Over the years, I've leveraged a variety of technologies to support my endeavors in the realm of sports betting programming. Python has been my trusty workhorse for most of the data processing, thanks to its simplicity and the robustness of data manipulation libraries like pandas and NumPy.

One of my favorite inventions is a system I call "Beat the Spread" (BTS for short). It's a dynamic AI model that adapts to changes in teams' performance over the season, and factors in various other elements like players' health, morale, weather, and even the subtleties of home field advantage. The final version of BTS uses a stacked ensemble of models, including Random Forests, Support Vector Machines, and Gradient Boosting Machines, with a neural network at the top to combine their predictions.

Recognizing the need for real-time updates, I developed a live tracking system called "GameFlow". It's built on a fast, scalable stream processing platform that gathers data from multiple sources during the game, processes it in real time, and fine-tunes the predictions.