Well, since I don’t mess with ML or Mil stats, I can only comment on what I have for HS. And, since I’ve only been looking at this for a few days, I don’t really have all that much. Take a look at http://www.infosports.com/scorekeeper/images/pripit1.pdf and I’ll do my best to explain what you’re looking at.
The 1st column of course is who’s pitchers the corresponding numbers are for. This 1st report is a accumulation of all of our pitchers for the last 4 seasons, and all of our opponents.
In order to actually look at things in a way that made sense to me, since I was only interested in seeing what happened after a previous pitch in an at bat was a strike or a ball, I had to filter out certain things.
The 1st were at bats that weren’t “official at bats”, and weren’t either walks or HBP’s. That would be things like Sac Bunts, Sac Flies, or when an at bat wasn’t completed, such as when the 3rd out was made or the game ended on something like a SB or a CS.
The next Major column, “ABs ending on the 1st pitch” is something else I didn’t want contaminating the final data. The reason is, since the AB finished on 1 pitch, there couldn’t have been a ball or strike thrown before in the AB. Just to keep things consistent, I showed the ABs, hits, and the BA for that condition. Just so everyone understands, contrary to popular belief, hacking at that 1st pitch doesn’t necessarily mean it always works out well for the pitcher.
I didn’t have to because it really didn’t add a lot to the report, but I did show the K’s, BB’s’ and HBP’s after both strikes and balls.
The next Major column, “ABs after strikes” just shows what happens when BA is computed using only ABs where the pitch before the one that ended the AB was a strike, and the “ABs after balls” column s of course the one where that next to the last pitch was a ball. The final column “Total At Bats” is of course the two possibilities added together.
When I ran that report against all 4 years, the 1st thing I noticed was a .062 point increase for their batters against our pitchers from the next to the last pitch being a ball rather than a strike, an .084 point increase for our hitters, and a .081 increase together. That really caught my attention, and when I ran it against other data sets, like our JV and Fall Ball teams, my son’s HS team, and his JUCO teams, that’s pretty much the same thing that happened every time.
Of course there were exceptions, and that’s what prompted me to take the next step, which you’ll see on the next 3 pages. What I had to do to try to explain why there were situations where things reversed, was to break everything out a bit more. Our team is broken out by individual pitchers and our opponents by team.
At this point in time, to be honest, the only think I can say for sure is, throw as few balls as possible! lol! But seriously, early on in something like this, its really difficult to come to many valid conclusions, because every question that gets answered, gives rise to 2 new ones. FI, does there being runners on have a bearing, how about the pitcher’s strike percentage, or maybe being ahead or behind in the count affects it, and there’s about a gozillion more questions that come up.
When I do things like this, what I’m really doing is attempting to find reasons for what takes place on the field, hoping to give solid reasons for working on something rather than just doing it, mostly because I feel it helps me better understand the game, and because its fun too.