Eric is a software engineer at a small biotech company, so he fits my mental profile of a Machine Madness competitor. He went to Kansas and his education has focused on robotics, computer vision, and some machine learning. Needless to say he's a big Jayhawks fan.
Sorry about that Stanford game, Eric.
He's also passionate about baseball (KC Royals fan, naturally) and is contemplating trying to predict baseball games. It's a slippery slope, this prediction business. For outside hobbies he's building an ASV (autonomous seasurface vehicle) and is looking at build a quadcopter.
Like many of the competitors, he got hooked into this via Number Crunching Life. One of his work colleagues did some number crunching to try to get an edge in the office pool, Eric got intrigued, Googled around and found Number Crunching Life and got sucked in. Looking at the competitors from last year I don't see Eric's name, so I think this is his first year competing.
His algorithm uses Danny Tarlow's probabilistic matrix factorization method using the 2D model with a vector for both offense and defense. Data was taken from the Kaggle site. Stochastic gradient decent was used to train the model, but with aging added. After an initial training, the teams were ranked based on the offense and defense vectors, then training continued using a higher learning rate based on the rank of the opponent being faced.
Eric also entered the Kaggle competition and was as high as #7 at one point on the first day, but has since dropped to #224. If he'd taken the opposite of his predictions he'd be in 25th place :-).
Right now Eric is in 6th place in the Machine Madness competition. Eric has a Virginia-Louisville final predicted, with Louisville winning it all. If that happens he'll certainly jump upwards in the standings!