Back in late March, I discussed six of 2019’s biggest breakouts, as defined by gain in end of season dollar value as calculated here. Obviously, reviewing how these six players ended up performing in 2020 is pretty silly considering we only played 60 games, amounting to just 37% of a regular full season. I’m going to do it anyway, because it’s still fun for me to look back to see how I did on the many calls I made. So let’s find out how the six 2019 breakouts performed in terms of generating fantasy value and whether I was right or wrong in calling them REAL (2020 EOS $$ was within $10 of 2019 EOS $$) or a BUST ((2020 EOS $$ was $10 or more below 2019 EOS $$).

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After a disappointing 139 plate appearances over this short season, Hunter Renfroe has signed a one-year deal with the Red Sox to take over as the team’s starting right fielder. Let’s consult the park factors to learn how the soon-to-be 29-year-old’s performance may be affected.

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On Saturday, reports indicate that 30-year-old catcher James McCann has agreed to a four-year deal with the Mets. He heads to The Big Apple after two seasons with the White Sox. Let’s check out the park factors to determine how the park switch might affect his offensive performance.

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Last Thursday, Adam Eaton officially signed a one-year contract with the White Sox to take over as their starting right fielder. The now 32-year-old is coming off a massively disappointing short season, as his wOBA dipped below .300. Will the park switch help fuel an offensive rebound? Let’s consult the park factors.

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On Tuesday, Carlos Santana signed a two-year deal with the Royals, likely to take over as their starting first baseman, with the occasional start at DH I’m sure. The move pushes Hunter Dozier back to third base and essentially signals that the team has given up on Ryan O’Hearn. Let’s find out how the change in home ball park might affect his performance.

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Yesterday, I used batter Hard% as the first example of why using averages might be deceiving. Along with an explanation as to why, I listed and discussed 15 hitters whose fly ball Hard% most exceeded their overall Hard%. Let’s now shift to the other side and identify and discuss the hitters whose FB Hard% settled most below overall Hard%. These are the hitters whose home run power was actually worse than overall Hard% suggests.

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As more and more advanced metrics are publicized and used in analysis and discussion, opportunity for misuse and misinterpretation of these statistics continues to increase. First, remember that a statistic is a fact. The metric in and of itself isn’t wrong or bad, but rather the use and interpretation might be. Some metrics are easier to misinterpret than others. One such metric type is the use of an average, which applies to many metrics. Averages are fine if used properly. For example, we could say a .350 hitter recorded more hits per at-bat than a .300 hitter, but we absolutely cannot say that the .350 hitter is better than the .300 hitter, solely using the difference in batting average as the determining factor.

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Back on November 24, Charlie Morton signed a one-year deal with the Braves, as he makes his return to the National League, where he last pitched back in 2016. Of course, back then things were quite a bit different, like no designated hitter, which is something we expect to remain permanent in the NL moving forward. So there is now little different between leagues, and Morton’s isn’t going to get any boost, since he won’t be facing a pitcher at the plate. That said, let’s dive into the park factors to see how the park change might affect his performance.

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Yesterday, I listed and discussed the starting pitchers whose ERAs underperformed their SIERA marks by the most significant margins. I then reviewed the pitchers’ BABIP, LOB%, and HR/FB marks and identified which of the three metrics were driving the SIERA underperformance and what the chances for improvement in 2021 are. Let’s now shift over to the SIERA overperformers. Which of the three “luck” metrics drove such overperformance this season and can that last through next year? Let’s discuss the fantasy relevant names.

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Of the three ERA estimators available on FanGraphs (SIERA, xFIP, FIP), SIERA is the best at predicting future ERA, even though it was designed as a backwards-looking metric, like the other two. If you’re still using xFIP or FIP in your pitcher analysis, then stop, and immediately switch to SIERA. In a short 60 game season, focusing on SIERA, rather than ERA, is even more important when forecasting a pitcher’s future performance.

The underlying skills that drive SIERA stabilize more quickly and the metric isn’t influenced by the gyrations of the three “luck metrics” — BABIP, LOB%, and HR/FB — which don’t have enough time to settle around the pitcher’s true talent level. ERA is heavily influenced by how a pitcher performs in those three metrics, but there’s far too much randomness involved to place significant weight on them, even over a full 162 game season. Remember though, even SIERA isn’t perfect because there are pitchers who consistently underperform or outperform due to some skill or lack thereof that has been a challenge to identify.

So let’s review the pitchers who underperformed their SIERA marks most this season (minimum 40 innings pitched). I’ll identify which of the three luck metrics fueled that underperformance and discuss whether there’s a chance the pitcher underperforms again in 2021 or reverts closer to his SIERA (I’ll only discuss the fantasy relevant names).

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