Machine Learning Algorithm for Predicting Major League Baseball Team Wins
2019
- 2,212Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage2,212
- Downloads1,746
- 1,746
- Abstract Views466
Article Description
I was inspired to work on this project by the book “Moneyball” by Michael Lewis. The book discusses the advancements made in sports analytics, and it gave me the idea to take Major League Baseball analytics to the next level. Machine learning and artificial intelligence is the future of many industries, but analytics and statistics is a clear starting point. I decided to combine the power of neural networks with traditional baseball statistics to predict the win totals of teams. Ideally, this information would be used to inform teams on where they need to improve to secure more wins. This project lies at the intersection of sports, statistics, and computer science, which all appeal to my interests. This is also a project that I can continue working on past graduation, potentially transitioning it into a full-sized application and maybe even a product.
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