There was quite an interest with my recent foray into the statistical side of Major League Baseball and how we can apply it to our fantasy baseball teams. That intersect exists solely because we, as managers of our teams, are always looking for an edge. Yes, we are competitive despite living in our mother’s basements. Priorities baby. I have continued my quest to learn more and more about how we can harness this advanced information and, sort-of-speak, create a toolbox with nifty things in it to examine players of interest for both you, the readership, and myself.
The first step was to explain batter and pitcher thresholds, which, in a nutshell, was finding chronological points, whether it be plate appearances or batters faced, where certain skills stabilize. The power to say, with confidence, that a player doing x will continue to do so, or what he is doing is simply a mirage, is quite useful. The second step was then to start looking at interesting cases as each threshold was met. The first one was Contact%, and you can revisit my post on that topic here. But, right now, the very premise needs to be updated, as some things have changed.
Before we get to the new numbers, special thanks goes to Razzball commentator Fish, who actually was as interested as I was about this topic, maybe even more so, and discovered new data that changes the dynamic. So what does all of this mean? Are we blowing everything up? No, but we do have newer points to compare data with, and that are also more accurate as well. Now, these numbers aren’t that dissimilar from the ones I listed in the previous iteration of this post, but we now have better ones, and more of them, which, to me, is a good thing.
The past research I did was mainly rooted to a study that basically took samples of plate appearances from a pool of players, divided them in two, and then correlated them to each other to reduce noise and find a point of stabilization. But there was plenty of other noise going on, in that the study had some flaws inherent to the process that may have generated less than accurate results. Because of this, there have been a couple of new methodologies set forth, and you are welcome to explore them yourselves. They will be cited at the end of this post.
However, just to get to the meat of this matter, because, you know, steaks bro. Let’s go over the new and improved stabilization marks.
Please Note: BF is Batters Faced. BIP is Balls In Play. FB is Flyballs.
What does this mean in terms of how I’ve implemented the older studies to my analysis? Well, while the previous study was flawed, almost everything generally is when we try to do something like this. That’s basically why regression is such a strong concept in statistics. In that regard, the analysis can still be right or wrong, but the reasons why it was right or wrong have changed a bit. And the level of rightness and wrongness, sort-of-speak, have changed as well. It doesn’t make what we’ve already learned useless, it just means that the process going forward will be much more accurate. For example, if you look at the same data through the new thresholds, the conclusion will most likely end up being the same. Norichika Aoki is still a very good baseball player. Ryan Howard is very much not, just to name a couple players that I analyzed with the old data.
So yes, I’ll be changing up how these numbers are used in future posts following this theme, and will be sure to take note of players who have made changes based on the above benchmarks. I’m excited to delve back into the data.
Remember, I take no credit for the actual process. This is data stems from my own grasp of the information found, and was also helped by Razzball commentator Fish. I want to make sure credit is given where its due, so if you’d like to take a gander at the methodology of the studies used as the basis of this post, you can find it here and here.