Skip to main content
WVU Home
Would you like to search this site specifically, or all WVU websites?
  • Home
  • Events
  • NBA Career Prediction Club Competition

NBA Career Prediction Club Competition

Basketball

ACTIVE COMPETITION: NBA Career Prediction

Fall 2023 -- An internal competition to predict a rookie's career success based on their first 4 years of play. Will they be an All-star? Starter? Benchwarmer? Use the power of AI to make a prediction!

---
DATA AND SAMPLE CODE FOUND HERE

The objective of this competition is to predict the career success of NBA players based on their performance as rookies, using popular Python tools such as Pandas, Keras, and Scikit-learn to build your predictive models.

Competition Details:
Participants will have the opportunity to analyze historical NBA rookie data, including statistics, player profiles, and performance metrics. Using this data, contestants are tasked with creating machine learning models that can accurately predict the future career success of NBA players. Career success may be defined by various factors such as All-Star selections, NBA championships, or career longevity, allowing participants to choose their preferred criteria for prediction.

Key Components:

  1. Data Exploration: Participants will utilize Pandas to clean, preprocess, and explore the dataset, gaining insights into the factors that contribute to NBA rookie success.

  2. Feature Engineering: Contestants will extract meaningful features from the dataset, selecting relevant statistics and player attributes that are likely to impact a player's career trajectory.

  3. Model Building: The heart of the competition lies in constructing predictive models using Keras and Scikit-learn. Participants can employ a variety of machine learning algorithms, including neural networks, decision trees, and regression models, to make their predictions.

  4. Evaluation: Predictive models will be assessed based on their accuracy and performance in predicting the career success of NBA players. Cross-validation and other relevant metrics will be used to determine the winning models.

  5. Prizes: Prizes and recognition will be awarded to the top-performing participants, based on model accuracy and creativity in their approach

Why Participate?

  • Sharpen your data analysis and machine learning skills.
  • Gain valuable experience in working with real-world sports data.
  • Compete with fellow enthusiasts and showcase your predictive abilities.

Get Ready to Compete! Join us in this thrilling NBA Rookie Prediction Competition, where you can put your Python, data analysis, and machine learning skills to the test. Whether you're a seasoned data scientist or a basketball fanatic looking to explore the world of predictive modeling, this competition offers an exciting opportunity to learn, grow, and compete.