Two months ago, if you asked me to name the most disappointing member of the phenom class, I’d have said Bobby Witt Jr. With barely a month of professional games past high school under his belt, he was invited to spring training in 2021 and hit three homers and put up an .851 OPS, creating chatter around baseball that he might start the season with the parent club. That was a bit premature, though he did spend the next six months terrorizing minor league pitchers into thinking long and hard about their choice of occupation. But in 2022 and early on in ’23, brevity was no longer the soul of Witt, as his whirlwind professional progress slowed to become one of those inevitably anemic breezes on an unpleasantly muggy July day.

Things appeared to reach their nadir in late June, when his OPS almost dipped under .700 once again. Since then, however, Witt has been on a tear, hitting .350/.385/.662, not only bringing his OPS safely over the .700 line but also getting it over .800. Since the morning of June 30, he’s been one of the absolute best players in baseball, providing a rare highlight for the 2023 Royals:

I’ve included BABIP here for a very good reason: when players are having hot streaks, BABIP is usually a big reason why. After all, players playing at their peak are more likely to be playing above their abilities than below. Witt is no exception here, with his numbers fueled in part by a .370 BABIP over that period. But I include that figure not to defuse my thesis, but to reinforce it. While a BABIP that high is hard to sustain over the long haul, ZiPS’ zBABIP thinks that .370 mark only barely outperforms what he’s actually done in the last month and a half. Read the rest of this entry »

The Astros currently rank third in the American League in runs scored — not an uncommon sight for a franchise that has only been outscored by the Dodgers and Red Sox over the last decade. But they’ve done it with a lineup with some pretty big holes, with half of their eight players with at least 300 plate appearances this season posting an OBP under .300. The team’s offense has been driven this year mainly by four players: Kyle Tucker, Yordan Alvarez, Alex Bregman, and Chas McCormick, with an assist from Yainer Diaz. A year ago, Houston signed Alvarez to a six-year, $115 million contract extension that ensured he would remain in town until the end of the 2028 season. Tucker, though, does not have a long-term deal and is scheduled to hit free agency after the 2025 season. What would a possible deal look like?

There’s certainly interest from the Astros’ side, as there ought to be. The team has discussed an extension with Tucker in the past, though there are no active talks right now. But general manager Dana Brown did use his weekly radio spot in part to discuss making Tucker an “Astro for life,” so some kind of deal coming together is hardly implausible. Read the rest of this entry »

One of the strange things about projecting baseball players is that even results themselves are small samples. Full seasons result in specific numbers that have minimal predictive value, such as BABIP for pitchers. The predictive value isn’t literally zero; individual seasons form much of the basis of projections, whether math-y ones like ZiPS or simply our personal opinions on how good a player is. But we have to develop tools that improve our ability to explain some of these stats. It’s not enough to know that the number of home runs allowed by a pitcher is volatile; we need to know how and why pitchers allow homers beyond a general sense of pitching poorly or being Jordan Lyles.

Data like that which StatCast provides gives us the ability to get at what’s more elemental, such as exit velocities and launch angles and the like — things that are in themselves more predictive than their end products (the number of homers). StatCast has its own implementation of this kind of exercise in its various “x” stats. ZiPS uses slightly different models with a similar purpose, which I’ve dubbed zStats. (I’m going to make you guess what the z stands for!) The differences in the models can be significant. For example, when talking about grounders, balls hit directly toward the second base bag became singles 48.7% of the time from 2012 to ’19, with 51.0% outs and 0.2% doubles. But grounders hit 16 degrees to the “left” of the bag only became hits 10.6% of the time over the same stretch, and toward the second base side, it was 9.8%. ZiPS uses data like sprint speed when calculating hitter BABIP, because how fast a player is has an effect on BABIP and extra-base hits.

And why is this important and not just number-spinning? Knowing that changes in walk rates, home run rates, and strikeout rates stabilized far quicker than other stats was an important step forward in player valuation. That’s something that’s useful whether you work for a front office, are a hardcore fan, want to make some fantasy league moves, or even just a regular fan who is rooting for your faves. If we improve our knowledge of the basic molecular structure of a walk or a strikeout, then we can find players who are improving or struggling even more quickly, and provide better answers on why a walk rate or a strikeout rate has changed. This is useful data for me in particular because I obviously do a lot of work with projections, but I’m hoping this type of information is interesting to readers beyond that.

Yesterday, I went over how pitchers zStats for the first two months of the season performed over the last two months. Today, we’ll look at the updated data, through the games on August 10. Read the rest of this entry »

One of the strange things about projecting baseball players is that even results themselves are small samples. Full seasons result in specific numbers that have minimal predictive value, such as BABIP for pitchers. The predictive value isn’t literally zero — individual seasons form much of the basis of projections, whether math-y ones like ZiPS or simply our personal opinions on how good a player is — but we have to develop tools that improve our ability to explain some of these stats. It’s not enough to know that the number of home runs allowed by a pitcher is volatile; we need to know how and why pitchers allow homers beyond a general sense of pitching poorly or being Jordan Lyles.

Data like that which StatCast provides gives us the ability to get at what’s more elemental, such as exit velocities and launch angles and the like — things that are in themselves more predictive than their end products (the number of homers). StatCast has its own implementation of this kind of exercise in its various “x” stats. ZiPS uses slightly different models with a similar purpose, which I’ve dubbed zStats. (I’m going to make you guess what the z stands for!) The differences in the models can be significant. For example, when talking about grounders, balls hit directly toward the second base bag became singles 48.7% of the time from 2012 to ’19, with 51.0% outs and 0.2% doubles. But grounders hit 16 degrees to the “left” of the bag only became hits 10.6% of the time over the same stretch, and toward the second base side, it was 9.8%. ZiPS uses data like sprint speed when calculating hitter BABIP, because how fast a player is has an effect on BABIP and extra-base hits.

ZiPS doesn’t discard actual stats; the models all improve from knowing the actual numbers in addition to the zStats. You can read more on how zStats relate to actual stats here. For those curious about the r-squared values between zStats and real stats for the offensive components, it’s 0.59 for zBABIP, 0.86 for strikeouts, 0.83 for walks, and 0.78 for homers. Those relationships are what make these stats useful for predicting the future. If you can explain 78% of the variance in home run rate between hitters with no information about how many homers they actually hit, you’ve answered a lot of the riddle. All of these numbers correlate better than the actual numbers with future numbers, though a model that uses both zStats and actual ones, as the full model of ZiPS does, is superior to either by themselves. Read the rest of this entry »

12:00

Dan Szymborski: It’s a chat!

12:00

Dan Szymborski: Probably not in serious jeopardy. ZiPS projections for Singleton aren’t great, either

12:01

Dan Szymborski: some unsalted peanuts I happen to have here

Read the rest of this entry »

One of the strange things about projecting baseball players is that even results themselves are small samples. Full seasons result in specific numbers that have minimal predictive value, such as BABIP for pitchers. The predictive value isn’t literally zero — individual seasons form much of the basis of projections, whether math-y ones like ZiPS or simply our personal opinions on how good a player is — but we have to develop tools that improve our ability to explain some of these stats. It’s not enough to know that the number of home runs allowed by a pitcher is volatile; we need to know how and why pitchers allow homers beyond a general sense of pitching poorly or being Jordan Lyles.

Data like that which StatCast provides gives us the ability to get at what’s more elemental, such as exit velocities and launch angles and the like — things that are in themselves more predictive than their end products (the number of homers). StatCast has its own implementation of this kind of exercise in its various “x” stats. ZiPS uses slightly different models with a similar purpose, which I’ve dubbed zStats. (I’m going to make you guess what the z stands for!) The differences in the models can be significant. For example, when talking about grounders, balls hit directly toward the second base bag became singles 48.7% of the time from 2012 to ’19, with 51.0% outs and 0.2% doubles. But grounders hit 16 degrees to the “left” of the bag only became hits 10.6% of the time over the same stretch, and toward the second base side, it was 9.8%. ZiPS uses data like sprint speed when calculating hitter BABIP, because how fast a player is has an effect on BABIP and extra-base hits.

ZiPS doesn’t discard actual stats; the models all improve from knowing the actual numbers in addition to the zStats. You can read more on how zStats relate to actual stats here. For those curious about the r-squared values between zStats and real stats for the offensive components, it’s 0.59 for zBABIP, 0.86 for strikeouts, 0.83 for walks, and 0.78 for homers. Those relationships are what make these stats useful for predicting the future. If you can explain 78% of the variance in home run rate between hitters with no information about how many homers they actually hit, you’ve answered a lot of the riddle. All of these numbers correlate better than the actual numbers with future numbers, though a model that uses both zStats and actual ones, as the full model of ZiPS does, is superior to either by themselves. Read the rest of this entry »

One of the strange things about projecting baseball players is that even results themselves are small samples. Full seasons result in specific numbers that have minimal predictive value, such as BABIP for pitchers. The predictive value isn’t literally zero — individual seasons form much of the basis of projections, whether math-y ones like ZiPS or simply our personal opinions on how good a player is — but we have to develop tools that improve our ability to explain some of these stats. It’s not enough to know that the number of home runs allowed by a pitcher is volatile; we need to know how and why pitchers allow homers beyond a general sense of pitching poorly or being Jordan Lyles.

Data like that which StatCast provides gives us the ability to get at what’s more elemental, such as exit velocities and launch angles and the like — things that are in themselves more predictive than their end products (the number of homers). StatCast has its own implementation of this kind of exercise in its various “x” stats. ZiPS uses slightly different models with a similar purpose, which I’ve dubbed zStats. (I’m going to make you guess what the z stands for!) The differences in the models can be significant. For example, when talking about grounders, balls hit directly toward the second base bag became singles 48.7% of the time from 2012 to ’19, with 51.0% outs and 0.2% doubles. But grounders hit 16 degrees to the “left” of the bag only became hits 10.6% of the time over the same stretch, and toward the second base side, it was 9.8%. ZiPS uses data like sprint speed when calculating hitter BABIP, because how fast a player is has an effect on BABIP and extra-base hits.

ZiPS doesn’t discard actual stats; the models all improve from knowing the actual numbers in addition to the zStats. You can read more on how zStats relate to actual stats here. For those curious about the r-squared values between zStats and real stats for the offensive components, it’s 0.59 for zBABIP, 0.86 for strikeouts, 0.83 for walks, and 0.78 for homers. Those relationships are what make these stats useful for predicting the future. If you can explain 78% of the variance in home run rate between hitters with no information about how many homers they actually hit, you’ve answered a lot of the riddle. All of these numbers correlate better than the actual numbers with future numbers, though a model that uses both zStats and actual ones, as the full model of ZiPS does, is superior to either by themselves.

And why is this important and not just number-spinning? Knowing that changes in walk rates, home run rates, and strikeout rates stabilized far quicker than other stats was an important step forward in player valuation. That’s something that’s useful whether you work for a front office, are a hardcore fan, want to make some fantasy league moves, or even just a regular fan who is rooting for your faves. If we improve our knowledge of the basic molecular structure of a walk or a strikeout, then we can find players who are improving or struggling even more quickly, and provide better answers on why a walk rate or a strikeout rate has changed. This is useful data for me in particular because I obviously do a lot of work with projections, but I’m hoping this type of information is interesting to readers beyond that.

As with any model, the proof of the pudding is in the eating, and there are always some people that question the value of data such as these. So for this run, I’m pitting zStats against the last two months and all new data that obviously could not have been used in the model without a time machine to see how the zStats did compared to reality. I’m not going to do a whole post for this every time, but this is something that, based on the feedback from the last post in June, people really wanted to see the results for.

Starting with zBABIP, let’s look at how the numbers have shaken out for the leaders and trailers from back in June. I didn’t include players with fewer than 100 plate appearances over the last two months. Read the rest of this entry »

Well, that’s the end of that. The trade deadline has come and gone, and whatever teams have is, well, whatever they’re going to have for the stretch run. My colleague Ben Clemens has already done the traditional look at the winners and losers of the deadline, so now it’s the turn of ZiPS, as I do every year afterwards. This is a very targeted look, in that ZiPS isn’t really looking at whether teams did a good job on a general level, only how the deadline affects their 2023 chances. So a team like the Mets ranking at the bottom isn’t a reflection on their competence, but how the deadline changed their postseason probability.

The methodology is simple. I project the rest of the season (after the games on August 1) both with the current rosters and if I undid every single trade made in the final two weeks of the trade period. I’ve included the projections for playoffs, division, and World Series for each of the 30 teams, with the default sort being playoff probability. Read the rest of this entry »

While some of the biggest names available did not find new homes on Tuesday, a whole lot of relievers are wearing new duds. So let’s get down to business.

The New York Yankees acquired pitcher Keynan Middleton for pitcher Juan Carela

With all the relief trades made by the White Sox, Middleton must have felt a bit like the last kid taken in gym class this weekend. This has been the year he’s put it all together, thanks to a much-improved changeup that has become his money pitch, resulting in hitters no longer simply waiting around to crush his fairly ordinary fastball. He’s a free agent after the season and certainly not meriting a qualifying offer, so the Sox were right to get what they could.

I’m mostly confused about this from the Yankees’ standpoint. He does upgrade the bullpen, which ranks below average in our depth charts for the first time I can recall. But unless they really like him and hope to lock him up to a contract before he hits free agency, I’m not sure what the Bombers get out of tinkering with their bullpen a little when the far more pressing problems in the lineup and rotation went unaddressed. As for Carela, he’s been solid in High-A ball this year, but he really ought to be as a repeater. Just how much of a lottery ticket he is won’t be better known until we see if he can continue his improvement against a better quality of hitter. Read the rest of this entry »

About 12 hours after Bo Bichette left Monday night’s game with a right knee injury, the Blue Jays found his possible replacement, acquiring shortstop Paul DeJong and cash considerations from the Cardinals on Tuesday afternoon for minor league reliever Matt Svanson.

DeJong being relevant again might make you think that you’ve warped back to pre-2020 days, but hang on before you load up on masks and toilet paper; he’s actually had a nice little 2023 season. I don’t think anyone would argue with me if I said the Cardinals were having a season with a stunning lack of pleasant surprises, but DeJong’s year has been one of those rarities. While a triple-slash of .233/.297/.412 won’t get you to many All-Star Games, it’s a much prettier line than his .196/.280/.351 collapse from 2020 to ’22, reaching a nadir with a ’22 season in which DeJong needed a telescope just to see the Mendoza line. He still plays solid defense at shortstop, and his bat has rebounded enough that there’s once again a significant role for him on a major league roster. The only reason he was even in St. Louis this year was the six-year contract he signed before the 2018 season.

The #BlueJays are sending minor-league reliever Matt Svanson, 24, in return for Paul DeJong and cash considerations. DeJong, who's in the final year of a six-year, $26 million contract, earns $9M this year with a $12.5M option or $2M buyout in 2024.

— Bob Nightengale (@BNightengale) August 1, 2023

While this piece has the “2023 trade deadline tag,” that’s actually kind of a lie; this is a regular ol’ injury replacement trade that just happened to coincide with the deadline. The full extent of Bichette’s injury probably won’t be known before the trade market closes up shop for the fall, so the Jays had to act quickly unless they wanted to try to replace him in-house. And while they had options in the organization, all of them had at least one seriously concerning issue. Santiago Espinal is more an emergency shortstop than a starter, and Cavan Biggio only has two professional innings at short. And neither is providing enough offense to make you want to take that defensive risk. In the minors, Addison Barger has suffered through elbow problems this season and isn’t a natural shortstop either, and Orelvis Martinez just debuted at Triple-A. Ernie Clement turning into a weird plate discipline deity is interesting, but more for a bad team in search of an upside play, not a team in a tight, crowded pennant race that needs some certainty.

DeJong’s options are now a little more complicated. At $12.5 million and $15 million, I imagine the Cardinals would have declined them as they did with Kolten Wong after a solid 2020 season. He has a $2 million buyout for 2024, and there may be a scenario in which the Jays pick it up if Bichette’s injury turns out to be fairly serious one. Is $10.5 million (since $2 million is baked into the cake either way) minus whatever the Cardinals are sending with him not worth it for them to keep DeJong as the starting shortstop for an undetermined part of next season before making him a utility infielder?

There was no reason to expect the Cardinals to get a haul of prospects for DeJong. Svanson is having a bit of a breakout season in the minors as a reliever, but you can’t ignore the fact that it’s as a 24-year-old in High-A ball. My colleague Eric Longenhagen has his two-seamer at 92–94 mph and his slider at 84, and that he would be an honorable mention in the Jays prospect list but would not make the main rankings. ZiPS turns Svanson’s 1.23 ERA/2.55 FIP into a 4.30 ERA translation with the Cardinals in 2023 because, well, 24-year-old in High-A ball. But if he advances quickly — and he will need to in order to have any kind of career in the majors — he could show up at the back of the big league bullpen.

Toronto would much prefer that Bichette’s knee is a minor issue that resolves quickly, but they’ve rightly formulated a solid Plan B here. There’s literally no time like the present.

*****BREAKING NEWS UPDATE EMERGENCY SIREN JAZZ HANDS*****

There’s no serious damage to Bichette’s knee, though it’s not determined whether or not he will need to head onto the IL. At the very least, we might see Bichette play more DH as they work him back into the lineup, whenever it is, and DeJong still remains a quality Plan B in the case of a setback. Bichette’s injury not being a serious one does reduce the chance that the Blue Jays will pick up one of DeJong’s options.

As everyone has seen by now, an MRI showed there is no structural damage in Bo Bichette’s right knee, per #BlueJays manager John Schneider, who described it as best case scenarios. Bichette isn’t in the lineup today but he’s day to day. Still determining if an IL stint is needed

— Kaitlyn McGrath (@kaitlyncmcgrath) August 1, 2023

*****END COMMUNIQUE*****