The panel on ‘The Changing State of Sabermetrics: at the 2017 SABR convention in NYC with panelists Joel Sherman, Mark DeRosa, Vince Gennaro and Mike Petriello claimed that fewer balls are going into play and singles are actually down. They posed the question, “Are singles still a thing?”

With that in mind, we aimed to verify if these claims are true and what makes people feel that players are hitting fewer singles in today’s game.

We used data that’s current as of July 2, 2017.

NOTES:

• Data being visualized is from http://www.baseball-reference.com/leagues/NL/bat.shtml and http://www.baseball-reference.com/leagues/AL/bat.shtml on July 1, 2017.
• BABIP definition is taken from  http://www.fangraphs.com/library/pitching/babip/

Below you will see two charts illustrating the number of hits, home runs and strikeouts per game.

You can conclude three things from these graphs:

1. Over the past 10 seasons, strikeouts have been increasing dramatically — 1.94 K/Game in the AL and 1.52 per game in the NL.
2. Over the past 3 seasons, singles per game have dipped.
3. Over the past 3 seasons, HR per game have spiked higher than ever before.

To say that Kevin Siegrist has really struggled in 2017 would be an understatement. After allowing 15 earned runs in 31 appearances through June 22, he was placed on the DL with a cervical spine sprain. With an ERA near 5, Cardinals fans have been left wondering what happened to the player who led the league in appearances (81) and finished third in holds (28) in 2015.

At first glance, Siegrist has an obvious issue — a very clear and very serious velocity problem. Take a look at this graph.

The velocity of his fastball has decreased every year since 2013. It hovered around 95.8 mph at one point, but more recently it’s dropped well below 93 mph. That’s a significant decrease, as the steep slope indicates. And for the first time, Siegrist, who is a reliever, has a fastball velocity well below a league average that includes starting pitchers.

If you have ever looked at aging curves, for hitters or pitchers, then you know that skills decline with age. Certainly, pitching velocity is no exception to this rule. Still, Siegrist is an extreme case.

Velocity very clearly declines with age and Siegrist has fallen right in line with this trend. For the first two or three years of his career, his changes in velocity pretty closely matched the aging curve. However, for the last two years, there has been a marked decrease.

In case you haven’t gotten the point, here’s one more graphic that shows Siegrist’s velocity problem.

This slope looks more like something I would ski down than data you want to see from a pitcher’s velocity. Clearly, Siegrist had an excellent stretch in 2015 and he produced the numbers to back that up. Other than that, we see a pretty consistent decline.

So, is that it for Kevin Siegrist? A slow decline into oblivion? I don’t think so. I actually expect him to far surpass expectations in the second half of the year.

What if I told you, Siegrist has actually improved this year? He’s not telegraphing his pitches. He has improved his tunneling. (For extra reading, here are primers on tunneling from The Hardball Times, Baseball Prospectus, and FanGraphs.)

Essentially, tunneling is the ability of a pitcher to repeat his delivery with similar, if not identical, release points. If a pitcher is able to do this, a batter has less time to recognize the pitch and a lower chance of getting a hit. If a pitcher’s release points are completely different, say for his fastball and changeup, a hitter can more easily distinguish between the two and put a better swing on the ball.

These are Siegrist’s release points from 2015 (his most successful year).

And here are the release points from the first half of 2017.

Let’s keep in mind we’re talking about inches here, not feet. Still, the differences between these two years are significant. The release points from 2015 are more spread out than the data from 2017. Siegrist has improved his ability to replicate pitch deliveries. Unfortunately, due to his decreased velocity, this hasn’t resulted in any type of noticeable success.

In 2015, the changeup and the slider release points overlapped nicely, but the fastball release points stick out like a sore thumb. In 2017, with the addition of a cutter, there is much more overlap among the pitches. If he can keep this up, it should translate to long-term success.

Moving away from release points, pitch virtualization data confirms the same hypothesis: that Kevin Siegrist has improved his ability to replicate his delivery.

This is the data from 2015. To the average viewer, and even probably to you and me, this doesn’t look too bad. At the 55-foot mark, the pitches have pretty similar locations. Even at the 30-foot mark, it’s probably pretty difficult to distinguish between five of his six pitches.

If we compare it to the 2017 data, we see a considerable difference.

It’s pretty clear, right? At 55 feet, the release points aren’t “pretty similar,” to use my own wording, they’re practically identical. And the trajectories remain extremely close to one another until about the 20-foot mark, when they break. 20 feet at 93 miles per hour (an all-time low velocity for Siegrist) gives the batter about a tenth of a second to decide what to do.

There is no denying that Kevin Siegrist has a velocity problem that he would do well to fix. And if the first half of 2017 is any indication, it needs to happen fast. It is unfortunate that he has not been able to reap the benefits of an improved delivery. The consistency in release points that Siegrist has shown during an abysmal 2017 is encouraging and should provide a source of hope going into the second half of the season.

Throughout the baseball season, I like to estimate teams wins, but I don’t do it in the traditional way. Some time ago, I discovered that I could use innings pitched to get a close estimate. Here’s what I do:

1) Take team games played and divide by 2;

2) Take the team’s innings pitched and subtract the team opponents’ innings pitched;

3) Add 1 and 2.

For example, the Washington Nationals, as of the All-Star break, have played 88 games. They have 789.33 IP, and their opponents have 781.33 IP. So I take 88 divided by 2, which gives me 44. Then I take 789.33 minus 781.33, which gives me 8. Then 44 plus 8 gives me an estimate of 52 team wins. Checking the standings, I see that Washington indeed has 52 wins.

How does my method compare with the traditional Pythagorean? (The Pythagorean method, of course, takes runs scored squared and divides by runs scored squared plus runs allowed squared.) I’ve set up some charts to demonstrate. First, let me present the relevant statistics for all teams as of the All-Star break (all statistics courtesy CBS Sportsline):

Now let me present a chart showing how many teams wins are predicted by my method and the Pythagorean method (for the Pythagorean method, I’m using 1.82 as my exponent, as shown by MLB on their Standings page):

My method appears in the second column, and the Pythagorean method appears in the third column, with actual team wins in the last column. My method, as shown above, gives estimated wins directly. The Pythagorean method actually computes winning percentage. To get the estimated wins for the Pythagorean method, I multiplied the team’s estimated winning percentage by the team’s games played.

The methods are pretty close! On a couple of teams, though, the methods miss by a wide margin. I’m way off on the Angels, for example, while Pythagoras is off on the Giants. But which of these methods is closer overall? I did an r-squared between each of the estimated win columns and the actual wins and got these results:

Mine’s a little higher, but let’s use mean squared error (MSE) as a cross-check. Here are my numbers:

I’m not a numbers person, so if I’ve made made errors in my calculations, please let me know, and I will never, ever trouble you fine readers again with another post. But I’ve published previous studies of both methods (in other places, under other names) and have found each time that my method edges out the Pythagorean in both r-squared and MSE.

If my method works at all, it’s because better teams typically have to get more outs to finish off their opponents. If the Dodgers, say, are at home against the Phillies, chances are they’re already winning when they go to the bottom of the ninth, and so the Dodgers don’t have to come to bat. That means the Dodgers had to get 27 outs and the Phillies had to get only 24. Conversely, on the road, if the Dodgers are leading the Phillies, the Phillies have to come to bat in the bottom of the ninth, and the Dodgers have to get the full 27 outs to end the game.

One caveat: my method tends to be more descriptive than predictive, so it’s a better measure of how a team has performed, not a good predictor of how a team will perform in the future. The Pythagorean method is much better as a predictive tool.

So there it is! My estimated team wins method. I hope you find it useful.

Many of the talking heads on radio and TV have commented on how playing in the WBC and skipping part of spring training negatively affects player performance during the regular season. As a Texas Rangers fan who has wondered the same thing, I decided to do a quick and dirty analysis.

The Ground Rules

• WBC rosters were pulled from Wikipedia 2017 World Baseball Classic rosters.
• Player WAR data was pulled from FanGraphs on July 10, 2017.
• Only MLB players were included.
• Only players with MLB statistics in both 2016 & 2017 were included.
• A WAR differential is defined as the difference of the 2017 WAR and 2016 WAR (2017 WAR – 2016 WAR)

The Results

Here’s the RAW data as I compiled it from the above sources.

The last column in the spreadsheet is the difference of the 2017 WAR and 2016 WAR and has a mean of -1.1 for all the players in the list.

The histogram below shows how the data is skewed to the negative, which is easily seen in the list just scanning visually.

Another interesting chart depicts the correlation between 2016 and 2017 WAR. The slope of that trend line is 0.59.

Here are the top (bottom!) 20 players, and two of my Rangers are in the list. Rougned Odor is 36th on the list with a -1.8 WAR differential.

There could be many other reasons for the decline in WAR and it very well could have nothing to do with the WBC.  It was an interesting exercise and the numbers make me wonder if MLB has really looked at the WBC and how it affects the MLB players that participate.

Being an avid lover of both baseball and video games, I naturally like to participate in both from time to time, at the same time. In fact, San Diego Studio’s MLB THE SHOW 17 is quite possibly my favorite game at the moment considering how many hours I put into it. Anyways, the reason I bring this up is that the topic of this post (the under-the-radar talent that is Travis Shaw) was brought to my attention while watching a live-stream of my favorite MLB THE SHOW YouTuber. After hearing of the inevitable rise to power that Shaw should see within the next few weeks, I decided to look more into his stats and see just how plausible this claim was.

I assume that unless you are a Brewers fan, Shaw’s ability and stats could possibly be low on your radar, especially since he didn’t crack the National League’s All-Star lineup for 2017. But after taking a close look at his stats, maybe he should have. At the time of writing this article, Shaw is hitting .296 with 18 dingers and 61 RBI. This is impressive when you compare his stats to the rest of the N.L. All-Star starting lineup that collectively averaged a .320 average, 16 home runs (2 fewer than Shaw) and 55 RBI (5 fewer than Shaw). Then, we can take it a step further and compare him directly to the lineup’s starting third baseman (Shaw’s position), Nolan Arenado, who is hitting .298 with 15 homers and 63 RBI.

At first sight, it seems as if these two are on par with one another, with a slight advantage given to Arenado in the average and RBI department. This, however, is not the case when taking into consideration the advanced stats. Shaw pulls away from Arenado in ISO, weighted On Base Average (wOBA), and weighted Runs Created Plus (wRC+), averaging .268, .386, and 135 in each stat, respectively. These stats are known to tell more of the “story” of the player, giving more details as to what is going on. Shaw is hitting for more power, creating more runs, and overall is a bigger asset to his team than many other players in their respective situations that were graced with All-Star status.

I, of course, am not saying that Arenado or any other player should not have been awarded All-Star status because they are all amazing ball players with enormous talent. Really, the only point that I am trying to get across is that, based on stats, Shaw should have most definitely been a part of the current National League All-Star group. And as for the rest of the season, the future is very bright for Shaw, especially considering that he is now a sleeper candidate for National League’s Most Valuable Player, according to ESPN.

*Side note* This is my first post in the FanGraphs community! And while I am very excited, I at the same time want to be sure to improve with each and every post and write about things that people want to hear. If you, the readers, do not have anything to say about the content of the articles but do have some constructive criticisms please feel free to leave a comment! Have a good one!

If you follow baseball, you’ve heard of Joey Gallo. However, he’s on track to be a member of a list of players that includes Rob Deer, Ivan DeJesus, and Tom Tresh.

Who are these guys? My point exactly.

That list is of qualified players who have hit under .200 for a season in the last 50 years. It’s quite an exclusive club. Over the course of half a century, only 13 players have managed to accomplish such a feat. In fact, there are more players who have hit above .368 for a full season than under .200.

Still, Gallo provides above-average, albeit inconsistent, value. He boasts an above-average wRC+ of 108, which is extremely impressive considering his .194 batting average. His wOBA, at .342, is more than barely above average and he is among the league leaders in home runs — certainly a primary source of his value.

Of course, followers of the game know his tendencies and understand that he’s pretty much a strikeout-or-homer kind of guy. Although there is a growing camp of believers who trust he could actually develop into a great player if given the time, I’ll leave that discussion for another day and probably for another person.

Still, it is worth examining just how far outside the standard bell curve Gallo’s performance has placed him. One only has to look at his Brooks Baseball landing page to see the kind of player the young Ranger has become. Against every type of pitch, Gallo’s result is “a disastrously high likelihood to swing and miss.” Again, this is no surprise; we know what kind of player he is at the moment, but this shows just how absurd it is that he actually provides decent value.

This graph is one representation of Gallo’s performance (the glowing dot). Despite placing in the bottom three in batting average, he is well above the 50th percentile in wRC+. This really is incredible. No other player with an average within 20 points of Gallo’s has a wRC+ above 77. That’s 30+ runs below the power hitter.

As a previous article noted, Gallo made his way to the majors via the three true outcomes — walking, striking out, and hitting home runs.

Surprisingly, Gallo walks at a well-above-average rate. And he has for his entire, although short, career.

Aside from the HRs, this is a clear source of his value. However, his strikeout rate is more than 3x his walk percentage.

This is another graphic that is just absurd. Gallo strikes out more than any other player, but still manages to accrue statistics that show his positive value. Imagine if he lowered his K% and hit a few more doubles, or even singles for that matter. His value would skyrocket.

The last of the true outcomes is the HR. We know Gallo can hit, but here is a graphic that connects a few of the factors already discussed.

As you might have guessed, Gallo is the player leading the league in whiffs. This graphic details the relationship between whiffs and HRs with overlaid colors showing batting average. I expected there would be more darker blue dots (lower averages) around Gallo and toward the right half of this graphic. For the most part, however, the dots around Gallo are red, or at least grey. It’s a nice image that confirms what we already suspected: Joey Gallo essentially whiffs or hits a HR.

Some might look at his sub-.200 average and write him off, while others could look to the future with hope for a player who has produced solid value, going yard with the best of them and walking at a solid rate. Joey Gallo is a player with a tremendous ceiling, but for now, we know exactly what kind of player he is. To use any other word but strange to describe the value he provides would be inaccurate. He certainly has a certain value, even now at 23, that no other player in the game has replicated. And the Rangers will take it.

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.

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.

Editors Note: Analysis in this article was conducted using Baseball Info Solutions Hard Hit batted ball data.

To be clear, this did not begin as an example of investigative journalism. While I do occasionally enjoy media pieces such as Spotlight and S-Town, my curiosity in this topic all began with the incredible amount of attention given to a seemingly mediocre player named Nick Castellanos. To give some examples, below are three popular FanGraphs/RotoGraphs articles written about Castellanos:

• Nick Castellanos: The Greek God of Hard Contact (Rylan Edwards – March 30, 2017)
• Turning Nick Castellanos Into Nolan Arenado (Jim Melichar – March 4, 2017)
• The Wait for Nick Castellanos Will Soon Be Over (Eno Sarris – November 4, 2015)

In theory, the hype surrounding Nick Castellanos makes sense. High hard-hit rate, few ground balls, sustainable HR/FB%, and a decent home ballpark. If only he could get those strikeouts down and avoid bad luck, he could turn into Kris Bryant or Nolan Arenado. The analytics community, who have been waiting for the Castellanos breakout for five years, is more divided than ever on the Tigers third baseman. Some continue to beat the drum while others are abandoning ship, arguing that the breakthrough will never happen.

This season, Castellanos is not the only Detroit Tigers player who has received love from the analytics community:

• Alex Avila: The Tigers Have Found Another Slugger (Dave Cameron – May 22, 2017)
• Miguel Cabrera: How to Beat Statcast’s Hitting Metric (Craig Edwards – May 16, 2017)

The claims brought up by all of these writers have one thing in common: high or increased hard-hit rate. As presented in Matthew Ludwig’s article The Value of Hitting the Ball Hard, hard-hit rate and wRC+ have a positive correlation. In general, a player who hits the ball harder would be expected to have more favorable results when they make contact.

This brings us to the question, is it possible for so many Detroit Tigers players to be underperforming their batted-ball profiles? In order to gauge exactly how much harder the Tigers are hitting the ball than their opponents this year, I took a look at the hard-hit rate for the Tigers as a team. The point that is colored “Tiger orange” represents the Detroit Tigers.

It isn’t even close; the 2017 Detroit Tigers are currently the best team at making hard contact and the worst team at preventing hard contact. Thinking qualitatively, are the Tigers hitters really that much better at making hard contact than the hitters on the Astros, Nationals, or Diamondbacks? Are the pitchers really that much worse at preventing hard contact than the pitching on the Padres, Orioles, or Reds? If so, the results are not proving it. The Tigers currently rank ninth in runs scored and 20th in runs against. Park factors and other variables do apply, so it may be possible that the hitters are getting unluckier and the pitchers are getting luckier than the batted-ball data shows. Assuming that players’ abilities are transferable across stadiums, we should small differences in hard-hit rate for Tigers hitters and pitchers when looking at home/away splits.

Quadrant I (x,y) represents the teams that have a higher hard-hit rate for both hitters and pitchers on the road than at home. Quadrant III (-x,-y) represents the teams that have a higher hard-hit rate for both hitters and pitchers at home than on the road. The Detroit Tigers (orange point) rank as the team with the largest negative difference for both hitters and pitchers. One thing to note about the data is that 22 out of the 30 points lie within either quadrant I or quadrant III. This could give some validity to the assumption that hard-hit rate is not consistently measured from park to park. There could be a variety of reasons for this (humidity, air density, etc.). For more on this, I would point to Andrew Perpetua’s article Home And Road Exit Velocity. If there was truly something unique about Detroit causing these balls to be measured harder, this trend would be seen over a wider time period. Let’s look at where the Tigers ranked for the years 2012-2016.

See that orange circle almost directly in the middle of the chart? That is the Detroit Tigers. The only point that has a closer distance to the direct center is the Atlanta Braves, who now play in an entirely different city and stadium.

So what about all other stadiums? If hard-hit rate is being artificially increased at Comerica Park, it is likely that there are slight adjustments at all ballparks. Based on 2017 data, the difference for each stadium (hitters or pitchers) is listed below:

Looking at an individual-player level (min. 50 AB home and away, min. 20 IP home and away), let’s see how many Tigers batters appear on the top 20 away-home hard-hit-rate difference leaderboard for hitters and pitchers. Detroit Tigers players are highlighted in orange.

I can see four possible scenarios to explain why Detroit Tigers players may be experiencing this phenomenon:

1. Tigers hitters and pitchers have actually experienced large splits between home/away hard-hit rate this year (with no other variables changing)
2. Something about Comerica Park is causing increased error in the variables used for the quality of contact algorithm
3. Changes are being made to the ball or environment at Comerica Park, making it act differently
4. Small sample size bias is skewing the data

Unfortunately, this is about as far as I can take this piece. Something is going on in Detroit this year that is skewing the hard-hit-rate calculations. However, the whys and hows beyond the data are not clearly evident. Until then, I will continue to monitor this unintended project of investigative journalism from the sidelines.