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Re: 2015-2016 correlation coefficient matrix
Thanks for sharing this. I think it's very cool to see how the more complex game elements in 2015 (auto points) had the strongest correlation with the most complex game elements in 2016 (scaling).
I recently took an interest in FRC data analysis and was wondering if there were any adjustments you made when calculating contribution for game elements that have a commonly reached ceilings (ex. defense crossings, breach achieved). The calculated contribution values for these statistics seem to bunch up near the average value because the same result is reached in most matches. For example, with defense crossings at champs divisions, all of the calculated contribution values are between 2.2 and 3.2 when there were clearly some teams that focused more heavily on breaching than others.
Did you run in to this problem? If so, what techniques would you recommend for getting more accurate/adjusted calculated contributions?
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Jonathan Zwiebel
Driver, Project Manager, Programmer [Team 8, Paly Robotics]
2016 Central Valley Regional Finalist and Wildcard, Silicon Valley Regional Quarterfinalist, Curie Division, CalGames Quarterfinalist and Entrepreneurship Award, Capital City Classic Quarterfinalist
2015 Central Valley Regional Entrepreneurship Award, Silicon Valley Regional Entrepreneurship Award, Capital City Classic Semifinalist and Judges' Award
2014 Central Valley Regional, Silicon Valley Regional, Chezy Champs
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