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#News(IoTStack) [ via IoTGroup ]
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Imagine watching sports and being able to immediately find all plays that are similar to what just happened.
Better still, imagine being able to draw a play with the Xs and Os on an interface like a coach draws on a chalkboard and instantaneously finding all the similar plays and conduct analytics on those plays (i.e., when those plays occur, how many points a team expects from that play).
Additionally, imagine having the ability to evaluate the performance of a player or a team in a given situation and compare it against another player in exactly the same situation.
This approach is called interactive sports analytics, and Patrick Lucey explains methods to find play similarity using multi-agent trajectory data, as well as predicting fine-grain plays.
You’ll see examples using tracking data amassed from the Stats Perform massive sports data archives.
And you’ll learn how you can go beyond center-of-mass tracking (i.e., dots) and capture body-pose information from broadcast video to take analysis to the next level.
Read More..
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