Introduction of VizBG

PlayerUnknown Battlegrounds (PUBG) has been an extremely popular game globally since last year. We are just two of the millions of fans of the game. We decided to make some cool visualizations based on some gameplay data.

The game is an online multiplayer battle royal game, where 100 random players are dispersed from a plane to an isolated island to fight and kill. From the first moment of players’ landing, they move around in this space to pick up weapons and supplies so that when they meet other players they are in a better shape to fight. As time goes by in the game, the arena shrinks which forces the players to enter a smaller space and fight. Intuitively, the last player stays alive is the winner.

Below is a map from the game to give you some sense of the rules. The circle is where players are allowed to move without causing any damage.



Choosing your next position is very important in this game. The biggest motivation of this project is to learn from the player behavior and understand the strategy players adopt.

We were glad to find the data we needed from kaggle. This public kaggle dataset contains information about 720,000 competitive matches extracted from pubg.op.gg, a game tracking website.

Please feel free to explore the visualization tabs to interact with the data!

3D Death Network

In the below 3D network, we randomly chose one match and visualized the victims - all the players except the top 1 winner, and their corresponding killer. Each dot represents one player and darker color means higher rank

About the Authors

Liz Chen

Liz Chen is currently pursuing a Masters in Data Science at the University of San Francisco. She enjoys wrestle with complex data and apply machine learning and visualization to tell stories and solve business problems.

Github
ychen293@dons.usfca.edu

Mathew Shaw

Mathew Shaw is also currently pursuing a Masters in Data Science at the University of San Francisco. He enjoys working with user data accross several industries to gain a better understanding of behaivor and classification.

Github
mathewjshaw@gmail.com