This paper will apply “STEAMS” methodology on gaming analytics. In the 21st century, the vast majority of youths are playing video games for too long (according to some studies an average of 13 hours/week). Parents do not want their children to play video games as they think it has a negative effect on their children. Chosen based on its wide applications of physics, Hill Climb Racing is the video game used in this project. Technology is applied to increase the transportation safety. Based on the engineering failure mode analysis and return of investment (ROI), a systematic car upgrading system was developed through statistical modeling to optimize the car performance. Several physics applications such as kinematics, energy/power, momentum, friction, circular motion, and gravity were applied on the car racing mechanisms. The AI clustering analysis grouped similar field stages (based on challenges, terrain, physics, and etc.) and cars. This increases cost efficiency and helps avoid wasted investment on upgrading multiple cars with similar functions. The statistical Mixture DOE optimizes the upgrading strategy based on the limited budget while helping enhance ROI and better understand the vehicle mechanics. DSD and Neural algorithms are also compared with Mixture DOE to uncover different correlations.
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This paper will apply “STEAMS” methodology on Gaming Science and Analytics. Kids are playing video games too long and parents do not want kids to play video games since most video games are not developing critical thinking. Hill Climb Car Racing game was chosen based on learning Vehicle Physics Science. Technology is on Transportation and Safety applications. Based on the engineering failure mode analysis and return of investment (ROI), author can develop a systematic car upgrading system through statistical modeling to optimize car performance much quickly. The AI clustering analysis grouped the similar field stages with common challenges and science which has helped upgrade car to support multiple stages. Simple linear regression was conducted to quantify the ROI return (car travelling distance) of investment (car upgrading cost). The regression model accuracy has been improved from original 66% (random playing mode) to 92% (systematic playing mode). The ROI slope has been improved from 147.2 to 512.4 meter/upgrade unit. The statistical Mixture DOE is applied to optimize the upgrading strategy, enhance ROI, and to understand the vehicle mechanics. “STEAMS” methodology is very powerful to help conduct the Scientific Research.
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