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!