Authors: David Ryan, Kendrew Christanto, Jonathan Thai, Nicholas Chang

Python data analysis project for finding correlations between winning matches alongside various game statistics of players that perform in the Rocket League Championship Series 2021-2022.

We hope to accomplish by relating winning matches to data points such as number of goals scored, car boost amount, movement, positioning, and game controls with respect to wins. The languages/tools/technologies we plan to use are:

• Python - A high-level, interpreted, general-purpose programming language.
• NumPy - A library for adding support for large, multi-dimensional arrays and matrices.
• Pandas - A library for data manipulation and analysis. Useful for manipulating our dataset.
• Matplotlib - Comprehensive library for creating static, animated, and interactive visualizations in Python.
• Seaborn - Data viz library for easily creating statistical graphs. Built on top of Matplotlib.
• Scikit-learn - Machine learning library for features various classification, regression and clustering algorithms. We'll be using this library for doing regression analysis, k-means clustering, and k-nearest neighbors.
• Google Colab - A Jupyter Notebook interface created by Google Research that allows us to write and execute Python code. Great for easily sharing code.

The dataset we have is a Rocket League Championship Series dataset that includes game data for 34,000 teams and 101,000 players. The team dataset includes a team's number of goals, assists, saves, and assists, along with a team's boost amount collected, total distance of movement, amount of time spent on offensive and defensive positioning, and the number of cars demolished.

The player dataset contains the same features as the team dataset, with differences only in the amount of goal participation and whether the winning player is an mvp or not.

Our Dataset: https://www.kaggle.com/datasets/dylanmonfret/rlcs-202122

Because there's so much data, we'll look into specifically games_by_players.csv and games_by_teams.csv. The data will be cleaned to only include relevant data points of interest