Recently, research on artificial intelligence-based prediction of baseball has been gradually developed, and various studies are being conducted. In general, variables such as launch angle and launch speed are used to predict hitters' home-run and hit...
Recently, research on artificial intelligence-based prediction of baseball has been gradually developed, and various studies are being conducted. In general, variables such as launch angle and launch speed are used to predict hitters' home-run and hit. However, launch angle and launch speed(batting results) are correlated with the pitcher's pitching. Therefore, in this study, the artificial intelligence model that predict home-run and hit was developed using only the pitcher's pitching information, excluding the batting information of the hitter. To develop the artificial intelligence model, pitching data from the 2022 season of the Major League Baseball(MLB) collected. (independent variable : pitch type, release speed, zone, stand, pitch throws, ball count, strike count / dependent(prediction) variable : hit and home-run) The artificial intelligence model was developed using XGBoost, one of the machine learning method. As a result of the development, the artificial intelligence model performance was accuracy 86.92%, precision 99.90%, recall 86.95%, and F1 score 92.98%. Ryu Hyun-jin(MLB pitcher)'s pitching data was applied to the developed artificial intelligence model and confirmed characteristics of individual.