Metadata-Version: 2.1
Name: kblpbyp2
Version: 0.0.5
Summary: Web scraping and visualization tools for Korean Basketball League's play-by-play data
Author: JeongJun Moon
Author-email: jaymnetwork@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: selenium
Requires-Dist: beautifulsoup4
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: plotnine
Requires-Dist: scikit-learn
Requires-Dist: networkx
Requires-Dist: matplotlib
Requires-Dist: seaborn
Requires-Dist: plotly
Requires-Dist: requests

While replicating the high-quality PBP data of leagues like the NBA, WNBA, and NCAA for the KBL can be challenging, there are still alternative methods to compile such data. One approach involves utilizing web scraping techniques(i.e., Selenium) to extract information from real-time text updates provided by KBL and platforms like ‘NAVER.’ These updates via text offer descriptions of plays, which allow us to have a rudimentary form of PBP data for further analysis and study after cleansing. By using such form of data, the project aims to demonstrate the types of analysis that can be conducted in the KBL.
