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.

Recently, for my job, I got to mess around with Statcast data for fly balls. I have a good job. As part of the task I was working on, I attempted to calculate the maximum heights and travel distances of fly balls using my extensive ninth-grade physics knowledge. Now, I was excellent at ninth-grade physics, especially kinematics, but my estimates, compared to the official Statcast numbers, were terrible. Figuring the discrepancies must be due to air resistance, I did my best to remember AP physics (with the help of NASA) and adjusted my calculations for drag. The results improved, but were still way off. There are many additional factors that affect the flight of a fly ball such as wind, air temperature and altitude, but I think the biggest factor causing the inaccuracy of my estimates is batted-ball spin. (If you disagree, let me know in the comments.) Exit velocity and launch angle get all the attention when discussing batted-ball metrics, but the data I was looking at suggested that batted-ball spin merits attention too. Are there batters who are consistently better at spinning the ball than others, and if so, is this a valuable skill?

We already know that balls hit with top-spin sink faster than normal while balls hit with back-spin stay in the air longer. It’s unclear, though, whether it’s better for the batter to hit the ball with more or less spin, and whether top-spin or back-spin is more beneficial. Back-spin would seem to be better if you are a home-run hitter while top-spin might be more beneficial if you are a line-drive hitter.

As far as I know, Statcast doesn’t measure batted-ball spin, and if it does, it’s not available on Baseball Savant. So to act as a proxy for spin, I calculated the estimated travel distance (adjusted for air resistance) from its launch angle and exit velocity for every line drive, fly ball and pop up hit in 2016 and subtracted this number from the distance estimated by Statcast. The bigger the deviation between these two numbers, the faster the ball was spinning, theoretically. Balls with positive deviations (actual distance > estimated distance) must have been hit with back-spin and balls with negative deviations (actual distance < estimated distance) must have been hit with top-spin.

The following table shows the 20 hitters (min. 50 fly balls hit) who gained the most distance on average in 2016 due to back-spin:

With a few exceptions, these are not home-run hitters. This group of 20 players averaged 8.25 home runs in 2016. The players who are getting the most added distance on their fly balls are not the ones who need it most. (Note: four players on this list and three of the top four players played their home games at Coors Field. Did you forget that Daniel Descalso played for the Rockies last year? Me too.)

What about the other end of the spectrum? The following are the 20 players who lost the most distance on average in 2016 due to top-spin:

Kris Bryant, Miguel Sano, Ryan Schimpf: this list is full of extreme fly-ball hitters with an average of 24 home runs last year. The scatter plot below with a correlation of -0.58 shows the relationship between batting spin and fly-ball percentage for all players in 2016.

And this isn’t just a one-year phenomenon. I was relieved to find out that the correlation between 2016 average distance deviations and 2015 average distance deviations is 0.75. Players who hit balls with a lot of spin in 2015 overwhelmingly did so again in 2016. Again, the plot below shows the strong relationship.

Mechanically, this is not such a surprising result. Players with a more dramatic uppercut swing (like a tennis swing) will impart more top spin onto the ball while the opposite should be true for players with a more level swing.

It remains to be seen whether this knowledge is useful in any way or if it falls more into the “interesting but mostly irrelevant” category of FanGraphs articles. There is essentially no relationship between a player’s average distance deviation and his wRC+ (correlation = -0.13), so we cannot say that spinning the ball more or in either direction leads to better results. And I imagine it is difficult to alter one’s swing to decrease top-spin while still trying to hit fly balls. At best, maybe this is a cautionary tale for players who want to be more hip and trendy and hit more fly balls like James McCann (FB% = 0.41), but don’t have the raw power to absorb a loss of 28 feet per fly ball (HR = 12, wRC+ = 66).

Let me know what you think in the comments.

There is value in the fly ball. That statement isn’t something that will surprise any fan. Even someone who knows very little about baseball could piece together the logic behind it. The most valuable individual outcome is a home run. How do you hit a home run? Hit a fly ball. As Travis Sawchik found for 2016, fly balls produced a wRC+ of 139, while ground balls put up a mark of 27 wRC+.

Of course, the sabermetrically inclined will quickly point out that it’s not that simple. Judging the value of a hit based on whether it is a fly ball or a ground ball is a futile exercise. You have to consider batted ball distance, launch angle, and exit velocity. Much has been made about the recent “fly ball revolution” occurring throughout the league. And while some believe hitting more fly balls really does increase the value of a player, data suggests that the fly ball revolution is hurting as many batters as it’s helped.

It’s possible that there are benefits to hitting more fly balls, but that doesn’t seem to correlate to an increased value.

There really is no correlation between fly ball % and wRC+. So, it seems that value is added not by hitting the ball higher, but by hitting the ball harder.

Now this is a pretty clear correlation. Hit the ball harder and a better outcome is more likely. A soft liner toward the second baseman will probably be an out. But, a laser to right-center field could be a triple.

This trend is not a new development or a new discovery. As far back as 2002, when batted-ball data became available, there has always been a positive correlation between Hard% and wRC+. In fact, the average correlation (R-squared value) between these two variables over the last 15 years is .475.

Hard% also has predictive value. Take a look at the data for 2017 thus far.

Although the correlation from past years isn’t there, it doesn’t need to be. We should no more expect the data to already have an R-squared value above .4 than we would expect an MVP to have a WAR higher than 6 at this point of the season. Because there are quite a few outliers that will come back to the mean, Hard%, based on its historical data, has considerable predictive value.

Ignoring the one point above the 200 wRC+ line (Mike Trout, whose entire career is an outlier), let’s examine a couple outliers. First, the point on the far right toward the bottom. Nick Castellanos is hitting the ball harder than Aaron Judge, who just set a Statcast record for hardest home run ever hit, but only has a wRC+ of 82 — well below average. Towards the top of the chart at the 175 wRC+ mark, we see that Zack Cozart is making hard contact only 32% of the time.

It is reasonable to expect, based on this chart, that Castellanos’s numbers will start to improve and Cozart’s will regress. As it turns out, Andrew Perpetua found the same outliers by looking at exit velocity and xOBA in a RotoGraphs article last week. These statistics all point toward the same thing — Castellanos has been very unlucky and Cozart has been just the opposite. The takeaway here is that Hard% can be used as a predictor for value even over a smaller sample size.

If Hard% is such a good indicator of success, what is the actual value of hitting the ball hard? Hitting the ball hard has been a hallmark of both HR leaders and batting champions. Over the last five years, the HR champion has an average Hard% of 40.12 and the batting champion has one of 35.16%. Although the almost five-point spread is a lot, a Hard% above 35% is nothing to laugh at — it’s still in the upper half of all players.

For the last full season (2016), increasing Hard% by even just 5% added 13 points to the wRC+ value. That is pretty significant. For context, 13 wRC+ is the difference between Aaron Judge and Yonder Alonso so far this year. But has it always been this way? Not exactly. In 2002, a 5% increase in Hard% increased a player’s wRC+ by 20 points. This points toward an interesting trend.

For the last 15 years, the correlation between Hard% and wRC+ has decreased. In other words, hitting the ball hard is not as valuable as it once was. My initial thought was that players aren’t hitting as many HRs as they did in 2002. But that is simply not true. 14.2% of flies result in HRs — the highest rate ever recorded. Perhaps this trend is a result of defenses shifting. Are batters hitting the ball harder than ever, but fielders are now better positioned? The shift is certainly a powerful tool — it kept Ryan Howard out of the Hall of Fame. Still, I’m not convinced the shift is solely responsible for this eerie trend.

Hitting a ball hard is much more important than hitting it high, that is, if you can’t have it both ways. However, the value of hitting the ball hard has decreased for more than a decade. Looking at the data, is it possible that in 10 years we’ll see a sort of “v” shape, indicating a return to the value of hitting the ball hard? Maybe. But for now, this is an interesting trend with no clear indicator.

Earlier this week, 538 put out an article on Ryan Howard, arguing the shift had killed his career…

Rather than the fact he was 37 years old and could not hit or field.

The article paints a picture of a stubborn player who refused to adapt when the league had figured him out:

While some hitters try to overcome the shift with well-timed bunts or tactical changes, Howard always stubbornly refused. “All you can do is continue to swing,” Howard said in a 2015 interview with MLB.com.

Howard’s stubbornness is contrasted with a link to an ESPN article about how a similar slugger (David Ortiz) learned to adjust, and imagines an alternate shift-free universe where Howard remains an MVP threat and HoF material.

This is crap.

Ortiz did not “figure out” the shift. He is a good hitter, who ran a 13% strikeout rate last year. Howard’s is over 28% for his career. I’m sure that the shift hurt him to some extent, but Ortiz and him both had BABIPs around .300 for their careers. He could make that work when he was hammering 40-plus homers, but take that away and there’s not much left. My guess, old age is what did him in. But this lead me to wonder, how does the shift actually work?

Many people treat the shift like some mystic boogeyman, out there to either ruin the game, or certain players in particular unless they “adjust.” As a Twins fan, I know many people who blame Joe Mauer’s decline on the shift.

Personally, I would like to just throw this chart out there:

This is the MLB BABIP on groundballs over the last 16 years. Notice how it didn’t go down at all. I don’t have the numbers to prove it, but I think we all know shift usage has exploded since 2002. Not a huge change in ground-ball outcomes. So where has it changed the game? A decline in line-drive BABIP over time. However, counteracting that’s the fact that fly-ball BABIP has gone up. Again, to the charts!

I wondered if some “line drives” of the past were simply fly balls that landed for hits, while outs were labeled “flies.” I don’t actually know if that’s true, if the process where line drives/fly balls are defined has been altered, but I decided to take a look at combined “air-ball” BABIP to see if it has changed over time. So here is the BABIP on all non-ground balls:

2017 is pretty clearly an outlier, but considering less than half the season’s in the books so far, and I have no idea how “air-ball” BABIP moves over the course of a season (more hits find grass when weather is warmer? no idea), I wouldn’t put too much stock in that just yet. Another option I had considered was that maybe the breakdown of line drives vs fly balls has changed over time. Since 2002, 36% of air balls have been line drives, and while some years are higher and some lower, there doesn’t seem to be any particular “trend” with respect to that number; the first eight years average 36% and the last eight have as well.

I know the shift has an impact on run scoring in aggregate. But in my opinion, skyrocketing strikeouts and the home-run explosion are the markers of the modern version of this nation’s pastime, not on which side of second base the shortstop stands.

Earlier this month, Eno Sarris posted an article called “Could Ichiro Have Been a Power Hitter?,” which began with a launch angle and exit velocity analysis of Ichiro himself, and developed into a wider examination which led to the interesting proposition that “players may have their own ideal launch angles based on where their own exit velocity peaks.”  In this article, I’ll look at a larger sample of players whose fly-ball rates increased from 2015 to 2016 and see if their peak exit velocity range changed or stayed constant.  First I’ll re-examine Elvis Andrus, then I’ll look at Jake Lamb, Xander Bogaerts and Salvador Perez.

Elvis Andrus

As mentioned by Eno, Andrus’ average launch angle went from 8.1 in 2015 to 8.6 in 2016, but his fly-ball rate actually decreased.  It seems like he started the change in 2015, but was only able to translate it into results (a 112 wRC+) in 2016.  Regardless, let’s look at the data again, and see what we can find.

Instead of just qualitatively looking at the distribution and giving an approximate range of maximum exit velocity, I split the data set into launch angle buckets, and found the bucket with the highest median exit velocity.  For example, if I set the bucket size at 5 degrees and applied it to Elvis Andrus in 2015, I got a range (-2°, 3°) (I’ll omit the degree symbol from now on).  If I set the size at 10 degrees, I got a range (-2, 8).  For the rest of the article, I’ll keep it set at a range of 5 degrees.

The peak range for Andrus’ 2016 was (-3, 2).

Using the method outlined, the peak range for 2015 was (-2, 3), and for 2016 it was (-3, 2), so Andrus’ peak exit velocity range did not change much from 2015 to 2016, just as Eno pointed out, and as we can see with the two years overlaid.

Jake Lamb

Comparing 2015 and 2016, Jake Lamb raised his average exit velocity from 89.7 to 91.3 MPH, and his fly-ball rate from 32.4% to 36.7%.  His adjustments were chronicled by August Fagerstrom during his breakout (http://www.fangraphs.com/blogs/jake-lambs-revamped-swing-made-him-an-all-star-snub/).

The peak 5 degree range for Jake Lamb’s 2015 was (3, 8).

The peak 5 degree range for Lamb’s 2016 was (15, 20)!

Unlike Andrus, Jake Lamb’s peak exit velocity range increased along with his launch angle distribution!  This seems to be the kind of effective swing change that players attempting to join the fly-ball revolution strive for.  Lamb managed to revamp his swing to not only elevate the ball more, but to hit the ball harder at high launch angles, and actually increase the angle at which he hit the ball the hardest.  However, as the next two cases show, this is far from a guaranteed outcome.

Salvador Perez

Perez’s peak 2015 range: (9, 14).

Perez’s peak 2016 range: (0, 5).

From 2015 to 2016, Perez increased his fly-ball rate from 37.4% to 47.1%, and increased his average exit velocity from 87.3 to 88.8 miles per hour.  He also increased his average launch angle from 13.7° to 19.1°.  But curiously, his peak exit velocity range actually went down from (9, 14) to (0, 5)!  When I saw this, I thought I’d have to change my methods, because it didn’t make sense to me at first.  But if you look at Perez’s exit velocity vs. launch angle graphs for 2015 and 2016, these ranges actually seem to qualitatively fit.  Somehow, the Royals backstop managed to hit the ball harder and higher, but become more effective at lower launch angles.  This could be a rising tide lifts all ships situation, whereby his swing adjustments let him hit tough low pitches hard at lower angles, or it could just be a sample size issue.  By splitting the data set into buckets, the sample size gets dangerously small, and prone to strange results.  But I think the results fit the picture, and either Sal Perez needed to hit more balls for us to get reliable results, or he just had a strange batted-ball distribution.  We have a similar, more extreme situation with Xander Bogaerts next.

Xander Bogaerts

Bogaerts’ peak 2015 range: (5, 10).

Bogaerts’ peak 2016 range: (-6, -1).

Bogaerts, like the other three players here, hit the ball harder in 2016 than in 2015.  He raised his fly-ball rate and his average launch angle, and was rewarded with a 113 wRC+, a slight improvement on his 109 wRC+ from 2015.  But his peak exit velocity range for 2016 was, like Perez, lower than in 2015.  Looking at his plots, it looks like he hit his ground balls harder in 2016, while not changing the exit velocity of his line drives and fly balls as significantly.  I’m not sure what else to say about Xander, other than that he’s kind of a weird player, as already noted by Dave Cameron (http://www.fangraphs.com/blogs/xander-bogaerts-is-a-very-weird-good-player/).

Summary

The following table summarizes the findings for each player.

It seems like Andrus improved by simply hitting the ball harder and staying within his peak exit velocity range of launch angles (which fits Eno’s hypothesis), whereas Jake Lamb improved by hitting the ball harder, raising his average launch angle, and shifting his peak exit velocity range (which runs contrary to Eno’s hypothesis).  Perez and Bogaerts didn’t really improve, and their Statcast data yielded some strange results, which suggests that this method is far from foolproof, and that there may have been better choices of players to investigate.

Many thanks to Eno for the inspiration for this article, and to Baseball Savant for all of the Statcast data.

As the 2017 MLB Draft quickly approaches, I thought it may be fun to analyze some of the best college hitters available.  On May 23, Eric Longenhagen released the 2017 Sortable Draft Board on FanGraphs.  This article looks at the statistics of each college hitter on the list.  In this article, I tried to not lean on literature and scouting reports of the players.  Rather, I decided to calculate some statistics to use as guides in building an outsider’s perspective of their offensive profiles.  This body of work does not include much information about attributes or skills not published on a school’s statistics page on their website.

Nobody real cares about the counting statistics of college players.  So, for my table of numbers to fit on a page, I left them out.  The statistics I focused on are a hitter’s slash line (AVG, OBP, and SLG), OPS, BABIP, ISO, RC, K% and BB%.  These are relatively easy to calculate and provide some sort of worth when evaluating prospects.  AVG, OBP, and SLG are simple and widely understood.  OPS provides a good gauge of a hitter’s overall offensive ability.  BABIP is an important indicator of a hitter’s talent at the plate, but can be inflated or deflated depending on the talent level of the different defenses faced by the hitter.  ISO is a good indicator of how well each hitter demonstrated their power and XBH ability.  Runs Created (RC) is a crude but effective measurement of total, individual offensive output.  K% and BB% give us some idea of how well the batter demonstrated their understanding of the strike zone and discipline at the plate.  For more information on each statistic, as well as how to apply it, I suggest checking out the Glossary tab.

Below is the table of numbers I made.  Even further below is where you will find a quick summation of each hitter discussed.

Jeren Kendall (#9 on FanGraphs Sortable Draft Board)

Vanderbilt                   OF                   (B- L/ T- R)

Jeren Kendall is considered by many to be the best college hitter, outside of Louisville two-way player Brendan McKay.  Kendall showed some impressive pop out of center field this past year, knocking 15 balls over the fence in 235 at bats.  However, he also managed to record 50 strikeouts.  Kendall did manage to produce an excellent walk rate and ISO, but his total output was “middle of the pack” as far as the guys on this list go.  He should go off the board within the first 20 picks this upcoming draft.

Adam Haseley (#15 on FanGraphs Sortable Draft Board)

Virginia                       OF                   (L/L)

Hitting from the left side of the plate, Virginia outfielder Adam Haseley managed to put up the best statistical profile of any hitter on this list.  He comes into June’s draft with an impressive OPS (1.186) and an even more entertaining strikeout rate — a board-best 7.7% (only 19 punch outs in 205 ABs).  While Haseley’s power numbers may not translate at the next level, his affinity for driving the ball into deeper parts of the ballpark should make for a high doubles count at the next level.

Keston Hiura (#17 on FanGraphs Sortable Draft Board)

UC Irvine                     2B                    (R/R)

While Keston Hiura’s .486 BABIP may be a good indicator as to why his batting average is north of .400, it is also a good indicator of just how good he is with a bat in his hand.  He did not just hit singles — his 21 doubles come in second on the list.  He displayed an excellent walk rate, which contributed to the highest on base percentage on the shortlist.  While some teams may elect to take a prep shortstop over a college second baseman, Hiura still plays a premium position with solid presence at the plate and would fit in nicely in any class as a second to third-round pick.

Pavin Smith (#18 on FanGraphs Sortable Draft Board)

Virginia                       1B                    (L/L)

The second UVA Cavalier on our list slashed an impressive .348/.433/.581 this past season, and posted an impressive 3.2% strikeout rate.  While his numbers do not match those of his teammate Adam Haseley, Pavin Smith could very well be the first college first baseman off the board, assuming you do not count Brendan McKay as a first baseman.  His demonstrated knowledge of the strike zone, coupled with a list-best walk rate, are both very good indicators of a first baseman with a high ceiling.

Logan Warmoth (#20 on FanGraphs Sortable Draft Board)

North Carolina            SS                    (R/R)

Tar Heel shortstop Logan Warmoth, when compared to the rest of this list, does not really stand out.  However, he should be taken early, as he still has the best odds of being the first college shortstop off the board.  He hit well in the ACC this past season, compiling 18 doubles, 4 triples, and 9 home runs.  Though his demonstrated power will likely not follow him up the minors, any team would love to have a strong bat such as his at the most premium of all premium positions.

Jake Burger (#22 on FanGraphs Sortable Draft Board)

Missouri State            3B                    (R/R)

Our only hot corner prospect on the list is a power threat through and through, according to his numbers.  While his average will continually drop as he climbs the minors, Burger’s 20 homers showcased his raw power.  Although there may be some questions about his tendency to punch out, plus power paired with an excellent walk rate at a corner position are a recipe for success.  Everybody loves a little yak sauce on their Burger every now and then.

Evan White (#29 on FanGraphs Sortable Draft Board)

Kentucky                      1B                    (R/L)

A first baseman who hits from the right side is very common.  A First Baseman who hits from the right side but throws left is very uncommon.  A first baseman who hits from the right side but throws left with plus speed is downright unique.   Evan White legged out a list leading 23 doubles this past year, and posted all-around great offensive numbers.  He will be a very interesting draft choice, and his excellent statistics project a demonstrate a solid offensive background.

Brian Miller (#49 on FanGraphs Sortable Draft Board)

North Carolina            OF                   (L/R)

Rounding out our list is North Carolina outfielder Brian Miller.  Miller slashed a very impressive .336/.412/.504 line this past year, and should be a good mid-grade prospect in the upcoming draft.  His statistics do not lean to one type of offensive profile over another, but his high BABIP and excellent walk rate generate some reasons to believe his bat will continue to develop at the next level.

Again, this article is meant to simply provide a statistical overview of a few college prospects in the upcoming draft.  It should be looked at as a tool for anybody who cares enough to concern themselves with college statistics.

Theodore Hooper is an undergraduate student at the University of Tennessee in Knoxville.  He can be found on LinkedIn at https://www.linkedin.com/in/theodore-hooper/ or on Twitter at @_superhooper_