(Fun fact: this graph is a year old. Aaron Judge has hit a ton of balls harder than anything on this chart)
This graph should be pretty easy to read. The more purple a location, the less valuable a batted ball is. These are almost always outs. The more orange a location, the more valuable a batted ball is. Hitters can thrive in two basic bands: the line drive band in between 10 degrees and 25 degrees, where soft contact can yield hits, and the power hitting band, where (only) hard contact yields extra base hits.
Statcast essentially does the same thing that Rob Arthur's graph does to produce the statistic xwOBA, which we've used a lot on this blog in 2017. It takes each individual batted ball a player hits, calculates an expected value, and adds in strikeout, walk rates, and other information. The equation basically looks like this:
wOBA = Batted Balls*X1 + Strikeout Rate*X2 + Walk/HBP Rate*X3 + Error
We produce the values for X1, X2, and X3 here by taking in all of the data for players recorded by Statcast so far, including their actual wOBA. We then can take those coefficients and estimate the wOBA that a given batted ball profile from a player would on average produce.
The error term is important here. We are not going to perfectly predict the wOBA is a player using their batted ball profile and plate discipline. There are lots of other factors at play: luck, opposing defenses, park factors, speed, and horizontal angle (where on the field the ball lands). A really big error number suggests that the model we've designed to predict an outcome does not do a great job predicting it, even if the individual variables fed into it are significant predictors of the outcome.
For the individual player, a large error term means that a lot of things other than the model are probably contributing to the observed outcome. For example, Billy Hamilton is very fast, and turns a lot of batted balls into more valuable outcomes than the average player. Since xwOBA doesn't (yet) account for speed, we should expect it to undershoot Billy Hamilton's value.
However, luck is probably the predominate factor for most players. Sometimes, balls find holes in the field. These hits count in the game, but aren't likely good predictors of future performance. If a player is having a good season because a lot of balls that are normally low value are falling in, they are likely not going to be as good going forward, and vice versa.
Didi Gregorius's Breakout Performance According to Statcast
Gregorius has posted a wOBA of 0.354 this season, well above the AL average 0.322 wOBA. However, the above equation predicts an xwOBA of 0.284 - well below average.
381 players have at least 100 MLB plate appearances this season. Of those, Gregorius is the 4th highest overperformer. The other three are Mallex Smith, Marwin Gonzalez, Zack Cozart. Here's what this looks like on a graph: