It’s nearly February and the free agent pool is thinning out. Most of the big names have already flown off the board. By our projections, only five unsigned players forecast to amass at least 1 WAR in the upcoming season, only three of whom are position players. Most teams have already filled out their Opening Day starting lineups; now their focus shifts to improving the fringes of their 26-man roster, searching for a couple of additional wins or insurance in case of injuries. The Mariners and the A’s, two AL West teams with very different outlooks for 2023, each recently made such an addition, inking a veteran to bolster infield depth. Let’s take a look.
Formerly a bench infielder and designated pinch hitter for the Cubs (his league-leading 91 pinch hit appearances in 2018 has not been matched since), La Stella was traded to the Angels with two years of team control remaining for a prospect who never threw a pitch in Chicago’s system. In 2019, he maintained the contact skills and excellent plate discipline that made him a league-average hitter, but he improved in another facet of his game that was emblematic of the juiced ball era. That year, his fly ball rate, which had previously sat around the 20% mark, climbed to 25%; that, combined with a small increase in his pull rate, led to a power break out. Despite lacking traditional power indicators like barrels and a high maximum exit velocity, La Stella made the most of his aerial contact (and the favorable dimensions of Angels Stadium) to post a career-high .486 slugging percentage and hit home runs at a rate of 30 per 600 PA, an excellent mark even during the heightened offensive environment. His absolute refusal to swing and miss played a big part in this as well; his minuscule 8.7% strikeout rate gave him plenty of balls in play, many of which left the yard:
Jay Jaffe: I’ll have my five-year HOF election outlook piece on Monday, and another FGA spot for next Friday as well.
2:04
Tacoby Bellsbury: Who gets inducted first: Jeff Kent or Chase Utley?
2:06
Jay Jaffe: My guess is Kent, even though he has to cool his heels for the next three years. Utley’s facing quite a crowd on the next two ballots, and while I’ll find room for him, I don’t know that everybody who wants to vote for him necessarily will get to right off the bat.
Rooting for Team USA in international sports can be a little… touchy. America’s great rivals vary from sport to sport and come from every corner of the globe. Ours is a nation of vast influence and combative people, so adversaries pop up everywhere. (At one point, the U.S. women’s national soccer team had a vicious rivalry with Norway. Norway! You must really want a fight if you have beef with Norway.)
But our most intense rivalries were forged in the Cold War, when American politicians and media painted Communist nations as an unknowable other against whom we were pitted in a battle for survival. Before glasnost, the internet, and the professionalization of Olympic sports, teams from Communist countries were so mysterious they could only be feared. We did not see Soviet stars in the NBA or NHL as we do now, nor Cuban baseball players and boxers in western competition. We only encountered them as they appeared every four years to pit their mettle against that of American college athletes at the Olympics. That’s how the Miracle on Ice became such a definitive part of American mythology; rare is the scenario in which an American team — much less an American men’s team in a relatively popular sport — can credibly claim to have faced and overcome an insurmountable opponent. But what the Soviets were to hockey, Cuba was to baseball.
Hello, and welcome to an article where I’m wrong about everything. Like literally all of the things. Here’s what happened. I was thinking about the long, glorious farewell tour of Albert Pujols. After a five-year stretch during which he posted a wRC+ of 84, he put up a 151 wRC+ in 2022. That was the best he’d hit since his age-30 season. Pujols largely put up those numbers by smashing lefties. His 113 wRC+ against righties was good, but against lefties that number was 214. MVP Paul Goldschmidt was the only batter who performed better against lefties (minimum 130 plate appearances vs. southpaws).
Pujols’ resurgence really started in 2021, when he had a 145 wRC+ against lefties and a 35 against righties. That’s the season I was more interested in. As I thought about it, I started wondering whether the last part of his journey — established veteran defies the aging curve by settling comfortably into a platoon role — is happening more frequently. I had the sense that it was happening more frequently.
I was wrong. It is not happening more frequently. Here’s a graph comparing the last 11 years to the previous 10 years:
Chris Murphy is gaining helium. A sixth-round pick in the 2019 draft out of San Diego State University, the 24-year-old southpaw was No. 38 on our Red Sox Top Prospects list going into last season, with a modest 35+ FV. But on the heels of a 2022 campaign that saw him excel in 15 starts with Double-A Portland and then hold his own in 15 more with Triple-A Worcester, this year he will be moving up to the 14–16 range with a 40 FV, per our lead prospect analyst Eric Longenhagen. He also just participated in Boston’s Rookie Development Program, which focuses on easing the transition into MLB — an indication that Murphy could be in Boston as soon as this summer.
A self-proclaimed nerd who is well-versed in his vertical approach angle and pitch metrics, Murphy discussed his craft earlier this week at Fenway Park.
———
David Laurila: Let’s start with who are you as a pitcher. How do you get outs?
Chris Murphy: “That’s a good question. There have been times in my career where it’s very fastball heavy — come at you fastball/changeup primarily and then curveball/slider secondarily. I’ve generally been aggressive with the fastball up in the zone. I have good vertical break, good two-plane, and a pretty decent vertical approach angle. That’s why I get swings and misses up in the zone and why my changeup plays down in the zone. Using that to my advantage, being a shorter pitcher, is something that’s given me a career to this point. That and throwing from the left side.”
Laurila: How tall are you?
Murphy: “The book will say 6-[foot]-1, but I’m probably just under six feet. I weigh about 185, so I’m not the biggest guy.”
Laurila: You said that you get good vertical but also two-plane. Can you elaborate?
Murphy: “Yes, I get both ride and run. There are days where my fastball is more true and it’s just ride, but ride and run is ideally where I like it to be. And then with the changeup, it’s about killing the spin, killing the vert, and adding more horizontal. The goal this year is to be under six vertical and negative-18–19 horizontal.” Read the rest of this entry »
For the 18th consecutive season, the ZiPS projection system is unleashing a full set of prognostications. For more information on the ZiPS projections, please consult this year’s introduction and MLB’s glossary entry. The team order is selected by lot. Rounding out the 2023 projections? The Tampa Bay Rays.
Batters
The Rays lineup presents a tale of two offenses. On the good side is a team with one of the best 2B/3B/SS combinations in the majors. Yandy Díaz had a monster 2022 season, basically as offensively amazing as it’s possible for a corner infielder with below-average power to be. ZiPS is low on Díaz relative to Steamer and The Bat, and his projection here is still excellent. It’s a real shame that he’s somehow still underrated in the eyes of fans, even in an age when most people realize on-base percentage is a thing. Díaz isn’t quite Eddie Yost or Eddie Joost in terms of pure walk rate, but he’s a mold-breaker along similar lines. With Isaac Paredes needing at-bats of his own and Curtis Mead aggressively pushing his way up from the minors, the Rays will almost certainly continue to use Díaz at multiple positions. I’d be happy with any of those three as my starting third baseman.
Brandon Lowe had a disappointing, injury-filled season, but it would take some severe recency bias to forgot that he was an elite second baseman in 2021, which wasn’t exactly an eon ago. Wanderkind Franco had his own injury problems in 2022, but he’s still very young, certainly young enough that you shouldn’t fret about it too much (yet). With some luck in terms of health, the Rays will have one of baseball’s best infields even if they get very little out of first base. Unfortunately, the computer doesn’t expect the Rays to get much out of first base. ZiPS has never been in on Jonathan Aranda, even after his most promising minor league season yet. Given the offensive explosion in the minors, ZiPS doesn’t translate his .318/.394/.521, 18-homer season for Durham as well as you might think, only having him at a .276/.341/.420, 12-homer season. Combine that with a poor debut and you can see why ZiPS really hopes that Aranda’s future is at second, not first. The player ZiPS does like is Kyle Manzardo, who gets a translation of .267/.346/.456 for his age-21 season, his first full pro campaign. Manzardo has a very good chance to be the top first baseman on the ZiPS Top 100 Prospects list next month. Read the rest of this entry »
Below is an analysis of the prospects in the farm system of the Tampa Bay Rays. Scouting reports were compiled with information provided by industry sources as well as my own observations. This is the third year we’re delineating between two anticipated relief roles, the abbreviations for which you’ll see in the “position” column below: MIRP for multi-inning relief pitchers, and SIRP for single-inning relief pitchers. The ETAs listed generally correspond to the year a player has to be added to the 40-man roster to avoid being made eligible for the Rule 5 draft. Manual adjustments are made where they seem appropriate, but I use that as a rule of thumb.
A quick overview of what FV (Future Value) means can be found here. A much deeper overview can be found here.
All of the ranked prospects below also appear on The Board, a resource the site offers featuring sortable scouting information for every organization. It has more details (and updated TrackMan data from various sources) than this article and integrates every team’s list so readers can compare prospects across farm systems. It can be found here. Read the rest of this entry »
First up, Jay Jaffe welcomes Ryan Thibodaux, who leads the team at the Hall of Fame Ballot Tracker, to discuss Scott Rolen’s enshrinement and the wilddayleading up to it. Ryan has worked on the Tracker for a decade now, but this is somehow the first time he and Jay have had a non-text conversation. The duo look back on the influence the Tracker has had over the years, what compelled Ryan to work on it, and if he ever hears from any of the candidates. Jay and Ryan also take a look at the many exciting names on next year’s ballot. [3:05]
After that, David Laurila welcomes Pirates prospect Quinn Priester to the show. The pair first chatted after Priester was drafted (then again in 2021), and they discuss how he has developed as a pitcher in that time. We hear about training in Arizona this offseason, changing his approach to stay in games longer, comparing curveballs with teammate Mike Burrows, and playing with Henry Davis, who was the first overall pick in the 2021 draft. Finally, Priester tells us who were the best hitters he faced this year and who he is most looking forward to taking on in the majors one day. [31:40]
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A few weeks ago, I regressed as a writer. I regressed a lot, actually: twenty years worth of slash line data regressed against twenty years of run scoring data in various ways. But — and this is a dangerous sentence, and usually a bad one — someone asked me a question on Twitter and I want to answer it. Namely: was batting average always the weakest correlation to run scoring among the slash line statistics, or has it only become so recently?
This is going to be a quick hitter. I broke the game down somewhat arbitrarily, using eras defined by OOTP Perfect Team. I started in 1947 and went up until 2000 (the results of the 2000s were in my previous article). Here’s what those 2000s results look like, which should both give you an idea of the correlations today and preview the format for the rest of the article:
R-Squared to Runs Scored, Various Stat Pairs
Statistic
AVG
OBP
SLG
AVG
.355
.673
.841
OBP
.673
.668
.885
SLG
.841
.885
.840
Without further ado, let’s get started.
Golden Years, 1947–1960
Now, these weren’t the golden years for me, because I wasn’t alive, but I guess that’s what some people call this era of baseball. Jackie Robinson! Ted Williams! Stan Musial! Willie Mays! Batting average mattered more, but it still didn’t matter:
R-Squared to Runs Scored, Golden Years
Statistic
AVG
OBP
SLG
AVG
.655
.762
.771
OBP
.762
.707
.908
SLG
.771
.908
.688
What do I mean by that? Well, if you predict run scoring with OBP and SLG, you get a 0.908 adjusted r-squared to actual runs scored. Predict run scoring with the entire triple slash line, and you get an adjusted r-squred of 0.91. Batting average did better, on its own, as a run scoring predictor, but using OBP and SLG was the gold standard in the golden years.
Baseball Boom, 1961–1979
This is a broad era that folds in some pitching-dominant years that led to rules changes, the early part of the speed era, and some early-60s home run mania. It’s also an era where, if you know OBP and SLG, you don’t need to know batting average to predict run scoring:
R-Squared to Runs Scored, Boom Years
Statistic
AVG
OBP
SLG
AVG
.672
.810
.856
OBP
.810
.795
.922
SLG
.856
.922
.833
Like the 1947–60 span, using OBP and SLG as predictors does just as well as using all three statistics. More specifically, OBP/SLG had a 0.922 adjusted r-squared to runs scored. The full AVG/OBP/SLG regression checks in at 0.923. Average… if you’re already 99.89% of the there, it’ll get you that last tiny bit of explanatory power. That’s not exactly a ringing endorsement.
Defensive Era, 1980–1992
Even though I wasn’t alive for a big chunk of this era and wasn’t following baseball for the vast majority of it, it’s one of my favorite eras, thanks to Ozzie Smith, my single favorite baseball player and, per my mom, the person I’ve most emulated in my life. I spent countless hours mimicking the defensive plays I saw on my “Ozzie, That’s a Winner” VHS tape, which my uncle had recorded on local access TV in St. Louis. I’m a lefty, so I was doing them backwards and they never led to me becoming a defensive wunderkind, but none of that mattered to me; I just wanted to be like Ozzie. Uh, where were we? Oh, right. Average didn’t matter:
R-Squared to Runs Scored, Defensive Era
Statistic
AVG
OBP
SLG
AVG
.542
.713
.800
OBP
.713
.705
.863
SLG
.800
.863
.784
Using the criteria from above, OBP/SLG checks in at 0.863, and an all-three-slash-stats regression checks in at 0.864. It’s interesting to note that OBP and SLG explain the lowest percentage of variation in run scoring in this era, which I attribute to the huge range in team baserunning strategy and effectiveness, but that’s not the point of this study. The point is that if you already know a team’s OBP and SLG, you don’t need to know their batting average to predict how many runs they scored.
The Power Years, 1993–2000
I cut this one off at 2000, since my previous article already covered the 21st century, but OOTP extends it to 2004. Regardless, you guessed it:
R-Squared to Runs Scored, Power Years
Statistic
AVG
OBP
SLG
AVG
.655
.830
.839
OBP
.830
.821
.912
SLG
.839
.912
.811
This time, the adjusted r-squared is the same whether you look at OBP/SLG or AVG/OBP/SLG. So there you have it: throughout the eras, the correlations have remained the same. If you’re trying to predict a team’s run scoring and already have their on-base percentage and slugging percentage, you can stop there. Batting average won’t add anything to the equation.
If I asked you to visualize the prototypical stolen base, you’d probably picture a runner taking off for second. Conversely, if I asked you to conjure up the most thrilling stolen base you could imagine, you’d pick a play at the plate. Stolen bases at third, then, are the neglected middle child — too infrequent to warrant much conversation or analysis, but not unusual enough to drum up excitement. But third is more than just the base between second and home, and stealing third regularly and efficiently is a distinct skill.
For one thing, steals of third base make for a faster showdown between catcher and runner. The average pop time on a throw to second last season was 1.97 seconds; on a throw to third, it was 1.55 seconds — nearly half a second quicker. The distance between bases, however, is the same all around the diamond, which means a runner needs a much better jump when he’s going for third. Thus, stealing third is less of a race and more of a mind game. Pure speed is less important, but the perfect lead and a well-timed jump are invaluable. Read the rest of this entry »
Jay Jaffe
