There are 7 repositories under nba-visualization topic.
Visualization and analysis of NBA player tracking data
Create NBA shot charts using data scrapped from stats.nba.com and R package ggplot2.
Visualizations to better understand NBA shooting tendencies and efficiency and classification models to predict shot outcomes
NBAShotTracker is a data visualization tool to track player shot performance.
stats.nba.com library :basketball:
An implementation of six degrees of separation for mutual NBA teammates.
An R Shiny app to help make betting decisions for FanDuel player props
Displaying team performance against player rotations during NBA games
Interactive exploration of NBA roster turnover
本项目综合运用d3、echarts来完成可视化工作,实现了对nba两场比赛的可视化数据分析,包括球员运动轨迹、个人数据、传球次数以及得分位置等多种可交互式图表。通过可视化方法,我们能够进一步深入分析球队的具体情况,便于制定更佳的战术。
visualization course project
Find basketball players with similar shot charts
A conceptual dashboard to visualize Expected Possession Value (EPV) in the NBA.
An app to visually explore the density (and other related factors) of the schedule for NBA teams.
A working workbook looking at physical demands of plays in NBA using SportVU legacy data.
A Front-End project to show the hot shooting points of NBA players to help analysis.
NBA Shots visualization
🏀 NBA Hometown Heroes is a data visualization made with D3.js showing the places that NBA players help put on the map.
Project folder for all my Jupyter Notebooks and NBA data.
NBA scoreboard web scraping using Python's selenium and Bucks 10 first games analysis
Source plugin for pulling NBA data into Gatsby 🏀
对NBA常规赛(2016-2017)中平均得分TOP30的球星的做了热图和雷达图的可视化分析
Classification on the Kobe Bryant Shot Selection dataset (https://www.kaggle.com/c/kobe-bryant-shot-selection/data) using Decision Trees
Estimating player value based on Win Share and Salary data
Visualizing NBA margins of victory for 2016-2017
A wrapper site for NNNBA
A NBA player data explorer web app in Python using the Streamlit library
This project was conducted for the purpose of enhancing my skills with Python web scraping and data visualization through Tableau. The data used in this project is pulled directly from the NBA API and currently consists of games played through March 26, 2023.
NBA database in C#
3D visualization of basketball shots using plotly and NBA shot chart data