1) Data analytics can help understand player behavior and social relationships to create better game experiences. 2) Analyzing player data can provide insights into who players are, what they do, and how they interact socially. 3) This helps optimize games, but also raises issues around privacy, ethics in how data is used and potential negative impacts on players.

1. This is how others play your game!

2. S C I E N C E * P A S S I O N * T E C H N O L O G Y
HOW DATA IS SHAPING OUR GAMES
J O H A N N A P I R K E R , T U G R A Z , A U S T R I A
@ J O E Y P R I N K
J P I R K E R . C O M
2 8 J U N / / W E A R E D E V E L O P E R S

6. DATA ANALYTICS
▸Market optimization (e.g. web analytics, target-group marketing)
▸Data exploration / discovery (e.g. finding new markets)
▸Social network & relationship analysis (e.g. influencer)
▸Fraud detection (site integrity, uptime)
▸Machine-generated data analytics (remote sensing, location-
based information)

7. DATA ANALYTICS MEETS GAMES
▸Market optimization (e.g. monetization optimization in F2P, community
marketing, user generation, virtual currency rate prediction, retention,..)
▸Data exploration / discovery (e.g. finding new markets)
▸Social network & relationship analysis (e.g. understand multi-player
setups)
▸Fraud detection (identify cheaters, ..)
▸Machine-generated data analytics (location-based information, server
issues, game issues..)

8. CONFLICTS
▸“I want to tell my story/idea”
▸ “I want the player to
understand MY game”
GAME DESIGNER
▸“I want to play the game my
way”
▸“I want to be able to play the
game”
PLAYER
▸“make money”
▸“make as much money as possible..”

9. KEY QUESTIONS
▸ How much money can I get from the player?
▸ What can I get from the player?
▸ How can I improve the experience for the player and make more people be
able to play my games?
▸ Does the player understand / can play the game?
▸ How does the player play the game?
▸ How does the player understand the game?
▸ How can the game experience be optimized?

10. DATA ANALYTICS MEETS GAMES
▸Understanding player behavior to create better, more
inclusive and more innovative (social) game experiences
▸Understanding and identifying patterns in game data
▸-> who is the player?
▸-> statistics on player behavior (retention rate,
concurrency, ..)
▸-> social behavior of players
▸How do we interact with others? How are friendships
build? How does the game change us? Who are *we*?

11. DATA ANALYTICS MEETS GAMES
▸Understanding player behavior to create better, more
inclusive and more innovative (social) game experiences
▸Understanding and identifying patterns in game data
▸-> who is the player?
▸-> statistics on player behavior (retention rate,
concurrency, ..)
▸-> social behavior of players
▸How do we interact with others? How are friendships
build? How does the game change us? Who are *we*?
1) LEARN MORE ABOUT PLAYERS, LEARN MORE ABOUT THE GAMES

12. DATA ANALYTICS MEETS GAMES
▸Understanding player behavior to create better, more
inclusive and more innovative (social) game experiences
▸Understanding and identifying patterns in game data
▸-> who is the player?
▸-> statistics on player behavior (retention rate,
concurrency, ..)
▸-> social behavior of players
▸Who are *we*?
1) LEARN MORE ABOUT PLAYERS, LEARN MORE ABOUT THE GAMES
2) LEARN MORE ABOUT US

13. DATA ANALYTICS MEETS GAMES
▸Understanding player behavior to create better, more
inclusive and more innovative (social) game experiences
▸Understanding and identifying patterns in game data
▸-> who is the player?
▸-> statistics on player behavior (retention rate,
concurrency, ..)
▸-> social behavior of players
▸Who are *we*?
1) LEARN MORE ABOUT PLAYERS AND THE GAMES

14. This is how others play your game!

15. WHAT WE WANT TO LEARN FROM THE PLAYERS..
Understanding player behavior to create better or more innovative
social game experiences
• Behavior - what players actually do
• Understanding - what the players think they have to do / have done
• Engagement - why do they keep playing?
• Experience - what the players feel
• Social Experiences – social behaviour of players, recommender systems

16. WHAT WE WANT TO LEARN FROM THE PLAYERS..
Understanding player behavior to create better or more innovative
social game experiences
• Behavior - what players actually do
• Understanding - what the players think they have to do / have done
• Engagement - why do they keep playing?
• Experience - what the players feel
• Social Experiences – social behaviour of players, recommender systems
I) BEHAVIOURAL PROFILING (E.G. CLUSTERING)

17. WHAT WE WANT TO LEARN FROM THE PLAYERS..
Understanding player behavior to create better or more innovative
social game experiences
• Behavior - what players actually do
• Understanding - what the players think they have to do / have done
• Engagement - why do they keep playing?
• Experience - what the players feel
• Social Experiences – social behaviour of players, recommender systems
I) BEHAVIOURAL PROFILING (E.G. CLUSTERING)
II) SOCIAL NETWORK ANALYSIS (E.G. GRAPH APPLICATION)

18. I) BEHAVIOURAL
PROFILING

19. BARTLE’S GAMER TYPES

20. P L A Y E R H A B I T ( P L A Y E R F I N G E R P R I N T )

21. PLAYER PROFILES IN FORZA
‣ What Drives People: Creating Engagement Profiles of Players from Game
Log Data
‣ 120 mio race entries from 1.2 mil players
T., Nagapan, N., Guajardo, J. J., Cooper, R., Solberg, T., & Greenawalt, D. (2015, October). What Drives People: Creating Engagement Profiles of Players from Game Log Data. In Proceedings of the 2015 Annual Symposium on Computer-Human Interaction
Harpstead, E., Zimmermann, T., Nagapan, N., Guajardo, J. J., Cooper, R., Solberg, T., & Greenawalt, D. (2015, October). What drives people: Creating
engagement profiles of players from game log data. In Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play (pp. 369-379).

22. F L O W ( M I H A L Y C S I K S Z E N T M I H A L Y I )

23. GOALS
• Quelle: www.cynamite.de
JC2

24. FEATURES
‣ Spatio-temporal navigation
‣ combat performance
‣ progression through the main storyline
‣ side quests..
‣ Agency missions (+ reach specific level of Chaos)
‣ subset of features based on the core mechanics
‣ -> does not impact the analytical framework
‣ -> impacts the kinds of conclusions that can be derived

25. P L A Y E R P R O G R E S S I O N A L O N G T H E M I S S I O N S

26. • Low Performers: worst kill/death ratio, most deaths per minutes
• Rushers: complete the game the fastest (most chaos per minute
collected, high k/d ratio, most kills per minute )
• Elite Players: highest difficulty level / highest k/d ratio
• Explorers: slow pace, hijacked most vehicles etc

27. RESULTS
‣ How can we describe player behaviour of the different player
profiles?

28. P L A Y E R B E H A V I O U R A L O N G T H E S T O R Y L I N E
jpirker.com/jc2/aaSankey.html

29. SOCIAL NETWORK
ANALYSIS IN GAMES

30. SIX DEGREES OF SEPARATION
▸In 1967, Stanley Milgram (social psychologist at Yale & Harvard)
conducted the small-world-experiment that is the basis of the “six
degrees of separation” concept.
▸He sent several packages to randomly selected individuals in the
US, asking them to forward the package to a target contact
person in Boston. The average path length for the received
packages was around 5.5.
Further reading: en.wikipedia.org/wiki/Small-world_experiment

32. SIX DEGREES OF SEPARATION
▸In 2008, a study of Microsoft showed that the average chain
of contacts between users of MSN was 6.6 people.
▸In 2016, Facebook observed an average connection distance
between Facebook users of 3.57.
Further reading: en.wikipedia.org/wiki/Small-world_experiment

34. ISSUES &
ETHICS

35. GD-NUMBER
Collaboration Graph as Engagement Tool
Based on the social network measure a new form of social engagement can be created. Similar to the
Small World Problem or the Erdos number, the collaboration graph can be used to engage
jammers, to collaborate with new jammers, or jammers at different locations.
As gamification tools, jammers could be motivated through their ”degree”, or the path length to
another person (e.g. a famous game developer, the ”Carmack number”) to collaborate with new
jammers.
Carmack Number 0
Carmack Number n
Carmack Number 1

36. Collaboration Graph as Engagement Tool
Based on the social network measure a new form of social engagement can be created. Similar
to the Small World Problem or the Erdos number, the collaboration graph can be used to
engage jammers, to collaborate with new jammers, or jammers at different locations.
As gamification tools, jammers could be motivated through their ”degree”, or the path length to
another person (e.g. a famous game developer, the ”Romero number”) to collaborate with
new jammers.
Romero Number 0
Romero Number n
Romero Number 1
GD-NUMBER

37. Pirker Number 0
Pirker Number n
Pirker Number n-1
Pirker Number n-1
GD-NUMBER

38. ISSUES &
ETHICS

42. TEXT
https://shiring.github.io/networks/2017/05/15/got_final_files/fa

45. TYPICAL QUESTIONS
▸Analyzing individuals:
▸Who are well connected / important players in a network?
▸What is the influence of individuals?
▸Who is the player with the largest reach?
▸Who are players connecting different player groups?

46. TYPICAL QUESTIONS
▸Analyzing groups and communities:
▸How can we identify groups and communities?
▸How are players connected with each other?
▸Are players more engaged by playing along or together?
▸Are players in groups performing better than players playing on
their own?
▸Do connected players share common interests?

47. TYPICAL QUESTIONS
▸Analyzing social dynamics:
▸How do players connect to other players?
▸How do players build guilds?
▸When a player gets an interesting item to share with other
players, how far will it get transmitted?
▸How can we recommend players in PvP matches?

48. BUILDING PLAYER NETWORKS
▸Undirected networks (Links are undirected)
▸Directed networks (Links are directed)
▸Weighted networks (Links are weighted)

49. NETWORK RELATIONSHIP
‣ Player Network
‣ v: players
‣ e: match played together
/ against each other
‣ undirected, weighted graph
‣ (weight: # matches played together)
PLAYER 1
PLAYER 2
PLAYER 3
3
1

50. BUILDING PLAYER NETWORKS
▸Direct relationships: Direct (explicit) interactions between players
are identified and used (e.g. in-game messaging, friendships, clan
memberships).
▸Indirect relationships: Relationships also can be identified
through indirect (implicit) interactions (playing in same matches
or opponent matches, same playing time, same in-game location).

51. SOCIAL NETWORKS IN DESTINY
Rattinger, A., Wallner, G., Drachen, A., Pirker, J., & Sifa, R. (2016, September) Integrating and Inspecting Combined Behavioral Profiling and Social Network Models in Destiny,15th International Conference on Entertainment Computing (in press).

52. RETENTION ANALYSIS / INFLUENCER ANALYSIS
▸How to keep players?
▸Identification of important nodes

53. PERFORMANCE ANALYSIS
▸How perform players?
▸Players playing more often with the same players in teams have a
higher success rate

54. ENGAGEMENT ANALYSIS
▸How to engage players?
▸Players playing more often with the same players in teams play
more often and longer

55. DATA ANALYTICS MEETS GAMES
▸Understanding player behavior to create better, more
inclusive and more innovative (social) game experiences
▸Understanding and identifying patterns in game data
▸-> who is the player?
▸-> statistics on player behavior (retention rate,
concurrency, ..)
▸-> social behavior of players
▸Who are *we*?
2) LEARN MORE ABOUT US

56. TEXT
https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.202049

57. DATA ANALYTICS MEETS GAMES
▸Understanding player behavior to create better, more
inclusive and more innovative (social) game experiences
▸Understanding and identifying patterns in game data
▸-> who is the player?
▸-> statistics on player behavior (retention rate,
concurrency, ..)
▸-> social behavior of players
▸Who are *we*?
3) DATA AS PART OF GAME DESIGN

58. DATA ANALYTICS MEETS GAMES

59. PROBLEMS &
CONCERNS
DATA ANALYTICS MEETS GAMES

60. TEXT

62. https://meyerweb.com/eric/thoughts/2014/12/24/inadvertent-algorithmic-cruelty/

63. “