There are 21 repositories under nba-analytics topic.
NBA sports betting using machine learning
Visualization and analysis of NBA player tracking data
Labelling NBA action using deep learning :basketball:
Using data analytics and machine learning to create a comprehensive and profitable system for predicting the outcomes of NBA games.
Predicts Daily NBA Games Using a Logistic Regression Model
An R package to quickly obtain clean and tidy men's basketball play by play data.
Stattleship R Wrapper
Feature requests for the MySportsFeeds Sports Data API.
Short, offhand analyses of the NBA
R wrapper functions for the MySportsFeeds Sports Data API
NBAShotTracker is a data visualization tool to track player shot performance.
Python package for filling in information about players on court in NBA play-by-play data.
stats.nba.com library :basketball:
Displaying team performance against player rotations during NBA games
Interactive exploration of NBA roster turnover
Being able to perform gameplay analysis of NBA players, NBA Predictive Analytics is a basketball coach's new best friend.
NBA game prediction model
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
A conceptual dashboard to visualize Expected Possession Value (EPV) in the NBA.
🔮 Predicting NBA games using statistics (65% accuracy so far)
This simple program predicts the result of an NBA match. Uses Monte-Carlo simulation to give the probability of each team winning the matchup.
Jupyter notebook that outlines the process of creating a machine learning predictive model. Predicts the peak "Wins Shared" by the current draft prospects based on numerous features such as college stats, projected draft pick, physical profile and age. I try out multiple models and pick the best performing one for the data from my judgement.
Machine Learning project using 15 seasons of NBA data (2005-2020) to predict player position. Decision Trees, Random Forests, Support Vector Machines (SVMs) and Gradient Boosted Trees (GBTs) utilized. Example PCA transformation of X-data included as well. Specific predictions made at the end, leading to interesting insights into what players are out-of-position.
ETL + Exploratory Data Analysis of NBA 2K23 Game's data
A working workbook looking at physical demands of plays in NBA using SportVU legacy data.
Code for the article "Adjusting for Scorekeeper Bias in NBA Box Scores" published in DMKD and presented at Sloan.
R package to interact with NBA api
Predicting the outcome of shots based on the events and tracking data available for the 2015/16 season.
A Front-End project to show the hot shooting points of NBA players to help analysis.