Yesterday, I reviewed the 11 hitters I identified back in late April who my xHR/FB rate suggested had deserved dramatically better actual xHR/FB rates. Today, we flip to the overperformers, those who my equation suggested deserved significantly lower HR/FB rates over that first month. While the equation isn’t meant to be used for predictive purposes, a forecast would likely account for that apparent overperformance and project a lower HR/FB rate the rest of the way. Let’s see what ended up transpiring.

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At the end of April, I calculated hitter xHR/FB rate using my equation to determine who had most underperformed at the time. I identified and discussed 11 of those hitters. Let’s see how many of these hitters actually did improve their HR/FB rates over the rest of the season and how close they came to their xHR/FB marks. Remember that xHR/FB isn’t meant to be predictive, but descriptive. The difference being that xHR/FB rate helps us determine what should have happened, rather than what will happen in the future. It attempts to strip out luck, like the majority of our expected/deserved metrics.

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Last week, I reviewed and discussed various hitting and pitching metrics and how they have trended over the past five seasons, from 2015 through 2019. After also discussing the early Statcast trends back in mid/late April, let’s take a look again with the full season now in the books.

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Yesterday, I shared and discussed a plethora of hitting metric trends from 2015 to 2019. More strikeouts, more fly balls, and more home run power are the obvious trends. Now let’s look at how the early 2019 pitcher trends I discussed in mid/late April ended for the season.

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In mid-April, I took an early look at hitting trends in a plethora of metrics since 2015. Now that the 2019 season has concluded, let’s now revisit those metrics and see what 2019 ended up bringing us. Did it continue a trend, reverse a recent trend, or begin a new one?

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Today, let’s review how 10 slow-starting pitchers I first discussed in mid-April performed the rest of the way. Was it a profitable move to acquire any of these pitchers from a panicked owner? Let’s find out.

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I continue reviewing my pre-season, and now in-season, articles and this is one of my favorite topics. Even though we know (or should!) that any random two week stretch of performance has little to no predictive value, fantasy owners will never stop allowing results from such a short period cloud their evaluation of a player. The most common time for player evaluation clouding is the beginning of the season, when those are the only stats currently in the book. But the funny thing is, by the end of the season, we typically forgot who those slow starters were, especially if they completely rebounded and ended up earning what we paid for them after all. So let’s take a look back at the slow starting hitters I discussed in Mid-April, and checking in on their stats at that point and over the rest of the season.

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While I have finished reviewing my preseason lists, I wanted to continue the recap theme, but this time looking back at early season results and trends. Did any of the surprises we saw occur early on continue for the rest of the season? We’ll start with my starting pitcher Z-Contact% improvers, a metric that indicates the percentage of pitches inside the zone batters made contact with. The ultimate sign of dominance is when a hitter can’t hit your strikes. Below is the original table with stats through Apr 10. I included 2018 and 2019 strikeout rate to see if the Z-Contact% changes if any, also resulted in strikeout rate changes in the same direction.

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Backed by this study supporting the idea that pitcher strikeout rate over spring training does hold some predictive value, I identified and discussed 12 starting pitchers that had posted significantly higher strikeout rates during the spring than my Pod Projection. Let’s see how many, if any, of these pitchers actually outperformed my forecast.

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Today, I finish my comparison of Pod ERA projections vs Steamer with the downside guys. Given that league ERA was at its highest since 2006, this should be an easy win for Pod. But, I only listed and discussed four pitchers, probably because on the whole, Pod was more bullish on ERA than Steamer was (oops), so there were fewer pitchers I was projecting for a significantly worse ERA. Let’s see how the for performed.

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