On Tuesday, I hopped over to the pitcher side of the ledger to discuss nine fantasy relevant starting pitchers with strikeout rate upside this season. I used my xK% equation and compared what the formula spit out to what the pitcher’s actual strikeout was. Today, I’m going to share the ten pitchers who most outperformed their xK% marks.
I’ve spent nearly the entire off-season discussing hitters, as Statcast and xHR/FB rate took over my life. Let’s move on to pitchers for now, and begin with another of my xMetrics, xK%. I updated the metric’s coefficients last season and it’s probably the best xEquation out there given its sky high adjusted R-squared.
We all know a park’s dimensions, foul territory, hitter’s backdrop, atmospheric effects, etc. play a significant role in shaping our projections and on a player’s performance. Collectively, we know these effects as park factors. We are probably most aware of a park’s home run park factor. I’m sure that for many parks, you have a perception in your mind as to its home run friendliness. The data might say otherwise, but at least you think you know, unlike, say, triples, which I’m sure most haven’t a clue which parks are best for boosting the three-bagger. Unfortunately, while the idea of park factors is sound, they are extremely problematic to rely on.
After two days discussing individual players with apparent upside and downside given their fly ball pull percentages (FB Pull%), it’s time to really get to know the metric. Let’s begin by looking at the leaguewide trend over the last 10 years.
Yesterday, I began my discussion of another one of my xHR/FB rate equation’s components, FB Pull%, and shared the hitters who posted above average Brls/True FB and Avg FB Dist marks, but below average FB Pull% rates, hinting at upside if a change in approach is made. Today, I’m going to check in on the hitters who posted below average Brls/True FB and Avg FB Dist marks, but above average FB Pull% rates. These hitters are at greater risk of HR/FB rate regression given their heavy reliance on pulling their flies. If that skill erodes or approach is altered, there would be major downside.
One of the components of my new xHR/FB equation is fly ball pull percentage (FB Pull%). Sadly, I have spent significantly more time discussing barrels per true fly ball and average fly ball distance, so I’m going to change that. Let’s talk FB Pull%!
The 2018 Pod Projections are now available! For the first time, the package includes NFBC ADP, along with all historical Pod-developed xMetrics. My projections are based on the methodology shared in my eBook Projecting X 2.0, and the process continues to evolve and improve (thanks Statcast!). Given the hype and the difficulties of translating performance from a foreign league, it was obvious who the first player for this series should be — Japanese uber-athlete Shohei Ohtani.
Yesterday, I used my new xHR/FB rate to identify and discuss 15 hitters whose xHR/FB rates actually validate their HR/FB rate spikes in 2017. Today, I’m going to check in on the opposite end of the validations — those hitters who suffered severe declines in HR/FB rate that was confirmed by xHR/FB rate. Without xHR/FB rate, we cannot be sure if it’s luck or just skill changes driving the swings in HR/FB, so the metric assists in making that determination.
As suggested by commenter Konoldo in yesterday’s post discussing 10 2018 HR/FB rate decliners, today I am going to use my new xHR/FB rate to identify surprise 2017 power sources validated by the metric. These are the hitters that either came out of nowhere to post big HR/FB rates, or really upped their games, blowing past even the most optimistic of projections. The knee-jerk reaction is always to expect severe regression, but xHR/FB rates might make you think twice. Perhaps a repeat is more likely than you think for these hitters.
Yesterday, I used my new and improved xHR/FB rate equation to discuss 10 hitters whose xHR/FB rates sat significantly above their actual marks in 2017, suggesting serious 2018 upside. Today, I’ll talk about the other end of the spectrum, hitters whose xHR/FB rates were well below their actual marks, hinting at real downside risk in 2018, assuming the same underlying skills.
