MLB: 5 most predictable hitters for 2018
These are the five hitters who have the least variation in their 2018 MLB projections based on seven different sources.
So much of life is about anticipation. When we’re young, we look forward to being old enough to drive or get our first legal drink. Somewhere along the way, we dream about falling in love and our first kiss. We think about what job we’ll have in the future and where we might live. When we’re older, we spend the winter thinking about what we’ll do next summer. In the spring, we look forward to the fall. It’s an ongoing cycle. For many MLB fans, the end of the season is a time to think about the offseason moves our favorite team will make.
Will they make a big trade or sign a free agent? Is it time for that excellent, young MLB prospects to crack the Opening Day lineup? Does that aging veteran still have one more good season left in him?
When spring training rolls around and the rosters are mostly set, baseball fans can think about how their favorite players will do in the upcoming season. For diehard fans, the best resource for predicting player statistics are the many projections available online. I recently did this for 2018. I used the following sources for projections:
There are 302 MLB players with roughly 200 or more plate appearances who appear on all seven lists. Before comparing them to find the most predictable hitters, I adjusted to put all of the sources on a level playing field. For example, these 302 hitters were projected by ATC to have a .266/.335/.447 (.782 OPS) batting line, but ZiPS had them hitting .261/.328/.438 (.766 OPS). That’s a difference in OPS of .016.
It looks like ATC is projecting a higher run-scoring environment than ZiPS. To compare these players, I want them expected for the same run-scoring environment. If I left the player’s stat lines unadjusted, then ZiPS would likely be the low source for many players. I want them on a level playing field, so I adjusted each player so that the entire group of players for each source has a .264/.333/.443 batting line, which is the average of all of the groups combined.
Using these adjusted numbers, I found the standard deviation for each player in OPS (on-base percentage plus slugging percentage). Players with a smaller standard deviation have projections that are more similar than those with a higher standard deviation. I also used the standard deviation of their projected plate appearances as an additional factor. They are the most predictable hitters, based on these seven sources of projections (statistics for each player are his adjusted statistics).