
stream for a spectator?
34 / 38
Watch me playing, I am a professional

35. Conclusion and perspectives
Examples
Facebook, tweets, IRC events, etc.
NLP, sentiment analysis (each game has a specific vocabulary)
Graph-mining, network analysis
35 / 38
Watch me playing, I am a professional

36. Conclusion and perspectives
Examples
Artificial Intelligence
Abstracting (very!) noisy series of events, without knowing the
game state that remains to be approximated
36 / 38
Watch me playing, I am a professional

37. Conclusion and perspectives
Examples
37 / 38
Watch me playing, I am a professional

38. Conclusion and perspectives
Thank you!
All datasets used for this article are available
http://homepages.dcc.ufmg.br/~kaytoue/
Other datasets
kaytoue@dcc.ufmg.br
38 / 38
Watch me playing, I am a professional