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S C I E N C E * PA S S I O N * T E C H N O L O G Y
HOW PLAYSTYLES EVOLVE: PROGRESSION
ANALYSIS AND PROFILING IN JUST CAUSE 2
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
S I M O N E G R I E S M AY R , T U G R A Z , A U S T R I A
A N D E R S D R A C H E N , A A L B O R G U N I V E R S I T Y & 

T H E PA G O N I S N E T W O R K , D E N M A R K
R A F E T S I FA , F R A U N H O F E R I A I S , G E R M A N Y
S E P T- 2 8 : : I F I P I C E C 2 0 1 6 , V I E N N A
Design
AnalysisDesign
Games
Mechanics
Experiences
Immersion
Audio
Animation
Graphics / 

Objects
Character (1st / 3rd)
Interactivity
Interface
Challenges Quests, Puzzles,…
BARTLE’S GAMER TYPES
http://www.gamerdna.com/quizzes/bartle-test-of-gamer-psychology
FLOW EXPERIENCE
http://www.gamerdna.com/quizzes/bartle-test-of-gamer-psychology
GAME ANALYTICS
▸ Understanding player behaviour to create better
game experiences
▸ Understanding and identifying patterns in player data
▸ -> who is the player?
▸ -> statistics on player behaviour (retention rate,
concurrency, )
▸ …
Further reading: El-Nasr, M. S., Drachen, A., & Canossa, A. (2013).
Game analytics: Maximizing the value of player data. Springer
Science & Business Media.
BEHAVIOURAL PROFILING::CLUSTER ANALYSIS
▸ Finding patterns in behavioural game data
▸ Unsupervised learning strategies to find groups/
clusters of players playing in a similar way / fit various
patterns
▸ identify groups with similar behaviour and identify the
most important behavioural features in terms of
underlying patterns in the dataset
Further reading: http://blog.gameanalytics.com/blog/introducing-clustering-
behavioral-profiling-game-analytics.html
PROGRESSION ANALYSIS AND
PROFILING IN JUST CAUSE 2
MAIN CONTRIBUTION
▸ Behavioural profiling through clustering with
Archetypal Analysis (AA) combined with progression
analysis in an Open-World game
▸ The main storyline of Just Cause 2 to measure
progression along multiple vectors
▸ Sankey flow diagram for a visual inspection
JUST CAUSE 2
▸ Progression along different vectors, seven Agency-
related missions, missions from a number of Rebel
Factions, Stronghold missions
▸ All mechanics in game available from the beginning
(direct gameplay approach)
DATASET
▸ Dataset provided by Square Enix
▸ Play histories from over 5000 JC2 players (2010)
▸ Various behavioural features collected:
▸ actions with
▸ in-game geographical coordinates
▸ timestamps
▸ metrics from the gameplay
▸ e.g. total kills, total chaos, kilometres driven # of
stronghold takeovers ,…
▸ Data set pre-processing (cleaning):
▸ Outliers removed: scores outside 1-99th percentile
excluded
▸ (faulty tracking or errors)
FEATURES
▸ 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
ANALYSIS & RESULTS
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
PLAYER PROGRESSION ALONG THE MISSIONS
ANALYSIS
▸ Archetypal Analysis (AA) for behavioural profiling
▸ AA models applied to all seven agency mission bins
▸ Optimal # of clusters (k) determined for each
(analysis of the residual sum of squares for all k value
less than or equal to 20, and chose the number of
clusters with the elbow criterion)
▸ -> three main archetypes
PLAYER PROFILES
PLAYER BEHAVIOUR ALONG THE STORYLINE
RESULTS
▸ How does in-game behaviour and performance
change over the various missions?
▸ (see Sankey diagram) 



▸ player behaviour changes - players do not
remain in a single cluster (also due to the
nature of the mission design)
▸ domination in exploration-based features
(e.g. playtime)
RESULTS
▸ How many profiles enter players on average over the
course of the game?
▸ They change at least once
▸ Avg. 2.91 clusters
RESULTS
▸ How can we describe player behaviour of the different
player profiles?
GOALS
• Improve our understanding of the different player
behaviours and factors to improve engagement
• Find issues to avoid drop-outs
• Provide tools for game designers to (visually) analyse
the game and improve the understanding of players
• Find game design flaws early and maybe also
automatically/dynamically
THANK YOU FOR YOUR
ATTENTION.
JOHANNA PIRKER, JPIRKER@MIT.EDU, @JOEYPRINK


Further information:
andersdrachen.com
jpirker.com
Thanks to Simone, Anders, and Rafet!!
Thanks to Square Enix!
Thanks to the reviewers!

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How Playstyles Evolve: Progression Analysis and Profiling in Just Cause 2

  • 1. S C I E N C E * PA S S I O N * T E C H N O L O G Y HOW PLAYSTYLES EVOLVE: PROGRESSION ANALYSIS AND PROFILING IN JUST CAUSE 2 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 S I M O N E G R I E S M AY R , T U G R A Z , A U S T R I A A N D E R S D R A C H E N , A A L B O R G U N I V E R S I T Y & 
 T H E PA G O N I S N E T W O R K , D E N M A R K R A F E T S I FA , F R A U N H O F E R I A I S , G E R M A N Y S E P T- 2 8 : : I F I P I C E C 2 0 1 6 , V I E N N A
  • 3. Immersion Audio Animation Graphics / 
 Objects Character (1st / 3rd) Interactivity Interface Challenges Quests, Puzzles,…
  • 6. GAME ANALYTICS ▸ Understanding player behaviour to create better game experiences ▸ Understanding and identifying patterns in player data ▸ -> who is the player? ▸ -> statistics on player behaviour (retention rate, concurrency, ) ▸ … Further reading: El-Nasr, M. S., Drachen, A., & Canossa, A. (2013). Game analytics: Maximizing the value of player data. Springer Science & Business Media.
  • 7. BEHAVIOURAL PROFILING::CLUSTER ANALYSIS ▸ Finding patterns in behavioural game data ▸ Unsupervised learning strategies to find groups/ clusters of players playing in a similar way / fit various patterns ▸ identify groups with similar behaviour and identify the most important behavioural features in terms of underlying patterns in the dataset Further reading: http://blog.gameanalytics.com/blog/introducing-clustering- behavioral-profiling-game-analytics.html
  • 9. MAIN CONTRIBUTION ▸ Behavioural profiling through clustering with Archetypal Analysis (AA) combined with progression analysis in an Open-World game ▸ The main storyline of Just Cause 2 to measure progression along multiple vectors ▸ Sankey flow diagram for a visual inspection
  • 10. JUST CAUSE 2 ▸ Progression along different vectors, seven Agency- related missions, missions from a number of Rebel Factions, Stronghold missions ▸ All mechanics in game available from the beginning (direct gameplay approach)
  • 11. DATASET ▸ Dataset provided by Square Enix ▸ Play histories from over 5000 JC2 players (2010) ▸ Various behavioural features collected: ▸ actions with ▸ in-game geographical coordinates ▸ timestamps ▸ metrics from the gameplay ▸ e.g. total kills, total chaos, kilometres driven # of stronghold takeovers ,… ▸ Data set pre-processing (cleaning): ▸ Outliers removed: scores outside 1-99th percentile excluded ▸ (faulty tracking or errors)
  • 12. FEATURES ▸ 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
  • 14. 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
  • 15. PLAYER PROGRESSION ALONG THE MISSIONS
  • 16. ANALYSIS ▸ Archetypal Analysis (AA) for behavioural profiling ▸ AA models applied to all seven agency mission bins ▸ Optimal # of clusters (k) determined for each (analysis of the residual sum of squares for all k value less than or equal to 20, and chose the number of clusters with the elbow criterion) ▸ -> three main archetypes
  • 18. PLAYER BEHAVIOUR ALONG THE STORYLINE
  • 19. RESULTS ▸ How does in-game behaviour and performance change over the various missions? ▸ (see Sankey diagram) 
 
 ▸ player behaviour changes - players do not remain in a single cluster (also due to the nature of the mission design) ▸ domination in exploration-based features (e.g. playtime)
  • 20. RESULTS ▸ How many profiles enter players on average over the course of the game? ▸ They change at least once ▸ Avg. 2.91 clusters
  • 21. RESULTS ▸ How can we describe player behaviour of the different player profiles?
  • 22. GOALS • Improve our understanding of the different player behaviours and factors to improve engagement • Find issues to avoid drop-outs • Provide tools for game designers to (visually) analyse the game and improve the understanding of players • Find game design flaws early and maybe also automatically/dynamically
  • 23. THANK YOU FOR YOUR ATTENTION. JOHANNA PIRKER, JPIRKER@MIT.EDU, @JOEYPRINK 
 Further information: andersdrachen.com jpirker.com Thanks to Simone, Anders, and Rafet!! Thanks to Square Enix! Thanks to the reviewers!