Prospect Scouting & Stats — Hitter Power Part 2

Yesterday, I introduced you to my newest series, Prospect Scouting & Stats, and began by sharing correlations between the various scouting power grades and average exit velocity with minor league HR/FB rate and ISO. I generally found better correlations with HR/FB rate, and average exit velocity (EV), which is completely objective, actually correlated best. During my analysis, I wondered aloud whether we might find better correlations if the scouting grades and EV all agreed. So, I dove back into the data to find out. I decided to only look at HR/FB rate this time since we care more about it as fantasy players and the metrics correlated better with it.

There were 339 aggregate (stats across multiple 2019 minor league stints) player seasons that I had all three data points for (Game Power – Present, Raw Power – Present, EV). The plan was then to bucket each metric into a “Low”, “Middle”, or “High” value. So I counted how many players were assigned each scouting grade and recorded at each EV. I wanted the buckets between grades and EV to include a similar number of players (e.g., the “Low” bucket would include around the same number of players for Game Power – Present, Raw Power – Present, and EV), but didn’t care if the “Low” bucket had a similar number of players as the “Middle” bucket.

Amazingly, it worked out pretty darn well, as I was easily able to create buckets between metrics with similar counts. These buckets were as follows:

Once I had the minimum and maximums for each bucket in each metric, I determined which, if any, bucket the player line belonged to. The most important takeaway is whether there was a match between all three metrics or not. Results were as follows:

I then ran correlations in the three metrics to HR/FB rate, grouping them as Match and Mismatch. The hope was that the Match correlation would be higher than the Mismatch, hopefully significantly higher.

Success! When the three metrics are all in agreement with each other, there’s a pretty strong correlation with HR/FB rate. Interestingly, this time the prospect’s Game Power scouting grade proved most correlated, while EV was last, though the different wasn’t huge. That’s the opposite of what we found yesterday when the matched and mismatched lines were all included.

If there’s disagreement between the metrics, the best metric to use is EV. However, you should be far less confident that the hitter’s HR/FB rate is real than if the metrics all agreed.

In the Majors, we have all sorts of things we could look at to help us determine how real a hitter’s HR/FB rate was or currently is. Whether it’s using my xHR/FB rate equation, looking at any of the components individually, looking at historical HR/FB rates, etc, we have much more data and tools at our disposal than we do for minor leaguers. So it makes it that much more difficult for us to determine whether a prospect’s HR/FB rate is real or not. It’s not enough to look at history and just take their marks at face value assuming it’s their true talent level.

Now we have learned that the scouting grades add a valuable reality check. Obviously it’s not going to be correct 100% of the time. However, if you find that the two present power grades and EV all match, there’s a significantly higher chance that that the HR/FB rate is real than if the grades and EV do not match.