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 »

After a frantic trade deadline with an excess of buyers, a few committed sellers fielding offers, and a handful of in-betweeners on the cusp of contention, the month of August is doing its best to separate the real contenders from the rest. Ten days in, five teams — the Angels, Yankees, Guardians, Reds and Diamondbacks — have seen their playoff chances slashed by more than half, and a sixth (the Red Sox) has gone from 24.6% to 13.8%. Meanwhile, the Rangers responded to a late-July skid with an eight-game winning streak, the Cubs doubled their playoff odds with series wins over the Reds and Braves, the Phillies played their way into the top Wild Card spot in the National League, and the Mariners swept the Angels and Padres to leapfrog New York, Boston, and Los Angeles in the AL Wild Card race. It’s been quite the shuffle for a ten-day stretch.

Read the rest of this entry »

Another week, another chance to look around baseball and see something that amazes you. That’s part of what I love about the game: weird and wonderful things are always happening. As always, I noted a few that particularly tickled my fancy, and now I’m going to write a bunch of words about them in the hope that you like them too. Shout out, per usual, to Zach Lowe, who came up with this idea for a column years ago and became my favorite basketball writer as a result. Let’s get going.

1. Cavan Biggio’s Instinctual Brilliance
When the Jays’ trio of legacy-admission prospects were breaking into the majors, I was highest on Cavan Biggio relative to industry consensus. I’ve definitely been wrong in that assessment. Bo Bichette and Vladimir Guerrero Jr. turned into stars, but Biggio is more of a luxury backup. He can play a lot of defensive positions, but none of them particularly well, and he’s a league-average hitter. That’s a perfectly serviceable addition to your team, but it’s hardly going to set the league on fire, and he’s been worse than that in 2023.

But my Biggio crush is still around, and you better believe that I’m going to highlight his fun plays. Let’s set the scene: Monday night in Cleveland, a scoreless game in the top of the eighth. Biggio drew a start at second base, and with Daulton Varsho on first, he clubbed a no-doubter to dead center to give the Jays a 2–0 lead. Hey! Biggio heads, unite, he’s back in business. Read the rest of this entry »

The Toronto Blue Jays have devoted huge resources to their rotation, spending a first-round pick on Alek Manoah, doling out huge free agent contracts to Chris Bassitt and Kevin Gausman, and trading the farm for José Berríos. (And then giving Berríos a huge contract extension as well.)

But Toronto’s best starting pitcher over the past month — and in a three-way tie for the best pitcher in all of baseball, by WAR — has been Yusei Kikuchi, the guy who couldn’t stay in the rotation a year ago. Read the rest of this entry »

A large portion of every season’s prospect-related transaction activity takes place between the draft and the trade deadline, a window that, since the draft was moved to July, spans just a few weeks. We can use the way the FanGraphs farm system rankings are calculated to track movement during this period on the baseball calendar and hopefully come to more fully understand how successful rebuilds are born. Over time, we can better contextualize trade and draft hauls by using this methodology to build a historical understanding of prospect movement. Mostly though, these rankings track the depth and impact of talent in each farm system at a specific moment in time. Or, in the case of the below links and tables, four moments in time. There are some methodological caveats to pass along (I’ll get to those momentarily), as well as some very specific examples where the movement communicated in the tables below does not properly capture team activity during the last month of trades and draft signings (which I get into throughout this post).

Let’s start with some basic disclaimers. Remember that while the Craig Edwards research that facilitates this approach is empirical, my subjective player evaluations (and their resulting Future Values) feed the formula that spits out the farm rankings. Just one significant over- or under-evaluation of a player can shift the way a team lines up in these rankings pretty dramatically, especially if you’re focused on the ordinal rankings. The monetary values, in addition to providing an approximate measure and reminder of how the draft and international amateur processes suppress what these guys might earn on an open market, illustrate the ways systems are spaced and clustered with more nuance. If I’m way too light or way too heavy on any single impact prospect, I’m basically infecting a list with half a standard deviation’s worth of error in this regard because Craig’s math favors top-heavy systems rather than ones with depth. Read the rest of this entry »

Ben Lindbergh, Meg Rowley, and Patreon supporter Samuel Giddins banter about Samuel’s baseball background and history with Effectively Wild, before (9:52) discussing a Juan Soto quote and the type of disappointing team that’s most frustrating. Then (21:35) they answer listener emails about randomizing on-field decisions, the legibility of player autographs, whether teams should employ designated huggers, whether veterans are more clutch, whether we’re misusing the phrase “heating up,” what would happen if Shohei Ohtani asked the Angels to release him, whether teams in a robo-umps world should alternate tall and short hitters in the lineup, offering Ohtani an ownership stake, why we use miles per hour for pitch speeds instead of feet per second, using a matching process in the amateur draft, whether anyone could make a trade if they found a GM’s unlocked phone, mascots as zombie runners, and playoff teams without .300 hitters, plus a Future Blast (1:41:34) from 2045 and a follow-up postscript on a White Sox (un)fun fact.

Audio intro: Benny and a Million Shetland Ponies, “Effectively Wild Theme (Pedantic)”
Audio outro: Beatwriter, “Effectively Wild Theme”

Link to Samuel’s website
Link to Soto quote
Link to Soto quote source
Link to Neil Paine on the unlucky Padres
Link to The Athletic on autographs
Link to Russell on postseason experience
Link to Russell on pennant-race experience
Link to tweet about Judge/Altuve zones
Link to tweet about Judge’s zone
Link to article about Judge/Altuve zones
Link to old FG post on Judge’s zone
Link to older FG post on Judge’s zone
Link to old FG post on Altuve’s zone
Link to older FG post on Altuve’s zone
Link to EW Stanky Draft
Link to KG on player owners
Link to Goold on player owners
Link to details about Beckham’s contract
Link to list of MLB mascots
Link to data on teams without .300 hitters
Link to graph of teams without .300 hitters
Link to Ryan Nelson on Twitter
Link to EW listener emails database
Link to Rick Wilber’s website
Link to Future Blast wiki
Link to Ben Clemens on Littell
Link to tweet about the Sox
Link to Sox high-K games
Link to worst WP w/13+ K
Link to worst WP w/12+ K

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Do you remember the springtime? We were so young and carefree, so full of hope. We hadn’t even breathed in our first lungfuls of Canadian wildfire smoke. Pitchers were full of hope, too. They’d spent the whole offseason in a lab, or playing winter ball, or maybe just in a nice condo, trying to figure how to get better.

Amazingly, a lot of them settled on the exact same recipe for success: start throwing a cutter. You couldn’t open up a soon-to-be-shuttered sports section without reading an article about some pitcher whose plan for world domination hinged on whipping up a delicious new cut fastball. Now that we’re in the dog days of summer, it’s time to check and see how those cutters are coming along. Are they browning nicely and just starting to set? Or have they filled the house with smoke, bubbling over the sides of the pan and burning down to a carbonized blob that needs to be scraped off the bottom of the oven with steel wool?

I pulled data on every pitcher who has thrown at least 400 pitches in both 2022 and ’23, focusing on the ones who are throwing a cutter at least 10% of the time this year after throwing it either infrequently or not at all last season. These cutoffs did mean that we missed some interesting players like Brayan Bello and Danny Coulombe, but we’re left with a list of 25 pitchers.

So did their new toys turn them into peak Pedro? The short answer is no. Taken as a whole, they’ve performed roughly as well as they did last season. As you’d expect from any sample, roughly half our pitchers got better, and half got worse. Of the pitchers who improved from last year to this year, I don’t think I can definitively say that any of them reached new heights specifically because of the cutter. Read the rest of this entry »

How in the world can you explain a team like the Rays? There are a lot of strange and seemingly magical things going on there, but let’s focus on just their starters. They churn out top-of-the-line dudes like no one’s business. Shane McClanahan is nasty. Tyler Glasnow looks unhittable at times. Jeffrey Springs went from zero to hero and stayed there. Zach Eflin is suddenly dominant. They can’t seem to take a step without tripping over a great starter.

They’re also always hungry for more. Whether it’s bad luck, adverse selection, or something about their performance training methods, the Rays stack up pitching injuries like few teams in baseball history. Of that group I named up above, only Eflin hasn’t missed significant time in 2023, and both McClanahan and Springs are out for the rest of the year. The Rays not only have all these starters, but they also traded for Aaron Civale at the deadline, and they’re still short on arms.

They did what anyone would do: point at a random reliever in the bullpen and tell him he’s now an excellent starter. Wait, that’s not what anyone else would do? Only the Rays do that? You’re right, at least a little bit; surely you recall the Drew Rasmussen experiment from 2021. That one was a big hit until Rasmussen tore his UCL this year. 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 »