May 14, 2013 at 5:46 pm by Franklin Rabon under Atlanta Braves
We’ve heard a lot about putting/not putting the ball in play lately, ie strikeouts, homeruns, walks, etc. and their relative merits. I thought I’d do a brief case study to examine a player’s value when he “the true outcomes” (ie home run, walk or strikeout) with a player who does all of those a fairly decent amount, Justin Upton, and then compare that to when he puts the ball in the field of play (ie out to a fielder or hit).
In Field of Play:
Not In Field of Play:
The first thing to notice is the wOBA category, as it’s a generally well understood measure of a hitter’s overall value. Let’s first look at the league average values. We see that the league as a whole is more valuable when producing one of the three true outcomes, even though strikeouts make up 61.6% of those at plate appearances. This means that if an average player had the opportunity to choose between the BABIP “wheel of chance” and a randomly assigned “true outcome” the player would be better off taking his chances with the three true outcomes.
We also see this holds for Justin Upton, as he’s produced an otherworldy 0.542 wOBA in his TTO plate appearances, compared to 0.326 for his ball in play plate appearances (this is still really good though). That is, if Justin Upton could choose between putting the ball in play and a true outcome, he should be choosing a true outcome.
Next we see that Justin Upton’s K percentage is below the league average for three true outcomes plate appearances. That is, Upton actually strikes out in a lower percentage of his three true outcomes plate appearances than average, in fact he’s in the top 79% in this regard of striking out the least. Huh, wha? Doesn’t Upton strikeout a lot?! Well, yes, but the difference isn’t because he strikes out more in his TTO plate appearances, it’s because he produces more TTO plate appearances than your average player. And as we looked at above, for Justin Upton, TTO plate appearances are a very good bargain. I’ll never claim that we shouldn’t ever be concerned with strikeouts with a player, but we certainly shouldn’t be concerned with them from Upton, as his strikeouts are coming from a good place, an effort to walk and hit home runs at an incredibly prodigious rate.
Why do we group three true outcomes together? Because they have a lot to do with one another. A lot of why good players strikeout and hit home runs is because they’re “waiting for their pitch” and then swinging hard. Many times this leads to walks, a lot of times (in fact more than half the time) this leads to strikeouts, and sometimes it leads to home runs. Just swinging at any pitch near the strike zone often leads to taking a spin on the BABIP wheel of (mis)fortune, especially for a player like Justin Upton. We see this as well in the fact that Upton doesn’t choke up on the bat with two strikes, like a lot of old school people advocate. What does this mean for Upton? Simply put, it means he’s one of the best two strike hitters in MLB:
Justin Upton with 2 strikes:
Basically he’s off the charts good in most every measure with two strikes.
Justin Upton is really good offensively for three reasons: 1) He’s in the top 10% of value when he doesn’t put the ball in the field of play (be it by walk, strikeout of home run) 2) he’s still in the top 33% of value when he puts the ball in play 3) He doesn’t put the ball in play more often than the vast majority of players. His value comes from being very good in most all situations, and then also pushing his plate appearances towards the ways in which he’s relatively most valuable.
May 14, 2013 at 9:57 am by Franklin Rabon under Atlanta Braves
Hey guys and gals, this promotion we ran last year was incredibly popular amongst our readers, so we’ll be running it monthly again this year. Essentially you get to play fantasy baseball for free, win prizes and help support Capitol Avenue Club all at once! Further details below. Have fun!
DraftStreet.com turns the season long grind into quick hitting one night leagues and the best part is that you can win cash every single day. You draft a team for one night and get paid out as soon as the games end that night. DraftStreet is at the forefront of this new trend in the fantasy world and is giving us a great promotion to celebrate Justin Upton and Chris Johnson’s thrashing of their former team: a FREE one-day fantasy league with $200 in prizes exclusively for Capitol Avenue Club.
Click Here to sign up now!
This free contest will be Pick ‘em style drafting. The way Pick ‘Em leagues work is you have 8 tiers of players and each tier will have players to choose from. All you have to do is select 1 player from each tier. There’s absolutely nothing to lose and it takes 5 minutes to build a team. You can adjust your roster up until rosters lock on Friday (5/17) at 7:05 est and it will be pickem style just like last month, at which time your rosters will lock and the Live Scoreboard will be available.
May 13, 2013 at 3:48 pm by Franklin Rabon under Atlanta Braves
or directly download the MP3
May 9, 2013 at 8:52 pm by Franklin Rabon under Atlanta Braves
A lot of hands have been wrung throughout Braves fandom recently over the surprising struggles of once dominant closer Craig Kimbrel. He’s gone from being not just automatic, but completely demoralizing to the other team, to Durbin-esque in recent weeks, giving up key home runs repeatedly.
This has lead many Braves fans to speculate on what’s wrong with Kimbrel. I’ve seen repeated mentions of his velocity being down (possibly due to an undiagnosed injury, because Twitter doctors can smell injuries from 200 miles away), over-reliance on his fastball, different release point, or just plain old lack of mental toughness.
Let’s first address the physical claims, because most of them are easier to dismiss, because they’re just not factually true.
Here are Kimbrel’s careeer velocity, release point and movement numbers, courtesy of Brooksbaseball.net:
and here are his numbers for 2013:
We see that Kimbrel has ‘lost’ 0.1 MPH on his fastball. Given the sample size in 2013, this is even inside the possibility this is even just due to inaccuracy in radar guns. 0.1 MPH isn’t even remotely close to statistically significant, let alone being practically different from a ‘difficulty of hitting the baseball’ standpoint. Attributing Kimbrel’s struggles to loss in velocity is simply factually incorrect, and the product of lazy analysis by those who are simply unwilling to actually research their opinions before spewing them. I was recently engaged on twitter by a fan who said “it’s not up for debate that he’s lost velocity, I watch every game, it’s clearly 2-3 MPH less.” While he was right, that it’s not up for debate, it was because he was so factually incorrect that it’s not up for debate that he was wrong; this clearly shows why the common rebuttal to most analysis of “do you even watch the games?” is a non-starter. When we watch the games, as humans, we’re extremely prone to all sorts of known cognitive biases. I do believe this follower wasn’t being disingenuous, and actually did believe Kimbrel’s velocity was down, but due to confirmation bias, he was simply wrong about that factual matter. I watch every game, either from my seat on the 10th row along the first base side of the infield, where I have a pretty good view of what’s going on, or from my TV, where I have access to slow motion replay, and often times I do BOTH, thanks to MLB.TV replay. Even as such, I still don’t trust my eyes and memories when it comes to matters that can easily enough be looked up given the vast resources we have at our disposal today.
Looking at usage charts, we see that Kimbrel’s fastball usage is up by 8% (and since he’s a two pitch pitcher, his curve usage is down 8%). One line of argumentation goes that Kimbrel has over-relied on his fastball, allowing hitters to ‘tee off on it’ because they’re seeing it so much more. Simply put, an additional 8% of the time over such a small sample simply isn’t enough of a difference for that to be the case. Consider an at bat, this comes out to less than one additional fastball per at bat, which simply isn’t enough of a difference for a hitter to alter the way he approaches facing Kimbrel. Further, since Kimbrel will only face a hitter once per game, it’s not like seeing those additional fastballs allows hitters to better track the pitch, like might be the case with a starting pitcher. Even at that, given the nastiness of Kimbrel’s stuff, it isn’t clear that it would matter anyway. Steve Carlton was a starting pitcher with essentially the same two pitches, and it didn’t really matter. Carlton even always threw his pitches to the same two locations (fastballs up and in and sliders low and away).
Further, his pitches aren’t moving in a statistically different way. While his point of release has changed a bit (he’s releasing the ball from a slightly lower point), that should show up in contact rates (which haven’t really changed) more-so than HR/FB rates.
Another claim is that Kimbrel isn’t throwing the high fastball as much, and that the low fastball is easier to hit out. First, this defies all common baseball knowledge. Common baseball knowledge says that low fastballs are harder to hit out, while high fastballs are easier to hit out, but harder to make contact with. And while we can’t always trust ‘common baseball knowledge’ we’d at least need some sort of statistical evidence before we went around challenging it. And while there is some evidence that high fastballs are harder to make contact with, there isn’t any evidence that it would change HR/FB ratio without changing contact rate first. Kimbrel isn’t having trouble missing bats, his trouble is coming from what happens when contact is made. Finally, the location change is barely even noticeable anyway:
Fastball Location for his Career:
Some difference, but not really a substantial one. Certainly not enough to explain a more than doubling of his HR/FB rate.
A point we’ve alluded to that we’ll now make explicit is what has changed with Kimbrel’s results? Strikeout, walk and other rates, again, aren’t statistically significantly different. What has changed is Kimbrel’s home run to fly ball ratio.
The HR/FB ratio jumps off the screen, while other rates are roughly in line with his career.
Simply put, HR/FB ratio is by far the most unstable rate for a pitcher of any statistic there is. Even for starting pitchers, it usually takes 2 or more full seasons to have any idea what a pitcher’s real HR/FB ratio really is. In Pizza Cutter’s landmark rate stabilization study, HR/FB ratio didn’t even come remotely close to stabilizing for starting pitchers over an entire season. The point is especially poignant for relievers. As Pizza Cutter said:
Now consider the sample and stat that we’re fretting so much about with Kimbrel, the sample is 52 PA. It typically takes close to 2000 PA for a pitcher’s HR/FB to begin to stabilize.
What we have to consider here is the “signal to noise” ratio. That is, how much of the results we’re seeing are due to a real change in skill, and how much are random variation. Given the sample size and variance of the statistic that’s giving Kimbrel so much trouble, we’re talking about 98.75% noise and 1.25% signal. That means that for the 52 plate appearances Kimbrel has faced hitters this season, HR/FB ratio is literally 99% random chance. Not a change in skill, not something the pitcher is doing differently, but purely random variation. We’re essentially trying to come up with explanations for a coin flipping contest when we try to explain why Kimbrel’s HR/FB ratio has jumped so much. While there might be some change in skill involved, it’s several hundred times more likely that it’s just pure dumb luck, that it’s not even really worth considering “what’s wrong with Kimbrel” at this point. It’s dramatically more likely he’s the same pitcher he’s always been, just running into small sample weirdness. Fans hate “small sample size weirdness” as an explanation, but this is largely why average fans aren’t professional MLB general managers.
Craig Kimbrel had an incredible season last year, but the problem is, even over a full year, a reliever’s performance is over a relatively small sample, and is thus prone, as pizza cutter stated, to an absurd amount of random variation. This is why it’s an absurd idea to pay any reliever a lot of money, even coming off a year like Kimbrel had last year. Because even if they do perform at an obscenely great level, you simply cannot depend on that type of performance from year to year.
Why I’m saying that the good news with Kimbrel is also the bad news is because while nothing is wrong with Kimbrel, that’s the point, nothing is wrong with him; what we’re seeing is simply what we should expect from him from time to time. You can never expect a reliever to put up a season like Kimbrel did last year, and we should expect down years where Kimbrel blows 7-10 saves more often than a year like last year, even if Kimbrel is one of the greatest relievers in baseball history. Further, with Kimbrel, even though he’s approaching 100 saves, he simply hasn’t pitched enough for us to even know what his actual rates should and will be once they start to stabilize. While we fully accept that a starter can have an anomolously good or bad year, we often fail to realize that over his entire career, Craig Kimbrel has pitched less than an ‘ace’ starter’s full season workload. It’s entirely possible that when Kimbrel’s HR/FB ratio does stabilize, it will be higher than the ~8% rate he’s seen over the first couple of years of his career. The bottom line is that we don’t really know if Kimbrel is an ‘all-time’ level talent or merely very good. It’s not only possible that his career won’t be as good as Billy Wagner’s, it’s actually extremely likely.
Finally, one point I’ll briefly address is the claim that the reason why Kimbrel’s ‘problem’ hasn’t shown up in any of the measures we’ve examined above is because it’s mental. There is a claim floating around out there that Kimbrel has simply got into a mental funk. I’m actually not one to immediately disqualify the mental aspect of the game. Players absolutely can go through mental funks that negatively impact their performance. However, for it to be real, it has to negatively impact their performance in a tangible way. What mental toughness does is makes a pitcher less likely to throw poor pitches. A lack of ‘closer mentality’ doesn’t however make good pitches magically become hittable. Kimbrel’s 96 MPH fastball with the exact same amount of movement didn’t magically become easier to hit out of the park because it contained less will to win. Rick Ankiel’s ‘intangibles’ were absolutely real, and caused an absolutely real inability to throw strikes. If you’re going to claim ‘intangibles’ you have to be able to show how those intangibles actually impact the game, you can’t use ‘intangible’ as if a magic wand that explains away everything that isn’t immediately apparent.
If Kimbrel had ‘a problem’ and getting back to his 2011-2012 type results were simply as easy as ‘fixing the problem’ it’d almost be less worrisome, the reality is that there’s nothing to fix. We should marvel at Kimbrel’s 2011-2012 results, but to expect we’ll see it year in-year out, or ever again at all, is simply folly. And most importantly of all, we absolutely shouldn’t pay Kimbrel down the road like we expect his 2011-2012 performance.
April 29, 2013 at 4:17 pm by Franklin Rabon under Atlanta Braves
We laugh at ourselves entirely too much.
Direct Downlaod of the mp3 here:
April 26, 2013 at 2:06 pm by Franklin Rabon under Atlanta Braves
It’s been a while since we’ve done one of these sabermetric primer series things, so thought we’d address ‘regression’ and ‘regression to the mean’, since it’s about that time of the season when we start getting a lot of questions about how early season streaks are going to hold up. We hear the term “regression” used a lot in these circles, and some may only dimly get it, and some may in fact misuse it, so let’s talk about regression is, specifically in how it applies to a game like baseball.
Baseball is a game of skill and luck, like almost all games are. What this means is that any given sampling of a players performance will contain results that will deviate from the player’s true ability, due to luck, but the results will be ‘biased’ towards what his true ability is, especially as the sample grows larger and larger. Over a very large sample, we should expect a player’s performance to converge upon his actual true ability level. With baseball however, there is so much luck involved, and the differences in skill levels between players are so small, that it often takes extraordinarily large samples to be able to differentiate two players of differing abilities as actually being different. Fangraphs has a nice quick and dirty guide to when you can “begin to trust” a player’s numbers.
But are a player’s numbers useless prior to reaching those points? Not totally. So what can we do with numbers before (and to some extent, after) they reach those milestones? We can apply regression to the mean.
The idea behind regression to the mean is based on the observation that any time luck is involved in a performance measure, those at the highest levels tend to not only be the most skilled, but also the most lucky. This is especially true at the extremes, because extreme performance, by its very definition, is difficult to achieve, it tends to only be achieved when the player is both good and really lucky. While Justin Upton is in fact a tremendous homerun hitter, I don’t think anybody really expects him to make a run at 100 homers. While we’re very high on Mike Minor, I don’t think anybody really expects Mike Minor to have a sub 2.00 ERA. On the flip side, we certainly don’t expect Jason Heyward to hit sub .200 when he comes back.
The first thing to address with regression to the mean though, is what it isn’t. We’ll often see things like “it’s going to be ugly when player X’s regression kicks in.” Unless the implication is that the player is terrible and is purely lucky, then this is simply a dressed up version of the gambler’s fallacy. Regression is not active. That is a player performing poorly, or well, doesn’t make him “due” for a correction. The level of performance we should expect of a player should be exactly the same as the level of performance we expected prior to the streak. In fact, we might even take the hot or cold streak into account as at least a possible partial reflection of a skill level change. That is, if anything a cold streak should slightly lower our expectations and a hot streak should slightly raise our expectations, not the reverse. If you see a player going through a cold streak and think “he’s due” for a hot streak, that’s the gambler’s fallacy in action. When we see a player like Jason Heyward struggle, we shouldn’t expect he’ll all of the sudden put up a .350/.420/.610 triple slash for the next month, we should expect his performance to reflect a very minutely, almost imperceptably downgraded belief of what we thought about him prior to the cold streak. Similarly, Justin Upton’s home run binge should only give the tiniest nudges upwards in our beliefs about him. But we certainly shouldn’t expect Upton to get cold all of the sudden or Jason Heyward blazing hot.
The problem we run into in baseball is that we very rarely know what a player’s true ability level is. During the prime of Chipper Jones’ career, we could probably assume if he was healthy he was somewhere around .300/.400/.500 ability, but most players don’t have track records as well established as Chipper did. This is especially problematic because players change as they age, and they don’t always change at uniform rates of improvement and decline. This is especially problematic with rookies, since while helpful, minor league data isn’t perfectly extrapolatable to what a player will do in the majors (though it is getting better year by year as we better learn how to use it). Take a rookie who comes up and hits .410/.450/.550 for a month, is he that good? Almost certainly not. But how good is he? Is he average? Below average? Above average?
A quick and dirty method is simply adding league average plate appearances to his record until he reaches the numbers linked in the fangraphs piece above. This is a method that Tom Tango favors. It’s not really precise, but it’s close enough for most applications. So let’s say a player had a .440 OBP over his first 100 PA at the MLB level. It takes roughly 500 PA for OBP to stabilize, so we’d need to add in 400 PA of league average OBP (I’ll use .320 here, just to make the math easy) to estimate his true level. When we do that math, we get that our best estimate of the player’s true OBP ability, given the information we do have about him is .344. Substantially better than average, but well shy of the astronomically good rate he put up over his first 100 PA. This is because we’re not throwing the data we gathered from the first 100 PA away completely, but we’re also not completely trusting it either. Now here comes the funny part, what should we expect that player’s OBP to be over 500 PA? Didn’t I just say .344? No. I said .344 is our estimate of his true skill level. To figure out what his OBP will be over the course of a 500 PA season, we then have to add in 400 PA of .344 OBP to the .400 OBP he’s already earned. So, given a player whose first 100 PA produced a .440 OBP, we should expect him to have a .363 OBP over those actual first 500 PA. This may seem paradoxical at first, but what we’re essentially saying is that we don’t throw those first 100 PAs away when we’re caluclating his full season OBP. We used them, combined with 400 PAs of league average PAs to regress him to his true level of .344. We then said, we expect, after those first 100 PAs, that he’d have a .344 OBP after that point. But those first 100 PAs of .440 OBP still count.
Now, the next issue then becomes even though the player had a .360 OBP his first season, and even though he has reached the 500 PA where OBP normally is stabilized, our player isn’t “normal”. .360 is still a really high OBP, so it in fact becomes probable that some of that .360 OBP was due to luck (specifically his luck in his first 100 PA). Now here, we must regress his results towards the league mean, because we really don’t know that his ‘true’ level is .344. How much should we regress him towards the leage mean of .320? That’s a topic for a lot of debate, it’s part of why all the various projection services, like Bill James, PECOTA, etc come up with very different results (that along with differing aging and injury predictors). Further, many that are used are linear, and regression isn’t usually linear, because the more a given result deviates from the mean, the more likely it is to have been due to a lot of luck. Our theoretical player with his .360 rookie season OBP should probably be regressed down to somewhere in the .335-.345 range.
After we’ve gathered multiple seasons of data however, the situation changes. Now we begin to be more sure of what a player actually is, and we can now start to expect a player to regress towards his population mean, ie the mean value of the seasons he has already put up, perhaps weighting recent seasons slightly more and then accounting for aging curves. The point here is that when we get enough data, the player’s own history starts to become a better (though not infallible) predictor for success or failure than a pure league average. The problem is that there isn’t a magic line where this phenomenon overtakes the rookie, who we had to regress so severely to the league mean. It’s a gradual adjustment, which makes for difficult math. Also, there aren’t real clear, established guidelines for how the weighting slowly switches from regressing towards the league mean to the player’s mean. But the important point for the purposes of this discussion is that we simply understand that for young players, we should regress them to the league mean, for well established players we should regress them towards their own established means, and for players in between (say 2 and 4 seasons) some sort of mix between their established record and the league’s mean.
Essentially regression towards the mean involves a few assumptions, and understanding those assumptions is really more important to us as fans trying to understand the game, than the exact mathematical details. The important assumptions are: 1) a player’s performance is a combination of luck and skill, and large deviations away from the mean are most often partially due to more luck. 2) a player’s luck has no future predictive value, ie regression is not active, a player performing abnormally well or poorly won’t experience counterbalancing good or bad luck to ‘even things out’, we should thereafter expect merely ‘normal’ amounts of luck 3) a player’s skill does have predictive value 4) regression is an attempt to make an educated guess at how much was due to luck and how much was due to skill 5) when we don’t have very much information about a player, our best guess as to his ability level is that he’s closer to the mean than his performance suggests 6) when we know a lot about a player, we assume his ability level is closer to his historical performance than his current performance.
April 25, 2013 at 2:53 pm by Franklin Rabon under Atlanta Braves
Mike Minor has been incredible this season, and that’s perhaps an understatement. At first I believed that Ben really was busy at work, which why is he’s been a little less present recently, but lately I’ve come to believe that he’s just been watching Mike Minor’s starts on endless loops, 24 hours a day.
In any regards, let’s first get a look at Minor’s results and peripherals, to get a sense of exactly how good he’s been:
and here are some of the numbers compared to league averages:
Here we see his results. Which are obviously excellent. A few things jump out, first and foremost the K/BB ratio. 22.1% K’s are good, but not great, but then his insanely low walk rate combined with that is the biggest indicator of his success. When you’re K’ing guys at a slightly above league average rate, but walking nobody, good things are going to happen. Next, his absurdly low homerun rate (1.1%). There’s probably a decent amount of luck involved here, and some regression in this regard is probably due, but you can also make the case that Minor has induced weaker fly-ball contact than in previous seasons, and that real skill is at least a small part of this number, as well as good luck. A last number that somewhat jumps out is the relatively average for Minor BABIP, it jumps out precisely because it doesn’t jump out. His .275 number is actually a good bit higher than his BABIP from last season (.252) and certainly can’t be used as an explanation for Minor’s results. Normally a guy on a streak like Minor’s is riding some great BABIP luck, and that just doesn’t seem to be the case here, which is encouraging.
Notice in the league comparison chart how much Minor is getting guys to chase, combined with a better than average called strike rate. This shows us he’s getting batters to swing at bad pitches and take good pitches, which is, uh, like a good thing to do if you’re a pitcher. The big key is that he’s been able to be better than average in BOTH regards. For most pitchers, they kind of have to pick one or the other, it’s a special pitcher that can do both. That’s Roy Halladay in his prime type stuff. Minor is also getting a lot of whiffs, being in the 80th percentile in miss%, while also getting guys to swing at a lot of pitches. Again, great stuff. He’s keeping guys from taking a lot of pitches, but also getting them to miss a lot. Further, this indicates if there’s this much swinging and missing going on, there’s also likely a lot of weak contact.
Next, let’s look at the hits he has given up:
As we can see even his hits allowed have mostly been of the soft variety. Not a whole lot of smashed hits down the gaps. Even the one home run he allowed didn’t go over 380 feet.
In his contact type rate numbers, nothing really jumps out as different from his norms, except the already noted HR/FB rate, which as we stated is probably due for some regression:
So, in summation, Minor has probably had some good luck with his homerun rates, but also seems to have made real strides in control, strikeouts and inducing weak contact. These are all admittedly short samples, but by and large they’re all great signs. He may be on a hot streak, but outside the homerun numbers it hasn’t necessarily been a lucky streak.
Next let’s look at the “how” he’s gotten these results, ie heat maps:
Essentially we see that Minor is effective against RHB, and devastating against LHB. He mostly seems to use his change-up to neutralize righties, and then his rising, riding fastball up in the zone with his curve to just brutalize lefties. On batters of both types, we see Minor likes his fastball up in the zone. Despite not being an overwhelming pitch velocity wise (it sits right at 90 MPH), his fastball does have good rise and run on it. It doesn’t necessarily actually rise, it just doesn’t drop as much as most pitchers’ fastballs do. The running action seems to get the ball off the end of the bat to righties and in on the handle to lefties. Floating at just 90 MPH and letter high, the pitch must often look imminently hittable to a lot of hitters, to only find Jason Heyward jogging over to make a routine catch.
Let’s get a bit better view of the movement and velocity of his pitches:
The center point of the graph represents the average movement of all pitches, thus the fact that his fastball (denoted in orange) sits up and in doesn’t mean it actually rises, just that it ‘comparatively rises’, since all pitches drop. What we see is that he has a hard riding fastball that sits at 90 MPH, a change-up that comes in on a similar plane, but with a bit more fade and less rise. Then a much slower curve that drops straight down, and his slider which is somewhere between his fastball and his curve. For a four pitch arsenal, this represents a very good spectrum of pitches. It’s been especially effective this season, as he’s managed to command all four pretty regularly.
Ultimately, we do see some signs pointing to a bit of regression for Minor (most notably the homerun rate), but that’s to be expected when your ERA sits at 1.80 and your name isn’t Greg Maddux. However, we also see a lot of signs of genuine skill development that might be at least somewhat sustainable. At the very least it’s hard to be anything but encouraged with everything we’ve seen of Minor thus far. He hasn’t been a case of somebody getting really lucky with balls in play and run support. He’s displayed excellent command, has been getting guys to swing at balls and take strikes, he’s been getting guys to make weak contact. There isn’t a single thing he’s done that’s been a harbinger of future doom this season.