-3bf97310184a13a8c1e-1bd5 20230204051438795 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 02 28 2023 12 3 Assistant Professor, Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA Lu Xiong https://orcid.org/0000-0003-2471-1256 Kechen Tian https://orcid.org/0000-0002-5481-0012 Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA Yuwen Qian https://orcid.org/0000-0002-3210-5961 Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA Wilson Musyoka https://orcid.org/0000-0002-6102-4597 Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA Xingyu Chen https://orcid.org/0000-0001-5716-2121 Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA Baseball is a sport of statistics. The industry has accumulated detailed offensive and defensive statistical data for over a century. Experience has shown that data analysis can give a competitive advantage compared to teams without using such analysis. In the last two decades, with the development of machine learning and artificial intelligence, we have had more advanced algorithms to analyze data in baseball. In the following research, we will run different ML models using sci-kit-learn and H2O on Colab, and the Caret package on RStudio to examine the datasets (hitting dataset and salary dataset) and determine the undervalued players by predicting the number of runs scored in the next year. We will compare machine learning regression algorithms and ensemble methods and give comprehensive explanations of the result. The suggestion of which model is superior in terms of prediction accuracy will be determined. 02 28 2023 17 24 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true No, I did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Lu Xiong: the overall research strategy, direction, ideas, team management, and writing. Kechen Tian: coding and results summary. Yuwen Qian: literature review and citations. Wilson Musyoka: data description and general introduction of SVM. Xingyu Chen: model evaluation and interpretation. 10.35940/ijitee.B9406.0212323 https://www.ijitee.org/portfolio-item/B94060112223/