Statistically Speaking: Fast: Can we classify every pitch?

Since Dalkowski is no longer pitching...I guess there would be enough time. Mike Fast takes a look at the PITCHf/x system revolution.

What if we knew what type of pitches every major league pitcher threw? What if we had detailed pitch-by-pitch data about how he used those pitches in every game situation? What if this information was accurate and freely accessible to baseball researchers?

...Clearly, one of my priorities should be improving my understanding of and facility with clustering techniques. In my opinion, an effective clustering algorithm should include speed, spin direction, spin rate, vertical spin deflection, horizontal spin deflection, and handedness of the pitcher and hitter. It’s possible that additional variables such as strike zone location, release point, and ball-strike count might be helpful, but I have a feeling their addition to a clustering algorithm might cause more problems than they solve.

I would like to develop a classification system that would be available to any researcher and accurate enough to distinguish splitters from changeups, sinkers from four-seam fastballs, and cutters from sliders and fastballs. I confess to being a bit daunted by the task and not very experienced in some of the statistical math that may be required, but I’m going to keep chipping away at this problem until we get there.