I’ve compiled a couple of projects that demonstrate my skills as a data scientist, statistician, and programmer. Many of my projects are sports-related, but the techniques that I use are applicable in many fields. The code for these projects can be found on my Github.
Mathematical Finance Projects
I wrote a paper exploring the theory of hidden Markov models and their applications in quantitative finance. I wrote Python code to implement the Baum-Welch algorithm for 2-state Bernoulli hidden Markov models, and used a bootstrapping technique to estimate the expected Fisher information matrix for latent processes with various parameters. I fit a 2-state Gaussian HMM to real-world S & P 500 data and analyzed its potential in predicting future market volatility.
I conducted a study of teaser bets using 25 years of NFL data. By applying logistic regression, I identified conditions under which teaser legs could yield positive expected returns. To read my analysis of the profitability of NFL teasers, click here.
I wrote a follow-up article that proposes an original, market-neutral strategy for finding positive expected value teaser bets using arbitrage.
I built a real-time win probability model trained on play-by-play data using XGBoost and neural nets. To learn more about how I created my NFL win probability model, click here (in progress). Use my model with the app below