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NBA Draft Value Analysis (2000–2020)

Who Were the Biggest Steals and Busts?

A data science project analyzing 21 years of NBA draft data to quantify which players over and under performed their draft position, which teams draft best, and whether career value can be predicted from draft slot alone.


Research Questions

  1. What career value does each draft position typically produce?
  2. Which players massively outperformed their draft slot
  3. Which high picks underperformed expectations
  4. Which teams are the most and least efficient drafters?
  5. How well can draft position predict career value?

Dataset

Source: Basketball Reference (scraped via requests + BeautifulSoup)
Coverage: NBA Drafts 2000–2020 (~1,300 picks)
Key stats: Win Shares (WS), VORP, PER, Games Played

Value Metric: Composite score = 0.6 × WS + 0.4 × (VORP × 3)
Combines the two most respected cumulative NBA value metrics.


Methods

Method Purpose
Web scraping (BeautifulSoup) Collect draft + career data from Basketball Reference
Expected value baseline Median value score per pick position across all years
Value Over Expected (VOE) Actual − expected → identifies steals and busts
Polynomial regression Predict WS from pick number (baseline model)
Gradient Boosting Predict value from pick + era + round (improved model)
Team efficiency ranking Avg VOE per pick per franchise

Setup & Usage

# 1. Clone
git clone https://github.com/Agastya191/nba-draft-analysis.git
cd nba-draft-analysis

# 2. Install dependencies
pip install -r requirements.txt

# 3. Run the notebook (scraping happens automatically)
jupyter notebook notebooks/analysis.ipynb

Note: First run scrapes ~21 pages from Basketball Reference with a 4-second rate limit (~5 minutes). Subsequent runs load from the cached CSV.


Key Figures

# Figure
1 Draft pick value curve — actual vs model predicted
2 Top 15 greatest draft steals
3 Top 15 biggest draft busts (picks 1–20)
4 Win shares and bust/steal rate by pick tier
5 Best second-round picks of the era
6 Draft quality comparison by era
7 Team drafting efficiency ranking

License & Data

Statistics sourced from Basketball Reference for educational/research use.

About

Analyzing 21 years of NBA draft data to find the biggest steals and busts. Web scraping, gradient boosting, and a Value Over Expected metric.

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