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For those that enjoy reading, seeing visuals, and having immediate access to data sources that are used. The frequency of posts varies greatly and merely depends on when I have an idea come to mind and when I have the time to address it. Feel free to contact me with any blog ideas or desires.
Hey everyone, just wanted to share an update on my Hall of Fame predictive model, which I had entered into the USCLAP competition as the final project for my ST 445 class in my final semester of college. The results have come out, and I ended up finishing 2nd in the intermediate category! While I'm sure there are other reasons why I didn't come in 1st, I did notice that I was the only top finisher that did not submit a group project. I also noticed that the topic of my project was slightly less serious than the other top finishers; their projects dealt with things like inequity & public defense funding, political affiliation & Covid, and driving alone & mental health, rather than baseball & the Hall of Fame. You can check out the final results and view each of the project reports here: https://www.causeweb.org/usproc/usclap/2021/fall/winners.
If you haven't had the chance to read my report yet, I would encourage you to do so. You can use the link above to find my submitted report and read it, or you can revisit my earlier blog post where I dug a little deeper into how I created the predictive model. That earlier blog post can be found here: stattinglineup.weebly.com/blog-posts/predicting-the-hall-of-famers-on-the-2022-ballot. I really enjoyed making this model and was pleased that it performed well in the competition. As I mention in the blog post, the recent era committee inductions effected the model somewhat and made it slightly worse, and the induction of David Ortiz also effects the results of the model. In the future I will update and re-tune and optimize all of the model's parameters and predictors with the recent induction results, so that the model will be all set and at its best to make predictions for the 2023 ballot. As a reminder, the model only deals with position players that didn't have non-statistical issues (i.e. gambling and steroids) with their careers. I look forward to creating a similar model for pitchers, as well as potentially having a "character clause" predictor that would allow me to rationally include all potential players in the model.
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