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Comprehensive Analysis of NBA Draft Trends

Overview

This project aims to analyze NBA draft trends with a focus on evaluating career longevity, performance metrics, and the success of top 3 picks. Inspired by my passion for basketball and favorite teams, this project leverages data science techniques to uncover insights about player trajectories and team strategies.

Objectives Evaluate career longevity of NBA players. Analyze performance metrics including points per game, rebounds, assists, and more. Assess the success rate of top 3 draft picks. Compare player performance trends across different draft years and teams. Data and Tools

Dataset: NBA Draft Data

Programming Language: Python Libraries: Pandas, Matplotlib, Seaborn, Scikit-learn, Statsmodels Techniques: Data Cleaning, PCA, Clustering, Regression Analysis

Analysis and Insights Career Longevity Objective: Identify factors influencing the career longevity of NBA players. Methodology: Used OLS regression to model the impact of various performance metrics on career longevity. Key Findings: Points per game, games played, and value over replacement significantly impact career longevity.

Performance Metrics Objective: Compare performance metrics across draft years and teams. Methodology: Visualized data using pair plots, bar charts, and scatter plots. Key Findings: Teams like the Lakers and UNC Tar Heels consistently produce high-performing players. Success of Top 3 Picks

Objective: Analyze the success rate of top 3 draft picks. Methodology: Compared win shares, points per game, and years active for top picks. Key Findings: Top picks often have longer, more successful careers.

Personal Inspiration As a fan of LeBron James, the UNC Tar Heels, and the Lakers, this project combines my passion for basketball with my data analysis skills. It was rewarding to see the data validate the strategic decisions behind draft picks and player success.

Learning Outcomes Enhanced knowledge of machine learning techniques and sports analytics. Improved skills in data cleaning, preprocessing, and visualization. Gained insights into factors contributing to NBA player success.

How to Run the Analysis Clone the Repository:

bash Copy code git clone https://github.com/yourusername/nba-draft-analysis.git cd nba-draft-analysis Install Dependencies:

bash Copy code pip install -r requirements.txt Run the Jupyter Notebook:

bash Copy code jupyter notebook nba_draft_analysis.ipynb Future Work Explore more advanced machine learning models for predicting career success. Analyze the impact of college performance on NBA success. Compare draft strategies across different NBA eras. Contact For any questions or collaboration opportunities, feel free to reach out!