Code, Creations, and Circuits

The NFL Data Bowl 2025 is an analytics competition hosted on Kaggle that focuses on analyzing NFL player tracking data. The competition challenges participants to develop machine learning models to predict various aspects of player performance and game outcomes using advanced data science techniques. The entire project is open source and available on my GitHub repository.

Competition Overview

In this competition, participants are provided with a rich dataset containing player tracking information, including player positions, movements, and interactions during NFL games. The goal is to leverage this data to build predictive models that can provide insights into player behavior and team strategies.

My Approach

For this competition, I employed a combination of neural networks and Monte Carlo simulations to analyze the tracking data. I decided to use the Go programming language over python for it’s performance benefits when handling large datasets and performing simulations. The ability to easily manage concurrency in Go also allowed me to efficiently run multiple simulations in parallel, significantly speeding up computation time. Great since I’m running the simulations on a steamdeck! I decided to go with the ‘gomlx’ as my machine learning library due to its ease of use for building and training neural networks and models.

Here’s a brief overview of my approach:

1. Data Preprocessing: I cleaned and preprocessed the raw tracking data to ensure it was in a suitable format for analysis. This involved handling missing values, normalizing features, and creating additional derived features based on player movements. I decided to import the data as a gomlx dataset for easy manipulation and batching. Since the dataset was quite large, I had to implement data loading in batches to avoid running out of memory. I also had to make sure they were thread-safe for concurrent processing.

2. Feature Engineering: I engineered new features that captured important aspects of player performance, such as speed, acceleration, and spatial relationships between players on the field. These features were crucial for understanding and improving both the monte carlo simulations and the neural network’s modeling.

3. Model Development: I built a neural network model using ‘gomlx’ to predict key performance metrics. The model architecture included multiple hidden layers with ReLU activation functions and dropout regularization to prevent overfitting. The model training was parallelized using Go’s goroutines to speed up the process.

4. Monte Carlo Simulations: To enhance the robustness of my predictions, I incorporated Monte Carlo simulations to account for uncertainty in player movements and game dynamics. This involved running multiple simulations with varying initial conditions to generate a distribution of possible outcomes.

5. Model Evaluation: I evaluated the performance of my model using appropriate metrics such as Mean Squared Error (MSE) and R-squared values. I also performed cross-validation to ensure the model’s generalizability. I compared the results from the neural network with those obtained from the Monte Carlo simulations mostly for fun.

Results

My final model achieved a competitive score on the leaderboard, demonstrating the effectiveness of combining neural networks with Monte Carlo simulations for analyzing NFL player tracking data. The insights derived from my analysis could prove valuable information on player performance trends and potential strategies for teams.

Conclusion

Participating in the NFL Data Bowl 2025 was an exciting opportunity to apply advanced machine learning techniques to real-world sports analytics challenges. The experience enhanced my skills in data preprocessing, feature engineering, and model development using Go and ‘gomlx’. I look forward to working with ‘gomlx’ in the future for similar data science projects.