Predicting success in professional sports is hard — especially during the draft. NBA franchises invest millions in scouting players, yet even lottery picks can become busts, while others rise unexpectedly to stardom.

In this competition, you'll work with a dataset covering over 30 years of NBA Draft history, using player information and career statistics to model long-term performance. The goal is to predict a player’s Value Over Replacement Player (VORP) — an advanced metric that summarizes their contribution throughout their career.

The dataset has been carefully compiled to include:

Draft position and scouting rank

College or international team

Career statistics such as games played, points per game, assists, rebounds, and more

Advanced performance metrics like Win Shares, Box Plus Minus, and VORP

**Why This Matters**
From a data science perspective, this challenge combines:

Real-world prediction under uncertainty

Regression modeling with mixed data types

Feature engineering around career trajectories

Applications in sports analytics and talent evaluation

From a basketball perspective: it's your chance to prove you could have out-drafted actual NBA GMs.

Good luck — and may your models find the next MVP hiding in plain sight.