How Playstyles Evolve: Progression Analysis and Profiling in Just Cause 2
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Presentation at IFIP ILRN 2016 in Vienna Authors of the Paper: Johanna Pirker, Simone Griesmayr, Anders Drachen , Rafet Sifa Link to paper: http://link.springer.com/chapter/10.1007%2F978-3-319-46100-7_8
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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
- 8. PROGRESSION ANALYSIS AND PROFILING IN JUST CAUSE 2
- 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
- 17. PLAYER PROFILES
- 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!