I would like to see a lot less aggregation of the data. Dividing into equal thirds is convenient for making tables, but may obscure where the real action is, or where the breakpoints between success and failure lie. Maybe the ideal launch angle is near the center of that 2-25 degree "bucket", or maybe it's closer to one end than the other. Conversely (somewhat), maybe the really good angles are more prevalent, because major league batters are, you know, good at their jobs, and more of the buckets would demonstrate that.
A table broken down into buckets of a single degree might be overkill, but I wouldn't assume that to be the case until I saw it. True, there is a risk of "small sample size" if you have too many buckets, but with n=20000 or so I don't worry too much about that, and one can eyeball where the anomalies start to show up and decide that the right level of aggregation is 3 degrees or whatever, to smooth out the presentation of data.
Another thing is that when you have two metrics for success (OBP and SLG here), it's possible that the peaks for each occur at very different launch angles. Combining into 3 buckets obscures this possibility.
Seeing the disaggregated data is a better guide for where the aggregation should take place, than simply equalizing all the buckets. Said another way, you can always aggregate data if it's too finely divided, but you can't easily go the other way.