Kaggle's annual March Machine Learning Mania competition drew 442 teams to predict the outcomes of the 2017 NCAA Men's Basketball tournament. In this winner's interview, Kaggler describes how he came in 5th place by stepping back from solution mode and taking the time to plan out his approach to the the project methodically. I have been working in credit risk model development in the banking industry for approximately 10 years. I have been lucky to receive exposure to big data and data science through previous roles but decided I wanted to teach myself R and to improve my machine learning knowledge. The best opportunity seemed to be to utilise Kaggle datasets where I could to help with this.

Kaggle's 2017 March Machine Learning Mania competition challenged Kagglers to do what millions of sports fans do every year–try to predict the winners and losers of the US men's college basketball tournament. In this winner's interview, 1st place winner, Andrew Landgraf, describes how he cleverly analyzed his competition to optimize his luck. I am interested in sports analytics and have followed the previous competitions on Kaggle. Reading last year's winner's interview, I realized that luck is a major component of winning this competition, just like all brackets. I wanted to see if there was a way of maximizing my luck.