It's actually quite interesting. The real question is how do you judge the merits of individuals in a team where there aren't a lot of individual measures. That is, in basketball there are a lot of individual measure, rebounds/assists/points/steals/blocks/turnovers. But in soccer far fewer (and occur with less frequency). So the question is, how do you model this sparse data. Pretty interesting IMO.
It really is how do you model the sounds amoung interaction in a 3D modeled space. I think this is darn near basic computational science, although one that I've never really seen anyone study before.
If these are the two shining examples of poor research funded by the NSF then the NSF has done an incredible job.
It's actually quite interesting. The real question is how do you judge the merits of individuals in a team where there aren't a lot of individual measures. That is, in basketball there are a lot of individual measure, rebounds/assists/points/steals/blocks/turnovers. But in soccer far fewer (and occur with less frequency). So the question is, how do you model this sparse data. Pretty interesting IMO.
The second research, sound effects, is just downright interesting: http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0905...
It really is how do you model the sounds amoung interaction in a 3D modeled space. I think this is darn near basic computational science, although one that I've never really seen anyone study before.
If these are the two shining examples of poor research funded by the NSF then the NSF has done an incredible job.