Evaluating a Procedural Content Orchestrator Gameplay Data and Classifying User Profiles
Background: Content Orchestration is a novel research field focused on coordinating distinct types of algorithmically generated game content. Purpose: Thus, the lack of research in this area hinders the analysis of gameplay data and player profiling in games with orchestrated content. Methods: This...
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| Format: | Article |
| Language: | English |
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Brazilian Computer Society
2025-07-01
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| Series: | Journal on Interactive Systems |
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| Online Access: | https://journals-sol.sbc.org.br/index.php/jis/article/view/5630 |
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| author | Leonardo Tórtoro Pereira T. Yuji Teoi Claudio Fabiano Motta Toledo |
| author_facet | Leonardo Tórtoro Pereira T. Yuji Teoi Claudio Fabiano Motta Toledo |
| author_sort | Leonardo Tórtoro Pereira |
| collection | DOAJ |
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Background: Content Orchestration is a novel research field focused on coordinating distinct types of algorithmically generated game content. Purpose: Thus, the lack of research in this area hinders the analysis of gameplay data and player profiling in games with orchestrated content. Methods: This paper is an extension of a work that collected and analyzed gameplay logs of 15 players who played 119 game sections of 12 different dungeons of a top-down action game. The game’s Levels, Rules, and Narrative content were orchestrated and adapted to player profiles defined from a pre-test questionnaire. PCA and clustering techniques were used to highlight relevant gameplay metrics for distinguishing play styles. In this extension, we used the gameplay data alone to train classifiers with and without data augmentation to predict a user’s profile, measuring the accuracy, precision, recall and f1-score with a train-test split and a 5-fold cross-validation for a more robust accuracy. We also implemented data augmentation on our gameplay metrics sample. Results: We identified, through the previous work, two components of PCA explaining a total of 65% of data variability, containing data such as Lock Usage Rate, Enemy Kill Rate, Map Completion, and Completed Immersion Quests. We also found game difficulty as an important level component for impact clustering. Through data augmentation, we achieved novel results, such as a mean accuracy
of almost 95%, measured with a 5-fold cross-validation, for the Histogram-based Gradient Boosting classifier when predicting a player’s profile based on their gameplay data, even with our small sample size. Conclusion: Our work guides developers and researchers to choose relevant gameplay metrics to determine players’ play styles. Our extended results suggest that we can predict player’s profiles through gameplay metrics and data augmentation, even for small samples. More studies are needed to validate our findings, with a larger and more diverse player-base.
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| format | Article |
| id | doaj-art-4873c725356e4998a4f0f07abf4402d7 |
| institution | OA Journals |
| issn | 2763-7719 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Brazilian Computer Society |
| record_format | Article |
| series | Journal on Interactive Systems |
| spelling | doaj-art-4873c725356e4998a4f0f07abf4402d72025-08-20T02:35:41ZengBrazilian Computer SocietyJournal on Interactive Systems2763-77192025-07-0116110.5753/jis.2025.5630Evaluating a Procedural Content Orchestrator Gameplay Data and Classifying User ProfilesLeonardo Tórtoro Pereira0T. Yuji Teoi1Claudio Fabiano Motta Toledo2São Paulo State University (UNESP)University of São PauloUniversity of São Paulo Background: Content Orchestration is a novel research field focused on coordinating distinct types of algorithmically generated game content. Purpose: Thus, the lack of research in this area hinders the analysis of gameplay data and player profiling in games with orchestrated content. Methods: This paper is an extension of a work that collected and analyzed gameplay logs of 15 players who played 119 game sections of 12 different dungeons of a top-down action game. The game’s Levels, Rules, and Narrative content were orchestrated and adapted to player profiles defined from a pre-test questionnaire. PCA and clustering techniques were used to highlight relevant gameplay metrics for distinguishing play styles. In this extension, we used the gameplay data alone to train classifiers with and without data augmentation to predict a user’s profile, measuring the accuracy, precision, recall and f1-score with a train-test split and a 5-fold cross-validation for a more robust accuracy. We also implemented data augmentation on our gameplay metrics sample. Results: We identified, through the previous work, two components of PCA explaining a total of 65% of data variability, containing data such as Lock Usage Rate, Enemy Kill Rate, Map Completion, and Completed Immersion Quests. We also found game difficulty as an important level component for impact clustering. Through data augmentation, we achieved novel results, such as a mean accuracy of almost 95%, measured with a 5-fold cross-validation, for the Histogram-based Gradient Boosting classifier when predicting a player’s profile based on their gameplay data, even with our small sample size. Conclusion: Our work guides developers and researchers to choose relevant gameplay metrics to determine players’ play styles. Our extended results suggest that we can predict player’s profiles through gameplay metrics and data augmentation, even for small samples. More studies are needed to validate our findings, with a larger and more diverse player-base. https://journals-sol.sbc.org.br/index.php/jis/article/view/5630Procedural Content GenerationPlayer ProfilingContent OrchestrationGameplay Metrics EvaluationMachine Learning |
| spellingShingle | Leonardo Tórtoro Pereira T. Yuji Teoi Claudio Fabiano Motta Toledo Evaluating a Procedural Content Orchestrator Gameplay Data and Classifying User Profiles Journal on Interactive Systems Procedural Content Generation Player Profiling Content Orchestration Gameplay Metrics Evaluation Machine Learning |
| title | Evaluating a Procedural Content Orchestrator Gameplay Data and Classifying User Profiles |
| title_full | Evaluating a Procedural Content Orchestrator Gameplay Data and Classifying User Profiles |
| title_fullStr | Evaluating a Procedural Content Orchestrator Gameplay Data and Classifying User Profiles |
| title_full_unstemmed | Evaluating a Procedural Content Orchestrator Gameplay Data and Classifying User Profiles |
| title_short | Evaluating a Procedural Content Orchestrator Gameplay Data and Classifying User Profiles |
| title_sort | evaluating a procedural content orchestrator gameplay data and classifying user profiles |
| topic | Procedural Content Generation Player Profiling Content Orchestration Gameplay Metrics Evaluation Machine Learning |
| url | https://journals-sol.sbc.org.br/index.php/jis/article/view/5630 |
| work_keys_str_mv | AT leonardotortoropereira evaluatingaproceduralcontentorchestratorgameplaydataandclassifyinguserprofiles AT tyujiteoi evaluatingaproceduralcontentorchestratorgameplaydataandclassifyinguserprofiles AT claudiofabianomottatoledo evaluatingaproceduralcontentorchestratorgameplaydataandclassifyinguserprofiles |