Data visualization becomes a crucial component in data analytics, especially data exploration, understanding, and analysis. Effective data visualization impacts decision-making and aids in discovering and understanding relationships. It leads to benefits in data-intensive software development tasks e.g., feature engineering in machine learning-based software projects. However, it is unknown how visualizations are used in competitive programming. The idea of this paper is to report early results on what visu- alizations are prevalent in competitive programming. Analyzing the visualizations of 7 high-rank competitors (i.e., Grandmaster) in Kaggle, we identify and present a catelog of visualizations use to both tell a story from the data, as well as explain the process and pipelines involved to explain their coding solutions. Our taxonomy includes nine types from over 821 visualizations in 68 instances of Jupyter notebooks. Furthermore, most visualizations are for data analysis for distribution (DA Distribution), and frequency (DA Frequency) most used. We envision that this catalogue can be useful to better understand different situations in which to employ these visualizations.