After taking a few months off from releasing new experimental data visualization options in the FanGraphs Lab, I’m happy to announce that we’re back at it. Meet the Count Progression tool:
With the advent of the ABS challenge system, this year’s league-wide walk rate has been inthenews. The first month of the season featured a huge number of walks, and while subsequent months haven’t been quite as extreme, we’re still tracking for the highest full-season walk rate of the 21st century (2020 was weird, naturally). When I was tracking down where those extra walks were coming from back in May, I came up with the idea of looking at each count in isolation to hunt for changing behavior. This tool is an offshoot of that line of research. Read the rest of this entry »
I say this with nothing but charity in my heart: It’s generally not a good sign when your team has a load-bearing Derek Hill. When the Phillies traded for Hill in June, he was arguably less valuable than the international bonus money they picked up in the same deal.
Hill is a career fifth outfielder with a strikeout rate (31.5% in 806 career plate appearances) that would make almost any hitter unplayable, especially a hitter with a career .281 on-base percentage and a career .132 ISO. But he can run, he can play center field, and he can hit lefties a bit. Best of all — and again, I’m not trying to be unkind here — Hill could not have dreamed up a scenario in which the bar for success was lower. Read the rest of this entry »
Ben Lindbergh and Meg Rowley banter about the deepening monetary morass surrounding Mark Walter and whether it will eventually ensnare the Dodgers, the struggles of Edwin Díaz and Kyle Tucker, L.A.’s incentive to try for a bye, the Dodgers’ and Mets’ offseason oopsies, Pete Crow-Armstrong’s heroics, the hot hitting of Denae Benites and the WPBL’s offensive environment, Joshua Báez and MLB’s debutant ball, Colt Emerson’s hidden hurt, the AL’s wild card mid-off, a four-team whirlwind for Jake Rogers, the longest team trade droughts, and more.
Want to build a projection system? There are a lot of things you want to prepare for ahead of time, like fans thinking that you have a secret vendetta against their favorite team. The most practically important thing to know is that you are going to be wrong. Not just occasionally, but all the time. And assuring people afterwards that yes, 10% of players are supposed to fail to even meet their 10th-percentile projection isn’t going to get you off the hook, no matter how technically correct you are. Being wrong, however, is your opportunity to improve your model. Indeed, models learn a lot more from being wrong than being right.
When it comes to projecting the number of wins for a team, it mostly comes down to two big factors: how good the players are and who actually plays. Predicting the future is an incredibly difficult activity, and that is reflected in the variance in the probability “buckets.” ZiPS entered the season highly confident that Paul Skenes was a darn good pitcher, but even with that belief, ZiPS was only 50% sure that his 2026 ERA would fall between 2.37 and 3.20, and only 80% sure that it would land between 1.80 and 3.65. But as hard as it is to project how effective a player will be, that’s still easy compared to predicting who will play. Models can get a general idea of the likelihood of injuries from some specifics, like whether a player has missed a lot of time recently or just had Tommy John surgery. But what they don’t know, and certainly never will, is who will tweak their hamstring next Thursday and be out for the rest of the season, who will suddenly lose their velocity in their Labor Day start and need a UCL repaired, or who will be abducted by giant mutant ants and forced to toil in underground sugar caves.
So, which teams have suffered the most due to injury in 2026? If you ask fans, especially those of underperforming clubs, they’ll tell you that their favorite team has been uniquely put upon by injuries this season. But we’re numbers people here. We can do a bit better than that and see which team’s fans can justify their sad laments about the trials and tribulations of the hometown 26. Read the rest of this entry »
Tommy John spent 26 seasons pitching in the majors from 1963–74 and then 1976–89, more than any player besides Nolan Ryan, but his level of fame – and indeed, his contribution to the game — stems as much from the year that cleaves that span as it does from his work on the mound. As the first recipient of the most famous sports medicine procedure of all time, the elbow ligament replacement surgery performed by Dr. Frank Jobe in late 1974 that now bears his name, John endured an arduous year-long rehab process before returning to pitch as well as ever. His recovery gave hope to generations of injured pitchers whose careers might otherwise have ended.
By the count of analyst Jon Roegele, keeper of the Tommy John Surgery List, over 2,700 professional players have undergone some variant of Jobe’s pioneering surgery on John during either their amateur or professional careers. That list ranges from Brent Strom — who did not make it back to the majors after becoming the second recipient of Jobe’s ulnar collateral ligament reconstruction technique but went on to become a successful pitching coach — to Hunter Greene, who just underwent his second Tommy John last week. So far, only three are in the Hall of Fame, namely Paul Molitor (the first position player to undergo the surgery), John Smoltz, and Billy Wagner, but Justin Verlander, Chris Sale, and Shohei Ohtani will almost certainly follow in their wake, with more to come. By Roegele’s accounting, over 39% of all major league pitchers in 2025 had undergone Tommy John surgery.
On the mound, John was the epitome of the “crafty lefty,” so good at his vocation that he arrived on the major league scene at age 20 and made his final appearance three days after his 46th birthday. His 700 starts and 288 wins are both more than any other 20th- or 21st-century pitcher outside the Hall of Fame except Roger Clemens, but John’s longevity, his more dominant cohorts — including six 300-game winners — and the surgery that bears his name have overshadowed the rest of his on-field accomplishments. Between going 0-for-15 on BBWAA ballots (where he topped out at 31.7%) and 0-for-5 on Era Committee ballots (where he peaked at 43.8% on the 2025 Classic Baseball ballot), he never came particularly close to election, though in 2013, he got a share of the spotlight when the Hall honored Jobe. Read the rest of this entry »
Jay Jaffe: Good afternoon, folks! Welcome to another edition of my Tuesday chat. I’m just back from a trip to Salt Lake City to celebrate my father’s 85th birthday and the upcoming birthdays of my daughter and niece. While in transit, I learned that Tommy John had passed away. My tribute to him just went live moments ago: https://blogs.fangraphs.com/tommy-john-1943-2026-crafty-lefty-and-firs…
12:03
David: If you could populate the Hall of Fame with the same number of players, would there would be a 90% overlap with those who are actually enshrined? Or do you think the writers and others who have voted over the years got it right more often than 90% of the time – or less?
12:06
Jay Jaffe: Not sure about 90%, but maybe 75%. I’d put in more Negro Leagues and pre-Negro Leagues Black baseball players and strip out some of the 1930s guys who rode the Frisch/Terry Express. More starting pitchers from the 1980s to 2000s. More figures whose influence went beyond the numbers, including John Donaldson, Lefty O’Doul, Fernando Valenzuela, and yes, Tommy John (and Dr. Frank Jobe).
12:06
doink000: I don’t understand why Webb wasn’t more seriously talked about in terms of potentially being on the move. I know he got the coveted “Buster Posey Assurance,” but best case scenario is this time will be on the up and up by 2028/2029 and Webb isn’t getting any younger/more durable. As a potential number 1, top shelf number 2 on a reasonable contract, seems like he would’ve landed a pretty decent prospects haul. Or, could’ve packaged him with a salary dump like Adames and still could’ve gotten good value.
12:08
Jay Jaffe: I think some of it comes down to a perception and public relations. “Let’s get rid of these expensive acquisitions that were mistakes to acquire (Devers, Adames) and keep the homegrown guys we’ve nurtured (Webb) and that the fan base has grown to love.”
12:09
Jay Jaffe: But you’re right in that Webb has more trade value than a contract that is underwater or bordering on it
On Saturday, the Detroit Tigers pulled off a truly delightful 1-5-4 double play. A picture’s worth a thousand words, and a video even more, so I’ll save everyone a lot of work and show it to you.
If you’re reading this, and you’re old enough to have lived through the war between small ball and sabermetrics in the early part of this century, you’re probably delighted to see a sacrifice bunt go awry like this. I lived through college baseball in the early-to-mid-2010s — if there was a war over sabermetrics, college ball at this time was its Stalingrad — so that was definitely my first reaction as well. Read the rest of this entry »
There are a lot of great statistics in baseball these days. They measure everything! Swing path tilt? Contact point against center of mass? Exit velocity? Hard-hit rate on opposite field line drives? You can get a report on any of those things at the wave of a few fingers. Expected wOBA? Barrel rate? There are acronyms and stat blends and projection aggregates for days. It’s analysis paralysis; the hard part isn’t finding stats to describe hitters, but figuring out which ones not to use.
I don’t have a foolproof method for seeing through all the noise for you. But I do have a fun experiment in how to work with statistics in such an overwhelming environment. I want to suggest a method that I call, These Things Are Good. Basically, it boils down to this: Come up with some statistics that you think are good without pre-determining which players you’re looking for, measure how good the entire league is at your statistics in question, and see what shakes out.
How should we do this in practice? I started by saying that I think pulling the ball when you hit it in the air is good. That gives me the first statistic I care about: air pull rate. I turned that into standard deviations for every player in baseball so that we could compare apples to apples across multiple statistics, then started looking for who the standouts were across all categories.
Air pull rate is nice, but it’s certainly not the only statistic that I’m interested in. Next, I wanted guys who hit the ball hard when they put it in the air. After all, exit velocity matters far more when batters can elevate. I did the same thing and put that in z-score terms via standard deviations. If you’re wondering what kinds of hitters these two categories identify, Kyle Schwarber, Griffin Conine, and Junior Caminero are the top three. Read the rest of this entry »
If you want to point to an example of parity in baseball, just look at the AL Wild Card standings. There are six teams within 2.5 games of the final Wild Card berth. Yes, all six are below .500, but having that many clubs competing for one playoff spot at this point in the season should make for some very exciting baseball down the stretch.
Our power rankings use a modified Elo rating system. If you’re familiar with chess rankings, 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 »
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Jay Jaffe
