For all the background information on the test I used RMSE and data sample reference the first article. These tests take forever to run and at some point, I kept getting the same answers smartly aggregating the projections , so I stopped running any new ones for hitters.
Here are the results for the tests I ran. And here are the home projections prorated per plate appearance. The big surprise was that Marcels remained near the top even when the home runs were turned into a rate stat. A new order of projections on this raw stat … well besides the averages performing near the top. Here they are as a rate stat.
I combined the two because the results were consistent aggregators kicking ass and I just wanted to see if any of the results stood out like with stolen bases. Nothing changed. Correctly guessing playing time allows a projection to dominate these rankings. The answer is simple, get an aggregation of projection. ATC and Zeile already do the combination. Note: I cut and diced the available information in what seemed a different ways.
The following are the two best examples I found for why projections miss. From some of my unpublished work, I have determined that projections miss based on age, previous playing time proxy for health , and talent projected OPS. I wanted to find out why Marcels performed better than the standard projections. In , here are the players the 4 Big Projs projected for more playing time than Marcels.
A table full of prospects e. Carter, Cruz or injured players e. Lewis, Hoskins. This verifies some of my previous findings that players with checkered playing histories miss their playing time projections.
