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.
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.
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.
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.
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.
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).
Yesterday, I identified and discussed the hitters whose SwStk% marks improved the most versus 2019. Today, let’s check in on the opposite end of the list. Remember that “decliner” in this context actually means these hitters’ SwStk% marks have increased, so their skill declined, but the metric we’re using to evaluate their skill has risen.
Let’s continue on with our look at the plate discipline metrics by moving onto swinging strike percentage (SwStk%). With a strong correlation of 0.77 (qualified batters from 2015-2019), SwStk% is a good proxy for strikeout rate and the two will usually move in the same direction. Because SwStk% uses a denominator of total pitches, versus K%’s use of plate appearances as its denominator, it takes far less time for SwStk% to become meaningful. As such, the value of the metric increases in a shortened season like 2020. So let’s review the 2020 batter SwStk% improvers.
Yesterday, I identified and discussed the hitters that improved their O-Swing% the most versus 2019. Today, I’ll check in on the other side of the ledger. While I titled this post “decliners”, what that really means is a decline in skill, assuming a lower O-Swing% is representative of greater skill. So these are the hitters whose O-Swing% increased the most versus 2019.
The Plate Discipline stat section provides us with a lot of “underlying skill” information. Since these metrics use a denominator that grows more quickly than more traditional metrics such as strikeout and walk rates, they are more useful over small sample sizes, especially after a short season. So let’s now move on to hitter O-Swing% to learn which hitters reduced their swing rate on pitches outside the strike zone and which swung more often at such pitches. Since 2019, O-Swing% had about a -0.74 correlation with walk rate, so it’s clearly that holding back on swinging on pitches outside the zone is an excellent indicator of plate patience and current/future walk rate.
