TRAIN Scholar Steven Gonzalez's Blog

    Hi all, Over the past week, I wrapped up the statistical calculations for the data collected last semester. Additionally, I decided to invest some time in deepening my comprehension of the statistical elements associated with our research. This involved watching lots of YouTube videos on statistics and exploring topics such as normal distribution, binomial distribution, standard deviation, and the 68-95-99.7 rule, among others. Apart from my research commitments, I've been busy preparing for midterms. I hope everyone is making good progress with their research projects, and I wish you all the best of luck as we approach midterms.

  Hi all, Throughout this past week, my focus has been on delving into the statistical analysis phase of our project. Given that so much of the previous semester was dedicated to data collection, I'm thrilled about transitioning into the mathematical aspects of our work. Since I haven't taken a statistics course yet, I am enjoying this hands-on learning of topics such as confidence interval through our research. This week's meeting has been rescheduled to the next, so I'm looking forward to reconnecting with the rest of the team next week to discuss our findings and transition to the next phase of our research.    

  Hi all, for this weeks blog I am submitting the research proposal assignment I worked on this week.   1. Introduction: Importance of Research: The realm of sports analytics is consistently in pursuit of innovative approaches to deepen our comprehension of the elements influencing match outcomes. In this context, the significance of investigating the correlation between team travel factors and performance in USL soccer matches becomes apparent. Recognizing the impact of travel, encompassing distance and time zone differences, on team performance is imperative for crafting more precise predictive models. This research contributes to the refinement of strategies for soccer teams in the USL, offering invaluable insights for both teams and analysts. Predictive models emanating from this research can find applications in sports betting, facilitating more informed predictions for betting markets. The discoveries can shape league policies, fostering fairness and competitiveness throug