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Simulated Arcade Game Statistics
Data Science and Analytics
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About
This data product offers statistical measurements for level performance in a game developed following the "Block Breaker" section of a C# Unity Developer course. It is a critical resource for developers needing to quantify the challenge of game levels. By analysing how an automated player performs on different configurations, users can gain insights into balancing mechanics and measuring design efficiency.
Columns
- Date: The date and time when the automated game trial was executed.
- Level: The unique name identifying the level played during the trial.
- NumBlocks: The total count of blocks that must be destroyed to successfully complete the level. Values range widely, from 18 up to 112 blocks.
- IsWin: A Boolean field indicating the outcome of the trial: True if the auto-player successfully broke all blocks, or False if the ball was lost past the paddle.
- ElapsedTime: The time, measured in seconds, until the game trial concluded (either by winning or losing). Note that the game was run at 4x speed during data collection.
- Score: The total score achieved by the auto-player at the moment the game concluded.
- Accuracy: A parameter used to tune the auto-player bot, chosen randomly for each trial. Higher values for accuracy are correlated with a higher probability of the bot achieving a win.
Distribution
The dataset, titled
GameStats.csv, currently contains 6,814 distinct valid records across seven columns. It is typically distributed in a structured format such as CSV and has a file size of approximately 524.3 kB. All recorded fields show 100% validity, meaning there are no missing data points across the observation set.Usage
This dataset is ideally suited for researchers interested in standardized testing methodologies, especially within simulation environments. It can be used by data analysts for data visualization projects focused on time-series performance or for building machine learning models to predict level difficulty based on initial design parameters (like
NumBlocks). It is also invaluable for video game developers focused on design iteration and balancing.Coverage
The recorded data spans a very narrow time frame, covering trials run from September 7th, 2019, through to September 9th, 2019. Since the data is generated entirely by an automated script running a simulation, there is no geographic or demographic scope associated with these records. The coverage is strictly limited to the performance metrics of the bot across the configured levels during that brief period.
License
CC0: Public Domain
Who Can Use It
- Game Developers: To objectively measure the difficulty of their levels and refine game balancing.
- Data Scientists: To practise statistical analysis on simulation-generated time-series and outcome data.
- Academics/Researchers: To study the relationship between input parameters (like
Accuracy) and performance outcomes in structured game environments.
Dataset Name Suggestions
- Blockbreaker Bot Performance Log
- Auto-Play Level Difficulty Metrics
- Simulated Arcade Game Statistics
- Game Level Balancing Data
Attributes
Original Data Source: Simulated Arcade Game Statistics
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