| dc.description.abstract | Archery is a sport that demands a high level of accuracy and consistency from athletes. Currently, many archers in Indonesia record their training results in the form of scorecards or photos of their shots on target boards. However, these records often remain as archives without further processing. In fact, analyzing shooting data can provide deeper insights into an athlete's performance and assist in evaluating training patterns. Therefore, this study aims to develop data visualization and performance analysis methods for archers based on shooting results over a specific training period. The data used in this study consists of two main datasets: TF122, which contains training results from an individual archer over a certain period, and LAC 2024, which includes the top eight male and female archers from the Lancaster Archery Classic tournament. This research introduces three new performance metrics—consistency, focus, and best point expectation—and applies a linear regression model to predict an athlete's score in future competitions. Data collected from scorecards is processed through data preparation stages, including collection, cleaning, and structuring, before being visualized using radar charts. The results indicate that data visualization provides better insights into an athlete’s performance development, while the predictive model offers accurate score estimations | en_US |
