Yesterday, I took the first dive into the intriguing waters of Statcast’s Sprint Speed metric. After comparing correlations with other speed-related stats to Spd Score, I now want to see which players are actually faster and slower than Spd would have use believe. Because Spd uses some context-dependent metrics, it could sometimes give us a false impression of a batter’s true raw speed.
In April, Statcast quietly introduced a new speed metric dubbed Sprint Speed. It wasn’t until late June that Baseball Savant made the leaderboards publicly available and we now have data going back to 2015. I have been meaning to dive into the data to find any incremental value, and finally the day has come. From the leaderboard page, the metric is described as thus:
Sprint Speed is Statcast’s foot speed metric, defined as “feet per second in a player’s fastest one-second window.” The Major League average on a “max effort” play is 27 ft/sec, and the max effort range is roughly from 23 ft/sec (poor) to 30 ft/sec (elite). A player must have at least 10 max effort runs to qualify for this leaderboard.
I’ve never performed a mid-season review of my pre-season bold predictions because I typically forget what I predicted and like to be surprised when I recap them six months later. But the article I wanted to write cannot be done, so here I am stepping into uncharted waters. Can you feel the excitement?! My goal this year is to beat my personal record setting 2016 performance of four correct bold predictions. Let’s see if I have any chance whatsoever.
Since 2013, I have polled you dashingly attractive readers on which group of pitchers you think will post the better aggregate ERA post all-star break. The two groups were determined based on ERA-SIERA disparity, pitting the underperformers versus the overperformers during the pre-all-star break period.
Yesterday, I identified a group of hitters worth buying for their home run upside, given the discrepancy between their actual HR/FB rate and xHR/FB rate. Today, I move over to batting average, as I identify the hitters whose xBABIP marks most exceed their actual BABIP marks. These are the guys to target for batting average that you may be able to get at a discount.
The All-Star break provides a much needed rest and a perfect opportunity to assess your team(s). The last time I used xHR/FB rate to identify hitters due for a home run spike was in the middle of May, so now is an excellent time to name names once again. These are the fantasy relevant hitters whose xHR/FB rates most exceed their actual HR/FB rates, which represents a strong list of acquisition targets if you’re looking to bolster your standing in the home run department and are hoping to buy at a discount.
On Thursday for my American League starting pitching slot, I used my expected strikeout rate metric to determine which starters have the most strikeout rate upside given the components of the equation (strike percentage, along with swinging, looking, and foul strike rates). Today, I’ll look at the starters with strikeout rate downside hinted at by xK%, but expand my group of pitchers to both leagues.
Earlier this year, I updated my pitcher expected strikeout rate metric, or xK%, with new coefficients. The equation uses a pitcher’s overall percentage of strikes thrown, as well as the breakdown between the types of strikes he generates — swinging, looking, and foul. We could use xK% over a smaller sample given that its denominator is pitches, rather than batters faces, so it likely stabilizes much more quickly.
If you have been searching for a Tyler to add to your Tylerless fantasy roster, you’ve come to the right place! Today, I share two Tylers with you, each of whom could very well fit all your Tyler needs.
Yesterday, I listed the 20 pitchers with the largest gaps between their ERA and SIERA marks, with their ERA marks sitting significantly higher than their SIERA marks, suggesting serious potential for improvement moving forward. Today, I’ll list the pitchers on the other side of the coin, the 20 with ERA marks significantly lower than their SIERA marks, suggesting real potential for ERA regression.
