Ben Lindbergh and Meg Rowley banter about the latest lima-bean-related revelations about Tarik Skubal’s elbow surgery, the Giants’ censored synchronized thrust and the history of homoerotic celebrations in baseball, Coby Mayo’s dog’s devotion, the Orioles’ rotation issues (and a Rico Garcia update), Atlanta’s unexpectedly productive DHs, Ben’s invisible baseball neighbor, and Konnor Griffin’s glow-up, plus (1:12:33) a check-in on the hitters and pitchers who’ve over- or underperformed their projections the most through the first quarter of the regular season, and the players who’ve made cases to become new MLB main characters.
This is an analysis I’ve wanted to do for a while. It’s not that important or complicated, and most of it is fairly obvious. But it gets at something that comes up from time to time in the various places baseball is discussed online. The conversation tends to start like this: Team A should sign Player X and move him to a new position. Inevitably, one of the first questions asked about such a plan is whether Player X has the arm strength to play that new position.
The number that gets cited to “yay” or “nay” such a follow-up is arm strength, in miles per hour. But ask any baseball fan to sit with this for a moment, and they’ll raise a concern. Arm strength, to some degree, is a function of position. A third baseman has a longer throw to make than a second baseman. A right fielder has a longer throw to make than a left fielder. This means players with better arms tend to play those positions, as we can see in this plot:
Yandy Díaz was in the news last week when he recorded his 1,000 career hit, making him the 20th Cuban-born MLB player to reach that milestone. The plaudits he received were well deserved, and they were also relatively uncommon. Playing in a lower-profile market, the Tampa Bay Rays stalwart flies under the national radar. Be honest. Outside of when he captured the American League batting title with a .330 average in 2023, when was the last time you paid more than a modicum of attention to the player who is arguably the top hitter in Rays franchise history?
You’re excused if you weren’t aware of just how good Díaz’s numbers are. Now in his eighth season with Tampa Bay after parts of two in Cleveland, he boasts a 133 wRC+ with the Rays, the highest in team annals among hitters with at least 1,000 plate appearances. Over 3,627 plate appearances with his current club, the 34-year-old corner infielder/DH has a .291/.373/.447 slash line and 104 home runs.
Díaz was admirably humble when asked about his milestone the following day.
“I never thought I’d get to 500, let alone 1,000,” he told reporters. “When I signed with Cleveland, I honestly never really thought I was going to get to the major league team. I thought, yeah, I was going to be a professional, but maybe I was going to get cut — specifically because it’s a different style of play over in Cuba. I thank God that I made the team and have been able to do it for so long.”
I asked the Sagua la Grande native how much he’s changed — and how much he really hasn’t changed — since coming stateside to play professionally in 2013. Read the rest of this entry »
It used to be said that old guys couldn’t stick at shortstop.
“You know, it’s kind of like a running back after 30 [years old],” then-Rangers manager Chris Woodwardsaid back in 2020, marveling at the defensive longevity of Elvis Andrus. “Shortstops after 30? There’s not too many of them.”
As of late, though, something has changed. Here is a plot showing the number of primary shortstops qualifying for the batting title in their age-30 season or older. In 2026, there are nine pacing to qualify, almost touching the previous peak in 2014: Read the rest of this entry »
Since the start of the year, I’ve been watching Jordan Walker mash the ball as I try to figure out something to say about it. As a card-carrying Walker booster – I’ve got a Top 50 Trade Value ranking to prove it – I’m very willing to believe in Walker’s promise. But as a sometime Cardinals fan – being a professional baseball writer makes fandom complicated – I’m afraid of getting burned. Walker has already gone from one of the most heralded prospects in the game to one of its worst-performing full-time players. Now he’s one of the best-performing players? Being a little skeptical is just a matter of self-preservation.
Now that we’re a month and a half into the season, though, I can’t keep myself from investigating. Walker hasn’t had stretches this productive since his rookie year. He hasn’t had stretches where he’s hit the ball on the ground this rarely as a major leaguer, period. He’s been 14.5 runs above average offensively in 2026 – after being 13 runs below average offensively for his entire career before now. If that isn’t screaming for an article, I don’t know what is.
If you know two things about Walker, they’re probably these: He swings hard, and he can’t get the ball off the ground. That makes it easy to think through how he might improve: keep swinging hard and stop hitting it on the ground. When I designed the Squared-Up Explorer for the FanGraphs Lab, Walker was actually one of my favorite examples to use. Look at where his best swings are, compared to another guy who swings very hard:
The bubble size represents frequency, and being further right means more squared-up contact. Before 2026, Walker squared up the ball most frequently on grounders, and he hit a ton of them. For his part, Judge isn’t squaring the ball up every time he hits it or anything, but he’s following a simple recipe. He swings really hard, he gets the ball in the air a lot, and then he profits. The harder you swing, the more valuable hitting the ball flush becomes; Judge doesn’t need to hit it pure every time to clobber dingers at a historic rate. Read the rest of this entry »
Spencer Arrighetti has twice been featured here at FanGraphs in standalone fashion, yours truly having interviewed the 26-year-old Houston Astros right-hander in April and August of his 2024 rookie season. On both occasions, he displayed an impressive knowledge of pitching analytics, as well as a thoughtful overall approach to his craft.
Our third conversation ended up focusing on his curveball. Arrighetti has been throwing the pitch at 31.4% clip this season, and not only has it been his most-used offering, it has been highly effective. As of this writing, it has yielded a .121 batting average and a .151 slugging percentage while eliciting a hefty 50.9% whiff rate. Arrighetti, who took the mound just seven times last season due to a fractured thumb and then right elbow inflammation, has made five starts this year to the tune of a 4-1 record and a 1.88 ERA over 28 2/3 innings. I spoke with him about his curve at Fenway Park earlier this month.
———
David Laurila: You’re throwing a lot more curveballs than in years past. Why is that?
Spencer Arrighetti: “Before I got hurt, it was a top-10 curveball in baseball. That makes me feel confident to throw it to whomever, and at any time in the count. Having a pitch like that goes a long way, especially as a starting pitcher. I’ve just leaned into it a little more this year. In the past, I had the thought process that to get a chase or a whiff on a curveball, you had to set it up with a fastball — something harder in the zone — in order to make a hitter be early on it, or to be off of the shape. I’ve kind of found that there are guys that I can just spam it to. I can throw it as many times as I want, in the zone, out of the zone, and get good results. Read the rest of this entry »
In a world defined and cheapened by soulless and repetitive optimization, the Cleveland Guardians are intractably themselves. Make no mistake, the Guardians’ quirks and foibles are the result of those same nihilistic capitalist forces; they’re trying to compete against teams with less-tightfisted owners in more fashionable locales. Those restrictions have shaped the Guardians into something gnarled and odd and occasionally unsightly, like a knotted tree sprouting from a rockface, or a squid that’s evolved to live in darkness 10,000 feet below the ocean surface.
It’s not always traditionally pretty, but it’s unique.
Here we are, in the middle of May, with Cleveland once again in sole possession of first place in the AL Central. (Don’t look at anyone’s record within the division, I’m making a point.) Not everything has gone smoothly for the Guardians so far this year, but they’re getting contributions where it counts. Especially from Parker Messick. Read the rest of this entry »
A few weeks ago, I presented some in-depth research on the size of the 2026 strike zone. The results were clear and unambiguous: The called strike zone is smaller this year than it was last year, and most of that shrinking is coming at the top of the zone. But saying that the strike zone is smaller is different than saying that the smaller zone is causing the overall major league walk rate to increase, and walks are up by a lot this season. Last year, batters walked in 8.4% of their plate appearances. This year, through May 8, they’ve walked in 9.5% of plate appearances. Still, walk rates move around all the time for reasons unrelated to the strike zone. That meant I had another question to answer: Are the walks coming from the smaller strike zone, or are they coming from something else?
First, I decided to look for which counts have had the greatest impact on the increase in walks. To do so, I used a technique called Markov chain decomposition. Think of each plate appearance as falling through a Plinko board. Every plate appearance starts at 0-0, and then it progresses in one of four ways: ball, strike, ball in play, or hit-by-pitch. Ball in play and hit-by-pitch results end the plate appearance, of course, but ball and strike outcomes on 0-0 feed into other buckets: 1-0 and 0-1 counts. In each of those counts, the same thing happens, with the next pitch resulting in either a ball, strike, ball in play, or hit-by-pitch. That keeps happening – with foul balls behaving like do-overs in two-strike counts – until you get to three strikes, four balls, a ball in play, or a hit-by-pitch. The reason that this is helpful is because you can start with small events – balls, strikes, balls in play – and build bigger outcomes, like walks and strikeouts. In that way, you can use per-pitch results to learn things about per-plate-appearance results.
That’s a Markov chain. To figure out how much each count’s changing results are contributing to the change in walk rate, we need to do a little decomposition, which means that another example is in order. Imagine a 2-2 count. Next, imagine that the only possible results are ball and strike. Further, imagine that there’s a two-thirds chance of a ball on 2-2, and a 50% chance of a ball on 3-2. You can work out the odds of a walk – one-in-three – and the odds of a strikeout – two-in-three – from those numbers. Now, let’s imagine a world where the walk rate balloons from 33% to 40%.
How can that happen? One of two ways: batters reaching 3-2 more frequently, or batters walking more frequently when they reach 3-2 counts. If 2-2 pitches go from being balls two thirds of the time to being balls 80% of the time, the walk rate would hit 40% without anything at all changing in 3-2 counts. Likewise, if 3-2 pitches go from being balls half the time to being balls 60% of the time, the walk rate would hit 40% without anything at all changing in 2-2 counts. In both of those scenarios, the walk rate goes up by the same amount, but in each case, the change in walk rate can be directly attributed to changing behaviors in a given count. As the likelihood of each individual result in each count varies, a Markov chain can calculate how much that affects the overall results.
In real life, the decomposition is a bit more complex, because there are more intermediate states and more outcomes, and because the results in each count are all changing at once. But that’s really just a matter of more math; it doesn’t alter the core concept. That means that you can look at a change in walk rate between two years and break down which counts are contributing to it the most. I did just that. I took every pitch from the 2025 and 2026 seasons and used them to create Markov chains. Then I decomposed them by count to see what’s going on with more granularity:
Contribution To Change In Walk Rate, 2025-2026
Count
Contribution To Walk Rate Change
3-2
0.23%
3-1
0.18%
2-0
0.18%
0-0
0.15%
1-0
0.13%
2-2
0.07%
1-1
0.06%
2-1
0.06%
3-0
0.04%
0-1
0%
0-2
-0.02%
1-2
-0.04%
Note: Markov chain decomposition of change in walk rate attributable to each count, full-season 2025 and 2026 data
There’s an easy story here. Walks aren’t increasing because hitters are recovering from disadvantageous counts more frequently. Walks are increasing because when hitters get ahead in the count, they’re turning that advantage into a walk more frequently. The biggest contributing count is 3-2, with 2-0 and 3-1 close behind. It’s interesting to see 0-0 in the mix, but I think it’s very notable that four of the five counts that are contributing most to the higher walk rate feature more balls than strikes. The only reason 3-0 isn’t on that list is because the count hits 3-0 fairly rarely; it can’t contribute much.
Digging into why results in each count are changing requires leaving our Markov chain behind. If you compare 3-2 counts from 2025 and 3-2 counts in 2026, balls are happening 1.4 percentage points more often. Strikes are happening about one percentage point less often (the reason these don’t match the per-plate appearance results is that foul balls lead to a redo). But that doesn’t tell us why we’re getting more balls. To learn more, we’ll have to start integrating pitch location and batter behavior.
I’d say we should start with zone rate, but we run into a problem right away: “Zone rate” doesn’t mean the same thing anymore. There’s a new strike zone in town. And even putting aside the fact that the zone is being called more tightly, the zones listed by Statcast on each pitch have changed. I did a quick test: I took all the batters who have appeared in both 2025 and 2026, and measured the change in the listed height of their strike zone in those two years. If you weight it by the number of pitches that they faced in 2025, the aggregate league-wide strike zone, as defined by ABS, is about three inches shorter than it was last year, with most of the decline coming at the top of the zone. Only three batters in all of baseball have taller strike zones in 2026 than in 2025.
Since zone rate is a moving target, we’ll have to measure pitch locations relative to one consistent zone. I chose to use the 2026 zone, but really, we could use either. The key here is that we have to make sure we’re comparing apples to apples, as it were. That’s because we need to distinguish between two effects: pitchers throwing to the same place but getting called balls where they used to get called strikes, and pitchers throwing to less central locations.
I broke up the strike zone into 14 regions. There are four “just inside the zone” regions, four “just outside the zone in one direction” regions, four “just outside the zone, on the corner” regions, and then the heart of the zone and far from the zone. Using a consistent zone, pitchers are throwing the ball outside the strike zone slightly more often in 3-2 counts this year:
3-2 Pitches By Location, 2025 vs. 2026
Region
2025 Pitch%
2026 Pitch%
Change
Heart
49.25%
49.03%
-0.22%
Top Edge In
1.68%
1.40%
-0.28%
Bottom Edge In
1.75%
2.09%
0.33%
Inside Edge In
1.64%
1.53%
-0.11%
Outside Edge In
1.80%
1.64%
-0.16%
Just Above
1.52%
1.19%
-0.33%
Just Below
1.53%
1.56%
0.03%
Just Inside
1.63%
1.41%
-0.22%
Just Outside
1.69%
1.38%
-0.30%
Up In Corner, Outside Zone
0.05%
0.07%
0.02%
Up Away Corner, Outside Zone
0.03%
0.01%
-0.01%
Down In Corner, Outside Zone
0.04%
0.06%
0.02%
Down Away Corner, Outside Zone
0.09%
0.08%
-0.01%
Far Outside
37.32%
38.56%
1.24%
Note: Consistent strike zone defined based on player height, and applied to both 2025 and 2026.
For the record, “far outside” is defined here as far enough out of the regulation zone that a take will almost never lead to a called strike. I chose one inch as the cutoff for the size of my “just inside” and “just outside” zones, which worked fairly well to differentiate between close calls and easy ones. In 2025, only 2.3% of taken pitches in the “far outside” zone were called strikes. In 2026, only 0.8% of them have been called strikes, out of a sample of more than 3,000 pitches.
Not every one of those “far outside” pitches gets taken, of course. Here are swing rates in each region on 3-2 pitches in 2025 and 2026:
3-2 Pitch Swing Rate, 2025 vs. 2026
Region
2025 Swing%
2026 Swing%
Change
Heart
90.89%
90.52%
-0.36%
Top Edge In
86.13%
84.30%
-1.83%
Bottom Edge In
70.53%
72.93%
2.39%
Inside Edge In
78.19%
76.69%
-1.50%
Outside Edge In
77.34%
80.28%
2.94%
Just Above
83.65%
79.61%
-4.04%
Just Below
67.44%
78.52%
11.08%
Just Inside
69.84%
69.67%
-0.17%
Just Outside
74.14%
68.33%
-5.80%
Up In Corner, Outside Zone
75.00%
100.00%
25.00%
Up Away Corner, Outside Zone
75.00%
0.00%
-75.00%
Down In Corner, Outside Zone
63.64%
40.00%
-23.64%
Down Away Corner, Outside Zone
51.85%
28.57%
-23.28%
Far Outside
41.92%
40.37%
-1.55%
Note: Consistent strike zone defined based on player height, and applied to both 2025 and 2026.
You don’t have to worry too much about the changes in swing rates on corner pitches, because pitchers have only hit the corners a combined 19 times in our 2026 sample. It’s just not a very frequent area of attack on 3-2 – and really, we’re talking about hitting one-square-inch targets, so it’s not a very frequent area of attack generally.
I performed a more complete analysis by working out how many pitches batters took in each region in 2025 and 2026, accounting for both changing pitcher behavior (where they locate the ball) and batter behavior (how often they swing). In 2025, 23.4% of 3-2 pitches resulted in hitters taking a pitch that was located outside the consistent strike zone we defined. In 2026, 24.5% of pitches have resulted in hitters taking a pitch located outside the consistent strike zone. That adds to the rate of called balls, but not by 1.1 percentage points. That’s because not every pitch outside of the strike zone is called a ball, and vice versa:
3-2 Called Strike Rate, 2025 vs. 2026
Region
2025 Called Strike Rate
2026 Called Strike Rate
Change
Heart
93.88%
95.04%
1.15%
Top Edge In
63.89%
52.63%
-11.26%
Bottom Edge In
42.50%
77.55%
35.05%
Inside Edge In
58.56%
80.65%
22.09%
Outside Edge In
69.05%
78.57%
9.52%
Just Above
53.25%
14.29%
-38.96%
Just Below
34.42%
13.79%
-20.62%
Just Inside
32.89%
10.81%
-22.08%
Just Outside
43.70%
15.79%
-27.91%
Up In Corner, Outside Zone
25.00%
0.00%
-25.00%
Up Away Corner, Outside Zone
0.00%
0.00%
0.00%
Down In Corner, Outside Zone
0.00%
0.00%
0.00%
Down Away Corner, Outside Zone
0.00%
0.00%
0.00%
Far Outside
2.28%
0.80%
-1.48%
Note: Consistent strike zone defined based on player height, and applied to both 2025 and 2026.
That’s right: The areas at the fringes of the strike zone are being called differently. It’s not so much that the areas where strikes are most frequently called have moved (with the exception of the area just above the top of the zone, which as previously noted, is where the zone is shrinking). The difference is that balls outside the zone are being called strikes less frequently than before, while balls inside the zone are being called strikes more frequently than before.
Let’s set aside the top of the zone for a moment. On the other three edges, the transition from 2025’s all-umpire strike zone to the 2026 challenge/umpire hybrid zone has been, well, striking. Balls that are just barely in the strike zone on those three edges were called strikes 56.7% of the time in 2025; they’re being called strikes 76.7% of the time in 2026. Balls just off those three edges were called strikes 37% of the time in 2025; they’re being called strikes 13.5% of the time in 2026. In other words, the strike zone is getting less fuzzy. The shape is only changing at the top, but the number of incorrect calls in a given area is declining across the board.
How does that lead to an increase in walk rate? It’s a neat little mathematical relationship. The closer a pitch is to the center of the strike zone, the more likely a batter is to swing, particularly in two-strike counts. That means that an increase in accuracy across the board will add more balls than strikes, because there will be more takes, and thus more chances for the umpire to call a ball or strike, on pitches located outside of the strike zone.
Take the example we just used. Swing rates on the inside, outside, and bottom edges of the zone – but still in the zone – hover around 75%. Swing rates on pitches just off those edges are around 70%. That’s a small but non-negligible effect from a one-inch difference in location – and it’s bigger in counts that don’t feature two strikes, where swinging at a ball in the strike zone is optional. There’s an even bigger difference between pitches over the heart of the plate and pitches outside the strike zone. Centrally-located pitches are being called strikes 1.2 percentage points more frequently in 2026 than they were in 2025, while pitches far outside the zone are being called strikes 1.5 percentage points less frequently. But batters swing at 90% of the strikes and only 40% of the balls, so the net effect is that improving ball/strike accuracy in three-ball counts leads to more walks.
There are three effects driving the change in outcomes on 3-2 counts this year: pitcher/batter behavior, a change in the definition of the top of the strike zone, and increased call accuracy. I mathematically decomposed those into three parts using a simple test. First, I calculated what the walk rate would be if we took all of the actual pitches, swings, and takes from 2026, but used the called strike rates by zone from 2025 (based on the consistent strike zone definition detailed above) for taken pitches. This explains how much the walk rate would increase merely from changes in batter/pitcher behavior with a constant strike zone. A methodological note here: I only considered pitches thrown to batters who appeared in both 2025 and 2026 so that I could standardize the size of the strike zone for our analysis. That means that the overall numbers differ slightly from league-wide rates, though the divergence is minimal.
Next, I took the relevant pitches from 2025 and used the 2026 called strike rates for the top of the strike zone and the 2025 called strike rates for the rest. That gave me the increase in walk rate you’d expect if the only change was the shape of the top of the zone. Finally, I took the relevant pitches from 2025 and the 2026 called strike rates for everywhere except the top of the zone, where I kept the 2025 rates. That gave me the increase in walk rate you’d expect from increased ball/strike accuracy. I found that you can attribute 0.9% of the increased rate of 3-2 balls to changing batter/pitcher behavior, 0.1% to changes in calls at the top of the strike zone, and 0.4% to changes in correct call frequency in the rest of the strike zone.
That analysis explains the change in 3-2 results. To understand the whole picture, I just repeated the calculation for every count. That gave me values for how much changes in batter/pitcher behavior, changes at the top of the strike zone, and increased call accuracy changed the rate of balls and strikes in each count so far this year. Then, to complete the circle, I fed this data back into our Markov chain from above; I ran hypothetical Markov chains for each of the three effects independently, which let me calculate the change in overall walk rate attributable to each.
In the aggregate, you can split the change in walk rate into three parts. One is a change in pitcher/batter behavior. This covers changes in where pitchers locate, how frequently batters swing in each location, how frequently they make contact, and how frequently that contact is fair. Those changes have added 0.5 percentage points to the overall walk rate. Next, changes in the shape of the top of the strike zone have added 0.2 percentage points. Finally, an increase in the accuracy of calls has added 0.4 percentage points to the overall walk rate. That’s the headline finding of this study: Walks are increasing for three different reasons, all working in concert.
The next question I had was how much of that increased accuracy is due to challenges – not the overall challenge system, but specifically the pitches that players have challenged and in some cases overturned. There’s an easy way to test this: I just told my computer to take the original umpire calls instead of the final calls. The results are both interesting and intuitive: ABS challenges themselves have actually decreased the walk rate. That’s not surprising – more balls have been overturned into strikes than the reverse – but it sounds funny when you say it out loud. MLB switched to an ABS challenge system this year, and the direct effect of that system is slightly decreasing walk rates. Also, walk rates have increased by a striking amount, and more than half of that is attributable to changes in the way that balls and strikes are called, which appears to be an indirect effect of the ABS challenge system. Isn’t that weird?
Finally, I performed some analysis to ensure that my findings are robust. I varied the sizes of the slices I used to define the various zones in this analysis. Regardless of how large or small I made those slices, the contribution of pitcher and batter behavior to walk rate was stable at around 0.5 percentage points. But the relative contributions of the top of the zone and of increasing accuracy changed; the larger I defined the top of the zone to be, the more effect it had. For very large definitions of “top of zone,” the effect was roughly equal in magnitude to the effect of increased accuracy. In other words, it’s difficult to disentangle exactly how much of the walk rate increase can be attributed to increased accuracy of an existing zone and how much can be attributed to a change in the size of that zone, but both factors are important, and I think it’s quite likely that the accuracy component is of slightly greater import.
So 3,000 words in, what does it all mean? This year’s strikingly high walk rate isn’t just about pitchers and batters behaving differently, and it isn’t just about the size of the strike zone. It’s both, and it’s also about umpires making calls more accurately. I think that’s why the increase appears so dramatic; lots of things are all changing at once, and they all happen to be changing in the same direction.
This isn’t a stable equilibrium. Both pitchers and batters will continue to adjust to the new way that balls and strikes are being called. Batters are swinging less frequently this year, and pitchers will likely adjust to that by throwing in the strike zone more frequently. Now that the rewards to fishing off the edges have declined thanks to an increase in call accuracy, attacking the zone is being rewarded even further. And batters don’t have to take those potential changes lying down. If pitchers start throwing in the zone more frequently, batters will likely increase their aggression.
I’m not sure where walk rate is headed. But I do feel confident in saying that plenty of this year’s increase comes down to a change in the way balls and strikes are called. I also feel confident that a majority of that effect is about the increased accuracy of calls rather than a change in the size of the strike zone. Finally, challenges themselves aren’t contributing to this change; taken in isolation, they’ve actually decreased walk rate.
As is customary, I’ve included the dataset and Python code used to generate these results here. The study can also be expanded to previous years or run on different data; in fact, I couldn’t upload the 2025 data to GitHub for size reasons, so you’ll need to download that yourself. You can also replace those with your own similarly-formatted data if you’re interested in expanding the analysis.
The current standings between the two leagues are quite lopsided entering the second full week of May. Just three teams in the American League have winning records, while all five clubs in the NL Central are above .500. Then again, that division, which was not supposed to be all that strong entering the season, is doing a lot to prop up the National League as a whole; seven of the remaining 10 teams in the Senior Circuit have losing records.
Our power rankings use a modified Elo rating system. If you’re familiar with chess rankings or FiveThirtyEight’s defunct sports section, you’ll know that Elo is an elegant ranking format that measures teams’ relative strength and is very reactive to recent performance. To avoid overweighting recent results during the season, we weigh each team’s raw Elo rank using our coin flip playoff odds. (Specifically, we regress the playoff odds by 50% and weigh those against the raw Elo ranking, increasing in weight as the season progresses to a maximum of 25%.) The weighted Elo ranks are then displayed as “Power Score” in the tables below. As the best and worst teams sort themselves out between now and October, they’ll filter to the top and bottom of the rankings, while the exercise remains reactive to hot streaks and cold snaps. If you’re looking for a visual representation of the ups and downs of your team throughout the season, look no further than the brand new Power Rankings Board in the FanGraphs Lab.
First up are the full rankings, presented in a sortable table. Below that, I’ve grouped the teams into tiers with comments on a handful of clubs. You’ll notice that the official ordinal rankings don’t always match the tiers — there are times where I take editorial liberties when grouping teams together — but generally, the ordering is consistent. One thing to note: The playoff odds listed in the tables below are our standard Depth Charts odds, not the coin flip odds that are used in the ranking formula. Read the rest of this entry »
Sponsor Us on Patreon
Give a Gift Subscription
Email Us: podcast@fangraphs.com
Effectively Wild Subreddit
Effectively Wild Wiki
Apple Podcasts Feed 
Spotify Feed
YouTube Playlist
Facebook Group
Bluesky Account
Twitter Account
Get Our Merch!
