Background/Objectives: This study utilizes big data analysis to investigate whether the average daily temperature affects the batting performance of catchers.
Methods/Statistical analysis: The targets of the study are the batting records, by date, of catchers who participated in the 2019 KBO League, and the average daily temperature in the region where baseball games are played. To this end, data was collected from the official website of the KBO League and the Weather Data Open Portal. The data suitable for this study was then pre-processed, and then output as visualization data for scouting reports through the analysis.
Findings: As a result of this study, a higher average daily temperature was found to affect the batting performance of catchers. However, above 15 degrees, both batting power and hitting productivity began to decrease, and above 20 degrees, hitting accuracy began to decrease. The reason for this is that a player in the position of catcher has a lot of physical strength due to a lot of equipment and hard play. Through this study, we can see that the myths and stories told in the baseball field can be proven through data. Through the data and implications derived from this study, it is expected that the scouting reports will help the players, coaching staff, and the front of the baseball club's players to operate the games and the season.
Improvements/Applications: In less than thirty words, this research makes it possible to write about the results of the improved research and other applications.
Key words: Big Data Analysis, KBO League, Batting Performance, Average Daily Temperature, SEMMA Model.
|
