Ever wondered why there are only 5 positions in basketball or how a player’s position is determined? So did we. But now, using Ayasdi’s proprietary data analysis engine and decades of computational topology research at Stanford, we have mathematically categorized players into 13 new positions: the real positions of basketball (which will be unveiled in this presentation). I will describe this revolutionary insight and how it can add tremendous value for coaches, owners, general managers, and the everyday fan. By visualizing the shape of data in terms of performance-based positions, we are able to discover undervalued players, manage in-game decisions, optimize rosters, and draft more intelligently. And this increased granularity in basketball positions is only the beginning. I will also describe how topological data analysis can lead the way to more evolutions in thoughts in basketball and other sports.
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