After just about a week of the season, I published a list of eight hitters whose early fly ball rates were significantly higher than their 2017 marks. I dubbed these guys as potential fly ball revolution participators. Let’s find out if they were able to maintain their FB% gains and whether it led to a home run surge.
Yesterday, I reviewed the first preseason Pod vs Steamer projections article, beginning with the hitters I projected for home run upside. Today, we’ll flip to the other side, as I review the home run downside guys. Hopefully I perform better than my upside guys!
A fun activity for me each preseason is comparing my Pod Projections to the Steamer projections. While the forecasts in various categories for most players are negligible, of course there are some that are wildly different. One comparison I made was looking at which hitters I projected for more homers than Steamer. However, rather than compare the raw home run total, which is greatly influenced by the at-bat projection, I computed each projection’s AB/HR ratio. So let’s find out how the guys I identified as having AB/HR upside performed.
As has now been an annual tradition, I published a series of Pod Projections before the season begin, and then compared my projection to the rest of the systems available on the player pages. We’ll start the reviews with Whit Merrifield, who came out of nowhere in 2017 to become a fantasy stud, swatting 19 homers and stealing 34 bases. Let’s see how he performed compared to my projection and the computer systems.
As I have at the end of the first half since 2013, I grouped two sets of pitchers together and aggregated their results through the half based on the degree of SIERA outperformance and underperformance. I then asked you which group of pitchers would perform better from an ERA perspective over the second half, and which range each group’s ERA would fall into. This year’s poll and voting results are here.
At the end of spring training, I identified and discussed 10 pitchers whose spring training strikeout rate was significantly higher than my Pod Projection K%. Though the vast majority of spring training stats are completely meaningless, I did find many years ago that strikeout and walk rates do hold some predictive value. So let’s see how these 10 pitchers ended up performing.
Today I continue with my recaps, this time reviewing my early January post touting six home run sleepers. I compiled the list by initially sorting the 2017 Brls/BBE leaderboard in descending order and identifying sleepery hitters. Let’s see how they performed.
Yesterday, I reviewed my 2018 bold hitter league leaders. Today we move to the pitching side. These are easier to get right than hitters simply due to greater influence of luck on the results.
Gosh, if you thought getting bold predictions right was a tough task, the bold league leaders is even more difficult. Not only do I have to choose a player who would be considered bold to lead the league in the category in question, but the pick has to at least be realistic enough so there’s an actual non-zero chance of the prediction coming true. Let’s see how I did this season on the hitting side. Here are my original thoughts.
I can’t believe it’s already that time of year again, playoffs and recap season! As usual, I have a ton of reviews to share in the coming months. We’ll start things off with my 2018 bold predictions. I believe four correct is my best showing. Let’s try to beat that. You may also remember that I have a disturbing knack for making bold predictions about players who end up missing a significant portion of the season to injury. Let’s hope that trend has ended.
