Open Access

ARTICLE

Pitcher Performance Prediction Major League Baseball (MLB) by Temporal Fusion Transformer

Wonbyung Lee, Jang Hyun Kim^*

Department of Applied Artificial Intelligence, SungKyunKwan University, Seoul, 03063, Republic of Korea

* Corresponding Author: Jang Hyun Kim. Email:

Computers, Materials & Continua 2025, 83(3), 5393-5412. https://doi.org/10.32604/cmc.2025.065413

Received 12 March 2025; Accepted 09 April 2025; Issue published 19 May 2025

Abstract

Predicting player performance in sports is a critical challenge with significant implications for team success, fan engagement, and financial outcomes. Although, in Major League Baseball (MLB), statistical methodologies such as sabermetrics have been widely used, the dynamic nature of sports makes accurate performance prediction a difficult task. Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions. This study addresses this challenge by employing the temporal fusion transformer (TFT), an advanced and cutting-edge deep learning model for complex data, to predict pitchers’ earned run average (ERA), a key metric in baseball performance analysis. The performance of the TFT model is evaluated against recurrent neural network-based approaches and existing projection systems. In experimental results, the TFT based model consistently outperformed its counterparts, demonstrating superior accuracy in pitcher performance prediction. By leveraging the advanced capabilities of TFT, this study contributes to more precise player evaluations and improves strategic planning in baseball.

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Keywords

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