Every year, I pit my Pod Projections (now available!) against the Steamer projections in various categories. Today, I’m going to continue the annual smackdowns by calculating AB/HR rates and then extrapolating them over 600 at-bats. At that point, I’ll compare how many home runs each system is forecasting, given a 600 at-bat projection. I’ll start by sharing the names of hitters Pod is projecting for significantly more home runs than Steamer.
Yesterday, I shared my latest pitcher xK% equation and identified and discussed the nine starting pitchers that most underperformed those marks.
Let’s return to our review of 2021 performances, this time flipping over to starting pitchers. Back at the beginning of 2017, I revealed the latest version of my pitcher xK% equation. From an adjusted R-squared perspective, it was the best equation I had developed. I’m never satisfied though and decided to perform my annual xMetric review over the winter, which included xK%. Lo and behold, I was able to develop an even better equation, which seemed impossible, but nonetheless, actually happened.
The 2022 Pod Projections are now available and include nearly 550 player lines! As usual in my Pod Projection posts, I’ll dive into my projection methodology (detailed in Projecting X 2.0) by sharing my process on several hitters and pitchers.
It’s been a longer wait than in the past, but it’s finally Pod Projections time! The 2022 forecasts are now available and include nearly 550 player lines. As usual in my Pod Projection posts, I’ll dive into my projection methodology (detailed in Projecting X 2.0) by sharing my process on several hitters and pitchers.
Today, I’ll analyze former top overall prospect, Wander Franco. He made his eagerly anticipated debut last season and was as solid as expected, despite being just 20 years old. While a 14 homer and four stolen base pace over a full season certainly didn’t thrill fantasy owners, he posted a .348 wOBA and managed to maintain his sterling contact ability by striking out just 12% of the time. That’s mightily impressive for a rookie who wasn’t even of legal drinking age yet.
On Tuesday night, the competitors of the LABR Mixed draft virtually congregated for our annual mid-February 15-team draft. Drafts this early are challenging. On the one hand, the early timing benefits the prepared and the more highly skilled. On the other hand, there remains a great many unknowns that we need to make educated guesses for at best, and complete shots in the dark for at worst. In addition, and during a normal pre-season period, the extra time between the draft and opening day (versus leagues that draft a week to three before the seasons begins) means more opportunity for injuries to decimate your roster before the season even begins!
Yesterday, I used my newest hitter xBABIP equation to discuss the batters whose actual BABIP marks most underperformed their xBABIP marks. Now let’s look at the overperformers, or those whose actual BABIP most exceeded their xBABIP marks.
Nearly two weeks after introducing my newest hitter xBABIP equation, it’s time to unveil the list of underperformers. This is the group that most underperformed their xBABIP, which could result in undervaluation if your leaguemates are paying for a 2021 repeat, and not a 2022 rebound. Of course, remember that a higher 2021 xBABIP than actual BABIP is not a 2022 projection. However, if you’re using historical BABIP to forecast future BABIP, then I would highly advise you use xBABIP instead of actual BABIP as your historical marks, especially for hitters with a small sample of playing time. I’ll use a 75 ball in play minimum once again.
Yesterday, I listed and discussed a handful of hitters whose actual 2021 BABIP marks were within 0.010 of league average, which normally wouldn’t make you think twice about its repeatability for the 2022 season. However, these hitters posted significantly higher xBABIP marks at least 0.020 higher than their actual marks. Let’s now flip over to the hitters who posted near-league average BABIP marks, but this time finished with xBABIP marks significantly below those BABIP marks.
Today, we continue our exploration of my new hitter xBABIP equation by identifying hitters whose 2021 BABIPs were around the non-pitcher league average of .293, but whose xBABIPs were significantly different. When you see a BABIP of .380 or .220, that clearly raises red flags, with immediate reactions of decline, in the case of the former, or improvement, in the case of the latter, in the upcoming season. But no such reaction is triggered when you see a BABIP around the league average, right? However, just being around the league average doesn’t necessarily mean it’s legit. So today, let’s begin by discussing those hitters who posted BABIPs marks within .010 of league average (between .283 and .303), but xBABIP marks significantly higher. If your leaguemates are using 2021 BABIP to shape their 2022 hitter forecasts, these hitters’ batting average contributions could be undervalued.
