Visualization and analysis of NBA player tracking data
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Updated
Nov 24, 2017 - Python
Visualization and analysis of NBA player tracking data
Visualizations to better understand NBA shooting tendencies and efficiency and classification models to predict shot outcomes
Create NBA shot charts using data scrapped from stats.nba.com and R package ggplot2.
NBAShotTracker is a data visualization tool to track player shot performance.
stats.nba.com library 🏀
An implementation of six degrees of separation for mutual NBA teammates.
Displaying team performance against player rotations during NBA games
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。
Interactive exploration of NBA roster turnover
A conceptual dashboard to visualize Expected Possession Value (EPV) in the NBA.
Analysis of NBA player stats and salaries of the 2016-17 for the 17-18 season
visualization course project
Find basketball players with similar shot charts
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.
Web application to see latest NBA news and stats
A Front-End project to show the hot shooting points of NBA players to help analysis.
Source plugin for pulling NBA data into Gatsby 🏀
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