In this repository you might find
The goal of this data exploration is to understand more about Starcraft II Gameplay Thourgh data in order to extract metrics that could identify good players from bad Ones, correlation and hierarchy of features in Starcraft II gameplay and visualizations That could add value to current personal #pysc2 research regarding fairness , games and agent design
Data visualization, including
- Dendogram and heat maps (visual clustering)
- Correlation Matrix
- Correlogram
- Radar chart
- Density
Data exploration, including models
- PCA
- KMeans Clustering
- Feedforward Network
https://github.com/wuhuikai/MSC
http://ggtracker.com/landing_tour
https://github.com/IBM/starcraft2-replay-analysis
https://github.com/GraylinKim/sc2reader
## Predicting Win/Loss Records using Starcraft 2 Replay Data http://snap.stanford.edu/class/cs224w-2010/proj2010/31_final_project.pdf
https://kaigi.org/jsai/webprogram/2017/pdf/446.pdf
https://arxiv.org/abs/1105.0755
http://thomas-zimmermann.com/publications/files/huang-topics-2017.pdf
https://www.kaggle.com/alimbekovkz/starcraft-ii-matches-history/data
kudos : Michael Park