There are few things in baseball worth more than a star-level catcher. Playing a position that requires you to squat each day for a 162-game season makes for a lot of injuries and shortened careers, and the role of a catcher is so crucial between game-calling, baserunning control, framing, and blocking, that just playing your good defense and being pitcher-friendly will get you a long career no matter how horrible you are with the bat.

Fortunately for all teams in need of a receiver, the 2020 free agent market offers one of the rarest cases among the sport, a true five-tool catcher: J.T. Realmuto, the former Marlins and Phillies backstop, is available for his mere salary.

You want a steady bat, maybe with some thump? Realmuto walks in sporting a career .278 AVG, .455 SLG, and double-digit homers in each of his six “full” seasons in the league.

You need a reliable asset, one that punches the ticket and goes to work? He averaged over 130 games from 2015-19.

You need a rock-solid defender that can also help your guy on the mound? J.T. is there for you with a rocket arm (over 88 mph on his average throw) and a spotless fielding percentage, and while he’s behind the plate, he’ll steal strikes for your pitchers as a 95th-percentile framer does.

Heck, he’ll even run if you ask him to, dashing at over 28 mph, making him an 84th-percentile runner, an absurdity given his role on the field. Read the rest of this entry »

A recent post in the Effectively Wild Facebook group sparked my curiosity. A poster named Tim wrote: “Record I’d like to see set that isn’t inconceivable: Player gets 5 HR in a single game.” That record is not inconceivable, because it has been accomplished at least five times in the minor leagues.

In fact, the professional baseball record is eight home runs in a single game, set by catcher Jay Clarke of the Corsicana Oil Cities in a 51-3 win over the Texarkana Casketmakers in a Texas League contest in 1902. The last minor leaguer to hit five homers in a single game was Dick Lane of the Muskegon Clippers in 1948.

But of course the poster was in all likelihood talking about the MLB record of four in a game, which has stood since 1894. But it was a commenter on the post that really piqued my interest. They simply asked: “Would a team really continue pitching to a guy who’s already had 4 HR in a game though?”

It’s a valid question to ask, and it set me down a rabbit hole of seeing just how many players had a plate appearance with four homers already in a game, and how those plate appearances went. Looking back at history isn’t necessarily the best way to predict future behavior, but it is a fun exercise if nothing else, because frankly, before conducting this research I had no idea how many players ever had a crack at a fifth home run. Read the rest of this entry »

Everyone knows that a strong farm system is key to the long-term success of a major league organization. They make it possible for clubs to field competitive teams at affordable salaries and stay beneath the luxury tax threshold, but how much value can an organization truly expect from their farm system? How much more value do the best farm systems generate compared to the worst ones? I decided to take a closer look.

Methodology

The first thing I did was gather the player information and rankings from the Baseball America’s Prospect Handbooks from 2001-14 and entered them into a database. I then found players’ total fWAR produced over the next six seasons, and I added them together to find the values that each farm system produced. I chose six seasons to ensure that teams wouldn’t get credit for a player’s non-team-controlled years, since the value produced would not be guaranteed for the player’s current organization. This method will reduce the total value produced by players that are further away from the majors, but the purpose of this analysis is to focus on the value of the entire farm system and not an individual player’s value over the course of their career.

Let’s look at the 2014 Minnesota Twins as an example. Below is a list of the thirty players that were ranked and the amount of WAR that each player has produced by season. Read the rest of this entry »

This article is a casual version of my paper “Challenging Nostalgia and Performance Metrics in Baseball” published in Chance which showed, among other things, that wins above replacement (WAR) and the wide class of “versus your peers” statistics are incapable of accurately comparing players across eras. In particular, it was shown that WAR exhibits a very strong bias toward baseball players who played in earlier seasons. A collection of resources and an interactive web app within this framework can be viewed here.

How We Came To This Conclusion

In our research, we split baseball data into time periods and show that WAR includes players from the older era in its all-time rankings. Specifically, the older time period is defined by players who started their career in 1950 or before, and the newer group is defined by players who started their career after 1950. The split date of 1950 corresponds to the US Census that is closest to the integration of baseball in 1947. Prior to 1947, Major League Baseball was a largely all-white segregated sports league, but it slowly but surely integrated in America and the has steadily risen in popularity abroad. All the while, the world populations continue to grow as time progresses. Simply put, there are far more people in the baseball-eligible talent pool post-1950 than before.

We find that roughly 20% of the “realistic historic talent pool” belongs to the pre-1950 group. By “realistic historic talent pool” we mean the cumulative population of men ages 20-29 collected every 10 years arising from baseball playing countries (men ages 20-29 serve as a proxy for a concept of talent pool that is otherwise not well-defined). Before 1950, this population is basically just white American men. After 1950, this population includes all American men, as well as men from a plethora of baseball-playing countries. Read the rest of this entry »

If you’re reading this, you are surely a baseball fan, and as such, you’re probably aware that Major League Baseball is putting lots of options on the table when it comes to rule changes to shake up the game and make it more interesting. We’ve already seen the intentional walk become automatic and the limiting of mound visits. MLB also reached an agreement with the Atlantic League to experiment with some other ideas, such as robot umpires, a three-batter minimum for pitchers, starting extra innings with runners on base, moving the mound back, and banning the shift. Some of these ended up being adopted in the majors on a temporary basis for the pandemic-shortened 2020 season and may end up getting implemented more permanently, depending on how the upcoming CBA negotiations go.

But I have an idea that I think is better than any of these. It’s a small rule change; but it would radically change the game. Too radical even for this change-happy commissioner, I think. And here it is:

On a ball in play, a runner who reaches home can decide to continue on to first base and keep running.

Now, before I explain why I find this rule change so appealing, let me first get the logistics out of the way. How could it be determined if a player has decided to go to first or not? For this part, it would have to operate the same as a batter running to first base. (To be clear, I don’t think it should be a force play at first base, though it would still be fun if it was.) Read the rest of this entry »

There are 24 different “states” in baseball. The three bases can be filled in eight different ways, and there can be 0, 1, or 2 outs at any given moment. Each of these 24 base-out states has an expected run value associated with them. Each value represents the average number of runs that the team is expected to score by the end of the inning. These values change each season depending on the run environment, but they generally don’t vary much.

Consider the following situation: Lorenzo Cain is on first base with two outs. Now consider two possible hitters, one being Christian Yelich and the other being Ryan Braun. According to the 2019 averages, the run expectancy in this base-out state was 0.24, regardless of the hitter. While both players had impressive seasons, Yelich is unquestionably the superior player at this point in time.

As a result of their differences, the run expectancy should be higher when Yelich is at the plate. Consequently, the benefit Milwaukee gets from Cain attempting to steal second base should be adjusted as well. Why is this the case? Given Braun’s inferior power and hitting ability, there is more to gain from Cain putting himself in scoring position, but more importantly, there is less to lose if he were to get caught. On the other hand, Yelich is much more likely to drive the ball. With Yelich at the plate, the increase in run expectancy from a stolen base is slightly smaller than if Braun were hitting. However, the decrease in run expectancy from being caught is significantly greater. This is why we need RE+. Read the rest of this entry »

The 2020 season brought unprecedented rule changes, one of the most puzzling among them being the “extra inning runner.” Ostensibly in an effort to reduce the spread of COVID-19 and speed up play, commissioner Rob Manfred decreed that once a game progresses past the ninth inning, a runner would be placed at second base to begin the frame.

Manfred’s blatantly obvious motives turned baseball fans — a demographic notorious for their acceptance of changes to the national pastime — against it. If there is any defense to be made for the addition of the extraneous runner, it’s that shorter games helped save pitchers’ arms in what’s already been an utterly brutal season for pitcher injuries.

This seismic rule change also created a correspondingly large shift in how teams strategized after a game surpassed nine innings. Teams, even the more sabermetrically inclined among them, began to employ traditional tactics. In order to determine how clubs played with a free runner, I charted every extra inning of the 2020 season. Read the rest of this entry »

Julio Teheran’s career has been one largely defined by consistency. Over his seven full seasons in Atlanta (2013-19), he never made fewer than 30 starts or threw under 174 innings, with ERAs between 2.94 and 4.49. Arguably the most defining element of his reliability was how he consistently out-performed his peripheral numbers. In each of those seasons, Teheran considerably out-pitched both his FIP and xFIP, often by close to a full run.

On the surface, it would appear that Teheran was already declining significantly over the previous three seasons, even if his ERAs failed to reflect such a story. Like many veteran starters, the easy assumption for such a decline would be diminished stuff, but his 22.4% strikeout rate in 2018 was the best of his career to date, with his 21.5% in 2019 not far behind. Teheran’s decline in Atlanta was predominantly marked by a notable loss of control — jumping from a 5.4 BB% in 2016 to 8.9% in 2017, then 11.6% and 11.0% in 2018 and 2019, respectively. Teheran’s streak of consistent results came to a screeching halt in 2020 to the tune of an 10.05 ERA, 8.62 FIP, and 6.35 xFIP, a recipe that culminated in -0.9fWAR.

But the most worrying sign for Teheran is that this is not a continuation of the previous problem. His walk rate for 2020 was 10.7%, still worse than his career average, but a slight improvement on the previous two seasons. What’s particularly alarming is that his strikeout rate plummeted to just 13.4%, while no pitcher in baseball with more than 100 batters faced had a whiff rate lower than Teheran’s 14.6%. Teheran was only hit slightly harder than previously; while his 38.7 hard hit % was notably higher than 2018’s 36.7% and 2019’s 35.4%, his average EV allowed was only slightly worse than league average at 89.0 mph, identical to his 2019 season. When paired alongside his inability to miss bats though, this high volume of hard contact led to disastrous results. Read the rest of this entry »

Mike Port’s professional baseball career spanned more than four decades, from 1969 to 2011. When his aspirations to play in the big leagues ended with an injury shortly after signing with San Diego, he accepted a position in the Padres’ minor league system and worked his way up to the role of farm director. In 1977, he began a 14-year stint with the California Angels where he was promoted to general manager in September, 1984. Following 18 months as the first president of the Arizona Fall League, Mr. Port migrated to the East Coast to begin a 12-year run with the Boston Red Sox as an assistant GM and held the acting general manager title during the 2002 season.

He was named Major League Baseball’s Vice President of Umpiring in August 2005 and remained in that position through the 2011 campaign. I conducted a telephone interview with Mr. Port in September of 2020 in which we discussed the general manager’s role and responsibilities (to be included in my upcoming book, Hardball Architects: Volume 2). Our chat drifted into topics such as umpiring, instant replay, and various rule changes that have been implemented in the past decade.

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DB: A number of rule changes were implemented for the 2020 season – the three-batter minimum for pitchers, seven-inning double-headers, extra innings starting with a runner on second base, designated hitter in the National League. It remains to be seen which rules will stay on the books.

MP: I was told in early September that it’s “under consideration” for Major League Baseball to make all games seven innings. Certainly they’re going to forego a lot of concession revenue. As one former pitching specialist told me, “They’re playing these seven-inning double headers. Well, that’s still fourteen innings in one day. So, you’re getting the games in, but is it at some expense to the people on your staff?” Read the rest of this entry »

With the advent of the Statcast era, a lot of research has been done in attempts to measure the effectiveness of a particular pitch based on its flight characteristics. As has been noted in the past, quantifying a pitcher’s stuff and command is no easy task. However, over the past few months I have worked to build my own models in an attempt to evaluate the “filth” of any given pitch, taking more of a probability-based approach. I introduce to you my Probabilistic Pitch Scores and xWhiff metrics.

When evaluating the quality of a particular pitch, I focused my interest on three different binary outcome variables: whether or not the batter swung at a pitch, whether or not the batter whiffed on a pitch, and whether or not a pitch was thrown for a strike. Thus, my goal was to train three different types of classification models corresponding to each of these variables: a swing, a miss, and a called strike. For the actual outcomes of these models, I was less interested in the model’s decision and more interested in the predicted probability. For example, if a batter swings on a pitch with given flight characteristics, what is the probability that he will whiff? These probabilities were utilized as the basis of my metrics.

Read the rest of this entry »