Archive for May, 2014

Home-Run Environment And Win-Homer Correlation

Home runs are good, I think we can all agree on that, and in the presumably post-steroid environment they have been in decline.  Does that make the home run more or less important?  It is hard to say.  In some ways it means that they are more scarce, and you might expect that home run hitting teams might be at a larger advantage than previously.  On the other hand, teams that don’t hit a lot of balls out of the park will not be as far behind their peers if said peers are not taking the ball yard quite so frequently.  So which is it?

FanGraphs, of course, can give the answer.  I took every team in the expansion era (1961 and on) and then tracked two things year over year.  The first was how far each team was from the average home runs for a team, just home runs for a team minus the average of all MLB teams.  From there I calculated the correlation of those differences with the wins that the team accumulated in that year.  Then I tracked that correlation versus the overall home run environment.  To get them in the same scale I tracked home run environment as a percent of the max average home runs per team, so 2000 became 100%, or peak home run environment, as it was the highest average per team and every other year the average was some percent below that with the average in 2000 as the denominator.

I did omit 1994 and 1981 due to how much the seasons were shortened by strikes.  It made the overall graph harder to read.  The results look like this:

 

 photo HRenvironment_zps35a42fa7.jpg

 

And the answer is…it doesn’t matter!  Home runs are always positively correlated with wins, meaning it is never advantageous for a team to be below average when it comes to hitting home runs.  That correlation over time has a best fit line with a near zero slope.  Home runs are equally valuable with respect to winning in lower home run environments and the more recent high ones.  You can also see that the correlation is rather volatile ranging from barely positive to about .65 which is a fairly strong positive relationship.  Volatile, but never negative, so there are no years where a bunch of below average home run hitting teams took the league by storm.

The home run environment last year was back to 81.9% of the peak in 2000, and this year’s pace is a little slower than last with home runs in 2.38% of plate appearances rather than 2013’s 2.52%, which could reduce the total home runs hit by more than 8 per team for the year, though the heat of summer will probably close that gap up some.  It is likely though that the overall home run environment will be down to the levels we saw in 2011 and 2012, and maybe the drop off from 2000 has flattened out.

Anyway, I know everyone hates a non-result, there are published papers that have been published about the bias against them even, but this is still interesting to at least me.  You always want to hit home runs, we already knew that, but the value of the home runs should not be increased in times when they are scarce and they don’t become even more necessary during a homer boom.  This means that teams shouldn’t for instance overpay for a guy like Giancarlo Stanton right now because his power bat is more valuable in the current home run environment.  It means they should overpay so that their fans can enjoy the majestic blasts and feel content knowing they will be just as valuable as ever.


Foundations of Batting Analysis – Part 2: Real and Indisputable Facts

In Part 1 (http://www.fangraphs.com/community/foundations-of-batting-analysis-part-1-genesis/), we examined how the hit became the first estimate of batting effectiveness in 1867 leading to the creation of the modern batting average in 1871. In Part 2, we’ll look more closely at what the hit actually measures and the inherent flaws in its estimation.

Over the century-and-a-half since Henry Chadwick wrote “The True Test of Batting,” it has been a given that if the batter makes contact with the ball, he has only shown “effectiveness” when that contact results in a clean hit – anything else is a failure. At first glance, this may seem somewhat reasonable. The batter is being credited for making contact with the ball in such a way that it is impossible for the defense to make an out, an action that must be indicative of his skill. If the batter makes an out, or reaches base due to a defensive error that should have resulted in an out, it was due to his ineffectiveness – he failed the “test of skill.”

This is an oversimplified view of batting.

By claiming that a hit is entirely due to the success of the batter and that an out, or reach on error, is due to his failure, we make fallacious assumptions about the nature of the game. Consider all of the factors involved in a play when a batter swings away. The catcher calls for a specific pitch with varying goals in mind depending on the batter, the state of the plate appearance, and the game state. The pitcher tries to pitch the ball in a way that will accomplish the goals of the catcher.[i] The batter attempts to make contact with the ball, potentially with the intent to hit the ball into the air or on the ground, or in a specific direction. The fielders aim to use the ball to reduce the ability of the batting team to score runs, either by putting out baserunners or limiting their ability to advance bases. The baserunners react to the contact and try to safely advance on the bases without being put out. All the while, the dirt, the grass, the air, the crowd, and everything else that can have some unmeasurable effect on the outcome of the play, are acting in the background. It is misleading to suggest that when contact between the bat and ball results in a hit, it must be due to “effective batting.”

Let’s look at some examples. Here is a Stephen Drew pop up from the World Series last year:

Here is a Michael Taylor line drive from 2011:

The contact made by Taylor was certainly superior to that made by Drew, reflecting more batting effectiveness in general, but due to fielding effectiveness—and luck—Taylor’s ball resulted in an out while Drew’s resulted in a hit.

Here are three balls launched into the outfield:

In each case, the batter struck the ball in a way that could potentially benefit his team, but varying levels of performance by the fielders resulted in three different scoring outcomes: a reach on error, a hit, and an out, respectively.

Here are a pair of a groundballs:

Results so dramatically affected by luck and randomness reflect little on the part of the batter, and yet we act as if Endy Chavez was effective and Kyle Seager was ineffective.

Home runs may be considered the ultimate success of a batter, but even they may not occur simply due to batting effectiveness. Consider these three:

Does a home run reflect more batting effectiveness when it lands in front of the centerfielder, when it’s hit farther than humanly possible,[ii] or when it doesn’t technically get over the wall?

The hit, at its core, is an estimate of value. Every time the ball is put into play in fair territory, some amount of value is generated for the batter’s team. When an out is made, the team has less of an opportunity to score runs: negative value. When an out is not made, the team has a greater opportunity to score runs: positive value. Hits estimate this value by being counted when an out is not made and when certain other aspects of the play conform to accepted standards of batting effectiveness, i.e. the 11 subsections of Rule 10.05 of the Official Baseball Rules that define what are and are not base hits, as well as the eight subsections of Rule 10.12.(a) that define when to charge an error against a fielder.

Rule 10.05 includes the phrase “scorer’s judgment” four times, and seven of the 11 parts of the rule involve some form of opinion on the part of the scorer to determine whether or not to award a hit. All eight subsections of Rule 10.12.(a) that define when to charge an error against a fielder are entirely subjective. Not only is the hit as an estimate of batting effectiveness muddled by the forces in the game that are outside of the batter’s control, but the decision whether to award a hit or an error can be based on subjective opinion. Imagine you’re the official scorer; are these hits or errors?

If you agreed with the official scorer on the last play, that Ortiz reached on a defensive error, you were “wrong” according to MLB, which overturned the call and awarded Ortiz a hit retroactively (something I doubt would have occurred if Darvish had completed the no-hitter). Despite Chadwick’s claim in 1867 that “there can be no mistake about the question of a batsman’s making his first base…whether by effective batting, or by errors in the field,” uncertainty in how to designate the outcome of a play is all too common, and not a modern phenomenon.

In an article in the 6 April 1916 issue of the Sporting News, John H. Gruber explains that before scoring methods became standardized in 1880, the definition of a hit could vary wildly from scorer to scorer.

“It was evidently taken for granted that everybody knew a base hit when he saw one made…a group of ‘tight’ and another of ‘open’ scorers came into existence.

‘Tight’ were those who recognized only ‘clean’ hits, when the ball was not touched by a fielder either on the ground or in the air. Should the fielder get even the tip of his fingers on the ball, though compelled to jump into the air, no hit was registered; instead an error was charged.

The ‘open’ contingent was more liberal. To it belonged the more experienced scorers who used their judgment in deciding between a hit and an error, and always in favor of the batter. They gave the batter a hit and insisted that he was entitled to a hit if he sent a ‘hot’ ball to the short-stop or the third baseman and the ball be only partly stopped and not in time to throw it to a bag.

Some of them even advocated the ‘right field base hit,’ which at present is scored a sacrifice fly. ‘For instance,’ they said, ‘a man is on third base and the batsman, in order to insure the scoring of the run by the player on third base, hits a ball to right field in such a way that, while it insures his being put out himself, sends the base runner on third home, and scores a run. This is a play which illustrates ”playing for the side” pretty strikingly, and it seems to us that such a hit should properly come under the category of base hits.’”

While official scorers have since become more consistent in how they score a game, there will never be a time when hits will not involve a “scorer’s judgment” on some level. As Isaac Ray wrote in the North American Review in 1856, building statistics based on opinion or “shrewd conjecture” leads to “no real advance in knowledge”:

“The common fallacy that, imperfect as they are, they still constitute an approximation of the truth, and therefore are not to be despised, is founded upon a total misconception of the proper objects of statistical inquiry, as well as of the first rules of philosophical induction. Facts—real and indisputable facts—may serve as a basis for general conclusions, and the more we have of them the better; but an accumulation of errors can never lead to the development of truth. Of course we do not deny that, in a mere matter of quantity, the errors on one side generally balance the errors on the other, and thus the value of the result is not materially affected. What we object to is the attempt to give a statistical form to things more or less doubtful and subjective.”

Hits, these “approximations of the truth,” have been used as the basic measurement of success for batters for the entire history of the professional game. However, in the 1950s, Branch Rickey, the general manager of the Los Angeles Dodgers, and Allan Roth, his statistical man-behind-the-curtain, acknowledged that a batter could provide value to his team outside of just swinging the bat. On August 2, 1954, Life magazine printed an article titled “Goodby to Some Old Baseball Ideas” in which Rickey wrote on methods used to estimate batting effectiveness:

“…batting average is only a partial means of determining a man’s effectiveness on offense. It neglects a major factor, the base on balls, which is reflected only negatively in the batting average (by not counting it as a time at bat). Actually walks are extremely important…the ability to get on base, or On Base Average, is both vital and measurable.”

While the concept didn’t propagate widely at first, by 1984 on base average (OBA) had become one of three averages, along with batting average (BA) and slugging average (SLG), calculated by the official statisticians for the National and American Leagues. These averages are currently calculated as follows:

BA = Hits/At-Bats = H/AB

OBA = (Hits + Walks + Times Hit by Pitcher) / (At-Bats + Walks + Times Hit by Pitcher + Sacrifice Flies) = (H + BB + HBP) / (AB + BB + HBP + SF)

SLG = Total Bases on Hits / At-Bats = TB/AB

The addition of on base average as an official statistic was due in large part to Pete Palmer who began recording the average for the American League in 1979. Before he began tracking these figures, Palmer wrote an article published in the Baseball Research Journal in 1973 titled, “On Base Average for Players,” in which he examined the OBA of players throughout the history of the game. To open the article, he wrote:

“There are two main objectives for the hitter. The first is to not make an out and the second is to hit for distance. Long-ball hitting is normally measured by slugging average. Not making an out can be expressed in terms of on base average…”

While on base average has proven popular with modern sabermetricians, it does not actually express the rate at which a batter does not make an out, as claimed by Palmer. Rather, it reflects the rate at which a batter does not make an out when showing accepted forms of batting effectiveness; it is a modern take on batting average. The suggestion is that when a batter reaches base due to a walk or being hit by a pitch he has shown effectiveness, but when he reaches on interference, obstruction, or an error he has not.

Here are a few instances of batters reaching base without swinging.

What effectiveness did the batter show in the first three plays that he failed to show in the final play?

In the same way that there are a litany of forces in play when a batter tries to make contact with the ball, reaching base due to non-swinging events requires more than just batting effectiveness. Reaching on catcher’s interference may not require any skill on the part of the batter, but there are countless examples of batters being walked or hit by a pitch that similarly reflect no batting skill. A batter may be intentionally walked because they are greatly skilled and the pitcher, catcher, or manager fears what the batter may be able to do if he makes contact, but in the actual plate appearance itself, that rationalization is inconsequential. If we’re going to estimate the effectiveness of a batter in a plate appearance, only what occurs during the plate appearance is relevant.

Inconsistency in when we decide to reward batters for reaching base has limited our ability to accurately reflect the value produced by batters. We intentionally exclude certain results and condemn others as failures despite the batter’s team benefiting from the outcomes of these plays. Instead of restricting ourselves to counting only the value produced when the batter has shown accepted forms of effectiveness, we should aim to accurately reflect the total value that is produced due to a batter’s plate appearance. We can then judge how much of the value we think was due to effective batting and how much due to outside forces, but we need to at least set the baseline for the total value that was produced.

To accomplish this goal, I’d like to repurpose the language Palmer used to begin “On Base Averages for Players”:

There are two main objectives for the batter. The first is to not make an out and the second is to advance as many bases as possible.

“Hitters” aim to “hit for distance” as it will improve their likelihood of advancing on the bases. “Batters” aim to do whatever it takes to advance on the bases. Hitting for distance may be the best way to accomplish this, in general, but batters will happily advance on an error caused by an errant throw from the shortstop, or a muffed popup in shallow right field, or a monster flyball to centerfield.

Unlike past methods that estimate batting effectiveness, there will be no exceptions or exclusions in how we reflect a batter’s rate at accomplishing these objectives. Our only limitation will be that we will restrict ourselves to those events that occur due to the action of the plate appearance. By this I mean that baserunning and fielding actions that occur following the initial result of the plate appearance are not to be considered. For instance, events like a runner advancing due to the ball being thrown to a different base, or a secondary fielding error that allows runners to advance, are to be ignored.

The basic measurement of success in this system is the reach (Re), which is credited to a batter any time he reaches first base without causing an out.[iii] A batter could receive credit for a reach in a myriad of ways: on a clean hit,[iv] a defensive error, a walk, a hit by pitch, interference, obstruction, a strikeout with a wild pitch, passed ball, or error, or even a failed fielder’s choice. The only essential element is that the batter reached first base without causing an out. The inclusion of the failed fielder’s choice may seem counterintuitive, as there is an implication that the fielder could have made an out if he had thrown the ball to first base, but “could” is opinion rearing its ugly head and this statistic is free of such bias.

The basic average resulting from this counting statistic is effective On Base Average (eOBA), which reflects the rate at which a batter reaches first base without causing an out per plate appearance.

eOBA = Reaches / Plate Appearances = Re/PA

Note that unlike the traditional on base average, all plate appearances are counted, not just at-bats, walks, times hit by the pitcher, and sacrifice flies. MLB may be of the opinion that batters shouldn’t be punished when they “play for the side” by making a sacrifice bunt, but that opinion is irrelevant for eOBA; the batter caused an out, nothing else matters.[v]

eOBA measures the rate at which batters accomplish their first main objective: not causing an out. To measure the second objective, advancing as many bases as possible, we’ll define the second basic measurement of success as total bases reached (TBR), which reflects the number of bases to which a batter advances due to a reach.[vi] So, a walk, a single, and catcher’s interference, among other things, are worth one TBR; a two-base error and a double are worth two TBR; etc.

The average resulting from TBR is effective Total Bases Average (eTBA), which reflects the average number of bases to which a batter advances per plate appearance.

eTBA = Total Bases Reached / Plate Appearances = TBR/PA

We now have ways to measure the rate at which a batter does not cause an out and how far they advance on average in a plate appearance. While these are the two main objectives for batters, it can be informative to know similar rates for when a batter attempts to make contact with the ball.

To build such averages, we need to first define a statistic that counts the number of attempts by a batter to make contact, as no such term currently exists. At-bats come close, but they have been altered to exclude certain contact events, namely sacrifices. For our purposes, it is irrelevant why a batter attempted to make contact, whether to sacrifice himself or otherwise, only that he did so. We’ll define an attempt-at-contact (AC) as any plate appearance in which the batter strikes out or puts the ball into play. The basic unit to measure success when attempting to make contact is the reach-on-contact (C), for which a batter receives credit when he reaches first base by making contact without causing an out. A strikeout where the batter reaches first base on a wild pitch, passed ball, or error counts as a reach but it does not count as a reach-on-contact, as the batter did not reach base safely by making contact.

The basic average resulting from this counting statistic is effective Batting Average (eBA), which reflects the rate at which a batter reaches first base by making contact without causing an out per attempt-at-contact.

eBA = Reaches-on-Contact / Attempts-at-Contact = C/AC

Finally, we’ll define total bases reached-on-contact (TBC) as the number of bases to which a batter advances due to a reach-on-contact. The average resulting from this is effective Slugging Average (eSLG), which reflects the average number of bases to which a batter advances per attempt-at-contact.

eSLG = Total Bases Reached-on-Contact / Attempts-at-Contact = TBC/AC

The two binary effective averages—eOBA and eBA—are the most basic tools we can build to describe the value produced by batters. They answer a very simple question: was an out caused due to the action in the plate appearance. There are no assumptions made about whose effectiveness caused an out to be made or not made, we only note that it occurred during a batter’s plate appearance; these are “real and indisputable facts.”

The value of these statistics lies not only in their reflection of whether a batter accomplishes his first main objective, but also in their linguistic simplicity. Miguel Cabrera led qualified batters with a .442 OBA in 2013. This means that he reached base while showing batting effectiveness (i.e. through a hit, walk, or hit by pitch) in 44.2 percent of the opportunities he had to show batting effectiveness (i.e. an at-bat, a walk, a hit by pitch, or a sacrifice fly). That’s a bit of a mouthful, and somewhat convoluted. Conversely, Mike Trout led all qualified batters with a .445 eOBA in 2013, meaning he reached base without causing an out in 44.5 percent of his plate appearances. There are no exceptions that need to be acknowledged for plate appearances or times safely reaching base that aren’t counted; it’s simple and to the point.

The two weighted effective averages—eTBA and eSLG—depend on the scorer to determine which base the batter reached due to the action of the plate appearance, and thus reflect a slight level of estimation. As we want to differentiate between actions caused by a plate appearance and those caused by subsequent baserunning and fielding, it’s necessary for the scorer to make these estimations. This process at least comes with fewer difficulties, in general, than those that can arise when scoring a hit or an error. No matter what we do, official scorers will always be a necessary evil in the game of baseball.

While I won’t get into any real analysis with these statistics yet, accounting for all results can certainly have a noticeable effect on how we may perceive the value of some players. For example, an average batter last season had an OBA of .318 with an eOBA of .325. Norichika Aoki was well above average with a .356 OBA last season, but by accounting for the 16 times he reached base “inefficiently,” he produced an even more impressive .375 eOBA. While he was ranked 37th among qualified batters in OBA, in the company of players like Marco Scutaro and Jacoby Ellsbury, he was 27th among qualified batters in eOBA, between Buster Posey and Jason Kipnis; a significant jump.

In the past, we have only cared about how many total bases a batter reached when he puts the ball into play, which is a disservice to those batters who are able to reach base at a high rate without swinging. Joey Votto had an eSLG of .504 last season – 26th overall among qualified batters. However, his eTBA, which accounts for the 139 total bases he reached when not making contact, was .599 – 7th among qualified batters.

This is certainly not the first time that such a method of tracking value production has been proposed, but it never seems to gain any traction. The earliest such proposal may have come in the Cincinnati Daily Enquirer on 14 August 1876, when O.P. Caylor suggested that there was a strong probability that “a different mode of scoring will be adopted by the [National] League next year”:

“Instead of the base-hit column will be the first base column, in which will be credited the times a player reached first base in each game, whether by an error, called balls, or a safe hit. The intention is to thereby encourage not only safe hitting, but also good first-base running, which has of late sadly declined. Players are too apt, under the present system of averages, to work only for base hits, and if they see they have not made one, they show an indifference about reaching first base in advance of the ball. The new system will make each member of a club play for the club, and not for his individual average.”

Of course, this new mode was not adopted. However, the National League did count walks as hits for a single season in 1887; an experiment that was widely despised and abandoned following the end of the season.

It has been 147 years since Henry Chadwick introduced the hit and began the process of estimating batting effectiveness. Maybe it’s time we accept the limitations of these estimations and start crediting batters for “reaching first base in advance of the ball” and advancing as far as possible, no matter how they do so.


 

[i] Whether it’s the catcher, pitcher, or manager who ultimately decides on what pitch is to be thrown is somewhat irrelevant. The goal of the pitching battery is to execute pitches that offer the greatest chance to help the pitching team, whether that’s by trying to strike out the batter, trying to induce weak or inferior contact, or trying to avoid the potential for any contact whatsoever.

[ii] Technically, it only had a true distance of 443 feet—not terribly deep in the grand pantheon of home runs—but the illusion works for me on many levels.

[iii] The fundamental principle of this system, that a reach is credited when an out doesn’t occur due to the action of the plate appearance, means that some plays that end in outs are still counted as reaches. In this way, we don’t incorrectly subtract value that was lost due to fielding and baserunning following the initial event. For instance, if a batter hits the ball cleanly into right field and safely reaches first base, but the right fielder throws out a baserunner advancing from first to third, the batter would still receive credit for a reach. Similarly, if a batter safely reaches first base but is thrown out trying to advance to second base, for consistency, this is considered a baserunning mistake and Is still treated as a reach of first base.

[iv] There is one type of hit that is not counted as a reach. When a batted ball hits a baserunner, the batter receives credit for a hit while an out is recorded, presumably because it is considered an event that reflects batting effectiveness. In this system, that event is treated as an out due to the action of the plate appearance—a failure to safely reach base.

[v] Sacrifice hits may be strategically valuable events, as the value of the sacrifice could be worth more than the average expected value that the batter would create if swinging away, but they are still negative events when compared to those that don’t end in an out—a somewhat obvious point, I hope. The average sacrifice hit is significantly more valuable than the average out, which we will show more clearly in Part III, but for consistency in building these basic averages, it’s only logical to count them as what they are: outs.

[vi] There are occasionally plays where a batter hits a groundball that causes a fielder to make a bad throw to first, in which the batter is credited with a single and then an advance to second on the throwing error. As the fielding play is part of the action of the plate appearance—it occurs directly in response to the ball being put into play—the batter would be credited with two TBR for these types of events.


 

I’ve included links to spreadsheets containing the leaders, among qualified batters, for each effective average, as well the batters with the largest difference between their effective and traditional averages, for comparison. Additionally, the same statistics have been generated for each team along with the league-wide averages.

2013 – Effective Averages for Qualified Players

2013 – Largest Difference Between Effective and Traditional Averages for Qualified Players

2013 – Effective Averages for Teams and Leagues


Feasting on Garbage: Early Strength of Schedule and Team Offense

The Oakland Athletics and the Colorado Rockies are two of the most productive offenses in the league this year, both ranking in the top 5 teams by wRC+. By contrast, the Brewers and Cardinals have been below-average so far, with a 93 wRC+ and 96 wRC+ respectively. Could the strength of these teams’ early schedules be a factor in these varying levels of production?

To evaluate this, I tabulated the actual innings pitched by opponents of the Athletics, Rockies, Brewers, and Cardinals so far in 2014, and then tabulated the anticipated innings for upcoming opponents in June, assuming 9 innings per game. (You could pick any four teams you wanted; these were the ones that interested me). To evaluate the quality of the pitching staffs faced, I used SIERA (published here at FanGraphs) to evaluate the runs the pitching staffs would have been expected to give up, on average, in light of their actual skill sets. Last year, SIERA explained 63% (by r2) of the variance in runs given up by team pitching staffs, making it a good choice for this exercise. Because the pitchers faced in a game are largely outside an opposing team’s control, I used the current, team-average SIERA for each pitching staff, and weighted each inning of a team opponent by that value. I totaled the weighted values to get an aggregate SIERA for the collective opponents of each team.

Let’s start with quality of opposing pitchers for each team in the two months so far:

Opponent SIERA
Lg. Avg. Athletics Rockies Brewers Cardinals
3.73 3.86 3.65 3.58 3.62
AVG RUN EFFECT +7 -4 -8 -6

SIERA can be a difficult statistic to appreciate because it operates on a tighter curve than other pitching statistics (ERA, FIP), and small differences have a surprisingly large effect on runs allowed.  Remember that as with most pitching metrics, however, lower is better.

Let’s work from the league-average SIERA so far this year — 3.73 — to make some overall observations. First, the Rockies’ production is quite impressive, as they were facing above-average pitching skills yet managed to generate a 110 wRC+. The Athletics, on the other hand, generated the same 110 wRC+ as the Rockies, but the quality of competition was entirely different. For the past two months, they’ve had the privilege of teeing off on opponents with an average staff SIERA of 3.84. That is literally like facing a team slightly worse than the Astros (3.83 SIERA) every day for two months.

Contrast that with the task faced by the Brewers and Cardinals so far. To date, the teams faced by those two clubs have posted an aggregate SIERA of 3.58 (Brewers) and 3.62 (Cardinals). On average, that’s like facing a top-10 pitching staff every day for two months. Is it all that surprising, then, that these two teams, widely thought to be above-average offensively when the season began, have struggled to live up to offensive expectations so far?

How does this difference actually affect runs scored? That is a tricky fact to isolate. Drawing a zero-coefficient, least-squares line, each .01 of SIERA has been worth about half of a run so far in 2014. (That rate is comparable to the entire season of 2013, suggesting that this ratio stabilizes fairly quickly). By that measure, as shown in the above table, we would expect their tough schedule to have cost the Brewers almost a win (8 runs) over average in runs scored so far, and almost a win-and-a half as compared to Oakland (15 runs difference). The Cardinals are not far behind.

But that is just the average runs lost, and does not account for the outliers. It probably won’t surprise you to learn that the largest deviations (residuals, technically) from the relatively modest average tend to come from teams at the bottom half of the pitching barrel. When these teams have a bad day, they are really bad, and they are prone to getting blown out. These teams include the White Sox, the Rangers, and the Astros — teams that, as it so happens, have been well-represented on the Athletics’ schedule to date. Certainly, we should expect good teams to blow bad teams out, but when your offensive success consists substantially of beating up bad pitching, it’s hard to say how good your offense really is. The Brewers and Cardinals, on the other hand, have enjoyed healthy servings of the Braves, the Cubs, the Reds, and also each other. All of those teams are in the top half of the league by SIERA, and none of them has a tendency toward outlier scores that allow an opponent to super-size their run differential.

What’s particularly interesting, though, is that this imbalance is about to change in the month of June. Here is how it looks right now:

Opponent SIERA
Lg. Avg. Athletics Rockies Brewers Cardinals
3.73 3.67 3.48 3.87 3.75
AVG RUN EFFECT -3 -13 +7 +1

Things project to be different this month. In June, it is the Brewers’ turn to feast on garbage pitching, as they essentially get to bat against the Astros pitching staff for the entire month (3.87 SIERA). The Cardinals aren’t quite as fortunate, although they still get to face slightly below-average pitching (akin to facing the Rays every day), whereas the Athletics at least have to face a top-half schedule by aggregate SIERA. The poor Rockies, on the other hand, fare worst of all, with a schedule that could not be more grueling: the Braves, Brewers, Cardinals, Dodgers, and Nationals, among others. If the Rockies still come out of June with an above-average wRC+, we can safely say that they are probably a true-talent, above-average ball club, at least when healthy.

The point of all this is not to say that Oakland is some kind of fluke. That team’s out-sized run differential is also a credit to excellent pitching, and it is not Oakland’s fault that it was assigned what turned out to be a favorable early schedule. Yet, this analysis provides yet another reason to be careful when relying upon early-season run differentials.  Before you get too enamored with a team’s production to date, take a close look at the opponents a team has played. You may find that a team’s seemingly-extraordinary results appear to be less so, when you properly weight the skills of the opponents who allowed those results to come about.

Follow Jonathan on Twitter @bachlaw.

Jonathan writes a weekly column about the Brewers at Disciples of Uecker. He has also published at Baseball Prospectus.  


What Data Can Tell Us About Kansas City’s Home Run Struggles

After getting out homered 5-0 by the Angels this weekend, the Royals sit at an underwhelming 20 home runs in 49 games, good for 30th in the league and less than half of the league average of 45. Early in the season, it can be tough to distinguish if under-performance in a certain outcome is due to random fluctuation or an actual decline in talent. Luckily, we have a litany of data at our disposal that can help to answer this question.

Since Kansas City does not have a lineup stacked with power hitters, and playing in Kauffman Stadium makes hitting home runs more difficult than many other stadiums, it’s preferable to compare current production to a projection system instead of league average in order to get a sense of the scale of the Royals’ current power struggles. This already takes into account both the team’s lineup and ballpark factors, giving us a better comparison. In the preseason, Steamer projected that the Royals would hit 126 home runs in 2014. Applying that projection to the 49 games Kansas City has played, we get that the team was projected to have scored 38 home runs through this point in the season. Using the linear weights from the wOBA formula, we can calculate that had the Royals hit 38 home runs as Steamer projected, they would own a (league average) .317 wOBA and a wRC of 202. Instead, Kansas City has a team wOBA of .296 and a 173 wRC. In essence, these 18 home runs have cost the team 29 runs in total, or 2.9 WAR.

Things should change going forward, however. Steamer posts daily updated projections that change as more historical data becomes available (i.e. more games are played) . Taking into account the abysmal start by KC, Steamer has updated their projected year end total home runs from 126 to 102. We already know that 18 of that 24 home run difference is historical, so the change in home runs projected through the rest of the season amounts to only 6 for the remaining 113 games. After factoring in playing time adjustments, Steamer has now discredited Kansas City 9 home runs that were expected at the beginning of the season. Although this represents a non-trivial  drop in home run rates, it is significantly less severe than the pace the Royals have set so far this season.

This does make some sense. Steamer has years of major league performance data to shape player performance for each of Kansas City’s starters, and centuries of baseball data on which to base aging curves. It seems pretty unreasonable to significantly change a projection based on less than two months of data from the current season. This would be especially unreasonable given that home run rates do not stabilize for a given player until about 300 plate appearances. Eric Hosmer has the most PA on the team at 218, so it will probably be another month before we have an idea of whether or not the Royals’ power outage is anything more than random fluctuations.

Another reason we might expect that this trend will not sustain is that much of it appears to be luck-based. Over the past five years, the Royals have had a HR/FB of around 8%, and the lowest they posted over a full season in that time frame was 6.9% in 2010. So far this season, Kansas City has a HR/FB of 4.5%. In addition, the team has hit 7 more doubles than Steamer projected for the season so far, supporting the theory that the Royals have had more than their fair share of balls land just on the wrong side of the fence. This does not account for all 18 home runs that were projected to be hit and were not, however. Bad luck only explains so much, and the majority of KC’s offensive woes still should be credited to poor hitting.


Aaron Hicks: Why Won’t You Swing?

I’ll confess that I was once a firm believer in Aaron Hicks, and that I’d be remiss to cast him off after just 442 plate appearances in the major leagues. But the numbers don’t lie: he has been terrible in those plate appearances, sporting a .285 wOBA while amassing a -1.0 WAR.

Thus, I can understand why the switch-hitting center fielder would be a little reluctant to swing the bat. When he does, bad things usually happen. But a career Swing% of 38.7 is not going to work for a player of Hicks’ ilk. For perspective, Tim Lincecum’s career Swing% is 42.5. Yes, Tim Lincecum swings the bat more often than Aaron Hicks.

But what about drawing walks? Hicks certainly does that, and at an impressive rate (17.8% in 2014) to boot. Thanks to this increased number (which was just 7.7% in 2013), Hicks is getting on base at a respectable clip of .336, which aligns fairly well with his minor league career (.376).

While drawing walks is obviously a valuable trait, especially for someone who stole 32 bases as recently as 2012 in Double-A, Hicks is going to have to start swinging the bat at some point if he wants to have an extended major league career.

What’s most troubling about Hicks’ swing rates isn’t necessarily the low overall percentage; rather, it’s his penchant for taking pitches that are in the zone. This season, Hicks’ Z-Swing% is just 54.6, well short of the league average of 64.6. Further, 17 of Hicks’ 33 strikeouts this season have been looking. With a Z-Contact% of 88.6 and an overall Contact% of 80.2, this is unacceptable.

What happens when Hicks does actually swing the bat? Well, it’s admittedly not particularly pretty. He has a career LD% of 16.3, which puts him well below this year’s league average of 20.0. Almost half (48.1%) of his batted balls are hit on the ground, and the remaining 35.6% hit in the air.

But it’s fair to surmise that if Hicks would swing at more pitches in the zone, his LD% would see an increase, which would in turn increase his BABIP, which is just .247 in his career. Heck, even if he swung at more pitches out of the zone he’d probably see an increase in both of these numbers.

Looking at how pitchers are attacking Hicks gives further insight as to why his LD% is so low. This year, Hicks is seeing fastballs 54.4% percent of the time, compared to the league average of 57.6. Not a huge difference, but enough to suggest that pitchers feel more confident attacking him with offspeed and breaking pitches. Hicks is seeing 12.3% curveballs and 18.1% changeups, well above the league average of 9.8 and 10.4, respectively.

Ironically, Hicks’ LD per BIP against offspeed pitches is an astounding 28.57 this season, compared to rates of 19.57 against hard pitches and 11.11 against breaking pitches. Since 72.5% of the pitches that Hicks has seen this year have been either fastballs or changeups, it’s baffling that he remains so reluctant at the plate.

This data suggests that, while Hicks may never reach the expectations set when dubbed a top prospect, he can at least be a useful if not above average player for the Twins if he would just swing the bat more.


Ben Revere: Frustratingly Frustrating and False Hope-Inducing

I can picture it so clearly – Ruben Amaro Jr., on a cold December day in 2012, finalizing a trade with the Twins, patting himself on the back while sipping on the finest scotch in the office. He just cashed in on Vance Worley‘s irrationally high stock and added a top-of-the-order center fielder, filling both a defensive void and a lineup void – one that could push the impatient Jimmy Rollins out of the leadoff spot. That player, little Ben Revere, also happened to be one of the fastest in baseball. What was there to like about need-filling Revere in the winter of 2012?

Of the outfielders who logged 1,500 innings from 2011-2012, only four of them had higher UZR/150.

Name Inn UZR/150
Josh Reddick
1881 19.3
Jason Heyward 2346 18.3
Peter Bourjos 1771 17.5
Chris Young 2098.1 15.9
Ben Revere 1987 15.7
Jacoby Ellsbury 1969.2 13.9

And only Jason Heyward posted a higher RngR.

Name Inn ARM RngR
Jason Heyward 2346 -1 32.6
Ben Revere 1987 -2.4 27.4
Josh Reddick 1881 5.4 24.2
Chris Young 2098.1 1.1 23.5
Jacoby Ellsbury 1969.2 -4.7 22

Of the 262 players with 600+ PA from 2011-2012, only Marco Scutaro and Juan Pierre posted better Swinging Strike rates.

Name SwStr%
Marco Scutaro 1.80%
Juan Pierre 2.40%
Ben Revere 3.00%
Jeff Keppinger 3.00%
Brett Gardner 3.00%
Denard Span 3.00%

Over those two seasons in Minnesota (241 games), Revere stole 74 bases and posted a WAR of 4.7.

He was an incredibly fast, rangy center-fielder who put the ball in play and walked just enough to give his speed a chance to create extra bases. He had holes in his game, namely arm strength and a complete lack of XBH potential. Still, I was all-in.

Fast forward roughly 18 months, and I’m sitting in Citizens Bank Park, watching a bases-loaded, zero out line drive sink into left-field, where Carl Crawford slides to make the catch. I turn my head back towards the infield and Ben Revere is running in the wrong direction. “Oh no!” I blurted out. He raced back to third base to avoid being doubled-up instead of racing home on a tag.

I sat and watched Dee Gordon – he of speed and contact – foul off numerous pitches, eventually work a walk, and then steal second as a formality – all while double-checking that Revere still has a 2.0% walk rate this season. He still does.

His arm strength remains Pierre-esque, but his route-running has transcended legendary status to a place I’ve never seen before. It’s a dark place. It’s trepidation to a Westerosian extent. It’s a gasp, then a breath-hold, followed by either a head shake or an exhale, depending on whether Revere recuperated from his disastrous first step and somehow worse second through tenth steps to catch the ball.

Ben Revere

Exhale.

Ben Revere

Shake head.

Of the 98 outfielders with 750 innings played from 2013-2014, Revere’s panic-inducing time in Philadelphia, 69 of them have posted better UZR/150 than Revere.

His RngR has gone from 27.4 as a Twin to -1.8 as a Phillie. That’s good for a drop from 2nd in all of baseball to 62nd out of 98.

One of the fastest outfielders in the majors is posting a below average RngR. One of the best contact-makers in the majors is unwilling to work the count.

In a sports world of placing blame, where does it lie? Should the general manager have seen through the lofty defensive numbers and recognized flaws? Should Amaro Jr. have known this player won’t get on base enough to utilize his best asset? Do we blame the player for being unable to improve aspects of their game that have shown to be improvable over time?

Or do I blame myself, for looking at this player, looking at these numbers, and still seeing hope on the horizon? If only he could… If he just…

Revere’s 0.9 WAR in an injury-shortened 2013 season  – in which all of the aforementioned regressions occurred – gives hope for a 2-3 WAR per season player. He just needs to…


The Uncommon Careers of Adam Jones and Howie Kendrick

Brett Favre was fascinating to watch and not just because he won football games. Fans watched in awe of how he won football games. Favre was often referred to as a gunslinger with unorthodox mechanics and a propensity to make questionable decisions. Mike Holmgren claims to have “aged many years to that relationship” because Favre’s fundamentals and decision-making weren’t always enviable. And yet, Favre had an innate ability to overcome perceived weaknesses that many thought should have precluded him from success. Baseball players also succeed with apparent shortcomings and overcome the odds because they have some special talent in one area or another. Two current examples are Adam Jones and Howie Kendrick.

Adam Jones isn’t an elite baseball player in the same way that Favre was an elite football player, but Jones is very good. He also has a “flaw” that typically prevents hitters from being effective and yet, Jones has hit at an above-average level for his career. In this regard, Jones is a rare talent in the same vein as Favre and an interesting case.

Jones loves to swing the bat, as I’m sure all baseball players do. But Jones loves to swing the bat more than most. His career Swing% is close to 55% and has never fallen below 52% in any year of his career. He swings at a large percentage of pitches out of the strike zone as evidenced by his career 40.5% O-Swing%. This habit has led to a low career walk rate of 4.5%. The FanGraphs glossary would categorize this walk rate as “awful”, and it is. In fact, Jones’ walk rate is so low that we would expect him to be a below-average offensive player, which he is not.

In 2013, 101 players finished with a wRC+ above 100. Of those 101 players, 79 of them had a walk rate of over 7%. The top 16 players in terms of wRC+ had a double-digit walk rate. By contrast, only four players in all of baseball had walk rates above 10% and finished with a wRC+ of under 100. This data makes sense. Typically, players that know the strike zone and avoid swinging at poor pitches hit better than those who extend the zone frequently.  Of these 101 players with an above-average wRC+, only nine finished with a walk rate of under 5%. Those players are listed in the table below.

Player BB% wRC+
Starling Marte 4.4% 121
Shane Victorino 4.7% 119
Adam Jones 3.6% 118
Torii Hunter 4.0% 117
Howie Kendrick 4.5% 116
Jean Segura 4.0% 107
Daniel Murphy 4.6% 106
Salvador Perez 4.0% 105
Manny Machado 4.1% 101

Jones was the only player to have a walk rate under 4% and still have an above-average wRC+. He still finished in the top 50 in wRC+. His .285/.318/.493 slash line was solid, and he had an excellent .208 ISO.These are impressive numbers for someone who walked 25 times in 689 plate appearances.

You’ll notice Howie Kendrick on that list. Again, Kendrick isn’t a superstar, but he has been an above-average offensive player for his career. Kendrick also has a tendency to swing often, swinging about 54% of the time in 2013.  Even so, Kendrick slashed .297/.335/.439 while walking only 23 times in 513 plate appearances.

The 2013 season isn’t what makes these two players interesting. Many players have had solid seasons with poor walk rates. Jones and Kendrick are interesting because they have made a career out of walking very little and still producing quality offense. Both players debuted in 2006 and have around 4000 plate appearances. In the last 25 years, only six players with 3000 plate appearances or more have managed to maintain a walk rate of under 5% and a wRC+ of over 100.

Player BB% wRC+
Steve Garvey 4.6% 108
Howie Kendrick 4.6% 107
Adam Jones 4.5% 107
Tony Armas 4.7% 105
Ivan Rodriguez 5.0% 104
Brian Harper 3.9% 101

Pretty amazing. As their careers continue both players may have down years that take them off this list, but the fact that they have hacked their way to this level of production is astounding. They are truly rare players.

This rare talent also displays each player’s offensive weakness. Based on how other players have performed, Kendrick and Jones would likely be more productive offensive players if they swung at less pitches outside the strike zone and walked more. In the same interview linked above, Mike Holmgren mentions that Favre was able to tone down some of his poor tendencies in order to improve his performance. Favre was always going to look and play differently than other elite quarterbacks, but he started winning more consistently because those differences became less extreme.

Both Kendrick and Jones will likely need to improve their walk rates to remain good offensive players as they age. So far this season, Kendrick has shown signs of improvement. He has a 9.5% walk rate in 201 plate appearances.  Jones continues to swing as often as he can. He owns an almost unfathomable 2.5% walk rate. These numbers help explain why Kendrick has had two of the best months of his career while Jones has been mediocre.

Regardless of what happens going forward, Jones and Kendrick have had oddly productive offensive careers to this point. We can simultaneously appreciate their uniqueness while also seeing the blemishes related to that uniqueness. They aren’t elite offensive players, but they have remain productive in spite of a flaw that often keeps players from even reaching the major leagues.


Is Bud Norris this Good?

Back in January, while everyone was still waiting around for the Orioles to sign a free agent, I wrote this post on what I thought about Bud Norris at the time. I came to the conclusion that Bud Norris pitched too few innings, gave up too many walks and home runs, and struggled too much against lefties to be an effective starter for the Orioles. I believed he was better suited to a bullpen role where his stuff would play up some and Buck could protect him against his horrendous splits. To date, Bud Norris has proven that diagnosis incorrect. Norris has posted a 3.58 ERA and averaged 6.27 innings per start and has been one of the better starters on the Orioles this year. I wanted to figure out what has made Bud better this year and see if he could continue his early season success.

The worst part of Norris’ game prior to this season were his bad splits against lefties. Last season, he had .387 wOBA against lefties, this seasons its a much more manageable .312. His HR/FB rate is down as well from last year’s second half and sitting at a more reasonable 10.9% this season down from 12.9% in the second half of 2013. Also, his walk rate is down to 6.8% this year while it sat in the second half of last season at 10.8%. However, Norris’ strikeouts are also down, he is striking out only 16.1% of batters this season while he struck out 23.0% of batters in the second half of 2013.  With the reduced walks and strikeouts Norris has been able to pitch longer into games this season.

Norris is not walking as many batters, controlling his home runs, improving against lefties, but he is also not missing as many bats as he used to. At first glance at some of the peripheral statistics one would say Bud Norris has been very lucky to this point in the season. The main reason being that his BABIP to date is .253. League average is somewhere around .300, meaning that 30% of balls put into play fall for hits. Norris is yielding hits at only a 25% rate on balls put into play. That would indicate some luck. However, I hate it when people simply list BABIP as reason for good or bad luck. It’s a decent indicator, but how balls are but into play matter most. Line drives fall for hits more often than ground balls and ground balls more often than fly balls.

Looking at the batted ball numbers, Norris has shown some  improvement. He has 20.5% line drive rate, a 43.0% ground ball rate, and a 36.4% fly ball rate. In the second half of 2013 he had rates of 22.7%, 39.5%, and 37.8% respectively. The reduction in line drives and increase in ground balls are good indicators that batters are barreling up the ball on Norris less than they did last season.  (Side note, while fly balls fall less often for hits than ground balls do, its better for a pitcher like Norris to have a higher GB% because he is home run prone and the Orioles infield defense is plus). All of his batted balls rates are around league average thus far into the season.

However, I would not be a good analyst if I did not tell you how he has gotten batters to make weaker contact against him. Looking at the tape reveals no major mechanical changes, unlike Matt Wieters for instance. He is a little higher in his set this year, a little taller on the follow through, but nothing of particular note. Bud Norris is simply pitching better this season. Looking at the graph below, all of his pitches are lower in the zone (the middle of the graph is the middle of the plate). In particular, he is locating his change up nearly half an inch lower than he did last season, indicating he is sharpening his command and refining that pitch. Also, his whiff rate on his change up is double what is was last season (6.5% in 2013 and 13.24% to date in 2014). This improved change up is likely what is helping him against the lefties.

Brooksbaseball-Chart (1)

Furthermore, as seen in the graph below, Norris’ velocity has increased with every pitch this season. His average fastball velocity is 1 MPH faster than last season. Velocity is not everything, but higher velocities tend to, regardless of location, induce weaker contact and make it harder to make strong contact.

Brooksbaseball-Chart

There are good and bad sides to Bud Norris’ start to the 2014 season. The good side being a lowering walk rate, better results against the lefties, a controlled home run rate, increased velocity, and improved pitch location. The bad side of Norris’ start to date this year is the decreasing percentage of which he is getting hitters to swing and miss and his unsustainable low BABIP (even with the slightly improved batted ball numbers). His plummeting strike out rate and low BABIP even with the increased velocity and location are bad signs moving forward.  Bud Norris has been a better pitcher than he was last year, but I highly doubt he is a 3.5 ERA pitcher for the entirety of the 2014 season.


Votto vs Casey vs Perez: Battle of Reds First Basemen

It’s funny how various factors affect how we interpret reality. Growing up, my family owned a boat. We would go fishing, water skiing, and tubing on the Ohio River and on several lakes. When I was a kid, I thought of this boat as a yacht. It was huge! I had all kinds of space to move around and acquire different angles of my brother being thrown from a tube. We had snacks and life jackets in hidden compartments. The seats were wide enough for me to lay down after an especially heinous wipeout. In my mind, we could have lived on that boat.

One time, I came home from college and my uncle and cousin wanted to take the boat out. I jumped at the chance to board our cruise liner and relive some of my youthful adventure. When it came time to board the boat, I realized something: our boat is tiny. The boat could only carry four people on the water legally. The seats were perfect for 12 year old me to lie down, but the extended version of myself could barely stretch my legs at all. I quickly came to a startling conclusion: my perception of our boat had not been entirely accurate. As a kid I didn’t have all the facts. I didn’t realize that only four people could ride in the boat at one time. My senses had deceived me. And for a long time, my memory had deceived me. The boat got bigger to me each year I was away from home. These are two different problems. Our senses may create a narrative that isn’t based in reality. We may also lose perspective on events, people, or experiences as time goes by.

We often do this. We remember things as grander than they actually were. Some of those things were great to begin with, but we embellish them to lofty heights. I recently read a comment from a Reds fan where he stated that besides 2010, Joey Votto has basically been Sean Casey as an offensive player. The commenter also stated that Tony Perez was a better hitter than Votto and insinuated that Votto’s 2010 season was a norm for Perez. Before I address these comments, I need to say a few things. All three players had great careers to varying degrees (Votto’s career still on going). All three players had and have strengths and weaknesses to their games. By examining the facts, I do not intend to belittle anyone of these great players. Let’s look at some numbers.

Player Games AVG OBP SLG HR ISO wRC+ WAR
Sean Casey 1405 .302 .367 .447 130 .145 109 16
Tony Perez 2777 .279 .341 .463 379 .184 121 58.9
Joey Votto 929 .312 .419 .537 163 .226 155 33.8

These numbers tell us several things. While all three players were great offensive players, Votto and Perez are and were a few steps above Casey. Casey was better than Perez at getting on base, but Perez power numbers dwarf Casey’s. Votto trumps Casey by a wide margin in both on-base ability and power. Casey’s career 109 wRC+ shows that he was a good offensive player; he just isn’t on the level of the other two.

The real comparison is between Votto and Perez.  In fairness to Perez, who played long enough to have some seasons that drove his career numbers down some, I decided to take his six-year peak and compare it to Votto’s six full seasons. In the table below, Perez’s numbers are from 1970-1975; Votto’s numbers are from 2008-2013.

Player Games AVG OBP SLG HR ISO wRC+ WAR
Tony Perez 898 .288 .359 .496 161 .208 138 30.2
Joey Votto 905 .312 .420 .537 159 .226 156 34.0

At their peak (which likely continues with Votto if he can stay healthy), Votto is a little bit better. wRC+ is a good indicator of how each player compared to league average in their era. To this point in his career, Votto has a 156 wRC+. Perez surpassed this number only twice in his 23 year career. Votto gets on base at a much better clip and according to SLG and ISO, he surprisingly hits for more power.

Perez was a phenomenal player. He also had a phenomenal team around him. To this point though, Votto has been a better hitter. Those who remember Perez as a great offensive force are correct, he just wasn’t as good in his peak as Votto has been. Perez had much better teammates than Votto and that might account for some selective memory. The Reds of the 1970s scored an abundance of runs. Perez was a big part of that. Votto’s Reds do not score nearly as much but not because of Votto’s efforts.

While our senses may cause us to draw faulty conclusions, the numbers tell a more complete and accurate story. Reds’ fans should celebrate what Votto has done in his career. We will likely look back at him as one of the greatest Reds’ hitters ever.


Samuel Deduno’s Crazy Fastball, Explained…Maybe

Like many other Twins fans, Samuel Deduno fascinates me. Thanks to some terrible starting pitching from the likes of Jason Marquis and Carl Pavano, Deduno earned his first extended stint in the major leagues in 2012. Immediately he dazzled fans and frustrated hitters with his success relative to the rest of the rotation despite his lack of control and self-proclaimed “crazy” fastball (he’s admitted he has no clue how to control its movement).

Deduno started the 2013 season in the minor leagues again, but received another call-up and improved significantly. He cut his walks from 15% to 9% of batters faced, and though his strikeouts also dropped, he combated that by allowing fewer home runs.

A few weeks ago, I chronicled Deduno’s fastball by showing that whether by accident or on purpose, Deduno’s moving fastball has become almost exclusively a cutter. In the past, it also would sink into the hands of a righthanded batter, but for whatever reason that movement has been eliminated.

Another fascinating fact of Deduno’s fastball is how much it drops, regardless of horizontal movement. MLB pitchers average nearly 9 inches of “rise” (relative to a spinless ball with no gravity), but Deduno’s gets only 2 inches of rise. His fastball drops more than some pitcher’s sliders and change-ups, which is pretty impressive. And while I’m no expert of the sciences, I may have figured out what the hell is going on with his fastball.

But first, a quick anecdote that gets passed around by some of the Twins part-time staff (I’m an usher at Target Field and have heard this story a few times). A few years ago, the Yankees were in town and Derek Jeter faced Deduno. The story goes that Jeter grounded out, and upon returning to the dugout he started shaking his fist violently while saying, “His fastball moves all over the (redacted) place!” He then went to a security guard and asked who was the pitcher on the mound. The security guard replied with, “He says, ‘My name is Sam Deduno and I have a crazy fastball.'”

But I digress. There are plenty of pitchF/X websites out there, and for reasons I cannot explain, I’ve become rather attached to the Texas Leaguers website. I typically pay the most attention to velocity and movement, but something caught my eye the last time I was drooling over Deduno’s data. There is also a number charted for every pitch that is titled “Spin Rate” and Deduno’s fastball spin rate is absolutely absurd. If you want to look at his cutting fastball since 2013, it’s 606 revolutions per minute. His sinking fastball is at 727.

“Yeah, okay, but why does that matter?” the casual observer would note. Well, unfortunately Texas Leaguers does not keep track of the average spin rate for fastballs and cutters, but I’ll post a variety of pitchers for context. All data is from the start of the 2013 season to the present.

Fastballs

Glen Perkins – 2,475

R.A. Dickey – 1,543

Josh Collmenter – 2,259

Rick Porcello – 1,867

Joe Saunders – 1,944

Cutters

Mariano Rivera – 1,580

Kevin Correia – 1,243

Dan Haren – 1,243

Andy Pettitte – 1,521

Scott Feldman – 958

Now can you understand why I said that Deduno’s fastball spin rate was absurd. Remember when I said that his pitch rises far less than the average MLB fastball? Well you might be aware of the Magnus effect, where the rotation of a ball affects its flight path. Now that it’s warming up outside, you have another excuse to inflate a beach ball. Go ahead and do it, then try to throw it overhand. Because of the backspin on the ball, no matter how hard you try to throw it straight forward, it will also rise. If you try to throw it underhand, it’s going to sink because of the topspin. Major league fastballs appear to rise because of the Magnus effect, causing it to resist the force of gravity. Because Deduno’s fastball has (relatively) little spin on it, the Magnus effect is reduced and it doesn’t rise as much.

Now why does Deduno’s fastball have so little spin? This is one of those cases where I wish I could see a slo-mo of his fastball, because I get the feeling that the ball might slip out of his hand a bit, kind of like Robert Coello’s “forkleball.” Speaking of which, let’s check out some of those pitches that aren’t supposed to spin very much.

Other Pitches

R.A. Dickey knuckleball – 901

Steven Wright knuckleball – 1,001

Robert Coello “forkleball” – 950 (it’s classified as about 4 different pitches and I found the average)

Ladies and gentlemen, here is why Samuel Deduno’s fastball is crazy. It supposedly spins less than noted knuckleballer R.A. Dickey’s famed knuckleball. I’ll admit that seems absurd (especially after watching the “forkleball” GIF above) but remember, that GIF is merely a highlight of one of the best Coello has thrown. He’s thrown many more that had far more spin than that pitch. The same is true of Dickey’s knuckleball; he’s thrown tons of them that failed to achieve a lack of spin. While my understanding is likely wrong, I still prefer to think that Samuel Deduno is practically firing 90 MPH knuckleballs at Kurt Suzuki and Josmil Pinto. That’s probably why Derek Jeter said it was like the ball was shaking as it came to home plate in the anecdote above, Deduno has no idea where his fastball is going, and why his control has been below average for his career.

Samuel Deduno’s crazy fastball, explained. Maybe.