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When Statcast was launched, we were graced with incredible new stats such as Exit Velocity and Launch Angle, which revolutionized how we evaluate hitting. This new information confirmed obvious things like that Giancarlo Stanton hits missiles, but it also gave us a new breed of hitter. Daniel Murphy, Justin Turner, J.D. Martinez, and others looked at the data and made adjustments that started maximizing their power outputs. The standard evaluation method has become to look at EVs mixed with LAs to determine who is one tweak away from stardom. Hitting is a complex beast, with pitchers throwing 95-plus with nasty hooks to go with shifting defenses. Ultimately, a hitter is looking to produce solid contact regardless of where the ball goes. The goal of this analysis is to identify hitters who have an inefficient spray chart and see how they could optimize their profile by hitting more balls in a different direction to maximize production. Luckily with Statcast, we can now try to find these answers.

To do this analysis, I used Baseball Savant to gather 2018 Exit Velocity and xwOBA to Pull Side, Straight Away, and Oppo Side for all hitters with at least 50 plate appearances. I then used FanGraphs to pull the 2018 data for Pull%, Mid%, and Oppo% to discern how often a hitter attacks that field. I used 50 PAs as a filter since this is about where exit velocities become stable and helps weed out pitchers and other noise. This does create gaps in the data because some players didn’t register 50 PAs of a batted-ball direction. This dataset gives us the ability to look at how hard a hitter hits the ball to a field, what was their expected damage (xwOBA) to that field, and how often they went that way.

The first category I looked at was players who could use the opposite field more often. To do this, I looked at players who had an above average Oppo Side xwOBA and a below-average Oppo%. I used exit velocities to each field as a proxy to justify the directional swing change. Read the rest of this entry »

Far and away the most expensive current player on the Padres is Eric Hosmer, who has proven to be quite disappointing so far in his short time with the team. He’s due $21 million next season, which is three times the second-highest salary on the club. For a team that will almost certainly never have a payroll that compares with larger market teams, that’s a significant price to be paying.

Although he was signed in February of 2018 to be a cornerstone for the team, he promptly put up a mere -0.1 fWAR last season. He’s always been an inconsistent performer year-to-year, and this campaign was no exception. How could a player who put up 4.1 fWAR in value in 2017 see his level of play drop off so quickly in the following season? Can he bounce back in 2019 as he often does?

To begin, we need to know what held him back last season as a hitter. Hosmer’s a first baseman, so the majority of his value is going to be derived from his bat. Here’s a table showing the differences in his offensive production between the 2017-18 seasons:

Read the rest of this entry »

This article was originally published on my blog, cargocultsabermetrics.com. 

Developing a new pitch can be a great way for a pitcher to have a breakout season. In 2018, we saw big improvements from Trevor Bauer adding a slider, Adam Ottavino adding a cutter, and Patrick Corbin adding a curveball. A new pitch can sometimes be the missing puzzle piece when trying to figure out why a player is good and not great. For Jose Berrios, a cutter might be that missing piece.

Examining Berrios’ arsenal

Jose Berrios has one of the nastiest curveballs in baseball. Instead of having the typical downward break associated with curveballs, Berrios’ curve averages 15.5 inches of glove-side break. This results from Berrios imparting gyro spin (think bullet spin) on his curveball rather than 12-6 top spin. Because of this, Berrios generates close to no vertical break caused by Magnus force, which is just a fancy way of saying the only drop we see on Berrios’ curve is due to gravity rather than top spin.

To pair with his curve, Berrios has a four-seam fastball which generates 9.5 inches of arm side run and 16.5 inches of upward vertical break due to Magnus force (causing the pitch to drop less), a two-seam fastball which generates 16.5 inches of arm-side run and 11.0 inches of upward vertical break, and a changeup with 14.5 inches of arm-side run and 5.5 inches of upward vertical break (the changeup will drop even more than the fastball since it is thrown slower and gravity will have more time to bring the pitch down). Read the rest of this entry »

With so many complex statistics out there, I wondered if there was an easier way to project winning percentage or runs, a way that is simple yet more complex than Bill James’ classic Pythagorean Win Expectancy. To create a statistic like that, I would have to create one comprehensive stat for offense and one for pitching. Ultimately, I came up with the following and named them “Run Value” and “Pitching Run Value,” respectively.

RVAL = ( ( TB + BB – SO )/4) + RBI + HR  

PRVAL = ( ( ( H + BB – SO )/4 ) + HR) x FIP

These two metrics are used for teams. In the batting RVal formula, the higher the better. I tried to get down to the pure number of runs that a player or team produces by using the very relaxed definition of a run being four bases. In the pitching PRVal formula, the lower the better. I did something very similar to the batting stat by trying to get the pure run total. I then put the two stats into the win expectancy formula:

RVALWinExp = RVal^1.83 / ( RVal^1.83 + PRVal^1.83)

I then ran a program in R to see how closely this stat correlates to actual team win percentage for all teams from the 1998 season through the 2018 season. In addition, I tested to see how Bill James’ win expectancy formula correlates to team win percentage over the same period of time. The results are below. Read the rest of this entry »

Note: This is a piece I have blogged about previously for a British baseball site located here, and this is a slightly updated version.

Jeff Sullivan does pieces on the worst called balls and strikes at the halfway mark and end of each season. These are usually quite bizarre calls that have some unusual circumstances behind them, but for the most part they don’t have too much influence on the game. However, in this postseason, there was a poor “strike” call which had a huge impact on a game.

In bottom of the second inning of Game Three in the NLDS series between the Braves and the Dodgers, Walker Buehler was in a difficult situation with two outs and runners on second and third after an error from Cody Bellinger. The Dodgers decided to intentionally walk Charlie Culberson, loading the bases, to get to Braves pitcher Sean Newcomb – a fairly standard approach in the NL. But Buehler fired four balls to Newcomb and walked in the first run of the game, bringing up Ronald Acuna, who Buehler threw another three balls to to end up down 3-0 in the count.

Then came “ball four,” but it wasn’t called a ball despite being two inches above the top of the zone, as home plate umpire Gary Cederstrom called a strike. That meant Buehler threw another pitch to Acuna, who launched it for a grand slam, resulting in a score of 5-0 and not 2-0. The potentially “pitcher friendly” call by the umpire cost the Dodgers three runs in a game they ended up losing by just one.

To go to a hyperbolic extent, this meant they lost the game, they then had to play a further game in the series against Atlanta, they were then more tired than the Brewers in what became a seven-game series, they were then more tired than the Red Sox, and they therefore lost the World Series. Certainly a stretch, but it’s not hard to see the effect in the game considering the Braves managed just three runs in the other 35 innings of their four-game series.

Not every mistake made by an umpire has an easily identifiable ramification like that, but they do happen in most game, and it is no surprise that MLB and the WUA (World Umpire Association) want to have the smallest number of mistakes possible. Nowadays they can do this by looking at how many calls an umpire got right or wrong thanks to systems that track the speeds and trajectories of pitched baseballs. Read the rest of this entry »

Jurickson Profar finds himself at the top of the second base depth chart for the Oakland A’s, his new team following an offseason trade. That is very good news for Jurickson Profar, since it seems Jurickson Profar is struggling to throw the baseball.

The former top prospect made nine throwing errors in 2018… at two different positions. He made nine throwing errors at shortstop and nine throwing errors at third base. He also made one at second base, for a total of 19 throwing errors on the year. That is seven more than anyone else and the most in one season since the stat was first tracked in 2003.

Profar came up as a middle infielder, playing every game but one there in his minor league career. In 2018, he started at shortstop for the first three months of the season when Elvis Andrus got injured, and he struggled to throw the ball accurately. In mid-May, Levi Weaver of The Athletic was concerned Profar had the yips. In the article, Profar states how he’s struggled to make the transition defensively from Triple-A to the bigs:

“You know, this is the first time that I’ve gotten to play, for a long time, shortstop in the big leagues. Shortstop in the big leagues is not the same as shortstop in the minor leagues. You don’t get big guys like… Giancarlo Stanton, Aaron Judge, and Didi Gregorius…”

Profar was getting his first real chance to play shortstop in the majors, recognizing its difference from minor league ball, and thought he was benefiting from his time there. Just as he was getting his reps and starting to figure out the differences, the Rangers decided to move him to third base in July as Adrian Beltre’s hamstring acted up.

Profar started 46 games at third base, and the results weren’t great. He made as many throwing errors as Maikel Franco in half the innings. In an article published by Sportsday, former manager Jeff Bannister attributes Profar’s miscues to his lack of footwork:

“There are times when he gets a little out of rhythm,” Banister said. “That’s when you see those errant throws. … He continues to work on being consistent with the footwork.”

On a bunch of Profar’s errant throws this year, he seems unaware of the amount of time he has to make a play. He gets the ball very quickly out of his glove but the throws have a tendency to be off-line. He appears to be rushing his throws in situations that don’t warrant it. This goes along with his poor footwork. When he’s better able to recognize the amount of time he has to make a play, he can take the time to set his feet and make a proper throw to first base.

All that said, there are two main reasons that there is some hope for young Profar. Exhibit A is Marcus Semien, Profar’s new teammate. In 2015, Semien’s first year with Oakland, he made 18 throwing errors. The next season he made only eight, and his defensive numbers have continued to improve overall. Maybe there’s some coaches in Oakland who will be able to recognize a problem that the Rangers could not. It also doesn’t hurt that Matt Olson is one of the best defensive first basemen in the league. With that in mind, the change of location could really benefit Profar.

The other silver lining, and what you’ve probably been thinking this whole time, is that second base is a much easier throw. Profar played 79 innings at second base this year and made just one throwing error, coming on a play where he slipped, regained his footing, and then made a throw well wide of first. On plays in which he did not slip, Profar had a flawless record at second.

Even if Profar makes eight throwing errors this season at second base, the most made by any second baseman in 2018, that is fewer than half what he made last year. The Athletics currently have one of the best defensive infields in the game, and they are hoping they can help Profar stay on his feet and contribute with his arm.

When I was first asked about Chavez Young, I honestly vaguely recognized the name. I knew he was a Blue Jays outfielder, but that’s about it (and I work with prospects!). Just looking at the stat line, I was impressed. And the deeper I looked, the more I began to think that this might be the most criminally underrated prospect in baseball. David Laurila also took note recently here at FanGraphs.

Quick question: Who was the only prospect in baseball to record 50 extra-base hits and steal 40 bases in 2018? You guessed it: Chavez Young. Which prospect is rated as having the best defensive arm in the Blue Jays system by Baseball America, with 98 mph from the outfield recorded in high school? You guessed it again, Chavez Young. So how does a player like this go so under-the-radar? How does a 20-year-old in full-season ball who plays plus defense, hits third in his lineup, and had 50 extra-base hits and 40 steals not even make MLB Pipeline’s top 30 for the organization?

Let’s start off by saying who Chavez Young is. Young was born in the Bahamas, an area that is starting to get more buzz as a baseball country after producing prospects like Kristian Robinson and Jazz Chisholm. But it is also an area that up until recently was rarely visited by baseball scouts. Since 1983, only one major league player has made the MLB from the Bahamas (Antoan Richardson). As a result, Chavez left the Bahamas in high school to pursue baseball in the prestigious Georgia prep ranks. Read the rest of this entry »

Despite the generally slow free agent market and the continuing increase of bullpen usage, starting pitchers have done fairly well for themselves this winter. Patrick Corbin inked a nine-figure deal, blowing past most projections to get a guaranteed $140 million. The Rays shelled out their largest free agent contract ever, giving Charlie Morton $30 million over two years. Nathan Eovaldi parlayed a strong second half and postseason heroics into a four-year, $67.5 million pact to return to Boston, and J.A. Happ got half that from the Yankees for his age 36 and 37 seasons. Even past-their-prime options such as Lance Lynn, Anibal Sanchez, and Matt Harvey were given eight figures, the former two on multi-year guarantees.

Yet arguably the most accomplished hurler among this year’s crop of free agent starters remains unsigned – Dallas Keuchel. FanGraphs’ Crowd Source and MLB Trade Rumors projections both had the 2015 Cy Young winner in the neighborhood of four years and $80 million, which would exceed Eovaldi’s deal for the second-highest guarantee among starters.

Of available starters, Keuchel was worth the second-most WAR last year (3.6, behind only Corbin’s 6.3), and projects to be the second-most valuable next year (3.3 WAR, just behind Corbin’s 3.5). Much has been made of his decline in punchouts (his strikeout rate dipped to 17.5% in 2018, fourth-lowest among qualified pitchers), but his velocity has remained steady and he’s continued to limit both walks and homers while inducing lots of ground balls. In 2018, Keuchel topped 200 innings for the third time in five seasons, and he’s been an above-average starter in all of those years.

At 31, he’s not young, but he’s younger than Happ (36), Morton (35), Sanchez (35), and Lynn (32), all of whom received multi-year deals. It’s fair to say that Keuchel doesn’t have the upside of Corbin or Eovaldi (or maybe even that of Morton or Yusei Kikuchi), but his consistency and track record should appeal to plenty of teams in need of a rotation upgrade.

Happ, a southpaw with a similar reputation for durability and above-average-but-not-elite performance, and Keuchel have been almost identical over the past three years (518 innings and 9.1 fWAR for Happ, 518.1 and 8.6 for Keuchel). But Happ is four years older, so over the course of his next contract, Keuchel’s output could quite reasonably look a lot like Happ’s recent past – that is, a 170-inning, 3-win metronome.

However, there seems to be some concern or trepidation surrounding Keuchel, a pitcher whose raw stuff was never overpowering, and the sustainability of his results. And looking at some of his underlying metrics, it’s easy to see why. Read the rest of this entry »

Managers are always attempting to optimize their lineups for success. Whether they make in-game decisions like double-switches and lefty-righty matchups, or choose to change things up based on recent or historical performances, every move is meant to give their team the competitive advantage. What if they also made alterations based on pitcher groupings? In this article, I will attempt to determine if batter performance is impacted by pitcher clusters that are organized by pitch speed and pitch proportion.

The parameters used to cluster pitchers are below:

• Proportion of Pitch Thrown
• Average Pitch Speed

These statistics were calculated for the following pitch types:

• Changeup
• Curveball
• Eephus
• Cutter
• Four-seam fastball
• Sinker
• Two-seam fastball
• Knuckle-curve
• Knuckleball
• Slider
• Splitter

*All data in this study is from 2010-July 2017 (MLB Gameday). Read the rest of this entry »

The 20-to-80 scale is one of the core tenets of baseball scouting and allows evaluators to quickly interpret a player’s skillset. Kiley McDaniel wrote an excellent series of articles back in 2014 (and provided an update this past November) explaining the scale, and while the whole series is easily worth a read, one of the key notes is as follows:

The invention of the scale is credited to Branch Rickey and whether he intended it or not, it mirrors various scientific scales. 50 is major league average, then each 10 point increment represents a standard deviation better or worse than average.

On the surface, the scale is fairly easy to understand, but somewhat harder to conceptualize what each grade actually looks like. For example, how frequently does a hitter with a 45 power grade hit a home run? How does a 60 run grade translate to Sprint Speed? I decided to investigate, drawing inspiration from a 2013 article by Mark Smith. The idea of an objective 20-to-80 scale, while not a new concept, is worth revisiting at this time because of changes in the run environment and the development of new player evaluation techniques, most notably StatCast. We’ll begin with a brief rundown on methodology before looking at each tool: Read the rest of this entry »