Tonight is the opening night of the 2014-15 NBA season. This year, we will be running a machine learning algorithm aimed at estimating underlying features characterizing each team. With these features, we hope to identify interesting match-ups (including potential upsets), similar team-playing-style categories, and win-loss probabilities for future games. As of now, the only source data that we intend to feed our system will be win-loss results of completed games. As the season progresses, our algorithm will thus have more and more data informing it — It will be interesting to see if it can begin to provide accurate predictions by the end of the season. Stay tuned for periodic updates on this experiment!
Dustin got a B.S in Engineering Physics from the Colorado School of Mines (Golden, CO) before moving to UC Santa Barbara for graduate school. There he became interested in Soft Condensed Matter Physics and Polymer Physics, studying the interaction between single DNA molecules and salt ions. After a brief postdoc at UC San Diego studying the physics of bacterial growth, Dustin decided to move into the data science business for good - he is now a Quantitative Analyst at Google in Mountain View.
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