Let’s continue our recaps of my Pod vs Steamer projections series, this time with stolen bases. As a reminder, I compared my 2017 Pod Projection stolen base forecast to the Steamer projected, extrapolated over the same number of plate appearances I had projected. This group is composed of those whose Pod Projected stolen base total most exceeded the Steamer projection. Let’s see how they did.
Before taking a short vacation, I reviewed the first preseason Pod vs Steamer Projections series posts, focused on home run upside. That article discussed the hitters whose home run Pod Projection was significantly greater than his Steamer projection. Today, I’ll recap the players I identified as possessing significant home run downside compared with Steamer. With the record setting home run total, this shall be interesting.
Before the 2017 season, I decided to switch up my typical “Steamer and I” series posts where I discuss one player in which my Pod Projections differ from Steamer. Instead, I compared my projections in specific fantasy categories to identify upside and downside guys my forecasts hinted at versus Steamer, representing the crowd. Let’s begin our recaps of the new series with the home run upside group, which are those hitters I projected for significantly more home runs than Steamer. Note that I extrapolated the actual Steamer home run projections to match the same number of at-bats I projected, so playing time differences wasn’t a factor.
Yesterday, I reviewed my list of 2017 HR/FB rate surgers, utilizing my Statcast-fueled xHR/FB rate to identify the guys with significant upside. Today, I’ll recap the list of HR/FB rate decliners. Having not yet looked at my list, I’m nervous the leaguewide spike to record-setting home run numbers is going to make me look silly. Let’s find out!
Geez, I could have blindly selected a handful of hitters whose HR/FB rates were due to rise in 2017 and probably would have hit on the majority! As I continue to recap my preseason lists, let’s move on to my 2017 HR/FB rate surgers. I compared my 2016 Statcast-fueled xHR/FB rate to the hitter’s actual HR/FB rate and identified six hitters whose xHR/FB rates were significantly above their actual marks, suggesting serious upside. Let’s see how these hitters performed.
Last Thursday, I discussed the 10 hitters I identified in the preseason as potential 2017 BABIP surgers, due to xBABIP marks well above actual marks. Eight of the nine hitters that actually recorded an at-bat enjoyed a BABIP increase. Let’s see how the nine hitters I identified in the potential BABIP decliners list fared.
Let’s stick with my xBABIP equation and discuss the group of 10 hitters I identified as potential 2017 BABIP surgers. These were the guys whose xBABIP marks were significantly above their actual BABIP marks.
Sometimes it’s obvious when a hitter’s BABIP is due for a rebound or regression, but that isn’t always the case. For the first time before this season, I decided to discuss some of the hitters whose BABIP marks looked relatively sustainable, but my new xBABIP equation disagreed. Let’s see how this group ended up doing.
Yesterday, I reviewed the 2017 starting pitcher walk rate improvers using my updated xBB% equation. Today, I’ll recap my walk rate regressers, which includes those starting pitchers whose actual walk rates were significantly better than their xBB% marks.
In late January, I updated my pitcher xBB% was include and additional variable, 3-0%, which is the percentage of a pitcher’s plate appearances in which a 3-0 count is seen. Since Alex Chamberlain had already published such a formula, which built upon my original equation, I simply updated the variables of his. I then used the new formula to highlight a group of starting pitchers with an xBB% significantly lower their actual 2016 marks. This group was then discussed as potential walk rate improvers in 2017. Let’s see how they did.
