You have two lines about 20 feet apart. Each player is trying to protect his “end zone”, which is some 5-10 feet deep. You serve from behind your line (inside your end zone). Other than the serve, any player can stand anywhere on the field. Your objective is to get the birdie to land inside your opponent’s end zone, either by a direct hit, or off your opponent, which include an “own goal” if your opponent can’t clear his own end zone. Typically, you will try to “protect” your end zone by not doing a kill shot, but just blocking a good hit from your opponent. Serves rotate, except when a score is made, in which case the loser serves.

That’s it, hope someone out there has fun with it!

]]>

At balls that travel at most 75 feet, Aoki has 55% of his contacts going that way, while Moss is at only 20% (league average is exactly halfway at 37%). For balls that travel at least 315 feet, Aoki is 8% to Moss at 35% (league is 20%).

We can generate these charts for every hitter. The next step is to see where fielders are playing each hitter, whether for these two hitters, or anyone else in the league. Moss would seem to be a challenge to position in the outfield. His frequency is above league average in balls that travel 160 to 260 feet, the no-man’s land between infield and outfield; his frequency is below league average where you would normally find outfielders, and above league average in balls hit behind outfielders.

What StatCast is giving us is really a scouting profile for all hitters, fielders, and pitchers. The convergence of performance analysis and scouting observation is at hand.

]]>If you are a baseball fan, you might be able to figure this out right away. If you are not, it will escape you. So, what do you think this consideration is? I’ll give you the answer later today…

]]>***

As of Aug 17, 2016: Britton has faced 99 batters in “high leverage” situations, 42 in medium, and 48 in low.

http://www.baseball-reference.com/players/split.cgi?id=brittza01&year=2016&t=p#lever::none

High is defined as a leverage index of at least 1.5. Low is at most 0.7. Average is 1.0.

Chris Sale has faced 124 batters in high leverage situations and 311 in medium leverage (and 206 in low).

http://www.baseball-reference.com/players/split.cgi?id=salech01&year=2016&t=p#lever::none

So, just focus on the high leverage: 99 for Britton and 124 for Sale! In effect, Sale is acting like his own reliever.

***

Now, maybe the threshold for “high” leverage was not high enough. Fangraphs sets the level at 2.0 and above:

http://www.fangraphs.com/statsplits.aspx?playerid=3240&position=P&season=2016

Britton has 65 in high, 67 in medium, 57 in low.

http://www.fangraphs.com/statsplits.aspx?playerid=10603&position=P&season=2016

Sale is 51, 335, 255 respectively.

So, Britton has faced 14 more batters in the high leverage and 268 fewer in the medium leverage.

***

However, Sale has one of the worst wOBA in high leverage situations, while Britton is one of the best pitchers according to wOBA in high leverage situations…. but not #1. That would be Jacob deGrom and Edwin Diaz

:

]]>As you would expect, the longer the ball is in the air, the greater a chance that a fielder can cover the ground to make the out. If a ball is in the air for six seconds, you should see outs being made by any fielder if he had to cover 100 feet. But if he had only 6 seconds to cover 130-140 feet, well, you need to be a really good outfielder to make that play.

Similarly, a ball in the air for only 4 seconds means that it’s an easy out if a fielder only needs to cover 50 feet. A great fielder however can track down that sharply hit liner by covering some 75 feet.

If you noticed, all I’ve been talking about is distance and time. Feet and seconds. That is, what we’re interested in is the number of feet per second that each player can cover.

StatCast gives you “top speed”, which is a useful number to know: the top runners are around 21-22 mph, which is around 31 to 32 feet per second.

***

Interlude: I was interested to see how outfielders compare to 100m World Class runners. I got the split times for twenty runners from SpeedEndurance.com (Usain Bolt, Carl Lewis, Ben Johnson, Maurice Greene, etc) so I can see how long it took them to run 10m, 20m, 30m, and 40m. 40m is 130 feet, which is about the limit that we’d get any outfielder to run. The split times for these 20 runners are as follows:

- 10m (33 feet): 1.87 seconds
- 20m (66 feet): 2.90 seconds
- 30m (98 feet): 3.82 seconds
- 40m (131 feet): 4.71 seconds

Obviously, those first 30 feet are problematic, but look what happens when we plot it:

A fairly straight line, with an intercept at around 1 second. That is, while the “reaction time” of runners are reported as around 0.13 seconds, if you were to merge reaction time and acceleration in the first 30 feet into one metric, say “startup time”, these World Class runners had a startup time of around 1 second. By this metric, the best “startup time” in my sample of 20 runners was 1991 Dennis Mitchell at 0.90 seconds.

The slope of the 100m World Class runners line is 35 to 36 feet per second. That is, these runners are, effectively, running at a speed of close to 36 feet per second. Their actual “top” speed was 37 feet per second, but forcing a straight line onto a non-straight line requires us to lose a bit of precision in order to get something much more flexible.

***

Once I saw that plot I got to thinking: well, what if we simply drew a straight line at various feet per second for our outfielders? Since the “startup time” was around 1 second, I figure I could just use that as my intercept, and then plot lines at 36, 32, 28, and 24(*) feet per second. This is what it looks like when I impose it on Adam Eaton’s chart.

(*) The fastest mile run is 3:43, or 5280 feet covered in 223 seconds, or 24 feet per second.

As you can see, a complete failure to represent what Adam Eaton did. I repeated it for Matt Kemp to similarly disastrous effect.

It’s that “startup time” that was throwing me off. A World Class runner would obviously have a lower startup time, so maybe it should be a little higher for non-superspeedsters. But still, there’s another difference: the 100m runner has to do only one thing, and that is to run straight ahead as soon as he hears the gun. An outfielder can start to move on the crack of the bat, but he still needs a bit more time to know the direction to run.

So, @DarenW and I tried a few lines, and came up with 4 lines that fit the bill:

- Outstanding outfielder: 1.5 seconds of startup time, running at 32 feet per second
- Good outfielder: 1.75 seconds of startup time, running at 30 feet per second
- Average outfielder: 2.0 seconds of startup time, running at 28 feet per second
- Poor outfielder: 2.25 seconds of startup time, running at 26 feet per second

Once Daren generated charts with those lines, the results really coalesced into a story. We can see Adam Eaton’s ability standout, while at the same time noting that Kemp’s outs don’t require a high level of speed to catch.

Eaton actually leads the league in oustanding plays, with 10 plays in the redzone, meaning plays that require 1.5 to 1.75 seconds of startup time and 30 to 32 feet per second of running speed. In the entire league, we have 206 such plays for outfielders.

These are the league leaders:

- 10 Eaton, Adam
- 8 Hamilton, Billy
- 8 Inciarte, Ender
- 7 Cain, Lorenzo
- 6 Pillar, Kevin
- 5 Betts, Mookie
- 5 Bourjos, Peter
- 5 Bradley Jr., Jackie
- 4 Chisenhall, Lonnie
- 4 Heyward, Jason
- 4 Marisnick, Jake
- 4 Mazara, Nomar
- 4 Stanton, Giancarlo

Now, is it possible that Kemp had no opportunity to catch balls that required a faster speed to get to? Sure, it’s possible, and the fewer innings you play, the more possible that is. Look after all at standout Kevin Kiermaier’s chart:

He should have a bunch of outs in the red zone, but if there are not any balls in the redzone, then he’s got no opportunities to show his stuff. With only one ball caught in the redzone, it makes it seem as if Kiermaier is not an oustanding fielder, though his plethora of balls in the light redzone shows obvious above-average talent. If you compare him to Heyward, you can see how they are in the same ballpark.

The next step in this process will be to show the non-outs as well, so we can see what plays are falling in for hits. We’ll have two charts one for outs and one for hits. If for example Kiermaier has no balls that fall in for hits in the redzone and we already know he has only 1 ball that were caught in the redzone, this simply means that Kiermaier was given almost no opportunity to make a great play.

Then we’ll be in a position to merge them to create a fielding metric. We’re just getting started.

]]>Say a batter averages 90mph on balls that are contacted that ends the at bat, for 400 such occurrences. He averages another 80mph on foul balls that keep the at bat alive, for 200 such occurrences. And he swings and misses another 100 times for, obviously, 0 mph. Generally speaking, only those 400 occurrences get reported.

It’s obviously not fair to discard those 100 swings and misses, since a player, whose intent is to swing hard all the time gets a “free pass” any time he swings and misses. A player who displays more bat control may swing and miss less, but at the cost of exit speed once he does make contact.

What to do? Well, we can focus on linear weights and how the run value is impacted. Generally speaking, a swing and miss will cost you around .05 to .10 runs. That is, by swinging and missing you are placed in a more disadvantaged plate count (or worse, you might have struck out).

What happens when you hit a ball under 90 mph? Generally speaking, if you can’t meet the launch threshold, your wOBA is around .250, compared to an overall average of .350. And a .100 point difference in wOBA is around .08 runs. That is, a swing-and-miss leads to roughly the same outcome as a low-exit plate appearance.

So, I would think therefore that a swing-and-miss should “count” the same as an under-90mph contact. If the average of such contacts is say 75mph (I’ll have to check, but let’s go with this for the moment), then I would count the “equivalent” exit speed of a swing-and-miss at 75mph.

That’s my first thought on the matter. What do you guys think?

]]>

And here’s the third in the set, with vertical launch angle on the y-axis, speed on the x-axis, with slideshow by horizontal spray direction. As you can see, compared to the above two, the horizontal spray direction isn’t as impactful.

***

A few weeks ago, long-time blog-buddy and all-round good guy David Appelman at Fangraphs(*) said something like “what I like about your stuff is that it’s simple”. Now, it may not sound complementary, but it was all in the delivery. You may recognize a form of that from a popular quote: “everything should be made as simple as possible… but no simpler”.

(*) If you have a chance to work for David, you should.

That’s basically how I try to beat down a problem, amidst chaotic numbers, find some way to simplify them. This is best encapsulated with Marcel The Monkey Forecasting System, or The Marcels. I spent dozens(*) of hours trying to come up with a great forecasting system, only to always go back to the drawing board with one main takeaway: I don’t need to overengineer it.

(*) Hundreds actually, but for my sanity, I have to say dozens.

***

We have the overwhelming StatCast data and we need to come up with a Quality of Contact for the triplet of numbers of exit speed, vertical launch, and horizontal spray. We want to do it in a transparent manner, and have it run very quickly, with 188,180 data points (and climbing) at our disposal.

The first step is to do what many people do, and that is to bin all the data into units of +/- 1. So, a batted ball at 99.2 mph, 18.4 degrees of vertical launch, and -16.1 degrees of spray direction would go in the 100,18,-16 bin. We have 74,520 bins, which is every combination for 40 to 110 mph, -30 to +60 vertical launch angle, and -44 to +44 horizontal spray direction. We bin all our 188,180 data points appropriately.

The most popular bin is this one: 102, +14, +6, meaning 102mph (+/- 1), +14 degrees of launch (+/- 1), +6 degrees of spray (+/- 1). There are 41 batted balls in this bin. Unfortunately, we end up with 33,892 bins with no data at all, or 45% of all the bins. As you can see, very chaotic data, with almost half the bins empty, and the other half averaging only 4 or 5 batted balls. This for example is what it looks like, based purely on binning, at a horizontal spray direction of -36 degrees, with the vertical angle on the y-axis and exit speed on the x-axis:

The black spots are where we have no data at all. As you can see, lots of gaps. But also where we have contiguous data, we can start to see potential patterns.

Now, since we’ve used up all our data, where can we find more data? The next step was to expand each bin by creating a second circle(*) that was 3 times the size of the first circle. In essence, a rolling average, as each bin “borrows” data from neighboring bins (in addition to still keeping its own data). From that perspective, the most popular bin is this one: 102, +14, +4. It had 31 batted balls within the inner circle, and now 94 balls in this larger circle. Here’s what that looks like using the same -36 degrees chart:

(*) I may be saying circle, which is a 2-dimensional object, but I actually mean a spheroid, a 3-D object.

Some of the gaps start getting filled, and we get a bit more smoothing overall. And I keep going, tripling the size of each circle. The most popular bin here has 231 batted balls, in the 102, +16, +6 bin. (This bin had 20 batted balls in the most inner circle, and 72 in the next outer circle.) We’re now at this point with the -36 degree chart:

Most gaps are filled, strong patterns are emerging. And on and on we go, always tripling the size of each bin until I got to +/- 9 units of each of the horizontal, vertical, and speed parameters. Then I put in one final one, and that was the “mirror” of each of those bins, so that bin 102, +14, +6 would be matched to 102, +14, -6. (Remember that since 0 degrees of spray is up-the-middle, then +6 degrees is a mirror of -6.)

Now, that lays the groundwork for every bin, with 8 circles for each bin. The next step was to weight each circle, with each outer circle getting progressively less weight. In a few cases, I only needed to weight the innermost circle, while discarding all the outer circles because I had enough data. In other cases, I needed to use 2 circles, or 3 circles, etc. And in those cases where there was little batted ball data to go on because of the odd combination of the triplet, I ended up using all 8 circles.

The end result for this particular angle is this:

As you can see, all gaps are filled, and a fairly strong pattern emerges. We do have splotches here and there. Those can be removed by increasing the smoothing pattern. But, there’s a danger of over-smoothing, as some splotches *should* remain. We have 7 fielders on the field, all spread out, so, we have to be careful about over-smoothing. And that’s really the tradeoff between over-smoothing and under-smoothing.

Thanks to gifmaker.me, here’s a slideshow of the smoothing process:

Now, can this be improved upon? You bet. I can spend dozens(*) more hours refining this process, and make it more sensitive to various triplets. For example, certain triplets are more sensitive than others to its neighbors. Certain triplets have a stronger relationship to its “mirrors” on the other side of the field (think LF/RF gaps). The goal as always is transparency and speed. So, the entire SQL script, including preparing the source data from scratch, runs fairly quickly to generate the chart of 74,520 bins. Once that chart is generated, then we can retrieve results for any combination of triplets instantaneously. As it stands, the results came out fairly good, as you can judge by the earlier slide shows. And I should note that great work has already been coming out, notably over at Fangraphs and Hardball Times.

(*) Even hundreds.

***

As I was working on this project, I had a thought I couldn’t unthink. I have a path to create a version 2 for Quality of Contact that will go in a somewhat different direction. But that is months away, but it’s fairly exciting what will come of it. It’s complex and intricate, but actually very intuitive. It will be much easier to explain. And ultimately, it will be even faster to run, and be more robust. As its a paradigm shift, it just is going to take time for me to code version 2.

Until then, I coded up this version 1 so that we have a baseline reference. So, this first version is Marcel-like, a simple process, but no simpler. And in a few months, I’ll roll out the second version. At the same time, I can keep plugging away at version 1, improving it on the periphery. I can make adjustments to version 1 based on feedback here. This brings me back to those Marcel days, as every time I decide to move on to something else, it pulls me back in. We’re just getting started.

]]>Similar to the earlier chart, but this time the exit speed is on the y-axis, and each slide is 8 degrees of vertical launch angle. Just remember that at 28 degrees it’s ideal HR angle, and 12 degrees is ideal line drive angle. Everything else fits around that.

The green box is just a reference point, which is 100 down to 80 mph, at 0 degree of spray.

]]>This is an animated gif. Hopefully this comes out ok. The redder the cell, the higher the wOBA. The bluer the cell, the lower the wOBA. White is average.

- On the x-axis is the horizontal spray angle, from 3B line to 1B line.
- On the y-axis is the vertical launch angle, from -30 to +60 degrees.
- I took a snapshot of various exit speeds, from 40 mph to 110 mph in steps of 10. It starts at 110 down to 40, then back up again to 110, and it continues to loop. Thanks to http://gifmaker.me/

Note: If you want to get your bearings, when the image gets to the 100mph slide, locate the center blue spot, and that’s 0 degrees horizontal spray, 18 degrees vertical launch. Just hover your cursor over that spot, and that’ll center your viewing. Sorry for not thinking about it before I created the gif.

]]>As we discussed last time, we need to make a distinction between estimating what might have happened, and predicting would could happen.

When we estimate what might have happened, we start with what DID happen, and then we remove one or more variables. A simple example is if a team gets 14 hits and 3 walks in a game, but manages to not score a single run. If you accept that the 14 hits and 3 walks (and 27 outs) happened, but the SEQUENCING of those events can be changed, then we can provide an estimate of what might have happened… if sequencing of events was not fixed. A quick estimate would suggest that this team would score about six to seven runs.

What could we predict would happen in the future knowing that we have a team that got 14 hits and 3 walks? In that case, we would infer that a team that got 14 hits and 3 walks is an above average hitting team. We would NOT predict they’d get exactly 14 hits and exactly 3 walks, but we’d predict they’d get an above average number of hits+walks. And we might predict that such a team would end up scoring about five runs a game.

The difference boils down to: (a) focusing on the things that happened that you care about and ignore the other things that happened that you don’t care about, and (b) inferring what could happen based on what did happen.

If you choose (a), you are trying to estimate what might have happened if you could change one or more things. In other words, you are recreating the past.

If you choose (b), you are trying to evaluate the talent and environment so you can come up with an expected value. In other words, you are providing a forecast of events that have yet to unfold.

***

This chart shows the wOBA if all you know is the exit speed of the batted ball.

As you can see, there’s alot of up/down at speeds 20-60mph, as you have things like bunts and checked swings and slow rollers that conspire against getting a smooth line. The inflection point happens at close to 90mph, and once you hit 95mph, you are on a straight upward slope, up until 110mph or so. After that, you get into something we learned recently that you maximize your exit speed if you do NOT loft the ball. But that comes at a cost of performance, which is why we have so many 115-120 mph groundballs that lead to outs: the batter hit the ball too square.

So, if you are interested in what might have happened if all you knew was the exit speed of the batted ball, you would simply consult this chart. You are saying that the vertical launch angle and the horizontal spray angle (and by extension the distance and hang time) are variables that you don’t want to consider. You just want to know what might have happened if you got a “typical” launch and spray angles.

Let’s take the above chart, but zoom in at the 80-120mph range.

Between 80 and 90mph, we estimate a wOBA of .200 to .250. At 100mph, we estimate a wOBA of .600, and at 105mph, an estimate of .900 wOBA. That’s our estimate of what might have happened, if all we know is the exit speed.

***

Now, we know it takes extraordinary talent to hit a groundball 120mph. This is something that basically is in the wheelhouse of Giancarlo Stanton. If you have someone who can hit a ball that hard, regardless of whether it’s a negative launch angle, or an ideal launch angle, you are dealing with a great hitter. We can infer the talent level of a hitter who can hit a ball that hard. We can forecast the future, and we can provide an expected value for that hitter.

We can forecast by looking at what has happened in one time period, and see how it correlates with another time period. And when we do this, we come up with this chart:

We can see that we get an inflection point of 88mph. Up until then, any batted ball hit under 88mph provides no information as to the quality of our batter. Whether the batter got an 80mph contact or 60mph contact, it has no impact to determining the true talent level of our hitter. A ball hit at 100mph allows us to infer a talent level of .500 wOBA, and at 105 mph, an estimate of just over .600 wOBA. And at 120mph, it’s a wOBA of 1.200.

***

While these two charts are similar, we can see a few differences:

1. We don’t care about speeds under 88mph

2. The slopes are much different, about half the rise for the (b) chart than the (a) chart

3. Speeds above 115mph still maintain prominence for the (b) chart than the (a) chart

So, before we go on to consider the vertical angle (and eventually the spray angle), we need to appreciate what track of questions we are after.

]]>Suppose you “win” when your dice roll is higher than your opponent’s by at least 2 points. So, when your opponent rolls 1, you can roll 3 or higher to win. If he rolls a 2, you need to roll 4 or higher. He rolls 3, you need to roll 5 or 6. If he rolls 4, you need to roll 6. That gives you 27.8% chance of winning. That’s your expected value, that’s your true talent, that’s what we would forecast as your chances of winning.

Now, suppose we don’t know what your opponent rolled. We just know that you rolled a 5. Since you win if your opponent rolls 1, 2, or 3, rolling a 5 is worth “50%”. We therefore estimate that you would have won 50% of the time, if you kept rolling a 5 over and over and over again. What we are doing in this scenario is saying that your opponent’s roll IN THAT INSTANT didn’t matter. We estimate that KNOWING you rolled a 5, we will therefore estimate its value as 50%.

***

We know the run values of hits, walks, HR and strikeouts. We can estimate what might have happened if we did not know the sequencing of those events. And we would make that kind of estimate because we strongly suspect that sequencing of events is not a skill, but just a matter of circumstance (for the most part). We therefore KNOW the events, but not the sequencing in making our estimate.

But that is still NOT the same thing as forecasting would could happen. And that is because strikeouts are far more predictive of future events than non-HR hits. Even walks are more predictive than non-HR hits. And this is true to the point that the value of a walk is about the same as the value of a non-HR hit, in forecasting future runs. In other words, the “true talent” run value of a walk is the same as the true talent run value of a non-HR hit. That’s how we would forecast.

See, what happens is that walks are an indicator of talent, much more than non-HR hits. What we care about, in terms of true talent or forecasting (which is essentially the same thing), is what is innate. And walks are more innate to a player than non-HR hits.

FIP is an example of an estimate of what might have happened.

But if you want to predict what could happen, you would NOT use FIP, not in its current state. What you want is FUTURE FIP.

How do we change the classic FIP:

ERA = (13*HR + 3*BB - 2*SO)/IP + 3.2

Here then is my first stab at…

FutureFIP = (6*HR + 2*BB - 2.5*SO)/IP + 5.12

As you can see, in terms of estimating runs, the HR has twice the impact compared to predicting runs. And whereas estimating the impact to runs is more on the walk than on the K, it’s the K that is more predictive than the walk in terms of runs.

***

Batted ball exit speed: there’s no question that an exit speed of 95mph is much more preferable than 90mph IN THAT INSTANT: a .363 wOBA v .244 wOBA, as of today, a difference of close to 120 wOBA points. That is, to estimate what might have happened, and not knowing anything else, 95mph has much more value than 90mph. But in terms of forecasting, the value of 95 is not that much higher than 90. As we will learn in the coming weeks, it’s about half that much, about 60 points of wOBA. That is because we are trying to infer what does 95mph tell is about a hitter compared to 90mph.

So, we have to be careful what we are trying to do, when using data, if it’s trying to predict the future, which is the same thing as establishing the true talent level, which is the same thing as establishing the expected value.

Or if it’s trying to estimate what happened, which means taking as a point of fact the event that happened, and assuming SOME circumstances as having happened, and other circumstances as not having existed.

These two things have some relationship, but they are really two different answers to two different questions. And both questions are valid in their own way.

Remember this, as we get more into this in the coming weeks.

]]>The data is binned for ease, and I can certainly make it more granular. We’re plotting balls hit based on hang time, and how far Eaton’s starting position was to the ball’s final landing spot.The top chart is saying “given that an out was recorded, what percentage of those outs were made by Eaton”.

As you can see, if a ball was over 140 feet away, he was rarely involved. And over 180 feet away, he was never involved. That’s the data in brown.

The bottom chart is simply the BABIP. By itself, it doesn’t speak to Eaton. But in conjunction with the top box, it will. All that BABIP in the brown box has nothing to do with Eaton. We can discard it.

The purple boxes shows a ball with a long hang time and close to Eaton: Eaton made all the outs, and the BABIP was .000, meaning that every ball was caught. These are the easy outs. We can discard these too.

The green boxes are balls that are in the air a long time, and multiple fielders can hog the play for the easy out. We can discard all of these as well.

The orange boxes are balls where they were essentially 50/50 plays, and only Eaton was in a position to make the play. These plays we are HIGHLY interested in.

The little yellow box is the one where we worry about “zone sharing”, balls where multiple fielders COULD make a play, but it’s a 50/50 play. Basically, a ball hit right at the boundary between two fielders. Not shown here is the frequency of such plays: 1%.

So, there you have it, we’ll be in a position to figure out how many balls are “easy outs”, “easy hits”, “shareable tough plays”, and “individual tough plays”.

Winter is coming…

]]>

Alan has a terrific article on the subject, with this descriptive chart, where he shows you need a one inch offset to maximize distance, with a cost of a few mph of exit speed.

A word first on optimal launch angles. For a hitter, he maximizes hits at 12 degrees, but he maximizes home runs at 28 degrees. At 20 degrees, it’s a “worst of the best of both worlds”: you still get great results at 20 degrees, just not as good as at 12, nor as at 28. Indeed, one can even reason that a batter’s ideal launch angle is 20 degrees AS A MEDIAN, knowing that if he “mis-angles” a bit low or a bit high, he’ll get better results.

So, what I like to do is create launch angle bands in groups of 8 degrees. We have the two ideal launch angles of 8-16 (median of 12) and 24-32 (median of 28). Then we fill in the rest: under 0, 0-8, 16-24, 32-40, 40+.

Repeating the methodology of the study I did last night, I now focus on the vertical launch angle, to see if there’s any difference. And we do see a difference! Remember, I am looking at the same hitter-pitcher pairs year to year. And the hitters are getting fewer groundballs, a bit fewer balls in the traditional liner angle (8-16 degrees), and a bit more balls where we’d normally see HR (24-32 degrees).

Now, this could happen either because they are “mis-angling low”, meaning they are keeping the same attack angle, and just getting under the ball more. Of course if you do that, your exit speed will ALSO decrease. Which we don’t find. Or, the hitters are changing the way they are batting, just slightly enough, that they are launching the balls higher with a matching attack angle. And maybe improving their approach slightly so they get a bit more of the ball (1 mph of higher exit speed), and at an angle that leads to more HR.

UPDATE:

Doing the same “shifting” exercise, the numbers are fairly close if we compare counts this year to counts last year when off by 1 degree:

]]>