NBA Draft Prediction Using Machine Learning

Overview

This project applies machine learning techniques to predict NBA draft outcomes. The goal is to use data-driven models to forecast which players will be drafted and their subsequent performance in the NBA.

Table of Contents

• Introduction
• Motivation
• Data Extraction
• Data Cleaning
• Feature Engineering
• Exploratory Data Analysis (EDA)
• Data Transformation
• Model Selection and Training
• Model Evaluation and Interpretation
• Model Deployment
• SHAP Analysis
• Real-World Application
• Challenges and Solutions
• Final Thoughts

Introduction

This project aims to utilize machine learning to predict NBA draft outcomes, enhancing team decision-making processes and improving roster-building strategies.

Motivation

Building Successful NBA Rosters

Accurate predictions help teams assess rookie potential, ensuring long-term competitiveness.

Improving Recruitment Strategies

Data analysis enables more effective identification of promising rookies and improves draft success rates.

Personal Interest

As a data analysis enthusiast, this project provides a deep dive into the patterns and trends within NBA drafts.

Data Extraction

1. Connect to SQLite Database
   • Extract data from relevant tables: player attributes, team salaries, player salaries, draft, draft combine, and game data.
   • Merge draft and draft combine data tables.
   • Remove unnecessary columns containing 'set' and 'location'.

Data Cleaning

1. Fill Missing Values
   • Use mode for categorical data and mean for numerical data.
2. Remove Irrelevant Data
   • Eliminate redundant rows and columns.
3. Verify Data Integrity
   • Ensure the dataset's integrity post-cleaning.

Feature Engineering

1. Select Relevant Features
   • Based on domain knowledge.
2. Create New Columns
   • Capture position data.
3. Calculate Additional Metrics
   • E.g., BMI.

Exploratory Data Analysis (EDA)

1. Summary Statistics
   • Show distributions of key features.
2. Target Variable Analysis
   • Analyze the distribution of drafted vs. undrafted players.
3. Visualize Relationships
   • Correlation and feature relationship visualizations.

Data Transformation

1. Normalize/Scale Numeric Features
2. Encode Categorical Features
   • Use one-hot encoding.
3. Split Data
   • Into training, validation, and test sets.

Model Selection and Training

1. Select Multiple Models
   • Logistic regression, decision trees, random forests, SVM, KNN, gradient boosting, XGBoost.
2. Train Models
   • Using the training dataset.
3. Evaluate Models
   • Metrics: accuracy, precision, recall, F1-score, ROC-AUC, and specificity.

Model Evaluation and Interpretation

1. Compare Models
   • Based on key metrics.
2. Select Best Model
   • Highest recall preferred.
3. Feature Importance Analysis
   • Identify key predictive factors.

Model Deployment

1. Save Best-Performing Model
2. Real-Time Predictions
   • Load and use the model for predictions.

SHAP Analysis

1. Generate SHAP Values
   • Explain model predictions.

Real-World Application

1. Data Collection
   • Gather actual data for new players.
2. Feature Engineering
   • Standardize and engineer features.
3. Predict Draft Position
   • Use the model and SHAP analysis for predictions.

Challenges and Solutions

1. Missing Data
   • Apply imputation techniques.
2. Model Generalization
   • Use cross-validation to avoid overfitting.
3. Balancing Metrics
   • Focus on optimizing recall for better prediction accuracy.

Final Thoughts

This project demonstrates the power of data analysis and machine learning in transforming NBA draft predictions. Continuous learning and adaptation are crucial for success in rapidly evolving fields.

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Data Download For those interested in exploring the data used in this project, you can download the dataset provided by one of the authors from Kaggle. Click here to access the database: https://www.kaggle.com/code/edwinstanzah/nba-draft-prediction-part-1-getting-the-data/input?select=basketball.sqlite

Thank you for reviewing this project. For more details, please refer to the slides included.