Predicting Tetris Performance Using Early Keystrokes

Gianluca Guglielmo; Michal Klincewicz; Elisabeth Huis in 't Veld; Pieter Spronck

doi:10.1145/3582437.3587184

Predicting Tetris Performance Using Early Keystrokes

Gianluca Guglielmo, Michal Klincewicz, Elisabeth Huis in 't Veld, Pieter Spronck

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

Abstract

In this study, we predict the different levels of performance in a Nintendo Entertainment System (NES) Tetris session based on the score and the number of matches played by the players. Using the first 45 seconds of gameplay, a Random Forest Classifier was trained on the five keys used in the game obtaining a ROC-AUC score of 0.80. Further analysis revealed that the number of down keys (forced drop) and the number of left keys (left translation) are the most relevant keys in this task, showing that by merely including the data from these two keys our Random Forest Classifier reached a ROC-AUC score of 0.83. We conclude that the keylogger data during the early phases of a game session can be successfully used to predict performance in longer sessions of Tetris.

Original language	English
Title of host publication	Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG 2023
Editors	Phil Lopes, Filipe Luz, Antonios Liapis, Henrik Engstrom
Publisher	ACM
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781450398565
ISBN (Print)	978-1-4503-9855-8
DOIs	https://doi.org/10.1145/3582437.3587184
Publication status	Published - 12 Apr 2023
Event	FDG 2023: Foundations of Digital Games 2023 Lisbon Portugal - Lisbon, Portugal Duration: 12 Apr 2023 → 14 Apr 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	FDG 2023: Foundations of Digital Games 2023 Lisbon Portugal
Country/Territory	Portugal
City	Lisbon
Period	12/04/23 → 14/04/23

Keywords

Expertise
Machine Learning
Performance
Peripherals
Tetris
Video games

Access to Document

10.1145/3582437.3587184Licence: CC BY

Cite this

Guglielmo, G., Klincewicz, M., Veld, E. H. I. ., & Spronck, P. (2023). Predicting Tetris Performance Using Early Keystrokes. In P. Lopes, F. Luz, A. Liapis, & H. Engstrom (Eds.), Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG 2023 (pp. 1-4). Article 46 (ACM International Conference Proceeding Series). ACM. https://doi.org/10.1145/3582437.3587184

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title = "Predicting Tetris Performance Using Early Keystrokes",

abstract = "In this study, we predict the different levels of performance in a Nintendo Entertainment System (NES) Tetris session based on the score and the number of matches played by the players. Using the first 45 seconds of gameplay, a Random Forest Classifier was trained on the five keys used in the game obtaining a ROC-AUC score of 0.80. Further analysis revealed that the number of down keys (forced drop) and the number of left keys (left translation) are the most relevant keys in this task, showing that by merely including the data from these two keys our Random Forest Classifier reached a ROC-AUC score of 0.83. We conclude that the keylogger data during the early phases of a game session can be successfully used to predict performance in longer sessions of Tetris.",

keywords = "Expertise, Machine Learning, Performance, Peripherals, Tetris, Video games",

author = "Gianluca Guglielmo and Michal Klincewicz and Veld, {Elisabeth Huis in 't} and Pieter Spronck",

note = "Funding Information: The research reported in this study is funded by the MasterMinds and Data2Game projects, part of the RegionDeal Midland WestBra-bant, and is co-funded by the Ministry of Economic Affairs, Region Hart van Brabant, REWIN, Region West-Brabant, Midpoint Brabant, Municipality of Breda, Netherlands Research Organisation (NWO), and Municipality of Tilburg awarded to MML Publisher Copyright: {\textcopyright} 2023 Owner/Author.; FDG 2023: Foundations of Digital Games 2023 Lisbon Portugal ; Conference date: 12-04-2023 Through 14-04-2023",

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Guglielmo, G , Klincewicz, M , Veld, EHI & Spronck, P 2023, Predicting Tetris Performance Using Early Keystrokes. in P Lopes, F Luz, A Liapis & H Engstrom (eds), Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG 2023., 46, ACM International Conference Proceeding Series, ACM, pp. 1-4, FDG 2023: Foundations of Digital Games 2023 Lisbon Portugal, Lisbon, Portugal, 12/04/23. https://doi.org/10.1145/3582437.3587184

Predicting Tetris Performance Using Early Keystrokes. / Guglielmo, Gianluca ; Klincewicz, Michal ; Veld, Elisabeth Huis in 't et al.
Proceedings of the 18th International Conference on the Foundations of Digital Games, FDG 2023. ed. / Phil Lopes; Filipe Luz; Antonios Liapis; Henrik Engstrom. ACM, 2023. p. 1-4 46 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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N1 - Funding Information: The research reported in this study is funded by the MasterMinds and Data2Game projects, part of the RegionDeal Midland WestBra-bant, and is co-funded by the Ministry of Economic Affairs, Region Hart van Brabant, REWIN, Region West-Brabant, Midpoint Brabant, Municipality of Breda, Netherlands Research Organisation (NWO), and Municipality of Tilburg awarded to MML Publisher Copyright: © 2023 Owner/Author.

PY - 2023/4/12

Y1 - 2023/4/12

N2 - In this study, we predict the different levels of performance in a Nintendo Entertainment System (NES) Tetris session based on the score and the number of matches played by the players. Using the first 45 seconds of gameplay, a Random Forest Classifier was trained on the five keys used in the game obtaining a ROC-AUC score of 0.80. Further analysis revealed that the number of down keys (forced drop) and the number of left keys (left translation) are the most relevant keys in this task, showing that by merely including the data from these two keys our Random Forest Classifier reached a ROC-AUC score of 0.83. We conclude that the keylogger data during the early phases of a game session can be successfully used to predict performance in longer sessions of Tetris.

AB - In this study, we predict the different levels of performance in a Nintendo Entertainment System (NES) Tetris session based on the score and the number of matches played by the players. Using the first 45 seconds of gameplay, a Random Forest Classifier was trained on the five keys used in the game obtaining a ROC-AUC score of 0.80. Further analysis revealed that the number of down keys (forced drop) and the number of left keys (left translation) are the most relevant keys in this task, showing that by merely including the data from these two keys our Random Forest Classifier reached a ROC-AUC score of 0.83. We conclude that the keylogger data during the early phases of a game session can be successfully used to predict performance in longer sessions of Tetris.

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ER -

Predicting Tetris Performance Using Early Keystrokes

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