NBA Player Stats Prediction
In the previous article we shared the first product of team level prediction that went live by the collaboration of Tensor Labs with Hyper. We took the product one level further to the level of player level prediction. In this article we will talk about how we tackled the player level prediction problem and took that live.
Predicting player performance in NBA betting has become increasingly important as the industry continues to grow. With the rise of Artificial Intelligence (AI) and Machine Learning (ML), it has never been easier to predict the performance of individual players in the National Basketball Association (NBA). Tensor Labs, has leveraged SOTA machine and deep learning algorithms to create a player level prediction system that calculates the expected statistics for five key performance indicators: steals (stls), defensive rebounds (drebs), points (pts), rebounds (rebs), and assists (ast).
The process of developing the player level prediction system was extensive, starting with data mining and cleaning, followed by exploratory data analysis (EDA), feature engineering, and training and deployment. We used a variety of techniques to ensure that the data was accurate and relevant, such as removing outliers and filling in missing values. After the data was prepared, we used various feature engineering techniques to extract relevant information from the data. we then used machine learning algorithms to train the model and make predictions. We had a variety of machine learning algorithms possible for the approach and we had to choose just the perfect one to land on current ones. The options we considered include the following
- Linear Regression: This is a supervised learning algorithm that can be used to predict a continuous output. In this case, it can be used to predict the expected performance of a player based on various input features such as their past performance, age, injury history, and so on.
- Random Forest: This is an ensemble learning algorithm that can be used for both regression and classification problems. In this case, it can be used to predict the expected performance of a player based on the results of many decision trees, each trained on a subset of the input data.
- Gradient Boosting: This is an ensemble learning algorithm that is similar to Random Forest, but is typically more accurate. It works by combining many weak decision trees to form a strong prediction model.
- Neural Networks: This is a type of machine learning algorithm that is modeled after the structure of the human brain. Neural networks can be used to predict player performance by learning the relationship between the input features and the expected output.
At the end we resorted on using a variant of Gradient Boosting ensemble with other models. This extensive process finally gave us the results we are proud to share today.
The performance of the player level prediction system was evaluated using the Mean Absolute Error (MAE) metric. MAE measures the average difference between the predicted values and the actual values. In the case of player level prediction system, the MAE for stls, drebs, pts, rebs, and ast was 3.60, 1.15, 0.4, 1.5, and 0.9 respectively. These low MAEs indicate that the model is making highly accurate predictions for these performance indicators.
Artificial Intelligence is revolutionizing the way betting favorites are chosen. With the ability to make highly accurate predictions, AI is enabling bookmakers and bettors alike to make more informed decisions. By using AI to predict player performance, Tensor Labs is helping to level the playing field for those who want to bet on NBA games. No longer do bettors have to rely on gut feelings or expert opinions, as AI can provide a much more accurate and objective assessment of player performance.\
Conclusion
Tensor Labs has successfully developed a player level prediction system for NBA players that leverages the latest advances in AI and ML. With an MAE of less than 1 for several key performance indicators, the model is providing highly accurate predictions for the five key performance indicators. The player level prediction system is now live and available for use by bookmakers and bettors alike. By using AI to make informed predictions, Tensor Labs is helping to change the way betting favorites are chosen in the NBA.
We are proud of the success of this project and the value it has brought to our client. It is a testament to our expertise in developing and deploying machine learning products, as well as our ability to provide complete end-to-end solutions for clients. If you have an idea and want to bring it to fruition, we would love to hear from you. You can visit our LinkedIn or reach out to us at info@tensorlabs.io to learn more about how we can help you turn your ideas into reality.
What’s next?
In the next articles we will keep introducing more of our clients which in collaboration with us have turned their ideas into reality. Stay tuned for more updates 😃.
