Hi my name is Truman Daniels. I’m a software engineer from Portland, Oregon captivated by the mechanisms that structure games and the way those mechanics frame our understanding of how a game works. I use machine learning to predict the outcomes of games with a model called Predictive Outcome Analysis (POA). The goal of POA is to expand the mastery behind games by maximizing opportunity in MMA using data analytics.
I grew up playing strategy games, learning to read while playing Magic the Gathering with my brother and how to count playing poker with my mom. In school, I played sports (mostly basketball) but was frustrated by the presentation and understanding of in-game statistics by coaches and media. My relationship with Mixed Martial Arts first came from a curiosity with the technical aspects of fighting. Further research made me enamored by the possibilities of implementing data analytics in a game that has such a variety of techniques and strategy its players can employ.
Some of the skills I’ve acquired throughout this process:
- Programming in Python 3
- Working with the Command Line / Linux Terminal to execute programs
- Data manipulation, cleaning and wrangling using Pandas including merging and joining dataframes. Creating and connecting to an SQLite database and performing SQL commands to retrieve data.
- Applying dimensionality reduction techniques like SVD and PCA to reduce noise in sparse data and implementing feature embeddings using tensorflow
- Visual data analysis and creating easy-to-understand graphical representations using Plotly, MatPlotLib and Seaborn
- Data scraping and web crawling using Requests + BeautifulSoup and Selenium
Implementing machine learning models with the following libraries:
- Neural Networks using Keras / Tensorflow and Pytorch
- Gradient Boosted Decision Trees using Catboost
- Linear Regression using Scikit Learn with emphasis on feature selection for causality and interpretability using parameter regularization methods like Lasso and Ridge regression