Next Thursday night, we’re going to find out who won the biggest individual honors in baseball: the Most Valuable Player awards for both the American and National Leagues, as determined by the august and esteemed voters of the Baseball Writers’ Association of America. (Cue trumpet fanfare.)
MVP awards memorialize great individual performances and bestow immense historical significance upon the players who earn them. This is the kind of thing Hall of Fame cases are built on. So you’d think the entire baseball-watching public would be glued to MLB Network or refreshing the BBWAA website on Thursday evening. But… maybe not. Aaron Judge and Shohei Ohtani are almost certain to win, and I guess it’s worth checking social media after dinner just to make sure.
There’s surprisingly little drama over awards these days; postseason betting odds on the MVP races were a little hard to come by, as most bookies have taken the issue off the board. But consider this as a measure of public sentiment: In early October, BetMGM had Judge as a 1-to-50 favorite in the AL, and Ohtani as a 1-to-100 favorite in the NL. Despite a spirited contrarian push by the pro-Fancisco Lindor camp late in the season, it’s all over but the shouting. Read the rest of this entry »
Description
The Chicago White Sox are looking for a full-stack engineer to join their Baseball Systems team. This role involves designing, developing, and maintaining custom web applications that support various aspects of our operations, including scouting, player development, biomechanics and front office decision-making. This position requires a strong focus on creating user-friendly interfaces for our custom web applications. A strong interest in baseball is a plus, but a passion for problem-solving is essential.
Key Responsibilities
Develop and maintain custom web applications
Collaborate with cross-functional teams to implement new features.
Communicate with stakeholders about technical issues and new developments.
Identify and implement process improvements.
Qualifications and Experience
Bachelor’s degree in computer science, engineering degree or commensurate experience
2+ years of professional experience as a full stack developer
Excellent verbal and written communication skills, with the ability to work effectively with multiple departments and stakeholders.
Demonstrated expertise in front-end design, with a strong eye for creating intuitive and visually appealing user interfaces.
Experience with at least one frontend framework like Vue, Svelte, React, Angular, etc
Experience with at least one backend language like Node, Python, C#, Ruby, etc
Proficient in relational database design, experienced with MySQL and PostgreSQL, and skilled in writing direct SQL queries.
Nice to Have
UI/UX design experience or fundamentals
Experience with data visualization
Experience with mobile-first design principles, ensuring applications are optimized for performance and usability on mobile devices.
Experience with DevOps tools (Git, CI/CD), containerization and orchestration tools.
Understanding of cloud infrastructure management.
Experience in a sports data environment, preferably baseball.
Things to Note
Preferred you live in Chicago but remote is an option for the right candidate.
Since you will be maintaining the custom applications used by a baseball team, you might need to work non-traditional hours to ensure tools are operational.
APPLICATION DEADLINE NOVEMBER 22, 2024
Chicago White Sox is an Equal Opportunity employer committed to a diverse workforce. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability, or any other status or characteristic protected by applicable federal, state, or local law.
Description
The Chicago White Sox are looking for a Data Engineer to join their Baseball Systems team. This role is crucial for maintaining data integrity and ensuring optimal database performance for all users. Responsibilities include importing data from external sources, integrating diverse data sets, and collaborating with the R&D department to help put the data to practical use both on the field, with coaches, and in the front office. The Data Engineer will work closely with multiple departments, gathering feedback and making recommendations for improvements. A key aspect of this role will be leveraging cloud-based systems to enhance data accessibility, scalability, and performance. Ensuring the database performs efficiently in a cloud environment is essential for the success of the White Sox Baseball Operations. A strong interest in baseball is a plus, but a passion for problem-solving is essential.
Key Responsibilities
Build and improve data pipelines for efficient data flow, ensuring databases are fast and reliable.
Ensure data quality and reduce errors.
Design and optimize database structures, ensuring they are scalable and efficient both on prem and in the cloud.
Implement best practices for cloud data management
Design and maintain cloud systems
Qualifications and Experience
Bachelor’s degree in computer science, engineering degree or commensurate experience
2+ years of professional experience with cloud platforms, data ingestion and data management
Experience in building and maintaining scalable data pipelines with the ability to integrate data from various sources using ETL tools and practices.
Excellent verbal and written communication skills, with the ability to work effectively with multiple departments and stakeholders.
Strong skills in designing and optimizing database schemas, ensuring high performance and reliability.
Proficiency with Python, SQL and cloud computing platforms (AWS, Azure, GCP)
Nice to Have
Knowledge of additional languages like C#, Node.js, R and others is a plus.
Experience with DevOps tools (Git, CI/CD), containerization and orchestration tools.
Experience with workflow management tools (Airflow, Prefect, Luigi, etc.)
Understanding of cloud infrastructure management.
Experience in a sports data environment, preferably baseball.
Things to Note
Preferred you live in Chicago but remote is an option for the right candidate.
Since you will be maintaining the data pipeline, you might need to work non-traditional hours to ensure data availability.
APPLICATION DEADLINE NOVEMBER 22, 2024
Chicago White Sox is an Equal Opportunity employer committed to a diverse workforce. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability, or any other status or characteristic protected by applicable federal, state, or local law.
“Juan Soto hates swinging.” That’s a takeaway you’re sure to hear if you follow baseball this winter. His free agency is the biggest story of the next few months, and his offensive approach drives fans to distraction. Walks aren’t all that fun, and Soto feasts on them. How could you not bring it up when your team is pursuing him for a record-breaking deal?
From a certain standpoint, it’s true that Soto hates swinging. Of the 101 batters who saw at least 1,500 pitches with zero or one strikes this past season, Soto ranked 99th in swing rate on those pitches. When he isn’t defending the plate with two strikes, he spends a ton of time with the bat on his shoulder.
That’s not a specific enough way of looking at it, though. For an example, let’s chop the strike zone up into pieces. Soto saw 675 pitches that weren’t in the strike zone or even near it – what Baseball Savant defines as the chase and waste zones. He swung at 6.5% of those, 42nd out of the 44 batters who saw 500 or more such pitches. He was almost never fooled into swinging at awful pitches, in other words.
Next consider the edges of the zone – pitches that are either barely strikes or barely balls. There aren’t a lot of good options on these pitches. Hitters don’t generally crush the ball when it’s located on the corners, unless they’re sitting on either a pitch or a location. Sure, if you’re looking high and away, you might tag it, but more likely you’ll swing and miss or make weak contact. Soto swung at 31.3% of these pitches, the second-lowest rate in baseball.
Those pitches in the chase and waste zones? You shouldn’t swing at them. There, Soto’s patience is an obvious asset. The ones on the borderline? It’s less obvious. There are great hitters who take an expansive approach to borderline pitches, like Bobby Witt Jr. and Yordan Alvarez. There are awful hitters who do it too, as you’d expect. Swinging too much at offerings we call “pitcher’s pitches” is pretty clearly not going to pan out every time.
Ben Lindbergh, Meg Rowley, and FanGraphs’ Eric Longenhagen banter about how to abbreviate Twins executive Derek Falvey’s new dual role as president of both baseball and business operations, then (5:23) discuss Japanese phenom Roki Sasaki from a scouting perspective, touching on how good he could be, how a team could ease him into MLB, what tweaks he could make to his repertoire, the bonus pool system that could help determine where he signs, and more. After that (50:16), Ben and Meg talk to NPB journalist Jim Allen about Sasaki’s posting from a Japan-based perspective, exploring why Sasaki was posted now, how Japanese fans feel about his departure, and what the implications could be for NPB. Finally (1:21:01), Ben does a bonus Stat Blast about baseball overrepresentation in crossword puzzles.
Well, it’s that time of the year again. When the last gasps of summer weather finally die and everybody starts selling pumpkin spice everything, that’s when I make the magical elves living in the oak in my backyard start cranking out the E.L.fWAR cookies. Szymborski shtick, Szymborski shtick, pop culture reference, and now, let’s run down what the ZiPS projections are, how they work, and what they mean. After all, you’re going to be seeing 30 ZiPS team articles over the next two months.
ZiPS is a computer projection system I initially developed in 2002–04. It officially went live for the public in 2005, after it had reached a level of non-craptitude I was content with. The origin of ZiPS is similar to Tom Tango’s Marcel the Monkey, coming from discussions I had in the late 1990s with Chris Dial, one of my best friends (our first interaction involved Chris calling me an expletive!) and a fellow stat nerd. ZiPS quickly evolved from its original iteration as a reasonably simple projection system, and now does a lot more and uses a lot more data than I ever envisioned it would 20 years ago. At its core, however, it’s still doing two primary tasks: estimating what the baseline expectation for a player is at the moment I hit the button, and then estimating where that player may be going using large cohorts of relatively similar players.
So why is ZiPS named ZiPS? At the time, Voros McCracken’s theories on the interaction of pitching, defense, and balls in play were fairly new, and since I wanted to integrate some of his findings, I decided the name of my system would rhyme with DIPS (defense-independent pitching statistics), with his blessing. I didn’t like SIPS, so I went with the next letter in my last name, Z. I originally named my work ZiPs as a nod to CHiPs, one of my favorite shows to watch as a kid. I mis-typed ZiPs as ZiPS when I released the projections publicly, and since my now-colleague Jay Jaffe had already reported on ZiPS for his Futility Infielder blog, I chose to just go with it. I never expected that all of this would be useful to anyone but me; if I had, I would have surely named it in less bizarre fashion.
ZiPS uses multiyear statistics, with more recent seasons weighted more heavily; in the beginning, all the statistics received the same yearly weighting, but eventually, this became more varied based on additional research. And research is a big part of ZiPS. Every year, I run hundreds of studies on various aspects of the system to determine their predictive value and better calibrate the player baselines. What started with the data available in 2002 has expanded considerably. Basic hit, velocity, and pitch data began playing a larger role starting in 2013, while data derived from Statcast has been included in recent years as I’ve gotten a handle on its predictive value and the impact of those numbers on existing models. I believe in cautious, conservative design, so data are only included once I have confidence in their improved accuracy, meaning there are always builds of ZiPS that are still a couple of years away. Additional internal ZiPS tools like zBABIP, zHR, zBB, and zSO are used to better establish baseline expectations for players. These stats work similarly to the various flavors of “x” stats, with the z standing for something I’d wager you’ve already guessed.
How does ZiPS project future production? First, using both recent playing data with adjustments for zStats, and other factors such as park, league, and quality of competition, ZiPS establishes a baseline estimate for every player being projected. To get an idea of where the player is going, the system compares that baseline to the baselines of all other players in its database, also calculated from the best data available for the player in the context of their time. The current ZiPS database consists of about 145,000 baselines for pitchers and about 180,000 for hitters. For hitters, outside of knowing the position played, this is offense only; how good a player is defensively doesn’t yield information on how a player will age at the plate.
Using a whole lot of stats, information on shape, and player characteristics, ZiPS then finds a large cohort that is most similar to the player. I use Mahalanobis distance extensively for this. A few years ago, Brandon G. Nguyen did a wonderful job broadly demonstrating how I do this while he was a computer science/math student at Texas A&M, though the variables used aren’t identical.
As an example, here are the top 50 near-age offensive comparisons for World Series MVP Freddie Freeman right now. The total cohort is much larger than this, but 50 ought to be enough to give you an idea:
Top 50 ZiPS Offensive Player Comps for Freddie Freeman
Ideally, ZiPS would prefer players to be the same age and play the same position, but since we have about 180,000 baselines, not 180 billion, ZiPS frequently has to settle for players at nearly the same age and position. The exact mix here was determined by extensive testing. The large group of similar players is then used to calculate an ensemble model on the fly for a player’s future career prospects, both good and bad.
One of the tenets of projections that I follow is that no matter what the ZiPS projection says, that’s what the projection is. Even if inserting my opinion would improve a specific projection, I’m philosophically opposed to doing so. ZiPS is most useful when people know that it’s purely data-based, not some unknown mix of data and my opinion. Over the years, I like to think I’ve taken a clever approach to turning more things into data — for example, ZiPS’ use of basic injury information — but some things just aren’t in the model. ZiPS doesn’t know if a pitcher wasn’t allowed to throw his slider coming back from injury, or if a left fielder suffered a family tragedy in July. Those sorts of things are outside a projection system’s purview, even though they can affect on-field performance.
It’s also important to remember that the bottom-line projection is, in layman’s terms, only a midpoint. You don’t expect every player to hit that midpoint; 10% of players are “supposed” to fail to meet their 10th-percentile projection and 10% of players are supposed to pass their 90th-percentile forecast. This point can create a surprising amount of confusion. ZiPS gave .300 batting average projections to two players in 2024: Luis Arraez and Ronald Acuña Jr. But that’s not the same thing as ZiPS thinking there would only be two .300 hitters. On average, ZiPS thought there would be 22 hitters with at least 100 plate appearances to eclipse .300, not two. In the end, there were 15 (ZiPS guessed high on the BA environment for the second straight year).
Another crucial thing to bear in mind is that the basic ZiPS projections are not playing-time predictors; by design, ZiPS has no idea who will actually play in the majors in 2025. Considering this, ZiPS makes its projections only for how players would perform in full-time major league roles. Having ZiPS tell me how someone would hit as a full-time player in the big leagues is a far more interesting use of a projection system than if it were to tell me how that same person would perform as a part-time player or a minor leaguer. For the depth charts that go live in every article, I use the FanGraphs Depth Charts to determine the playing time for individual players. Since we’re talking about team construction, I can’t leave ZiPS to its own devices for an application like this. It’s the same reason I use modified depth charts for team projections in-season. There’s a probabilistic element in the ZiPS depth charts: Sometimes Joe Schmo will play a full season, sometimes he’ll miss playing time and Buck Schmuck will have to step in. But the basic concept is very straightforward.
What’s new in 2025? Outside of the myriad calibration updates, a lot of the additions were invisible to the public — quality of life things that allow me to batch run the projections faster and with more flexibility on the inputs. One consequence of this is that I will, for the first time ever, be able to do a preseason update that reflects spring training performance. It doesn’t mean a ton, but it means a little bit, and it’s something that Dan Rosenheck of The Economistdemonstrated about a decade ago. Now that I can do a whole batch run of ZiPS on two computers in less than 36 hours, I can turn these around and get them up on FanGraphs within a reasonable amount of time, making it a feasible task. A tiny improvement is better than none!
The other change is that, starting with any projections that run in spring training, relievers will have save projections in ZiPS. One thing I’ve spent time doing is constructing a machine learning approach to saves, which focuses on previous roles, contract information, time spent with the team, and other pitchers available on the roster. This has been on my to do list for a while and I’m happy that I was able to get to it. It’s just impractical to do with these offseason team rundowns because the rosters will be in flux for the next four months.
Have any questions, suggestions, or concerns about ZiPS? I’ll try to reply to as many as I can reasonably address in the comments below. If the projections have been valuable to you now or in the past, I would also urge you to consider becoming a FanGraphs Member, should you have the ability to do so. It’s with your continued and much appreciated support that I have been able to keep so much of this work available to the public for so many years for free. Improving and maintaining ZiPS is a time-intensive endeavor and reader support allows me the flexibility to put an obscene number of hours into its development. It’s hard to believe I’ve been developing ZiPS for nearly half my life now! Hopefully, the projections and the things we’ve learned about baseball have provided you with a return on your investment, or at least a small measure of entertainment, whether it’s from being delighted or enraged.
The following article is part of a series concerning the 2025 Classic Baseball Era Committee ballot, covering long-retired players, managers, executives, and umpires whose candidacies will be voted upon on December 8. For an introduction to the ballot, see here, and for an introduction to JAWS, see here. Several profiles in this series are adapted from work previously published at SI.com, Baseball Prospectus, and Futility Infielder. All WAR figures refer to the Baseball-Reference version unless otherwise indicated.
2025 Classic Baseball Candidate: Ken Boyer
Player
Career WAR
Peak WAR
JAWS
Ken Boyer
62.8
46.2
54.5
Avg. HOF 3B
69.4
43.3
56.3
H
HR
AVG/OBP/SLG
OPS+
2,143
282
.287/.349/.462
116
SOURCE: Baseball-Reference
One of three brothers who spent time in the majors, Ken Boyer spent the bulk of his 15-year career (1955-69) vying with Hall of Famers Eddie Mathews and Ron Santo for recognition as the National League’s top third baseman. An outstanding all-around player with good power, speed, and an excellent glove — but comparatively little flash, for he was all business – Boyer earned All-Star honors in seven seasons and won five Gold Gloves, all of them during his initial 11-year run with the Cardinals. In 1964, he took home NL MVP honors while helping St. Louis to its first championship in 18 years.
Boyer was born on May 20, 1931 in Liberty, Missouri, the third-oldest son in a family of 14 (!) children whose father, Vern Boyer, operated a general store and service station in nearby Alba, where the family lived. Ken was nearly four years younger than Cloyd Boyer, a righty who pitched in the majors from 1949–52 and ’55, and nearly six years older than Clete Boyer, also a third baseman from 1955–57 and ’59–71; four other brothers (Wayne, Lynn, Len, and Ron) played in the minors. As a teen, Ken often competed against a shortstop named Mickey Mantle, who played for the Baxter Springs Whiz Kids, based in Kansas, just across the border from Oklahoma.
At Alba High School, Ken starred in basketball and football as well as baseball, and received scholarship offers from more than a dozen major colleges and universities. The Yankees were interested, but with Boyer’s high school coach, Buford Cooper, serving as a bird dog scout from the Cardinals, he leaned toward St. Louis. In 1949, Cardinals scout Runt Marr recommended him for a special tryout at Sportsman’s Park, and the team liked him enough to sign him as a pitcher, paying him a $6,000 bonus, $1,000 under the limit that would have required him to remain on the major league roster (a “bonus baby”). While Boyer’s pitching results weren’t awful, he took his strong arm to third base when the need presented itself on his Class D Hamilton Cardinals team in 1950; he hit .342, slugged .575, and showed off outstanding defense.
In 1951, the Cardinals committed to Boyer as a full-time third baseman. At A-level Omaha, he overcame a slow start to hit .306/.354/.455, refining his game on both sides of the ball under the tutelage of manager George Kissell, a legendary baseball lifer whose six decades in the St. Louis organization spanned from Stan Musial’s pre-World War II days as a pitcher to Tony La Russa’s tenure as a manager. Boyer’s progress to the majors was interrupted by a two-year stint in the Army during the Korean War; serving overseas in Germany and Africa, he missed the 1952 and ’53 seasons. Upon returning, the 23-year-old Boyer put in a strong season at Double-A Houston in 1954, then made the Cardinals out of spring training the following year, and even homered in his major league debut, a two-run shot off the Cubs’ Paul Minner in a blowout. That was the first of 18 homers Boyer hit as a rookie while batting .264/.311/.425 (94 OPS+); he also stole 22 bases but was caught a league-high 17 times.
Boyer came into his own in 1956, batting .306/.347/.494 (124 OPS+) with 26 homers and making his first All-Star team. According to Sports Illustrated’s Robert Creamer, in the spring, Cardinals manager Fred Hutchinsonmarveled at his 6-foot-1, 190-pound third baseman. “He’s the kind of player you dream about: terrific speed, brute strength, a great arm. There’s nothing he can’t do,” said Hutchinson. “I think he has the greatest future of any young player in the league.” However, Boyer’s calm in the face of some second-half regression — he didn’t walk or homer at all in August while hitting just .219/.217/.254 — led to criticism from Hutchinson and general manager Frank Lane, as well as a stint on the bench. More via Creamer:
“Lane talked to me,” Boyer said. “He’s talked about drive and aggressiveness. I don’t think I really know what he means. I know that I try, that I give everything I have. I don’t loaf. I know that all my life people have been saying that to me, that I don’t look as if I’m trying. I guess I don’t look as if I’m putting out. But I am.
“I don’t think hustle is something you can see all the time. Like Enos Slaughter. Everybody talks about the way he runs in and off the field between innings. That’s the least important part of Slaughter’s hustle. The thing that counts is the way he runs on the bases and in the outfield. That’s what makes him a hustling ballplayer, not the way he runs off the field.”
Fortunately, Boyer finished the season with a strong September. It was the first year of a nine-season run across which he hit a combined .299/.364/.491 (124 OPS+) while averaging 25 homers and 6.1 WAR. He ranked among the NL’s top 10 in WAR seven times in that span, with five top-10 finishes in both batting average and on-base percentage, and four in slugging percentage. In 1957, the Cardinals took him up on his offer to play center field so as to allow rookie Eddie Kasko to play third base. Boyer fared well at the spot defensively (Total Zone credits him with being eight runs above average in 105 games) but moved back to the hot corner full time in 1958 when the team called up 20-year-old prospect Curt Flood, who had been acquired from the Reds the previous December. In 1959, the Cardinals named Boyer team captain.
Boyer set career highs in home runs (32), slugging percentage (.570) and OPS+ (144) in 1960, then followed that up with highs in WAR (8.0), AVG, and OBP while hitting .329/.397/.533 (136 OPS+) in ’61. He made the All-Star team every year from 1959–64, including the twice-a-summer version of the event in the first four of those seasons.
The Cardinals were not a very good team for the first leg of Boyer’s career; from 1954–59, they cracked .500 just once, going 87-67 in ’57. With Boyer absorbing the lessons of Musial and helping to pass them along to a younger core — Flood, first baseman Bill White, second baseman Julian Javier, and later catcher Tim McCarver — the team began trending in the right direction. The Cardinals went 86-68 in 1960, and continued to improve, particularly as right-hander Bob Gibson emerged as a star.
After going 93-69 and finishing second to the Dodgers in 1963 — a six-game deficit, their smallest since ’49 — they matched that record and won the pennant the following year, spurred by the mid-June acquisition of left fielder Lou Brock. They beat out a Phillies team that closed September with 10 straight losses despite the strong play of rookie Dick Allen, who is also on the ballot and was then known as Richie. Boyer hit .295/.365/.489 (130 OPS+) in 1964 while driving in a league-high 119 runs. In a case of the writers rewarding the top player on a winning team with the MVP award, he took home the trophy, though his 6.1 WAR ranked a modest 10th, well behind Willie Mays (11.0), Santo (8.9), Allen (8.8), and Frank Robinson (7.9), among several others.
Though Boyer hit just .222/.241/.481 in the seven-game World Series against the Yankees and his brother Clete, he came up big by supplying all the scoring in the Cardinals’ 4-3 win in Game 4 with his grand slam off Al Downing. Additionally, he went 3-for-4 with a double and a homer in their 7-5 win in Game 7. Clete also homered in the latter game, to date the only time that brothers have homered in the same World Series game.
Hampered by back problems, Boyer slipped to a 91 OPS and 1.8 WAR in 1965, his age-34 season, after which he was traded to the Mets — whose general manager, Bing Devine, had served as the Cardinals’ GM from late 1957 until August ’64 — for pitcher Al Jackson and third baseman Charley Smith. At the time, it was the biggest trade the Mets had made. Boyer, whom Devine had acquired as much for his veteran leadership as for his playing skills, rebounded to a 101 OPS+ and 2.9 WAR, albeit on a 95-loss team going nowhere. The following July, he was traded to the White Sox, who were running first in what wound up as a thrilling four-team race that went down to the season’s final day. The White Sox were managed by Eddie Stanky, who had been at the helm when Boyer broke in with the Cardinals. Though Boyer didn’t play badly, he appeared in just 67 games for the team before being released in May 1968. He was picked up by the Dodgers and spent the remainder of that season and the next with them in a reserve role.
The Dodgers asked Boyer to return as a coach for 1970, but he instead chose to return to the Cardinals organization so he could manage in the minors. He spent five seasons guiding various Cardinals affiliates in Arkansas, Florida, and Oklahoma, interrupted by a two-year stint (1971–72) as a coach on the big league staff. Bypassed when the Cardinals hired Vern Rapp to succeed Red Schoendienst after the 1976 season, he spent ’77 managing the Orioles’ Triple-A Rochester affiliate, but when the Cardinals fired Rapp after a 6-10 start in ’78, he returned to take over. The team went just 62-82 on his watch, but the next year, Boyer guided the Cardinals to an 86-76 record and a third-place finish.
Alas, when the Cardinals skidded to an 18-33 start in 1980, the team replaced Boyer with Whitey Herzog, whose tenure in St. Louis would include three pennants and a championship. Boyer accepted reassignment into a scouting role, and was slated to manage the team’s Triple-A Louisville affiliate in 1982, but he had to decline the opportunity when he was diagnosed with lung cancer. He was just 52 years old when he died on September 7, 1982. The Cardinals retired his number 14 in 1984, and 40 years later, he’s still the team’s only former player with that honor who’s not in the Hall of Fame.
On that subject, Boyer never got much traction in the BBWAA voting, either before or after his death. From 1975–79, he maxed out at 4.7%, and was bumped off the ballot when the Five Percent rule was put in place in ’80. He was one of 11 players who had his eligibility restored in 1985, and he was among the five players who cleared the bar to stay on the ballot, along with Allen, Flood, Santo, and Vada Pinson. He remained on the ballot through 1994, topping out at a meager 25.5% in ’88, nowhere near enough for election. Neither did he fare well via the expanded Veterans Committee in the 2003, ’05, and ’07 elections, maxing out at 18.8% in the middle of those years. Similarly, on the 2012 and ’15 Golden Era ballots, and the ’22 Golden Days ballot, he didn’t receive enough support to have his actual vote total announced; customarily, the Hall lumps together all of the candidates below a certain (varying) threshold as “receiving fewer than x” votes to avoid embarrassing them (or their descendants) with the news of a shutout.
All of which is to say that once again, Boyer feels more like ballast than a true candidate, here to round out a ballot without having much chance at getting elected. That’s a shame, because he was damn good. For the 1956–64 period, he ranked sixth among all position players in value:
That’s a pretty good group! Of course the comparison is manicured perfectly to Boyer’s best years, but even if I expand the range to cover the full extent of his career, he’s ninth on the list, in similar company (Kaline, Clemente, and Banks pass him), and one spot ahead of Santo. Boyer was a better fielder than Santo (via Total Zone, +73 runs to +20), and a better baserunner (+20 runs to -34, including double play avoidance), though not as good a hitter (116 OPS+ to 125).
Even though he probably would have reached the majors earlier if not for his military service, Boyer ranks 14th among third basemen in JAWS, just 1.8 points below the standard, with a seven-year peak that ranks ninth, 3.0 points above the standard. At a position that’s grossly underrepresented — there are just 17 enshrined third basemen, not including Negro League players, compared to 20 second basemen, 23 shortstops, and 28 right fielders — that should be good enough for Cooperstown.
To these eyes it is. I included Boyer on both my 2015 and ’22 virtual ballots, both of which allowed voters to choose four candidates from among a slate of 10. With the 2022 tweaks to the Era Committee format, voters can now tab just three candidates out of eight, and so for as much as I believe Boyer is worthy, the new math requires a more extensive ballot triage. His past levels of support illustrate that he’s never gotten more than 25% on an Era Committee ballot, suggesting that he’s a long shot. Even though he has a slightly higher career WAR, peak WAR, and JAWS than Allen (58.7/45.9/52.3), the fact that the latter — who endured considerable racism and shabby treatment during his career — has fallen one vote short in back-to-back elections opposite Boyer has already led me to dedicate one of my three spots to him. That leaves me just two to play with. For now, the best I can do is to leave Boyer in play for one of those spots, but I already think I’m leaning away from selecting him for my final ballot.
Every year when the postseason rolls around, we enjoyers of baseball try our best to make sure we’re properly appreciating the history unfolding on the field before us. We want to acknowledge when we’ve just watched a game so magical that it will be spoken of in tones of awe and disbelief for years to come. Downstream of that, we like to evaluate whether a game, a series, or even an entire postseason was a good one, mentally sorting them into tiers with other postseasons we’ve watched. Some measures of “good” are subjective, coming down to our personal preferences for certain strategies, styles of play, narratives, teams, or players. Other measures are more universally agreed upon and objectively quantifiable. In particular, most neutral observers value a close, exciting game, one that features both tension and action to keep observers engaged.
Win Probability Added (WPA) provides a reasonable proxy for measuring both tension and excitement. At the plate appearance level, it uses the score, inning, and base-out state (i.e. runner on second, two outs) to calculate a team’s win expectancy based on historical outcomes. The difference in a team’s win expectancy after a plate appearance relative to what it was before it represents the WPA during the plate appearance in question. WPA will be negative for the team whose odds of winning decreased while being positive for their opponent, but in this context, we’re going to focus on the magnitude of the change in win expectancy. Without a rooting interest, it’s less about which team wins and more about seeing big plays that impact the outcome of the game. Games with a large quantity of WPA have a lot of high-impact plays and lead changes that allow teams to pass win probability back and forth between one another.
Using WPA, we can evaluate the quality of the action in a given game by both looking at the average WPA per plate appearance and by adding up the game’s total WPA. Both methods provide useful insight. Average WPA per plate appearance controls for the variable number of plate appearances in a game, since games with more plate appearances have more opportunities to accumulate WPA. Sometimes that accumulation constitutes empty calories; other times it’s more substantial. Ultimately, we want the games that top the charts from both perspectives. Read the rest of this entry »
It’s time for another cycle of prospect lists, and as I’ve become accustomed to doing for the last few seasons, I’m starting with scouting reports on pro players in foreign leagues, with a focus on players available for MLB free agency this offseason. On The Board, you can see a fresh batch of scouting reports and evaluations for relevant players from Nippon Professional Baseball, the Korea Baseball Organization, and the Chinese Professional Baseball League in Taiwan, as well as reports on some young players I’ve identified as potentially impactful long-term prospects. For those who need a crash course on the age- and pro experience-driven lines of demarcation that dictate how MLB teams sign international players, I’d point you to a number of MLB.com glossary entries, including those on international free agency for those in Asian pro leagues, international amateur free agency and bonus pool restrictions, the Japanese posting system, and the Korean posting system.
It can be overwhelming to sift through so many different types of players on that section of The Board — it’s a real apples and oranges situation when we’re talking about some guys who are in their 30s and others who are still teenagers — so I’ve got many of them broken into digestible subgroups below. You’ll notice that some players appear across multiple categories. The Board has each player’s full scouting report and tool grades — think of this as more of a table of contents. Read the rest of this entry »
The following article is part of a series concerning the 2025 Classic Baseball Era Committee ballot, covering long-retired players, managers, executives, and umpires whose candidacies will be voted upon on December 8. It is adapted from a chapter in The Cooperstown Casebook, published in 2017 by Thomas Dunne Books. For an introduction to the ballot, see here, and for an introduction to JAWS, see here. All WAR figures refer to the Baseball-Reference version unless otherwise indicated.
2025 Classic Baseball Candidate: Dick Allen
Player
Career WAR
Peak WAR
JAWS
Dick Allen
58.7
45.9
52.3
Avg. HOF 3B
69.4
43.3
56.3
H
HR
AVG/OBP/SLG
OPS+
1,848
351
.292/.378/.534
156
SOURCE: Baseball-Reference
“Dick Allen forced Philadelphia baseball and its fans to come to terms with the racism that existed in this city in the ’60s and ’70s. He may not have done it with the self-discipline or tact of Jackie Robinson, but he exemplified the emerging independence of major league baseball players as well as growing black consciousness.” — William Kashatus, The Philadelphia Inquirer, April 2, 1996
At first glance, Dick Allen might be viewed as the Gary Sheffield or Albert Belle of his day, a heavy hitter seemingly engaged in a constant battle with the world around him, generating controversy at every stop of his 15-year career. It’s unfair and reductive to lump Allen in with those two players, however, for they all faced different obstacles and bore different scars from the wounds they suffered early in their careers.
In Allen’s case, those wounds predated his 1963 arrival in the majors with a team that was far behind the integration curve, and a city that was in no better shape. In Philadelphia and beyond, he was a polarizing presence, covered by a media contingent so unable or unwilling to relate to him that writers often refused to call him by the name of his choosing: Dick Allen, not Richie. Read the rest of this entry »
When I talked to him at last year’s GM meetings, J.J. Picollo told me that an offseason priority was to add “guys with experience” to a Kansas City Royals roster that was long on promising young talent but short on veteran presence. Picollo did just that — Seth Lugo, Hunter Renfroe, Will Smith, and Michael Wacha were among those brought on board — and while the additions only told part of the story, the end result was a best seller. One year after winning just 56 games, the 2024 Royals went 86-76 and played October baseball for the first time in a decade.
What does the AL Central club’s Executive Vice President/General Manager see as the top priority going into next season?
“We need to be a little more dynamic offensively, and by that I mean we need to get on base at a higher rate than we did this year,” Picollo told me earlier this week in San Antonio. “We’re trying to target players we can lengthen out our lineup with, whether it’s someone at the top, in the middle, or toward the back end. Our identity is more pitching and defense, base running, and situational hitting, so how can we add some guys that can complement what we already have that will allow us to score more runs?”
The Royals crossed the plate 735 times in 2024, the sixth-highest total in the American League. Their .306 on-base percentage was ninth-highest, while their .403 slugging percentage and their 170 home runs ranked sixth and tenth respectively. As power obviously helps provide more runs, I asked Picollo if OBP is indeed the priority. Read the rest of this entry »