Statcast, Scouting, and Statistics: An Objective Look at the 20-80 Scale for Hitters

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:

Methodology

As McDaniel noted, the 20-to-80 scale largely mirrors a normal distribution, with each 10-point grade jump representing an increase of one standard deviation. Following this logic, the first step towards creating an objective scale is finding “major league average.” This in and of itself presents a bit of a challenge, as major league average differs from the average of all regular (qualified) major league players. Taking the average of only qualified major league regulars would result in a skewed distribution, while including all major leaguers would result in far too many outliers (for example, a player batting .000 or .750). Therefore, the trick lies in finding a sample representative of more than just regulars but also one that isn’t easily skewed by tiny sample size variations. Many of the cutoffs selected are admittedly arbitrary but allow for a reasonable sample of players receiving significant time in the major leagues, and are largely based off of Russell Carleton’s excellent work on reliability. These cutoffs will be detailed with each statistic provided.

Returning to the methodology, once the “major league average” was established for each parameter, the standard deviation of the sample was calculated. The average was assigned a grade of 50, and each 5-point increase on the 20-to-80 scale resulted in the addition of half a standard deviation to the previous value. Likewise, a 5-point decrease corresponded with a decrease from the higher value by half a standard deviation. The following chart illustrates this pattern:

All data spans 2016 to 2018 and represents cumulative averages over that span. When applicable, each tool grade also provides a representative example player, as well as the number of players in the sample that fall under that tool grade. These examples are meant to be purely illustrative and are in place to simply give an idea of what each tool might look like rather than exist as a hard-and-fast grade. For FanGraphs-sourced data, cumulative data is directly pulled while cumulative three-year data from Baseball Prospectus and Baseball Savant was calculated with the use of weighted averages. Without further ado, let’s look at the tools:

Hit

For the hit tool, we’ll look at a number of measures, varying from traditional to StatCast-based. In order, those parameters are strikeout rate, batting average, batting average on balls in play, expected batting average, and contact rate. Strikeout rate and contact rate grades were calculated based on all non-pitchers with at least 200 PA between 2016 and 2018 (easily clearing Carleton’s standard of 60 PA for K%), while batting average was based off players with at least 910 at-bats (matching up with Carleton’s standard), and expected batting average and BABIP are based off all players with at least 820 balls in play over that span. Here’s a look at the hit tool across the league:

Power

Power can be measured in a number of ways, but we’ll look at four primary measures: home run rate, isolated power, average exit velocity, and barrels per batted ball event. Any player with at least 200 PA qualified for the sample for the ISO and HR% grades, while players with at least 100 batted ball events qualified for the StatCast metrics. It’s worth noting the absence of 20 or 25 power grade players, but this is possibly indicative of the emphasis teams place on hitting for power. It is nearly impossible for a player to provide enough value elsewhere to make up for a complete lack of power, as evidenced by the lack of extremely low power hitters in the league. Power may be one of the tools McDaniel mentioned in his primer on the scouting scale that doesn’t exactly follow a normal distribution.

Speed

Speed is a simpler tool to look at than just about any other tool thanks to the development of StatCast Sprint Speed. Any player season with 50 or more max effort runs (2016-2018) was considered for the sprint speed component of the tool. It is also worth adding baserunning into the mix as McDaniel notes that “baserunning and good jumps out of the batter’s box are also folded into the run grade.” The baserunning grade is based on a sample of all players with at least 150 times on base over the three-year span and the parameter is scaled to 150 times on base for ease of comparison.

Defense

While the hit, power, and speed tools are all fairly easily measured and interpreted, the defense and throwing tools present a new set of challenges. For one, it doesn’t make sense to compare players at vastly different positions on the same scale. For this purpose, I’ve broken down defense into three groups: catchers, infielders, and outfielders. Of the three groups, outfielders were the most straightforward. Outs Above Average provides a solid look at outfield defense (based on players with at least 1900 innings in the outfield, 2016 to 2018) but is absent for other positional groups. Additionally, the throwing components of DRS and UZR are more easily separated from the fielding-based components for outfielders, allowing for a separate defense grade (based on range and plays made) from the throwing grade.

This is similarly possible for catcher defense, but the catcher fielding grade is complicated by the myriad of factors that make up suiting up behind the plate. The catcher defensive parameters include non-throwing DRS, blocking runs, and framing runs. For infielders, it’s much more difficult to break down throwing runs from fielding ones, and for this purpose it is safest to simply present the two together. All Defensive Runs Saved and Ultimate Zone Rating data is from FanGraphs and includes any players with 2,000-plus innings played in the infield between 2016 and 2018. All UZR and DRS-based defensive metrics are scaled to 1,000 innings played. Catcher framing is scaled to per 5,000 framing chances while blocking is scaled to 2,500 blocking chances, based on all catchers with 1,000 defensive innings over the last three seasons (as are all catching defensive metrics for the purpose of this study).

Infielder Defense/Throwing

Outfielder Defense

Catcher Defense

Throwing

As discussed above, while there is no throwing grade for infielders, there are somewhat promising measures with which to evaluate outfielder and catcher throwing ability. Outfielder throwing is based on the arm run components of DRS and UZR, scaled to 1,000 innings. Catcher throwing includes both StatCast arm strength and caught stealing runs, scaled to 1,000 innings. Outfield metrics are based on all players with at least 2,000 defensive innings in the outfield while catcher throwing is based on all players with at least 1,000 innings caught since 2016. It is worth noting that the somewhat high cutoff for outfielders led to a number of players (Khris Davis, Derek Dietrich) that scored especially poorly in terms of throwing to not qualify for the sample, likely because teams have simply tried to avoid putting notably poor defenders and throwers in the field. Here’s a look at the throwing grade:

Outfielder Throwing

Catcher Throwing

It’s worth wondering whether the lack of catchers at either extreme in terms of defense and throwing is simply due to the limited sample of players or is being caused by a different phenomenon. It’s possible that the clustering effect is explained by the fact that terrible catchers either don’t remain behind the plate often or don’t make the major leagues. Whether this effect is due simply to a small sample, less than comprehensive defensive metrics, or something different, it is certainly interesting to observe compared to other positions.

Conclusion

For how often the 20-to-80 scale is discussed in baseball circles, it is interesting to note that it is rarely objectively analyzed to create a rough estimate of what each grade might look like at the major league level. The advent of StatCast has allowed for objective analysis of more tools than ever before and will hopefully continue to do so as the technology continues to develop. While my attempt at creating an objective view of the 20-to-80 scale is undoubtedly imperfect, the results certainly provide an interesting shorthand look at many of the measurable aspects of the scale.

Statistical data (AVG, BABIP, ISO, HR%, BsR, all DRS and UZR components, Contact%) from FanGraphs, pulled December 11, 2018. Catcher framing and blocking data taken from Baseball Prospectus, pulled on December 11, 2018. StatCast data (xBA, Exit Velocity, Sprint Speed, Barrels/BBE, OAA, Catcher Throwing Velocity) from Baseball Savant, pulled August 23, 2018. Cutoffs for metrics largely based on Russell Carleton’s work on reliability.

This piece was originally published on December 27, 2018 at the CheckSwings baseball blog.


Using Statcast Data to Estimate Minor League Home Run Distance

For a couple of years now, baseball fans have enjoyed publicly available Statcast data for the MLB level. This data allows us to examine the exit velocity, launch angle, estimated distance, and countless other aspects of every batted ball. This data has also resulted in “expected” stats, very useful additions to the toolbox of any baseball fanalyst. While this data is collected at the minor league level as well, it is not made publicly available, leaving us with a more limited toolbox when evaluating prospects via statistics.

Fortunately, one piece of Statcast-adjacent MiLB data is publicly-available. MLB’s Prospect site includes a search engine for MiLB statistics. For each batted ball, the site reports two “hit coordinates”: hc_x and hc_y. These coordinates appear to tell us the point on the field where the batted ball hit the ground or was caught. Using these hit coordinates, we can estimate (with some accuracy) the distance of home runs hit at the MiLB level.

Here are the hit coordinates of every batted ball by the Toronto Blue Jays in 2018 at the MLB level. The picture of a baseball diamond becomes even clearer if we multiply each hc_y by -1, flipping the image about the horizontal axis. Read the rest of this entry »


Are Analysts Affecting the Behavior They’re Observing?

Introduction and Hypothesis

One of the longest standing tenets of sabermetrics, stemming from Voros McCracken’s seminal 2001 work on DIPS (Defense Independent Pitching Stats) theory, is that pitchers ought to try for strikeouts rather than focusing on inducing weak contact. McCracken asserted that pitchers have little control over the quality of contact they allow. However, they do control if they strike the batter out (good) or walk him (bad) or allow a home run (even worse). Put another way, McCracken found a strong negative correlation between a pitcher’s strikeout rate (K%) and his runs allowed per nine innings (RA9). It is a simple logical step from here to conclude that pitchers ought to try to strike batters out.

Or is it?

Might McCracken’s DIPS observations only hold as long as pitchers are trying to generate weak contact? If they begin to focus solely on strikeouts, might this observed correlation weaken? Might we find more pitchers who are able to generate strikeouts but are not particularly successful at preventing runs?

As an analogy, consider a farmer whose goal is to get a big harvest of high-quality crops. To this end, he regularly waters and fertilizes his plants. He hires a consultant who does some studies and points out that fertilizing is closely correlated with the quality and quantity of the harvest. As a result, the farmer shifts all of his efforts to fertilizing and ignores watering altogether. Clearly this is not the best strategy. In the same way, might a pitcher be hurt by focusing on strikeouts and ignoring the quality of contact his pitches will generate if the batter does make contact?

With this in mind, might we, as analysts, in fact be affecting the very phenomena that we’re observing? Read the rest of this entry »


An Analysis of Pitch Movement at Coors Field

Since opening in 1999, Coors Field has provided the most offense-friendly environment in baseball. Despite the inherent volatility in park factors for single-season data, Coors has “won” the park factor title in 15 of the past 20 years, never finishing lower than third. The dramatic increase in home runs may be the most striking effect of the thin air about a mile above sea level, but all balls in flight, including pitches, are affected. Due to the lower air density, the spin-induced movement of a pitch thrown at high altitude will be lower than that of a comparable pitch closer to sea level.

Check out the average movement on Adam Ottavino’s pitches in 2017 and 2018 separated by home (purple) and away (black).

Ottavino pitch chart

You may recall Ottavino said recently that he is confident Babe Ruth couldn’t hit any of this stuff. Read the rest of this entry »


James Paxton Is Not the “Next Sonny Gray”

The Yankees kicked off their offseason by acquiring LHP James Paxton from the Seattle Mariners to bolster their starting rotation. You won’t find anybody willing to deny Paxton’s immense talent, but it’s natural for people to scrutinize big acquisitions, especially when the big acquisition is on his way to New York. This scrutiny is best exemplified by a conversation I had with my mother on the day the trade was made. My mom followed the Mets of the mid-to-late 1980s when she lived in Brooklyn, went years without watching baseball, and has watched the Yankees for the past decade by product of my fandom. This leads to the amusing circumstance that she is very familiar with current broadcasters Keith Hernandez and David Cone, all of the recent Yankees players, and almost nobody in between. Our conversation on the day of the Paxton trade went something like this:

Me: The Yankees picked up a hell of a pitcher named James Paxton. I think he’s going to do big things for the Yankees next year!

Mom: Yeah, sure. Isn’t that what you said about Sonny Gray?

Okay — she got me there.

Read the rest of this entry »


We Were Wrong About the Home Run Derby Curse

The Home Run Derby (HRD) is one of the most popular MLB events of the year, seemingly as popular among the players as among the fans. Everyone enjoys watching the best players in baseball launch 450-foot home runs while the non-participating All Stars towel the hitters off and cheer wildly for the most spectacular hits as they head over the outfield seats. But it is also one of the most controversial events, since it rewards something that every little leaguer is warned not to do — swing for the fences with every pitch. Some commentators believe that there is a pattern of derby participants exhibiting declining production in the second half of the season, and they argue that participation in the derby is to blame, because, they say, it ruins the swing plane of the participants. If we can put this theory to bed, then, if nothing else, it would take a little bit of stress off of a really fun night. If an effect does exist, however, it would be useful for front offices to know this before sending their stars to their potential demise.

It has become commonplace in the statistically minded baseball community to view the “Home Run Derby Curse” — the decline in productivity for HRD participants — as an example of misguided traditionalist folklore. The statistically savvy point out that people are selected for the derby exactly because they are overperforming their “true” talent level and because they will perform closer to that true talent level in the second half. Considering that, it is reasonable to assume their second-half performance will be worse than their first-half performance — a rather pedestrian example of regression to the mean. However, the argument usually stops here, as if somehow the concept of regression alone is enough to prove the non-existence of a curse.

The fundamental challenge in rigorously exploring whether or not the Home Run Derby caused a decline in production for an individual player is the same as for many arguments about causality — in order to firmly establish (or dismiss) the claim, we would need to imagine a counterfactual world in which that player did not participate in the derby and then we could see the difference in second-half production. That, of course, is impossible. One approach to addressing this challenge is to consider a collection of players whose statistics are similar to the HRD participant but who did not compete in the derby and look at the difference in second-half production. If we do this with all HRD participants, we should be able to see any general effects, if they exist. Read the rest of this entry »


Prospecting for the Mookie Betts of Pitching

Over the past several years, we have watched a number of hitters in the minors display good contact skills with average or below-average power be labeled with 45s and 50s only to burst onto the scene with an explosion of power they never showed any hint of previous. Mookie Betts might be the best example, along with guys like Jose Ramirez, who show up to the big leagues and announce themselves by mashing.  Naturally, prospect hounds, analysts, and the baseball community investigated how these guys went so overlooked (unless you were Carson Cistulli). It was surmised that contact quality mixed with good exit velocity and appropriate launch angles allowed hitters to maximize their output even without Aaron Judge levels of thump.

This investigation, however, is not a hunt for the next minor leaguer who will smash his way onto the scene, but rather a search for the pitchers who will try to stop them. With modern conditioning and institutions (read: Driveline) making it more possible than ever to gain velocity, one no longer must be naturally gifted a 6-foot-5 frame with easy 95 to be considered a prospect. Furthermore, with openers, bulk guys, firemen, and more, traditional pitching roles are going by the wayside.

This analysis attempts to seek out pitchers who possess above-average command or secondary offerings but lack the prototypical velocity grades we are seeing in today’s game. Identifying these pitchers would make them intriguing candidates for these high-intensity velocity training plans. While you may not find the next Luis Severino, you could uncover an explosive fireman reliever, matchup guy, or high-octane backend starter that pushes you closer to October glory.

The process for this analysis involved using the 2018 updated prospects list from THE BOARD, developed by Kiley McDaniel, Eric Longenhagen, and Sean Dolinar at this very site. I started by sorting for prospects who either currently have > 55 command or project for the same. This brought the sample to 85 pitchers. Next, I sorted out pitchers who have a present FB grade of > 55. Our sample now sits at 38 pitchers who have or project to have above-average command and an average-to-below-average fastball. Before diving into the next set of data, I wanted to provide some broader notes about this group. Notable pitchers with top 100–130 considerations on this list include Atlanta’s Kolby Allard and Joey Wentz, Miami’s Braxton Garrett, and the Angels’ Griffin Canning. There are 16 lefties and 22 righties. The Phillies lead the way with five of these guys, the Cubs and Rockies are tied with three each, and then the rest of the league has one or two on this list. Additionally, the average age of this group is 22.8 years old.

Now that we have our assorted pool, it is time to sort through this group’s off-speed arsenal. This part of the analysis was more subjective. I have attempted to group pitchers with similar traits that could fill a variety of roles. What follows is three tables of guys who could benefit most from additional velocity.

Elite Pitch Guys (70 Grade Pitch)
Name Pos Tm Age FB SL CH CMD
Eli Morgan RHP CLE 22.5 45 / 45 50 / 55 60 / 70 45 / 55
Logan Shore RHP DET 23.9 40 / 45 40 / 45 60 / 70 50 / 60

This first group features two right-handers with a current 60-grade pitch that projects for 70. Of the 38, these two are the lone members who feature a current 60 pitch. Of the two, Morgan has the higher upside based on his slider. Both have fastballs that sit around 90 mph, but additional velo training could push the value of these guys up a tier. Guys from this tier could be featured as openers or one-time-through-the-order relievers that rely on one elite pitch. The selling point of this group is that they have that elite pitch to lean on even without elite velocity.

Mid-to-Backend Starter Type (One 60 and 55)
Name Pos Tm Age FB CB CH CMD
Pedro Avila RHP SDP 21.8 50 / 50 55 / 60 55 / 60 45 / 55
Joey Wentz LHP ATL 21.1 45 / 50 45 / 55 60 / 60 45 / 55
Braxton Garrett LHP MIA 21.3 50 / 50 55 / 60 40 / 55 45 / 55
Foster Griffin LHP KCR 23.3 45 / 45 55 / 60 50 / 55 50 / 55

The next group features players with multiple 55-or-better future offerings, led by Padres righty Pedro Avila, who is rocking two future 60-grade pitches. Previously mentioned notables Garrett and Wentz also fall into this category. This group represents backend starter types who are useful during the season but less useful during the postseason. Additional velo here could push these guys into strong No. 3 starters or high-leverage multi-inning guys.

Kitchen Sinkers (High Secondary Scores)
Name Pos Tm Age FB SL CB CH CMD ARS
Griffin Canning RHP LAA 22.5 50 / 50 50 / 50 50 / 50 45 / 55 45 / 55 155
Peter Lambert RHP COL 21.6 50 / 50 45 / 50 50 / 55 55 / 60 45 / 55 155
Jose Lopez RHP CIN 25.2 50 / 50 50 / 50 50 / 50 40 / 50 50 / 55 150
Aaron Civale RHP CLE 23.4 45 / 50 55 / 60 40 / 45 45 / 50 50 / 60 155
Cole Irvin LHP PHI 24.8 40 / 40 45 / 50 50 / 50 40 / 45 45 / 55 145
Alec Mills RHP CHC 26.9 45 / 45 50 / 50 40 / 40 55 / 55 55 / 60 145
Cory Abbott RHP CHC 23.1 45 / 45 50 / 55 45 / 45 40 / 45 45 / 55 145

The last group of guys profile as backend starter types who live on off-speed stuff and have no margin for error with their fastballs. I identified these players by adding their FV non-fastball pitch grades together, noted as ARS in table (ARS = FCH+FSL+FCB). These guys walk the command and off-speed tightrope to end up as backend starters in the best case, or just middle-relief guys or up-and-down starters. Occasionally these guys become Kyle Hendricks, Tanner Roark, or Doug Fister, but these are exceptions and not the rule. Almost everyone in this group is older for a prospect, so the ceiling is limited, however, additional velo for these guys could turn them into more dynamic multi-inning relivers, bulk guys, or high-end No. 4-5 starters.

I should also note that all these guys fall into different buckets of age, level, and body types. Arguably, the most critical component of a prospect on this list would be targeting high-makeup guys who would be willing to experiment and acknowledge that they could use more gas to ascend to the next level. Some of these pitchers may be maxed out physically or unwilling to change what already seems to work. This analysis also looks past statistical performance, level, and even present pitch value a bit. What this analysis does do is identify guys who could rapidly improve with additional velocity due to advanced command and secondary. The margin for error is incredibly slim for this type of pitcher, but through intense training and velocity gains, pitcher X throwing 90-92 bumping to 94-96 with already above-average command and secondaries would vault them into a new tier of player. For teams looking to squeeze every ounce of value out of their farm system, this could be another way to target undervalued talent that has yet to be unlocked and developed.


The Ballad of Yandy Diaz

The Incredibles can be considered a masterpiece of modern cinema. Brad Bird’s 2004 Pixar film follows a family of superheroes in a world that’s openly hostile to their kind. As such, the family is forced to camouflage itself as a normal one. Mr. Incredible has an unfulfilling job. Mrs. Incredible thanklessly raises three kids. At the outset of the movie, we, the viewers, know the positive potential for superheroes from our prior knowledge of popular culture. For some reason, the movie tells us that the existence of superheroes is a bad thing, so we’re left with a major dissonance. Yandy Diaz’s situation in Cleveland was a lot like the beginning of The Incredibles.

In an age of celluloid superheroes, Diaz looks like a real-life version. The “most jacked player in baseball” according to stack.com (the mere existence of this website makes me uncomfortable) has put up minor league numbers of someone with super vision, not super strength. Indeed, his career-low in on-base percentage at any minor league stop with at least 20 plate appearances is .399. In 2018, he split time between Triple-A Columbus and Cleveland, where his OBPs were .409 and .375, respectively.

Unfortunately, like the existence of superheroes, Diaz’s power remains in our collective imaginations. Despite his mammoth yolkedness, his professional career high in home runs in any full season is nine. Even Delino Deshields hit 12 in the minors once, and he is a small, fast player, and he plays like it. You can usually tell what kind of player someone is by their body type, but not so with Diaz.

As you may guess, there have been zero ground-ball home runs in the Statcast Era. Indeed, cold hard data and common sense align on this topic. This is the great barrier separating our reality from SuperYandy, his radioactive spider so to speak. Diaz’s professional ground-ball rates have ranged from the low-50s to the mid-60s. To put that in context, the following chart shows the ten qualified MLB players with the highest GB%, along with Yandy’s 2018 season:

Ground Ball Percentage in 2018
Name GB% BB% BABIP wRC+
Ian Desmond 62.0 8.6 0.279 81
Eric Hosmer 60.4 9.2 0.302 95
Jon Jay 59.3 5.6 0.319 86
Jonathan Villar 55.9 8 0.339 94
Dee Gordon 55.2 1.5 0.304 77
Nomar Mazara 55.1 7.5 0.298 96
Trey Mancini 54.6 6.9 0.285 93
Lorenzo Cain 54.6 11.5 0.357 124
Matt Duffy 54.3 8.4 0.353 106
Yandy Diaz 53.3 9.2 0.371 115
Willson Contreras 52.0 9.7 0.313 100

The most valuable player here is Lorenzo Cain, and by a wide margin. As you can see, Cain posted one of the highest BABIPs and walk rates of the bunch, and he finished 24% above average offensively. Combine that with his stellar baserunning and center-field defense, and Cain was one of the most valuable players in the majors in 2018. However, the chart makes it clear that hitting grounders is generally not great for hitters’ production. Without a strong BABIP and BB%, it’s nearly impossible to be above average while hitting that many grounders.

Luckily for Diaz, he seems to be skilled at achieving high marks in both of those categories. We’ve already talked about his SuperVision, and the Steamer projection system expects Diaz to put up a .368 OBP while walking over 12% of the time in 2019. Meanwhile, Yandy’s BABIP is directly tied to his potential to become baseball’s Mr. Incredible.

BABIP has always been an attention-grabbing stat. When it first jumped into the analytical scene, it was dismissed as randomness. The thought was that hitters can’t control where the ball goes, or the quality and positioning of defenders, so we should expect batting average on balls in play to fluctuate with luck, and to an extent, it does. But more recent thought suggests that players do have some control over their BABIP. Cain is much faster than Trey Mancini, so even though they have identical ground-ball rates, we can expect Cain to beat out more of those grounders. Indeed, Cain had 27 infield hits in 2018, while Mancini finished with only 11.

So what other controllable factors can lead to higher BABIP? For one, batters can influence how hard they hit the ball, and they can influence how high they hit the ball; indeed, we find that each of these factors affects batting average on balls in play. From 2015-17, balls struck at 100 mph led to base hits 49.8% of the time, and that percentage only increases with harder hit balls. (It should be noted that that includes home runs, which are not included in BABIP). In addition, each type of batted ball is associated with BABIP performance, as outlined in the following chart:

Batted Ball Results
Type BABIP wRC+
Grounders 0.236 30
Liners 0.672 339
Fly Balls 0.117 133
Non-Flies 0.380 132
Non-Grounders 0.343 211

Diaz has always specialized in that ‘non-flies’ category: during his limited MLB career, just 20.8% of his batted balls have been fly-balls, which is the sixth-lowest during that time-frame. However, not all non-flies are created equally; we’ve already discussed the association between hitting the ball hard and reaching base successfully. Among the 480 players with 50 or more batted balls in 2018, Diaz finished 24th in average exit velocity, just behind the AL and NL MVPs, Mookie Betts and Christian Yelich.

This is the kind of hitter Diaz is as of this winter. He’s a non-fly-ball hitter who consistently makes great contact with an above-average eye and low strikeout numbers. In the field, Diaz had a strong reputation in the minors. Baseball America even named him the strongest defensive third baseman in the Carolina League in 2014. Additionally, throughout his minor league career, he played every position except shortstop, catcher, and pitcher.

Put all of this together and you have yourself a high-floor, multi-positional major league baseball player, which makes his time with the Indians franchise seem peculiar. Diaz began his American baseball career in 2014, and he cruised through the minor leagues as a consistently great hitter. By just 2015, he got his first taste of Triple-A ball, and 25 games into 2016, he was permanently at that level. And then… the Indians never really gave him a shot. For Columbus in 2016, as a 24-year-old, he hit .325/.399/.461, good for a 149 wRC+. No call-up. In 2017, he was even better: .350/.454/.460, 163 wRC+. Finally, the Indians called him up after over 800 extremely successful Triple-A plate appearances, and he fared okay in his cup of coffee. In 2018, he spent most of his time in Triple-A, again, despite his 132 wRC+ in his 120 MLB plate appearances.

For some reason, despite his overwhelming success as a professional baseball player, Cleveland barely gave him a chance to succeed. He’s potentially Mr. Incredible, an extremely strong player with great potential being held back. For one, it seems like Cleveland, and Terry Francona, viewed Diaz’s defense as “a work in progress,” despite countless public reports to the contrary. Maybe that’s the case, but even if we assume Diaz’s fielding isn’t actually as good as those reports make it out to be, I still find it hard to believe that Diaz would not have been a better first baseman than Yonder Alonso in 2018. We must consider that it is possible that the front office knows something about him that would make him less appealing.

To Tampa Bay, Diaz is not just a high-floor Ben Zobrist type, but he also has tantalizing upside. There’s no way of knowing this for sure, but I am willing to guess that the strongest three players in the major leagues are, in some order, Yandy Diaz, Aaron Judge, and Giancarlo Stanton. We don’t know if Cleveland tried to convert Diaz into someone with Judgian power, as John Sterling would say, but we can assume the Indians at least thought about it. Maybe they tried to change his swing and he was resistant. Who knows? There’s little doubt that this is the Rays’ plan though. They’re hoping that Diaz can change his swing a la J.D. Martinez and become SuperYandy, a slugger without the strikeouts. Even if that doesn’t work out, they still end up with a valuable player, and if it does, Diaz could end up as one of the most valuable players in baseball. It will take time before we know if he was one of the best players traded this offseason, but rest assured, he’s already one of the most interesting.


Where Did Madison Bumgarner’s Four-Seamer Go?

Something appears to have happened to Madison Bumgarner. Specifically, his four-seam fastball has gone missing. Depending on which data source you use, it figuratively and literally disappeared. Regardless of data source used, Bumgarner’s fastball isn’t performing.

Two leading data sources disagree on what has happened to Bumgarner’s fastball. Because of this, I chose to look at both sources independently: Pitch Info (through Brooks Baseball) and Statcast (through Baseball Savant). This analysis spans four seasons, 2015 through 2018, encompassing Bumgarner’s two best and two worst complete seasons.

According to Pitch Info, Bumgarner threw four-seamers in 2018 at a career-low frequency — 34.5% of the time in 2018, down from 48.2% in 2016 and 49.6% in 2015. It has been losing effectiveness since its peak in 2014. Using Pitch Info’s runs above average metric, we see Bumgarner’s four-seamer peaked in quality at 1.25 runs above average per 100 pitches in 2014 and has dropped each year since then: 0.97 in 2015, 0.39 in 2016, -0.35 in 2017, and -1.14 in 2018, a career low.

bum brooks.png

As seen in the Pitch Info Whiff Percentage charts above, Bumgarner’s four-seam fastball had its lowest whiff rate of our period of study in 2018 (seen on the left), likely leading to it’s ineffectiveness. Similarly, Bumgarner’s four-seam is measured to have had more vertical sink, independent of gravity, than it had throughout this period (seen on the right). Depending on the pitch, more movement generally increases whiff rates. A four-seam fastball moving more like a two-seamer, however, would lose swing-throughs: sinkers (two-seamers) generate more contact in the form of ground balls.

Screen Shot 2018-10-10 at 3.45.35 PM.png

Bumgarner produced his highest ground-ball rate with his fastball since 2013 while also generating the fewest whiffs with his fastball of his career. Couple the results with the change (increased vertical movement), and it appears his fastball began to behave like a two-seam fastball.

This seems to be clear already. According to Statcast, Bumgarner threw his four-seam fastball only once in 2018, as compared to 38.6% of the time in 2016 and 41.1% of the time in 2015. He replaced them mainly with two-seam fastballs, but also with some curveballs and changeups.

bum_pitches_16-18

When comparing Statcast to Pitch Info, I wondered if Statcast could have been misclassifying four-seam fastballs as two-seamers. Through looking at the above plots, however, it’s clear a cluster of pitches was missing in 2018. The above graphs are of every pitch Bumgarner threw, by horizontal (x-axis) and vertical (y-axis) movement, colored by Statcast pitch classifications. Even when ignoring pitch type labels, a pitch type is seen to be missing. Specifically, Bumgarner’s high-rising, fairly straight pitch was no longer thrown. On a side note, notice how inconsistent 2017’s movements were: likely because Bumgarner had to recover form a major shoulder injury and struggled.

With Statcast data, we can evaluate what happened with greater depth than through other methods. Below is a table of statistical changes in both Bumgarner’s two-seam and four-seam fastballs.

fastball stats

Velocity is measured in miles per hour, spin in revolutions per minute, extension is feet from the rubber, and horizontal and vertical movements are in inches from release point. Ignore 2017, as it was a very inconsistent year (as seen with the movement chart above). Both two-seam and four-seam fastballs in 2015 and 2016 had significant vertical rise due to spin. In 2018, however, Bumgarner couldn’t or wasn’t spinning his fastballs as much, resulting in less rise and more downward movement. This could be why Statcast is misclassifying his fastballs.

Why has Bumgarner lost spin on his fastballs? The data suggests two reasons why, both of which could be correlated. He’s lost velocity, and release speed correlates with spin rate. Similarly, Bumgarner has less extension on his fastballs than in 2016. His 2018 extension is similar to his 2015 extension, but because he’s lost velocity, the loss of extension could be penalizing. This loss of extension could explain the loss of spin if it’s related to grip or release.

Extension loss to home plate reduces the perceived velocity the batter anticipates, making it easier for the batter to time the pitch. Both loss of velocity and extension would, when combined, greatly benefit the batter at the expense of Bumgarner’s fastball.

What could have caused the loss of velocity and extension? Bumgarner is 29 years old, so there is a chance he’s entered his decline. The likely culprit, however, is injury: Bumgarner fell of a dirt bike in April 2017, injuring his left shoulder, and he broke his left hand on a line-drive comebacker in spring training in 2018, requiring surgery. Being left-handed, both injuries could have significantly affected his 2018.

One year away from free agency, Bumgarner likely hopes he can recover lost velocity and spin on his fastball. Whether it was an organizational change, a declining skill set, or driven by injury, his 2018 fastball difference was one to forget. His shoulder should be better healed, one year further removed from his accident, and hopefully his throwing hand does the same.

This and other postings like it can be found on my personal blog, First Pitch Swinging.

Best of the Bench in 2018

Every year, nearly every team in baseball receives significant contributions from unexpected sources. The 2018 campaign was no exception, with a number of players putting up productive seasons while primarily coming off the bench. For the purposes of this article, we’ll define a bench player as any player with (roughly) 100-plus plate appearances over the course of the season that did not appear as a team’s primary starter at any one position. Additionally, I sorted out players that were traded (or acquired) midseason (Manny Machado for example) or called up to start full time but did not record enough time to count as the team’s primary starter at their position (per Baseball Reference). Players with 500-plus PA were excluded from consideration on account of extensive playing time (apologies to Chris Taylor, Matt Kemp, Jurickson Profar, and Ben Zobrist), and one player was selected at each position, along with honorable mentions. Without further ado, let’s look at the best of the bench in 2018:

Catcher: Elias Diaz, Pirates

Honorable Mentions: Tyler Flowers (ATL), Luke Maile (TOR)

Diaz certainly served as a key bright spot during an up-and-down season for the Pirates, filling in admirably for the oft-injured Francisco Cervelli. The young Venezuelan had somewhat of a breakout campaign in 2018, posting a 114 wRC+ while contributing positive defensive value behind the plate. Overall, Diaz put up a solid 2.0 WAR in 277 PA on the season. After a weak offensive showing in 2017, Diaz appears to have made notable strides in plate discipline this season, improving his walk rate from 5.5% to 7.6% while dropping his strikeout rate from 19.0% to 14.4%. This decline in strikeout rate represents one of the 10 largest such drops in the league between 2017 and 2018 among players with at least 200 PA in each season. Combined with Cervelli’s excellent output, Diaz helped guide the Bucs to a league-leading 5.3 WAR from catchers:

While Flowers and Maile each put up solid seasons in their own right, Diaz ranked among the top 10 bench players in all of baseball by WAR this season and established himself as one of the better backup catchers in the game. Read the rest of this entry »