NBA Game prediction model using ML & Blockchain

Client Brief

The Sylvan Company approached us to develop a model that can predict NBA (National Basketball Association) game results which is a major basketball league in North America. They needed a tool that could learn from past NBA matches (like scores, team performance) to predict future game outcomes accurately.

Our Endeavor

We built the prediction model using Machine Learning and Blockchain with Python programming. This helps the system learn from past NBA games, team stats, and player performance to predict future match results. We trained the model using data from past NBA games, including scores, injuries, and team strategies, so it could spot patterns and make smarter predictions.

The model was connected to Sportstensor, a secure system that keeps data safe and unchangeable, to run the predictions. Once the predictions were made, Sportstensor processed and stored them. If any predictions were wrong, they were flagged, helping improve the model’s accuracy. The better the model performed, the higher its ranking and reliability.

Our team worked together closely to address challenges with the prediction results. They carefully analyzed the issues, adjusted the implementation, and improved the model’s accuracy and efficiency.

One challenge was obtaining accurate player data for NBA games. After some effort, we connected with alternative sources, solving the issue and ensuring the model delivered the desired precision and reliability.

Now, the client has a powerful tool that gives accurate NBA game predictions, helping users make smarter choices.

Technology/Platform

Python, Blockchain

Industry:

Retail & Wholesale