Batting average was rightly criticized as a measure for player performance when better stats arrived. Batting average only measures a part of hitting skill (getting hits), and not other crucial things like walks and power. Because of that, better statistics like OBP and OPS and finally wRC+ were used to evaluate players.

Over the years, Ks went up, but also contact quality increased, so batting average did not change that much, but about 20 years ago BABIP maxed out and has stayed at .300 ever since while Ks continued to climb. Still, the number of .300 hitters isn’t down that much; in 1997 it was 35 and last year it was 25. Hitting .300 always was a tough thing to do, and still is.

Hitting .300 is not productive by itself of course, but if you look at the .300 hitters last year it seems like the “empty .3o0 hitter” that was often used as an example of why BA is bad to judge hitters is basically not existing anymore. Last year only five of the 25 .300 hitters hit single-digit homers and none of the 25 had a below-average wRC+. It seems like slapping the ball in play is not enough to hit .300 anymore with today’s defenses. Basically the modern .300 hitter is a powerful hitter in most cases. The .300 hitters of last year averaged a mightily impressive 132 wRC+ and a whopping 4.7 WAR so it was truly an elite group (seven out of the top 10 in WAR hit .300).

You can illustrate the value of hitting .300 pretty well when you look at the dated stat of OPS. Its components are OBP and SLG. OBP is hits+walks+HP/PA and last year 71% of all on-base events were hits. And even slugging is heavily influenced by BA as it is BA+ISO. The importance of ISO has grown over the years but still SLG is about 60% driven by BA and only 40% by ISO. That means that BA still has a huge influence on batting production.

What we do see is that the .300 hitters are good contact hitters. On average they have a 15% K%, which is way better than the league average (around 22%). No .300 hitter had a K% of above 25% and only two were even above 20%. Still, K% does not have a big correlation with batting performance since there are still the weak slap hitters and productive TTO, but the below-15% K hitters as a group have a respectable 109 wRC+.

Again it looks like the weak slap hitters are a dying breed. We all have noted that the league is getting closer together in power and this could actually mean a comeback for contact and batting average. it doesn’t mean that slap hitters will come back, but since power seems to be about maxed out (still increasing but more because the bottom guys now also hit bombs), now players can get more productive by adding contact without sacrificing power; with the new ball or whatever, you don’t need to hit the ball that hard, just at the right angle, and that can be done without striking out more.

Now we all read the stories about players who got more productive by swinging harder and striking out more, increasing their power (Alonso, Freeman), and we all notice the huge power hitters with a ton of Ks being quite productive (Sano, Judge, Gallo, Stanton, Bellinger), and that is true, but those guys are all huge power guys (and still won’t be .300 hitters at least when you think that last year’s correlation of Ks and hitting 300 is still true).

The more desirable thing for the average player is probably to be like Murphy or Altuve, who don’t strike out and still hit 25 bombs. Those guys don’t hit the ball super hard (around average EV) but they make a ton of solid contact at good launch angles.

It is a bold statement, but I think the batter of the future would be a guy who hits the ball reasonably hard but makes good contact and hits the ball in the air. Hitting the ball in the air like Billy Hamilton won’t get it done, but once you are past a certain threshold, there are no extra points for more EV. A 120 MPH homer doesn’t plate more runs than a 100 MPH homer.

Now, of course, the extra power still has a value. Last year everyone hit 30 HR and still the leaders were only mid 40s, but this year we actually might get 50-bomb guys again. But still, I think that we won’t see a proliferation of Judge or Sano types. I think the new conditions actually hurt those guys a little because a team now can find a 25-HR guy who makes contact and defense more easily, making it harder for the big slugger to separate himself from the pack.

The Astros actually already incorporated that successfully. They improved their contact without really giving up power and they now really do well.

Low Ks don’t have an intrinsic value, but if power is already maxed out and the league is striking out so much, it is pretty easy to separate yourself from the pack. The low-K thing already was en vogue after the Royals won, but they still did it at a cost of low power. The Astros are basically the Royals 2.0 because they also have power.

One of the more subtle stories of the dawn of the statistical revolution in baseball was the case of Frank Thomas. Frank was a big slugger, but not exactly graceful in the field. Since he played in the American League for a long while, he often was slotted as the DH. On paper, this would make sense. A team could theoretically use his bat in the lineup while playing someone else in the field, to avoid his lack of fielding. Well, despite what current trends seem to say, baseball is not played on paper, and some factors that cannot exactly be explained can derail logical thinking, like the Thomas at DH theory.

What I mean by this is that for some reason, Thomas’ best seasons as a hitter came when he played in the field. In his two MVP seasons, he played only 17 games (about 5%) as the DH. In all five of his All-Star seasons, he was voted in as a first baseman. This idea is further explained in Tom Tango’s The Book. For some reason, he was just not as good as a hitter as a DH. Maybe he was not as engaged, maybe he was “cold,” or maybe it was some other weird reason. I tried to dive into the splits to see the exact numbers, but could not get the exact data. Yet, the idea still remains: hitters may perform differently when placed in different spots in the field, or when they are not in the field at all.

I decided to apply this logic to the Minnesota Twins’ All-Star slugger, Miguel Sano. Like Thomas, Sano hits the cover off the ball, but isn’t as skilled in the field. Some would think that this means the Twins should play him as the designated hitter. Again, from a superficial level, this would seem to make sense, as his defensive liabilities would not come into play.

I looked into his splits for the 2017 season. With about 250 plate appearances as a third baseman and about 60 as a designated hitter or a pinch-hitter, there were enough observations to perform a hypothesis test (specifically a Two Sample t-Test) on the subject.

My test criteria is as follows:

• Null Hypothesis: Batting Average/OBP/wOBA as 3B = BA/OBP/wOBA as PH/DH
• Alternative Hypothesis: Batting Average/OBP/wOBA as 3B ≠ BA/OBP/wOBA as PH/DH

My level of significance is 95% confidence, or a = 0.05.

For those who are not familiar with the two sample t-test for equal population means, or if you may have forgotten formulas for test statistics, degrees of freedom, point estimates, or anything else, you can find it all here.

Here are Sano’s splits. One can note the obvious difference in his batting statistics, but is this difference statistically significant?

Let’s dive in. Well, here is my test:

It appears that the difference in means for batting average and wOBA are significant, meaning we can reject the null hypothesis that Sano bats the same when in the field and when off of the field. OBP was not significant, so we fail to reject the null for that metric, but the p-value was still relatively weak.

From this analysis, one can see that Sano does in fact perform worse when he doesn’t play 3B. Perhaps the Twins have already keyed onto this, as Sano has four times as many ABs as a third baseman than as a designated or pinch-hitter. The Twins do have a few decent utility infielders, so maybe they are just squeezing in playing time for those guys when they move Sano to DH. But, in this case, the statistics don’t lie: please play Miguel Sano at third base.

On May 5th, the Cardinals called up Tommy Pham after both Dexter Folwer and Stephen Piscotty suffered from various injuries. Since then, Pham has been arguably the strongest driving force for the club – more so than in any of his other major-league stints. He’s been between AAA and MLB since 2015, so many Cardinals fans are wondering “what’s different this time?” This is something I set out to get an understanding on.

In the case of outlier performance, generally the first thing I try to understand is how much this can be ascribed to luck. The most common proxy for luck in baseball is BABIP (Batting Average on Balls in Play), so I tried to get a sense of how this season stacks up to Pham’s prior major-league experiences:

According to FanGraphs, BABIP can be the result of a few things: varying defense, luck, and talent – FanGraphs goes on to to say that the talent is related to how hard a player is hitting balls. Being that we’re looking at full year stats (and 2017 through 79 games, 50 he’s appeared in), varying defensive skills ought to be normalized out. There doesn’t seem to be any significant outliers as far as how hard he’s hitting balls compared to prior years, so it’s very possible that this is slightly biased by luck, thus should be framed under that presupposition.

Pham has always had severe eye problems, so much so that he was once legally blind in his left eye, but his performance this year seems to indicate that he’s seeing the ball much better.

If we look at his swing rate at pitches outside of the zone, it affirms this notion. He’s improved yearly, but this year significantly so. It’s reasonable after this to ask the converse – how is he swinging at balls in the zone?

Perhaps my naïve expectation was that he would swing at more balls in the zone – but that doesn’t seem to be true. He’s swinging less in the zone as well. In fact, if we look at his swing rate in general:

It appears he’s just swinging at fewer pitches in general. This is what I think Pham has corrected – he’s become a more patient hitter, and it’s paying off. It’s important to understand how increased patience at the plate would help his offense.

Here I’ve plotted how his swinging inside and outside the zone correlates with his batting average each year. The .290 BA is this year, .226 was 2016, and .268 was 2015. It’s interesting to notice that his best batting average of his professional career is associated with both a low swing rate outside and inside the zone. Patience pays off.

The point for 2015 is also interesting because he swung less in the zone, swung slightly more outside, and yet still improved his average. This emphasizes just how important it is for Tommy to see a few pitches before he swings away.

If he’s waiting for better pitches, it’s not a jump to assume that he should get better hits off those pitches:

Looking at slugging percentage, however, it seems as though he’s hitting about the same in 2015. It’s worthwhile to note that in 2015 he was swinging in the zone at a fairly similar rate, which makes sense – for quality contact, you need a pitch in the zone.

The last thing I wanted to look at was if his increased patience led to more walks.

The answer here was a bit surprising. Logically, I would expect more patience would yield more walks, but that doesn’t seem to be the case. He’s walking at about the rate he was in 2016, and only 0.1% better than in his rookie year.

So far, Pham’s newfound patience is working out for him, but I think it’s possible pitchers are still expecting the 2015 and 2016 Tommy Pham, who is more willing to swing out of the zone. It should be interesting to see how this evolves as the season continues and pitchers adjust to his new approach.

Plots made in python using the seaborn package.

Note: stats are as of morning June 27th.

Generally speaking, a slash line (ERA/FIP/xFIP/SIERA) of 5.58/4.28/4.77/4.80 isn’t very encouraging. These are the numbers that Pirates starter Chad Kuhl has put up to date this season through 69.1 innings. Last year, he threw 70.2 MLB innings, so we have comparable sample sizes. Yet, he seemingly hasn’t improved upon last year’s numbers. Yes, the strikeouts are up, from a 17.6% K-rate to a 19% this year. However, the walk rate is also up (6.6% to 9%), the ground balls are down (44.3% to 41.8%), and the home-run rate has risen accordingly (0.89 HR/9 to 1.04). What, you may be wondering, do I see in this guy?

Check out his plate discipline stats.

Improvements across the board. His chase rate has gone up while his in-zone swing rate has gone down. Hitters are making far less contact on pitches out of the zone, and even a bit less on pitches within the zone. This explains the increase in strikeouts. The walks shouldn’t be increasing, unless hitters are really going much deeper into counts, since they are making less contact. Nonetheless, this should change if Kuhl keeps things the same, because he’s throwing in the zone more often and getting more swings outside of the zone. Of the 118 pitchers who have thrown at least 60 innings this year, Kuhl’s chase rate ranks 43rd, his in-zone swing rate is tied for 48th lowest, his Z-Swing minus O-Swing ranks 37th, and most impressive, his swinging-strike rate is tied for 26th. In fact, his swinging-strike rate is the same as Yu Darvish — he even has a higher chase rate than him (30.5% and 29.3%), and Darvish has a superb 26.9% strikeout rate. The underlying statistics are optimistic, so if Kuhl keeps pitching this way, the strikeouts will increase and the walks will decrease. The bigger question is, what is the driving force behind these improvements?

According to PITCHf/x data on FanGraphs, Kuhl’s average four-seam fastball velocity has jumped from 93 last year to 95.5 this year, touching 99. Contrary to what his name might suggest, Chad Kuhl is throwing heat. In fact, all of his pitches have seen an increase in velocity (and he’s added a curveball, but he’s only thrown 38 of them and they have been largely ineffective):

The velocity increase has given Kuhl more confidence in his four-seamer, and his usage of the pitch has risen to 29% this year, up from a mere 10% last year. This explains part of why the ground-ball rate is dropping — the uptick in four-seamer usage has caused a drop in sinker usage (down from 57% last year to 37% this year).

In addition, while his sinker has seen an increase in arm-side run (1.6 inches more), the ground-ball rate is also dropping because the sinker has seen a decrease in drop (1.1 inches less). While the drop on his sinker has decreased, the rise on his four-seamer has increased. It is now above average, ranking 52nd out of the 118 pitchers who have thrown at least 60 innings as of morning June 27th. This is in part due to a slight change in vertical release point:

This year, Kuhl is throwing more over the top with all of his pitches. This graph shows that, for his sinker, he is on average releasing the ball about two inches higher. Now, Pitch Info (which powers this graph) says that Kuhl doesn’t throw a four-seamer at all, only sinkers, as opposed to PITCHf/x. Either way, at this point, Kuhl’s “sinkers” don’t sink very much. Using Pitch Info’s data, Kuhl’s sinker has the eighth-worst drop among the 87 starters who have thrown at least 200 sinkers this year. In that same group, the ground-ball rate on Kuhl’s sinker is also eighth-worst. Coincidence? I think not. His overall ground-ball rate of 41.8% this year is below average, ranking 78th-lowest of the 118 pitchers who have thrown at least 60 innings this year.

All of his pitches are generating more whiffs, looking at both Pitch Info and PITCHf/x. This is probably due to the improved velocity. Using Pitch Info’s data, his slider ranks 15th in whiffs per swing out of the 87 starters who have thrown 100 sliders this year (not to mention, it ranks 10th in average velocity), and his sinker ranks 17th out of the 87 starters who have thrown 200 sinkers this year. However, his changeup still gets whiffs at a below-average rate: it ranks 71st out of the 92 starters who have thrown 100 changeups this year. Although the changeup has gotten more run this year, it too has lost vertical drop and the velocity gap between it and the fastball has closed a bit. Generally, changeups are used to sit down batters of the opposite handedness, because they have arm-side run. Kuhl, a righty, has struggled against lefties this year, as they have a .445 wOBA against him, while righties have a mere .286 wOBA. At the same time though, he has gotten more strikeouts against lefties (30) than righties (29), despite having faced fewer lefties (147) than righties (163). Also, I’m not too worried that Kuhl will have struggles against lefties in the long run because his sinker has great arm-side run.

The fact that Kuhl has a diminished ability to get ground balls doesn’t bode well for his old skill set, where he relied on his control and inducing weak contact, but with an increased penchant for strikeouts, backed by improving velocity, it shouldn’t matter that much. I would still take a flyer on him; the strikeouts, walks, and platoon splits should improve, along with his ERA.

Data from FanGraphs, Brooks Baseball, and Baseball Prospectus. Picture from MLB.com. Thanks for reading!

The Seattle Mariners went into the offseason with a solid lineup and a questionable at best starting rotation, which was made even more so with the trade of Taijuan Walker for Jean Segura and Mitch Haniger on November 23^rd, 2016. On January 11^th, 2017 GM Jerry Dipoto made his eleventh trade of the offseason when he shipped off the recently-acquired Mallex Smith along with minor leaguers Carlos Vargas and Ryan Yarbrough to the Rays for lefty Drew Smyly.

In 2016, Smyly put up a rather uninspiring 4.49 FIP, but he did take the mound 30 times and throw a career-high 175.1 innings. He wasn’t supposed to be anything special for the M’s; he was just supposed to slot into the middle of their rotation behind James Paxton and Felix Hernandez.

That is, until March 15^th, when he started for the US in their World Baseball Classic game against Venezuela and Seattle teammate Felix Hernandez. If you don’t remember what happened that night, go read this article by Jeff Sullivan. Smyly was brilliant, allowing 0 earned runs on only 3 hits. He did not issue a walk, and had 8 strikeouts in 4.2 innings. Felix was just as good that night, going 5 shutout innings with no walks and only 3 hits allowed. But what caught everyone’s eye was the uptick in Smyly’s fastball velocity. As Jeff detailed, his fastball was more than two ticks above his career average, and this was coming in a mid-March start. Mariners fans had to be thrilled after watching that game. Was the King back? Had Dipoto traded for another power lefty starter to pair with Paxton? Smyly was also elated, saying a couple days after that start, “hopefully, I can carry that with me for the rest of the season, but it’s a long season. … It’s hard to maintain that for 30 starts, but if I can, that’ll be great.”

Well, in late March, the Mariners put Smyly on the DL with elbow discomfort, and then on Wednesday, Ryan Divish of the Seattle Times broke this news:

Drew Smyly has a torn UCL and will undergo Tommy John surgery

— Ryan Divish (@RyanDivish) June 28, 2017

As an M’s fan, it was a big blow to go from hoping for 30 starts of this new harder-throwing Smyly to knowing that he won’t even throw a pitch for the M’s this year (if ever). Smyly wasn’t the only Mariners pitcher to participate in the WBC and then have issues this season. I already mentioned that Felix started that same WBC game for Venezuela, and he spent two months on the DL with shoulder bursitis before returning on June 18^th. Yovanni Gallardo threw 4 innings for Mexico, and he was terrible this year before recently being replaced in the rotation. Also, last year’s rookie closer phenom Edwin Diaz has very ineffective this year after being almost unhittable as a rookie in 2016.

This had me thinking, it couldn’t just be bad Mariners luck, could it? Have the other pitchers that participated in the World Baseball Classic gotten hurt and/or been less effective this season? Could all those complaints and worries about the WBC messing with throwing schedules and programs be justified?

So, I gathered the data to look at how MLB pitchers who participated in the WBC have performed this year. I am comparing their 2017 season results to how they performed from 2014 – 2016. This is a very simple comparison, and there some caveats that you should know about the data I am using: I am only including pitchers who threw at least 3 innings in the WBC, I removed 4 pitchers who made their debut in 2017, and I also removed Drew Smyly since he hasn’t pitched in 2017. I do, however, leave in everyone who made their debut prior to 2017. For example, Jose Berrios is included in the sample although only he only had 58.1 career innings before 2017, all of which came in 2016. This leaves me with a sample of 36 pitchers who have pitched before and after participating in the year’s WBC. Now let’s get to the results!

First, here is the comparison of 2017 vs 2014 – 2016 for the sample as a whole using a weighted-average approach:

As you can see, quite a decrease in performance by our group in 2017. In fact, the sample group has been almost exactly league average in 2017. While the WBC rosters are not entirely comprised of All-Stars, I think we would assume that the players competing for their countries in the biggest international baseball tournament are better than league average, and the data from 2014 – 2016 suggests that they were.

Now, to look at this individually, here is a scatter plot comparing the FIP- from 2017 vs 2014 – 2016 for the 36 individual pitchers:

Clearly, we can see that there are some outliers that have performed much worse in 2017 than they did in the previous years. On the very right we have Sam Dyson (2017: 156, 2014 – 2016: 82), who was designated for assignment by Texas after his historically bad start to the season as their closer, and moving down from him to the left is Edwin Diaz (126, 48). But these outliers are made up for by Jose Berrios, who we see at the very top has been significantly better this year than in his first taste of the show last year (77, 145). So, we cannot attribute this decrease in performance to the outliers, but rather by the group performing worse, which we can see by how close most of the group is to the trendline, in addition to the Average point being located to the left of the trendline.

Here are also the biggest increases and decreases in 2017 performance compared to 2014 – 2016:

The point of this article is not to say definitively that the World Baseball Classic has caused this group of pitchers to suffer a decrease in performance and/or injuries. I realize that this decrease in performance could be completely random, and we only have a half season of data after the 2017 WBC, but I do think it is interesting that the group has performed worse in 2017 than they did in the previous years. There has been lots of discussions about when the best time to hold this tournament would be, or if it is even worth having at all. Maybe it is a bad idea to have this tournament before the season starts when the arms aren’t fully stretched out. Maybe teams won’t allow their top pitchers to participate in future tournaments. Or, maybe it is just a bad idea to pitch in the WBC.

This weekend, we learned of 22-year-old stud closer Roberto Osuna’s anxiety and how it’s keeping him from taking the field. Tim Brown of Yahoo Sports stepped back and humanized the concept of a quality professional not feeling suitable for work because of something like this. It’s a thought that too often feels foreign because of the status we give pro athletes.

Dominant on the field, Osuna appeared to be overwhelmed in his quotes about his well-being. From Brown’s piece (emphasis mine):

“I really don’t know how to explain it,” he said. “I just feel anxious. I feel like I’m lost a little bit right now. I’m just a little bit lost.

“This has nothing to do with me being on the field. I feel great out there. It’s just when I’m out of baseball, when I’m not on the field that I feel just weird and a little bit lost.

“I wish I knew how to get out of this, but we’re working on it, trying to find ways to see what can make me feel better. But, to be honest, I just don’t know.”

In a single sitting, Osuna says “just” five times. And it might be the most dangerous word that could be used in this context.

Though we’ve made strides in accepting anxiety as a legitimate medical concern, there is still a stigma that surrounds it.  But because it doesn’t inherently come with a fever or cast it’s often looked at as something that someone just needs to deal with. Meanwhile, symptoms can mimic a heart attack.

It’s not even strictly a mental obstacle. It’s chemical. Anxiety is tied to cortisol levels in the body. Cortisol is regarded as the stress hormone and is critical to our natural fight-or-flight instincts. It is adrenaline’s tag-team partner. It’s triggered by high-leverage situations with a lot on the line, which happen to be the kind from which Osuna makes a living. So when he says his current state has nothing to do with him being on the field, it’s probably fair to say that’s actually highly unlikely.

The body doesn’t release these chemicals like a faucet. There is no convenient handle to portion out the amounts one might receive at any given moment. It’s possible that Osuna gets into games and simply can’t turn off the very thing that makes him so damn good on the mound once the game is over; that cortisol floods through his system unchecked.

And why would he know how to turn it off? What background might he have to keep it in check? We’re generally not a culture that prepares for the come-down. At 22, he’s already got two-plus years experience in the bigs. But dealing with anxiety? That’s probably not a focus through the developmental process in baseball operations, even though there are well-vetted methods that can easily be implemented.

The brain loves patterns and automation. For the most part, it wants you to be able to go about your day without having to stress too much. But danger may arise quickly when the stress response it’s equipped with for protection gets folded into patterns of automation that are designed for comfort. That’s why “just” can be an alarming word to pair with statements about feeling “weird” and “a little bit lost.”

How Osuna and the Jays handle this is ultimately their business, and only their business. But I fear an announcement in the coming days saying he’s fine. He’s already been back on the mound. Osuna may not be out of the woods for some time, though, and if it’s stopped him from entering games, it could be severe for him. It can take years of practice and strategy to appropriately address anxiety. I only hope that he and the team comes to that conclusion on their own. If they don’t, the situation could get much worse.

Speed kills. At least, that’s what people say.

Speed is certainly a good tool to have. All else equal, any manager would pick the faster guy. Of course, speed is a huge asset in the field, especially for outfielders. Good speed increases range, providing a sort of buffer zone for players who don’t get a good jump on the ball or who don’t read the ball well off the bat. No one in their right mind, when given the choice, would pick the player with less range (again, all else equal). And so we can all agree that speed very clearly increases a player’s value in the field.

Whether or not speed increases a player’s value at the plate is a different story. The faster guy may leg out an infield hit every now and then or stretch a single into a double or a double into a triple, but this won’t significantly increase a player’s value outside of a small uptick in average.

Luckily, Baseball Savant’s sprint-speed leaderboard gives us some interesting data to examine (you can find the interactive tool here).

Here, we can see that the league average sprint speed is 27 ft/s. Catchers, first basemen, and designated hitters are typically below league average. And it comes as no surprise that outfielders, especially center fielders, are typically above league average.

If we look at the fastest player at each position for 2017, we can come to a better understanding of the value of speed.

Notably, of the nine players on this list, only four of them have a wRC+ above 100 — league average. Is this significant? Probably not as a stand-alone statistic. But it is safe to say that speed does not directly correlate to value. And it certainly doesn’t correlate to value at the plate. Even when examining the WAR column, you won’t be blown away. Dickerson and Bryant are having great years, but for the most part these players represent a pretty average group.

As mentioned previously, only four of these players are above average in terms of creating runs (highlighted in red and orange). The players with wRC+ values in red have not had success because of their speed. They all have ISOs that are at least 50 points above league average. Basically, their success can be attributed to power, not speed.

However, JT Realmuto’s ISO is essentially league average. Did speed boost his value that much? (NOTE: speed is not taken into account when calculating wRC+; still, the value of each outcome, which is considered in the calculation, can be affected by speed) Realmuto’s speed puts additional pressure on opposing defenses, especially relative to other catchers, but I would be very hesitant to say that speed alone created a difference of 9 wRC+ between him and the average player.

Billy Hamilton is the fastest player in the league. And while most would call him a plus defender, very few would call him a good all-around player. His wRC+ value of 57 is seventh-worst out of all qualified players (highlighted in blue). Although he leads the league in stolen bases, even that wasn’t enough to raise his WAR above a dismal 0.5. We can safely say that speed does not correlate to success.

What about specific teams? Do teams compiled of speedsters at every position win more games?

Here is the same image as above with only Marlins players highlighted. Miami has a player with above-average speed at every single position, save for Justin Bour at 1B who has been a top-20 player in the MLB based on offensive production this year. Without question, the Marlins have a lot of speed, but still, they are six games under .500 and 10.5 games out of the wild-card race in the National League.

Here is the same image with San Diego players. The Padres are a speedy team. They have not one, but two players above league average at three different positions. Even their catcher, Austin Hedges, is only slightly below league average while still significantly faster than the average catcher. Despite having one of the fastest teams in the MLB, the Padres are 14 games below .500 and 19 games out of first place in the NL West.

Speed isn’t a stand-alone tool. It is a great complement to someone who makes contact at high rates (see: Ichiro) and it can put pressure on a defense, forcing fielders to rush to make a play. Furthermore, it is a crucial tool in the field, increasing range for all players, most significantly for outfielders. However, speed in and of itself is by no means an indicator of overall value. In baseball, speed doesn’t kill.

When learning the game of baseball, players are often taught about the importance of hitting the ball where it’s pitched.  This means that, if the pitch is inside, it should be pulled, if it’s in the middle of the plate, it should be hit back up the middle, and if it’s outside, it should be driven to the opposite field.  This is advice that generally makes intuitive sense.  I’m sure we’ve all seen batters reach to pull an outside pitch and roll over it for a soft ground ball.  We’ve also seen batters trying to fight off inside pitches and hit a weak ground ball or pop up to the opposite field.

However, the reemergence of the home run has led me to wonder just how valuable this guidance is.  Over and over again, Bryce Harper has been able to extend on an outside pitch and deposit it into the right-field bleachers for a home run.  Now, Bryce Harper is very often the exception to the rule, and an approach that works for him may not be suitable for 99% of the league.  That being said, more and more home runs are being hit, and not very many of them are being hit to the opposite field.  Based on Statcast data from Baseball Savant, in 2016 approximately 79% of home runs were hit to the pull side of the field.  Therefore, maybe it does make sense to load up and try to pull everything with the hope of hitting for more power.

To further investigate this, all batted balls from 2016 were queried from Baseball Savant and analyzed.  These batted balls were bucketed into four groups based on the pitch and batted-ball locations, separating each pitch as inside or outside (relative to the middle of home plate) and pulled or hit to the opposite field (using the middle of the field as the dividing line).  A few offensive statistics for each group are shown in the following table.

Maybe it is a good idea to just pull everything after all.  For both the inside and outside buckets, batters hit the ball harder and are more successful when pulling the ball.  The results on outside pitches are relatively close.  However, it is definitely not a good idea to try to hit inside pitches the other way.  I don’t think any batters are intentionally doing this right now or this is something that would come as a shocking discovery, but the data shows that by far the most weakly hit balls are inside pitches hit the other way.  I’d imagine a lot of these are scenarios where a batter gets jammed as opposed to trying to take the ball to the opposite field.

While it does appear that pulled balls are hit harder, the buckets here are pretty broad.  Right now we’re grouping pitches a half inch away from the middle of the plate in the same group as pitches on the outside edge of the strike zone or outside the strike zone entirely.  Therefore, it might be worth looking at the outside pitches further while using slightly more narrow buckets.

The table below shows pitch locations bucketed into two groups: slightly outside and way outside.  To get these two groups, the plate was split into quartiles.  Slightly outside pitches are located in the 3^rd quartile when counting from inside out, while pitches further outside than the 3^rd quartile were considered way outside.  In other words, the dividing line was the midpoint of the outside half of the plate.  As the table shows, the results aren’t as simple as saying that every pitch should be pulled for maximum effectiveness.

For pitches that are just barely outside, batters experienced much more success in 2016 by pulling the baseball.  However for pitches that were well on the outer half of the plate or even further outside, hitting the ball to the opposite field is by far the better option.  There are several key takeaways to note here.  When looking at wOBA, the success of hitting to the opposite field does improve when the pitch is further away, but only slightly.  However, the results of pulling the ball absolutely crater when moving from pitches that are just barely outside to way outside.  It’s really hard to pull a ball that far outside with any authority.  Those pitches are much more likely to result in the batter rolling over the ball for a weak groundout.

The home-run-percentage numbers are also interesting in the table above.  Even when the pitch is way outside, pulled baseballs are more likely to result in home runs.  For balls hit to the opposite field, home runs are higher when the pitch is slightly outside, even though wOBA is lower.  The gains in hitting balls to the opposite field when they’re further out come from improvements in average, not power.

In our previous table, we’ve accounted for the fact that there are varying degrees of how far out a pitch can be.  In the same manner that there’s a difference between a pitch that is way in/out and just barely in/out, there are also varying extremities of how severely a ball is pulled or hit to the opposite field.  To help account for this, we are going to calculate horizontal spray angles for each batted ball using the formula from this extremely helpful Hardball Times article.  As stated in the article, the calculations may not be perfect, and they may not align exactly with the pulled and opposite field values used earlier, but they should be very similar and will allow us to analyze batted balls at a much more granular level than we have thus far.

Once spray angles were calculated for each pitch, batted balls were split into nine separate groups.  Pitch locations were divided into inside, middle, and outside, with middle pitches consisting of the central third of home plate.  All pitches further out than that were considered outside, with all pitches further in considered inside.  Pitches were also divided into three groups along batted-ball location, with balls hit to the middle 30° of the field placed into the middle group, and balls that were hit further in or away grouped accordingly.  The table below shows the average launch speed for each of the nine groups.

As the table shows, inside pitches should be pulled, with the optimal angle drifting closer to the opposite field as the pitch gets further away.  However, even for outside pitches, balls are still hit harder to the middle third of the field than to the away third.  We can also look at wOBA for these groups, which will show relatively similar results.

One interesting result here is that batters are actually slightly more successful when pulling the ball than hitting it up the middle if it’s in the heart of the plate.  We still see the same shift, however, where the further away a pitch is, the further away it should be hit.  Maybe the old conventional wisdom is on to something after all.  We can help visualize this with the following heat map, which shows how batted-ball launch speed varies based on the horizontal location of the pitch and the spray angle.

In the plot above, negative spray angles are balls that are pulled, with positive spray angles being hit to the opposite field.  Zero is the middle of the field.  The horizontal pitch location follows a similar layout, with zero being the middle of the plate and negative values representing pitches that are inside.  While it is subtle, we see that, as the distance of the pitch away from the batter increases, the spray angle of the hardest-hit balls increases as well.  However, for opposite-field hits, this seems to taper off around the 20° mark, which we don’t really see for pulled balls

One other interesting note is that the average launch angles vary quite a bit between the different groups, as shown in the following table.

Average launch angles are much lower for pulled balls, and launch angles decrease among all batted-ball locations as the pitch moves further away.

So, is the conventional wisdom to hit the ball where it’s pitched correct?  Yes, the optimal location to hit a baseball varies with the location of the pitch.  As the pitch gets further outside, the optimal angle moves further towards the opposite field.  However, it’s important to note that the optimal spray angle isn’t centered relative to the middle of the plate and the center of the field, and is actually offset towards the pull side.  It’s also worth noting that batters still have less power when hitting to the opposite field, so it’s likely worthwhile to be selective and wait for a pitch that can be driven up the middle or pulled when possible.

There are still a lot of other ways to cut this data outside of what I’ve described here, and I really think we’re just scratching the surface regarding the optimal offensive approach.  While balls that are pulled are hit harder, batters who are more selective and wait for a pitch to pull are also more likely to get deeper in counts and strike out more often, so there’s definitely a trade-off that has to be considered.  Another important note is that the tables I’ve shown above are looking at all batted balls.  It could be valuable to pull similar results when only looking at pitches in or near the strike zone.  It would also be interesting to see how the optimal spray angle varies based on other factors, such as the pitch type and the vertical location of the pitch.

The current analysis groups all major-league hitters together.  I’d love to see a future analysis that breaks out results by the type of batter and perhaps even shows different optimal spray angles for different batter profiles.  While the analysis here does help to demonstrate that there is validity to the conventional wisdom of hitting the ball where it’s pitched, there are still factors not being accounted for.  One of these is the fact that we may be dealing with some sample bias, as the most powerful hitters are also likely to be the ones who attempt to pull every pitch.  Accounting for different types of hitters would be a great next step in furthering this research by adjusting for the fact that hitting doesn’t have a one-size-fits-all approach.

The Cardinals’ have struggled to run the bases for the better part of two years now. So far, the only tangible effect has been third-base coach Chris Maloney’s “reassignment” to the minor leagues. Nevertheless, Cardinals manager Mike Matheny has continued to preach aggressiveness on the basepaths.

I intend to show the effect the Cardinals’ outs on the bases have had on their ability to score runs. A run-expectancy matrix can help. A run-expectancy matrix shows you the number of runs, on average, a team can expect to score from a given on-base state to the end of the inning. For example, with the bases loaded and no outs, a team can expect to score about 2.2 runs by the end of the inning. On the other hand, with nobody on and two outs, the offensive team’s run expectancy is about 0.098 runs. Here’s the basic run-expectancy matrix:

To estimate the number of runs the Cardinals have left on the bases, I charted every out on the bases thus far in 2017 (53). In each of those 53 instances, I charted the actual outcome and the outcome had the mistake not been made. Then, I subtracted the run-expectancy of the actual outcome from the mistake-free one.

In total, the Cardinal’s actual run expectancy is about 22 runs lower than it would be without baserunning mistakes. If you add those 22 runs to the Pythagorean record formula, the Cardinals should be 38-37, or 1.5 games behind the Brewers.

Not all outs on the bases are created equal, though.

All those formulas are useful, but they make a few key assumptions. First, they assume average speed on the bases. Second, they assume an average hitter at the plate. The creators of run-expectancy arrived at the above numbers by studying the results of MLB games over a six-year period. That’s thousands of innings and at-bats for the numbers to even out. But, when you look at just 53 instances, it’s possible for there to be some small-sample-size error. So let’s look at a couple of specific plays from this season.

April 18

With the Cardinals leading the Pirates 1-0 in the 5th, Greg Garcia came to bat with Jose Martinez on first. With nobody out the run expectancy was 0.8.

Garcia lined a double into center. Martinez rounded third and scored easily, but Garcia was thrown out trying for third. Now, it’s possible a throw from the outfield was cut off by the first baseman and redirected to third to nab Garcia. However, quick review of the video shows that not to be the case.

With one run in, the Cardinals could have expected about 1.1 more runs had Garcia stayed put at second. Instead, with nobody on and one out, their run expectancy dropped to .59. There’s about 1/2 of one of those 22 runs.

Luckily the Cardinals hung on for a one-run win.

May 13

Leading the Cubs 3-1, Magneuris Sierra was on first with one out and the pitcher, Carlos Martinez, at bat. Sierra tried to steal second (Lester was on the mound) but was thrown out for the second out.

Run expectancy says the Cardinals went from scoring about .5 a run on average to .2. But the pitcher was hitting. Assuming Carlos would have bunted him over, the run expectancy would have risen to .319. Lower than it was, but higher than if Carlos would have, say, struck out.

This is an example of a time where run-expectancy breaks down. In the National League, pitchers hitting has a tendency to ruin even the best laid plans. And because most formulas make the basic assumptions mentioned above, it’s hard to criticize Sierra’s mistake.

May 18

I bet you’re surprised I got this far without mentioning Matt Carpenter.

Well, on May 18 Carpenter committed one of the stupidest, irresponsible, boneheaded, bordering-gross-criminal-negligence baserunning mistakes I’ve ever seen.

Carlos had pitched an utter gem, and the game was 0-0 in the 9th. Carpenter lashed a sure double into left. It appeared the Cardinals were well on their way to a win, as their run expectancy rose from about half a run to 1.1.

Then Carpenter rounded second, and headed for third.

He was nailed at third easily. The Cardinals run expectancy dropped all the way down to 0.25. They didn’t score in the inning, and went on to lose the game.

As you can see, run expectancy isn’t the perfect tool for evaluating baserunning. Sometimes calculated risks have to be taken based on the speed of the runner or quality of the hitter, two things run expectancy ignores.

Taking the extra base is always a calculated risk. By ignoring the times the Cardinals have been successful, I was setting them up for failure in this scenario. But when you are among the league leaders in outs on the bases, the particulars of those outs require some serious consideration.

The conclusion is this: the Cardinals reckless baserunning has cost them as many as three wins thus far this season.

This article first appeared in The Redbird Daily.

On June 22 the Chicago Cubs sent a struggling Very Large Human, Kyle Schwarber, to the minors. The Warbird earned it, with the 20th worst fWAR and 6th worst bWAR among qualifying hitters. His OPS is just two points shy of Albert Pujols‘, a goal that you kids at home should no longer aspire to. Schwarber’s inoffensive offense has led to much discussion, most of which revolves around two competing theories:

1. This is just a slump, and Scwharber will come out of it. He’s way better than he’s shown in 2017. Craig Edwards said as much in these pages a couple of weeks ago.
2. Schwarber’s fallen and he can’t get up. There is something fundamentally wrong with him that is going to take considerable time to correct, if it is correctable at all. The demon that possessed Jason Heyward in 2016 has found a new human host.

The Cubs, predictably, are publicly sticking with Theory 1, and not without reason. As Edwards pointed out, there is plenty of statistical evidence suggesting Schwarber is basically the same hitter he was during his torrid 2015 campaign. The walk rate is about the same. The K rate is about the same. The power is still there — how many guys with an ISO over .200 get sent down? And that most basic of slump detectors, BABIP, is flashing red: Schwarber’s BABIP is a minuscule .193, last among qualifiers.

Or is this all whistling past the graveyard? A deeper look at Schwarber’s numbers reveals some seemingly alarming trends. Specifically, he’s been virtually helpless against the slider this year, “slugging” it at an .042 clip. In 2015 he murdilated sliders, slugging .593 against them. For those of you not near a calculator, that means between 2015 and 2017 Schwarber lost 551 points of slugging against one of baseball’s more common pitches — losing more than most hitters will ever attain.

There were specific sliders that Schwarber found particularly tasty in 2015 — those down and over the plate. This year, not so much. As his FanGraphs pitch value tables indicate, the slider has become garlic to Schwarber’s vampire. (Not that I am in any way suggesting that Schwarber is an undead being of any sort.) Other teams, aware of this newly opened hole in his swing, have accordingly started feeding Schwarber a steady diet of sliders.

Except that they haven’t. Schwarber is actually seeing slightly fewer sliders this year than he did in 2015. Maybe major-league front offices would benefit by reading more brilliantly researched blog posts like this one.

Or maybe there really isn’t anything there after all. One good way to evaluate a hitter is to watch how other teams are treating him, and Schwarber’s opponents haven’t pitched him much differently than they did in 2015, at least as far as pitch selection is concerned. This doesn’t seem to be a Heyward situation, where a gaping hole did open in his swing, and pitchers began attacking him mercilessly with high fastballs.

Another good way to evaluate a hitter is to watch how his own team treats him, and the Cubs have been almost painfully patient with Schwarber. He was bad in April before getting much, much worse in May. A power spurt in early June was not enough to save him from Iowa.

Last year, the Cubs had good reason to be patient with Heyward, even though he was producing about as much reliable power as Pakistan’s grid. There were two reasons for this: (1) he was making substantial contributions with his glove; and (2) from about April 15 on, the Cubs had a divisional lead of at least 75.5 games. No, really. Look it up.

The Cubs patience with Schwarber is less obviously explicable. Replacements for his limp bat were at hand in the minors, including Ian Happ and (more recently) Mark Zagunis. Schwarber adds nothing to the team’s defense, and the Cubs this year are in a remorseless fight to the death in the NL Central.

So the Cubs and their opponents have been behaving (for most of the season, anyway) as though Schwarber is in a slump, rather than suffering from some more fundamental problem. The Cubs might be looking at his track record — in his brief minor-league career Schwarber’s OPS is 1.042, and, you know, that 2015 season was so awesome!

But Schwarber didn’t have a season in 2015, he had less than half a season: 273 plate appearances to be exact. He’s had 261 PAs this year. So to this point, Schwarber’s entire career does not yet amount to the equivalent of even one full major-league season. His career has been strange in that his PAs have been so highly segregated: 273 fantastic PAs followed by 261 awful ones, with some World Series heroics in between that would melt the hardest of non-Cleveland hearts. Put it all together though, and you have one short, not-all-that-impressive career thus far. His career OPS+ is 102, and his career wRC+ is 104. Those numbers simply aren’t good enough for an essentially postionless player. Here’s a list of left fielders with a career OPS+ of 102. For those of you who can’t click through, trust me, it’s short. The Cubs didn’t plan to use the 4th pick of the 2014 draft because they wanted the next Garret Anderson.

Past performance does not fully predict future results, and there are some reasons to think that the real Warbird is closer to the 2015 version than the 2017 one. As noted above, his minor-league stats were through the roof, and a very competent front office used a very high draft pick to get him. His trouble with the slider looks more like a reframed BABIP slump — that is, a run of bad luck during a small sample size — than a genuinely exploitable hole. He’s still only 24.

And yet, the Cubs did send him down. This probably has more to do with the pennant race than with Schwarber; the Cubs simply can no longer afford to give away outs. The move takes some pressure off of Schwarber himself (though he may well not see it that way), and takes the pressure off of Joe Maddon to either write Schwarber into the lineup every day and answer a bunch of annoying questions about it, or not write Schwarber into the lineup and answer a bunch of annoying questions about it. But if Schwarber’s last 261 PAs are simply down to bad luck, why couldn’t the first 273 PAs be down to good luck?

I don’t really believe Schwarber is the next Garret Anderson, but I’m less certain than some Cubs fans that we know who the real Schwarber is yet. Schwarber’s demotion will help the Iowa Cubs sell tickets. Whether it helps the Chicago Cubs sell playoff tickets remains to be seen.