Code 42 Advisors

Code 42 is an algorithmic based sports insights firm aimed at providing accurate point-spread forecasts. Armed with a passion for collegiate and professional football and a Bachelors of Science in Statistics and a Masters of Science in Applied and Computational Mathematics and Statistics from the University of Notre Dame, I created Code 42 with the dedication and excitement to accurately forecast point spreads and beat the Vegas lines. We continue to offer some of the most successful advise on forecasting collegiate and professional football point spreads in the market using complex machine learning algorithms.

The algorithms used in both collegiate and professional football betting are comprised of main two parts, one comprised of linear regression models and the second consisting of probability theory. Part one involves the collection, organization, and combination of football statistics from a spread of data sources and applying them to linear regression models to forecasting spreads. Extensive research experience and academic studying in statistics helped me master the concepts of linear regression and other important statistical tools used to in the final algorithm. As a graduate student in the ACMS (Applied and Computational Mathematics and Statistics) department, I was taught to approach problems with an open mind; not limiting myself to the mainstream focus points of football statistics, which has given me an unconventional view towards the sport. The second part of my algorithm involves probability theories used to determine which teams have the greatest chance of beating the spread. This part uses more theoretical methods I have learned in Notre Dame ACMS department classes. 

The name of my company might confuse some people being that it has nothing to do with sports. "Code 42" was derived from the version of code that finally produced the successful algorithm that beat collegiate football Vegas point spreads with 65.7% from 2006 to 2014. The "42" is derived from version 4.2 of the algorithm.

Please feel free to contact me with any questions via the contact page found here.

To learn more about the principles used in my algorithms please read a summary of the research report I wrote while studying for my Masters in Statistics.