The Los Angeles Angels can’t count clubhouse chemistry as the primary reason they entered Memorial Day weekend with the third most wins in the American League. Marquee players such as Mike Trout, Shohei Ohtani, and Anthony Rendon — not to mention Patrick Sandoval and Taylor Ward — bear a far larger responsibility for the club’s success. Which doesn’t mean that intangibles haven’t mattered. In the opinion of Angels GM Perry Minasian, they’ve actually mattered a lot.
Asked about his approach for building a winning team, Minasian responded with a rhetorical: “Do we have enough time for this?” The 42-year-old baseball lifer then proceeded to champion the value of non-quantifiable characteristics.
“Philosophically, we’re not only trying to get talented players,” Minasian told me when the Angels visited Fenway Park earlier this month. “We’re trying to get the right DNA, the right mix of guys from a makeup standpoint. That’s really important to me. Growing up around the game — I’ve been fortunate to spend a lot of time in big-league clubhouses — I really believe in mix and fit. It’s hard to quantify, but I think it has a huge impact. The room makes a big difference.”
So too does on-field talent. Identifying it — ditto projecting it — will always be an integral part of a general manager’s job. Minasian knows that as well as anyone. Read the rest of this entry »
Ben Lindbergh and Meg Rowley answer listener emails about why we don’t see more hidden-ball tricks in MLB, why big leaguers practice fielding grounders between innings, comparing the careers of Paul Goldschmidt and Freddie Freeman, how baseball broadcasts should discuss domestic-violence suspensions, whether Rickey Henderson would still have stolen bases if he’d been inflicted with Glass Ass Syndrome, and whether teams could get more favorable calls by riding umpires even harder than they already do, then (58:01) field “How can you not be pedantic about baseball?” questions about hometown discounts for players who aren’t native to a town, who qualifies as a “phenom,” and pitches “on the corner,” followed by (1:08:53) Stat Blasts about pitchers who’ve lost to every current franchise and how patterns of starter wins and losses have changed, how often game times exactly match game start times, and whether Roger Angell was right about a bet in 1975.
An old friend returned to the big leagues yesterday in a relatively unexpected place. After 11 seasons in the majors, all with the Cardinals, Matt Carpenter found himself searching for a new team this winter; in the end, he only managed to snag a minor league deal with the Rangers. Now, though, he’s found himself in New York with the Yankees, signing a major league deal with them that was announced on Thursday and hitting eighth in the starting lineup for their game against the Rays that same day. Does Carpenter have a second wind, or has too much time passed since he was an effective contributor?
That Carpenter found little interest in his services this winter was hardly surprising. Some players age gracefully, but he fell off a cliff after the 2018 season, dropping from a wRC+ of 140 to 96. If that had been the extent of his collapse, he’d still have a role in the majors; he still managed to collect 1.7 WAR in 492 plate appearances in 2019, thanks to not being awful at second or third base. But after hitting .176/.313/.291 combined over 2020 and ’21, even that saving grace didn’t provide quite enough grace. What rope remained after the COVID-shortened 2020 rapidly ran out of slack the following year, and his role was reduced to the extent that only 11 of his 53 games after the All-Star break were as a starter.
It strikes me as likely that Carpenter only survived on the roster because of his long history with the franchise; infielders who can’t hit are a dime a dozen, and he didn’t have an exploitable platoon split advantage the way a steeply declining Albert Pujols did. And while much has been made of Carpenter’s struggles against the shift, and while he’s been worse throughout his career relative to a traditional infield configuration, it’s not sufficient to explain the collapse. He hit just fine overall as the approach against him shifted (no pun intended) yearly toward all-shift after 2015; by the time 2018 rolled around, when he was still a dangerous offensive player, he was almost exclusively hitting against a stacked right side of the infield. Read the rest of this entry »
Shane Bieber has been one of the best pitchers in baseball in recent years. From 2019-21, he used pristine command and nasty breaking balls to rack up elite levels of strikeouts while posting an ERA- of 64 (tied for second-best in baseball over that span); he even won the AL Cy Young Award in 2020. That elite level of performance lasted until a 2021 shoulder strain cost him over three months of the season; he returned just in time to make a couple of late September starts before officially shutting it down. Coming into the 2022 campaign, Bieber said he was “100%.” Yet through his first eight starts of the season, there are warning signs all over his underlying metrics:
Shane Bieber’s Struggles
Year
IP
K%
BB%
ERA-
FIP-
SwStr%
Barrel%
FA Velo
2019-2021
388.1
33.0%
6.0%
64
68
15.2%
7.8%
93.3
2022
45.2
24.1%
7.3%
95
83
13.2%
10.8%
91.2
Bieber has still been an effective pitcher. He has an above-average ERA and an even better FIP and SwStr%, but these numbers still represent a drop in performance. For a pitcher who is just shy of 27 years old, it’s certainly notable, but we might chalk it up to a wonky eight-start rough patch were it not for the dip in velocity and the injury last season. But Bieber has lost three ticks on his heater from his Cy Young peak, when he averaged 94.3 mph. I wonder if we might be seeing the lingering effects of his shoulder injury. Just prior to the injury, Bieber’s velocity dipped to 92 mph and during his late September cameo, it was down to 91.4 mph. Throughout the season’s early going, Bieber has been asked repeatedly about his velocity dip and insisted that he feels fine and expects his velo to trend up as the season goes on. As we reach the end of May, however, no such increase has occurred:
Regardless of the cause, it isn’t a good development. And beyond just the lost velocity, there are some interesting mechanical changes that are worth taking a closer look at. We’ll start with an establishing shot of Bieber’s mechanics in 2020 and then this season. You may be able to pick up on some changes, like how this year’s windup is quite a bit faster:
Next we’ll look at his arm path. The following clip is synced to when his front foot lands, giving us a better look at his hand position at that pivotal point in a delivery. To me, it looks like his entire upper body is more closed off, giving Bieber a more circuitous hand path from his glove separation all the way through to the release of the ball. The result is an arm that needs to play catch-up as his foot lands:
These mechanical changes could mean a number of things. It’s possible this is an intentional change to try and add velocity, or to mitigate a recurrence of his shoulder issue. Or these could be subconscious changes as Bieber’s body tries to find a pain-free path to delivering a pitch. If you want to assume that this change is part of the loss in velocity and that it’s helping him stay healthy, then it could be a worthwhile tradeoff. However, if this is a less intentional tweak and it’s the result of his body subconsciously favoring his shoulder, it could lead to added stress on other areas that are currently healthy.
Given that we’re about a quarter of the way into the season, we have a pretty good sample by which to gauge how Bieber is trying to pitch through his velocity decline. It might seem like the obvious solution to a diminished fastball would be for Bieber to lean in to his two good breaking balls. Look no further than the success of Clayton Kershaw in recent years for a blueprint to mimic. Kershaw has evolved from throwing 94 mph fastballs 60% of the time to throwing a 91 mph fastball only 36% of the time. He is now a slider-first pitcher and it has allowed him to have a tremendously successful decline phase – if you can even call his 72 ERA- over the last five seasons a decline. A Kershaw-like evolution from Bieber may not be so straightforward, however, as he’s never relied on his fastball nearly as much as Kershaw did and has always heavily featured his breaking stuff. In fact, it may come as a surprise to see that Bieber hasn’t decreased his fastball usage at all; at 38.3%, it’s pretty much right in line with his 2020 season:
Shane Bieber’s Pitch Mix
Year
Fastball
Slider
Curveball
2020
37.4%
11.6%
26.3%
2021
35.3%
25.3%
31.2%
2022
38.3%
40.9%
18.0%
Bieber’s continued use of his declining fastball has come at a cost. While his wOBA allowed on his fastball is only .364 so far this season, his xwOBA has risen somewhat ominously up to .435, compared to .320 last season and .290 in 2020. Instead of throwing fewer fastballs, Bieber has opted to change his breaking ball usage, throwing more sliders in lieu of his curveball. The problem is that his slider hasn’t been that effective for him this season (.300 wOBA, 18.8% SwStr%) after being one of the best sliders in baseball last year (.207 wOBA, 24.8 SwStr%). More than just its increased usage, the movement profile of the pitch has undergone a change as well:
Bieber is getting significantly less depth on his slider while gaining a small amount of horizontal movement. These changes come from him putting more back-spin on the pitch than in years past; previously, the spin he imparted on the ball was much closer to that of his curveball. As a result, his slider has more cutter-like qualities and he’s lost about 200 rpm from last year, leading to a pitch that is generating fewer whiffs and fewer swings outside the zone than at any other time in his career. If you look closely, you can see the subtle change in his fingers, which are pushing a bit more behind the ball, giving the pitch a touch more backspin than before:
These changes to his slider have given him a bigger movement and velocity difference between his slider and curveball, which is helping to diversify his arsenal after years of having a slider and curveball that were eerily similar. But it’s hard to say whether this is a good change, especially considering that it is now his most used pitch. His curveball, on the other hand, has been his best pitch (.201 wOBA, 16.8% SwStr%) and may be benefitting from these changes to his slider.
This new movement profile coupled with the mechanical changes highlighted earlier may be creating some issues when it comes to his typically great command of the pitch. Bieber is currently leaving double the number of sliders up in the zone compared to 2020. Check out his slider heat map and you’ll get a better sense of the frequency of his mistakes this season:
There’s been a lot of doom and gloom in this piece so far, and I would like to leave things on a more optimistic note. For that, I’ll mention Bieber’s most recent start against Detroit last Sunday afternoon. Things were a bit rocky in the beginning, with Miguel Cabrera ripping a hanging slider for an RBI double, but Bieber eventually settled in to what turned out to be a dominant performance. It was the best his slider has looked since before his injury. He found success with the pitch by consistently commanding it to the low-and-away corner, earning whiffs all afternoon — he generated 11 whiffs on 39 such pitches thrown (28.2% SwStr%) with an 84.9 mph average exit velocity. On the day, he notched a season-high 10 strikeouts over seven innings.
Of course, it’s only one start and it came against the Tigers and their league-trailing offense. Bieber’s fastball velocity didn’t tick up any, and even though his slider was great, none of the underlying metrics suggest it was all that different from what it has been in previous starts this season, still lacking the spin rate and vertical bite of years past. But that doesn’t have to mean that this start is an aberration. Bieber could be finding better ways to use his slider, and his command of the pitch could be improving as he gets more used to its new movement profile. Bieber really needs his breaking pitches to carry the weight if he’s going to remain successful with below average fastball velocity, and this outing serves as a proof of concept for how he can still dominate a lineup even if his Cy Young stuff stays firmly in the rear view mirror.
Below is an analysis of the prospects in the farm system of the Kansas City Royals. Scouting reports were compiled with information provided by industry sources as well as our own observations. This is the second year we’re delineating between two anticipated relief roles, the abbreviations for which you’ll see in the “position” column below: MIRP for multi-inning relief pitchers, and SIRP for single-inning relief pitchers.
A quick overview of what FV (Future Value) means can be found here. A much deeper overview can be found here.
All of the numbered prospects below also appear on The Board, a resource the site offers featuring sortable scouting information for every organization. It has more details than this article and integrates every team’s list so readers can compare prospects across farm systems. It can be found here. Read the rest of this entry »
Offense hasn’t been as quiet as it was in April, and bullpens have gotten a bit shaky — even before being forced to shrink (though it was announced after recording that the pitcher roster limit deadline will be pushed back a bit). Ben and Jason discuss these trends, as well as just how fun the sport has been, including Ben’s early pick for game of the year. The duo also talk about the St. Louis Cardinals, who are having fun and running wild on the basepaths while once again featuring a crop of promising rookie talent. We hear about players like Juan Yepez, Nolan Gorman, and Brendan Donovan, before yours truly asks Ben and Jason to make some fun predictions about rest of the team’s season.
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Ben Lindbergh and Meg Rowley banter about the apparent end of Mike Trout’s long-lasting SuperPretzel sponsorship, Willians Astudillo’s return to the majors, the Phillies’ fielding and a seeming surfeit of amusingly bad defensive plays across the league, the arrival of overall no. 1 prospect (and Orioles catcher) Adley Rutschman and how to tell when a prospect is really ready, the postponement of the 13-pitcher limit, the Yankees signing Matt Carpenter, different facial hair changing players’ appearances, the fallout from Josh Donaldson calling Tim Anderson “Jackie,” and Anderson’s great start to the season, plus a few followups.
Audio intro: Great Lakes, “End of an Error” Audio outro: Joel Plaskett, “Tim”
Dan Szymborski: OK, just regular chat, but adding Thunder to things always makes them more exciting
12:04
Old Hoss Radbourn: I know xStats are flawed (even more so this year because of the change in offensive environment), but they’re showing Abraham Toro getting insanely unlucky. Is there some bad luck masking a real breakout here?
12:04
Dan Szymborski: I think ther’es some bad luck
12:04
Dan Szymborski: xStat stuff is having *calibration* problems, but it should be an issue of *bias* more than accuracy really
12:05
Dan Szymborski: ZiPS stats are more predictive (with slightly differing methodology) and is similarly overexpecting results
Last week, I wrote an article about the influence of batted ball spin. The takeaways were simple: For one, even though confounding variables like temperature and wind speed are hard to eliminate, it’s entirely plausible that batted ball spin alone can subtract crucial amounts of expected distance. Also, while hitters may display a penchant for certain types of spin, they seemed to have little control over it on a daily basis. Potential inaccuracies aside, these findings made sense; hitting a baseball is hard, and batted ball spin is just another piece of the puzzle.
After the article ran, I didn’t expect to revisit this topic anytime soon. But two things inspired me to start exploring again. First, a Twitter mutual was kind enough to provide me with Trackman data of college baseball games that include — you guessed it — batted ball spin axis, which opened up multiple avenues of research. Second, Dr. Alan Nathan, a physics professor at UIUC, summarized his own findings on batted ball spin in the comments. Armed with new data and knowledge, it was time to dive back in. Read the rest of this entry »
As you’re probably aware, Apple TV+ has stepped onto the baseball broadcasting scene this year, airing two games every Friday. They’re stylistically different from your average baseball broadcast, even at a glance. The colors look different, more muted to my eyes than the average broadcast. The score bugs are sleek, the fonts understated. The announcers are mostly new faces. And most interestingly, to me at least, the broadcast displays probabilities on nearly every pitch.
As a big old math nerd, I love probabilities. They appeal to something that feels almost elemental. Every time I watch a baseball game, I wonder how likely the next hitter up is to get a hit – or to reach base, or strike out, or drive in a run. It’s not so much that I want to know the future – probabilities can’t tell you that – but I would like to know whether the outcome I’m hoping for is an uphill battle or a near-certainty, and how the ongoing struggle of pitcher against hitter changes that.
The Apple TV+ broadcasts gets those probability numbers from nVenue, a tech startup that got its start in an NBC tech accelerator. According to an interview with CEO Kelly Pracht in SportTechie, the machine learning algorithm at the heart of nVenue’s product considers 120 inputs from the field of play in making each prediction.
Machine learning, if you weren’t aware, is a fancy way of saying “regressions.” It’s more than that, of course, but at its core, machine learning takes sample data and “learns” how to make predictions from that data. Those predictions can then be applied to new, out-of-sample events. Variations in initial conditions produce different predictions, which is why you can think of it as an advanced form of regression analysis; at its most basic, changes in some set of independent variables are used to predict a response variable (or variables). Read the rest of this entry »
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Dan Szymborski
