Developing Arm Action in Early Youth Throwers

Trip Somers • January 24, 2022 • Youth Sports

The title is a little grandiose, but this isn't going to be a post with a strict plan or loads of cited research. I'm going to forego the background of explaining the assumptions, so anyone who is curious about the scientific evidence behind these statements will have some extra reading to do elsewhere.

First and foremost, you absolutely do not need to worry too much about the arm action of a 6U athlete.

6-year-olds typically find 3-pound weights to be quite heavy, so obsessing over a child's shot-put throwing style, for example, is an exercise in frustration. Patience is going to be your friend because trying to correct a "bad" arm action with mechanical cues and excessive or unhelpful feedback simply won't work.

Put yourself in the mindset of a young athlete. Some guy is telling you that your arm needs to here and do this. You are trying. It is not working. Occasionally, he says, "Great job!" Occasionally, he says, "No, like this." You don't understand the difference. To you, it looks and feels the same. Great, now he's saying, "Reach back!" and "Finish!". You're pretty sure you did both.

There are three common paths out of there:

  • You luck into a good enough arm action that the coach shuts up.
  • You get frustrated and eventually tune out the coach.
  • You focus on your arm being there and doing that instead of just throwing the ball.

The most unlikely scenario? The kid executes and repeats the internal mechanical cues perfectly and instantly has a prettier arm action.

Is that prettier arm action the path to future success, though, or is the kid now too focused on arm action instead of throwing? Did you just put a restrictor plate in that kid's engine?

In other words: even if it works, did it really work?

The most important thing you can do is keep it fun because that's the real purpose of the game.

So now you've promised yourself that you won't nitpick arm actions or teach "mechanics" to smaller kids, but now what? How can you help them develop without the risk of doing more harm than good?

The real answer here is that you can leave the 6-year-old alone to play the game. As kids grow and develop, a lot of early athletic development issues, like a wonky arm action, work themselves out naturally.  There's some survivor bias in that statement, since the kids that don't work it out don't tend to keep playing, so what can you actually do to nudge things in the right direction?

Keep the cues simple:

  • Throw it hard.
  • Throw it where you want it to go.

Create micro-games like target and distance challenges. These can range from gamified catch, to landing throws in buckets, to measured long-toss. Sometimes simply playing catch at the right distance is enough.

The last bit of advice I have to share here is to be careful not to make micro-games too competitive. No one wants to finish last all the time, and the wrong kind of competition could drive a kid away before they have a chance to develop. Sometimes kids need to compete with each other, but the real competition should be focused on improving scores/results over time.

Keep it short. Keep it simple. Keep it fun.

Pitch Movement, Part V: (Sp)in Your Eyes

Trip Somers • February 9, 2021 • Analysis

It's been a while since Part IV in this series, and since that time, I realized that I had been holding all of the information I needed to make a case that spin deception is a thing. The theory, most simply put, goes like this:

Wake effects deceive the batter by creating unanticipated movement.

The long-winded version is bit of a walk, but it's an easy one. Professional hitters have really good vision. Like, crazy good. They also have reps upon reps upon reps against live pitches. The result is that they are exceptionally adept at seeing and reacting to spin. Given the time hitters have to react to pitches at the professional level, it's practically a reflex. When a pitch moves differently than this reflex expects, the pitch is harder to hit.

Identifying Wake Effects

Since MLB switched over to the Hawk-Eye system, pitch tracking data has been elevated in a way that helps us identify this effect. The old "spin direction" measurement was really movement direction all along. With Hawk-Eye reporting directly on the pitch's actual spin direction, we can now fairly easily compare these two numbers.

Movement direction is no longer directly reported, so we have to dust off those old trigonometry functions and do some MLB-specific adjustments to get movement direction on the same scale as spin direction. Luckily, most coding languages and SQL have a handy atan2(y, x) function that does a lot of the heavy lifting. (I will answer emails and tweets about this math, but I won't further extend this lengthy post by elaborating on trigonometry.)

Once we have spin direction and movement direction, we can very easily figure out how far apart they are. The catch is that you can't assume a whole lot from such comparisons, and here's why.

A specific difference -- spin direction minus movement direction for a specific pair of values -- means something different for different tilts. An easy example is that a +30° difference for a pitch with a 180° spin direction (12:00 tilt) means it had a 150° movement direction (11:00 tilt), but for a pitch with a 0° spin direction (6:00 tilt), a +30° difference means it has a 330° movement direction (5:00 tilt). At first glance that's going to seem perfectly logical to you, but the first pitch moves to the right (batter's perspective) more than expected and the second pitch moves more to the left than expected!

A further wrinkle is found on sliders, tight curveballs, and any other pitch that finds itself in a gyro cluster near 0 vertical movement and 0 horizontal movement. Pitches with high gyroscopic spin are extremely sensitive to small variations in spin direction (tilt). This leads to exaggerated differences between the two direction angles. You set your sample data to exclude certain pitch classes, but that merely reduces the geometry problem instead of accounting for it. So...

Let's factor in movement distance. There are two "clear" approaches to this. You can be completely serious, like Glenn Healey and Lequan Wang, and use physics to reasonably calculate the side force, or you can be like me and use a somewhat reasonable alternative that assumes equal movement at both angles and measures the difference between the two endpoints. Healey and Wang definitely have the more accurate mathmatical approach, but while they are asking, "How much did side force affect this pitch?", I am asking a less specific question: How far did this pitch wind up from where the batter expected it to wind up? (The difference in approaches results in my values being smaller than Healey and Wang.)

I decided to express the directions as Spin Tilt and Movement Tilt since "tilt" is well understood and widely used. I settled on the name Tilt Difference for the difference between them and Deception Distance for the final value. But that's not all...

I did a truly excessive amount of thought before I touched any numbers, and decided that I also wanted to look at the absolute value of these differences. It didn't seem right to me that a pitch that generally has either +2" or -2" Deception Distance should average to a near-zero value because in reality there is an average 2" difference! This value gives us a second way to look for hidden value. We can check not only the average Deception Distance, but also the average Absolute Deception Distance.

Free Data!

I published a spreadsheet with all of this data: Spin Deception Data. It's a pretty neat little toy that lets you use the Data -> Filter views selection to shuffle between pitcher handedness and pitch type. The dataset is the complete pitch type summary set for 2020 as reported by the public MLB StatsApi. The filters are limited to pitchers that threw at least 20 of the filtered pitch type. Some dependent variables included in the sheet are swinging strike rate, foul rate, and in-play rate broken down by batter handedness as well as overall exit velocity and launch angle.

The second sheet in that document contains scatter plots with trend lines. Here are the most intriguing results, which I'm sure greatly please @NotRealCertain. The first set of charts is sinkers thrown by RHP.

RHP Sinkers

RHP Sinkers - Deception Distance vs Exit Velocity
RHP Sinkers - Deception Distance (in) vs Exit Velocity (mph)
RHP Sinkers - Deception Distance vs Launch Angle
RHP Sinkers - Deception Distance (in) vs Launch Angle (deg)
RHP Sinkers - Absolute Deception Distance vs Exit Velocity
RHP Sinkers - Absolute Deception Distance (in) vs Exit Velocity (mph)
RHP Sinkers - Absolute Deception Distance vs Launch Angle
RHP Sinkers - Absolute Deception Distance (in) vs Launch Angle (deg)

Those are some pretty strong trendlines suggesting a positive relationship between both versions of Deception Distance and how poorly a ball is hit, and we see them again with the LHP sinkers. The fun thing about the LHP sinkers charts is that the trendlines are in the opposite direction because LHP sinkers have "positive" Tilt Differences.

LHP Sinkers

LHP Sinkers - Deception Distance vs Exit Velocity
LHP Sinkers - Deception Distance (in) vs Exit Velocity (mph)
LHP Sinkers - Deception Distance vs Launch Angle
LHP Sinkers - Deception Distance (in) vs Launch Angle (deg)
LHP Sinkers - Absolute Deception Distance vs Exit Velocity
LHP Sinkers - Absolute Deception Distance (in) vs Exit Velocity (mph)
LHP Sinkers - Absolute Deception Distance vs Launch Angle
LHP Sinkers - Absolute Deception Distance (in) vs Launch Angle (deg)

Further research

I'm not an analyst, and I just do this for fun, so this is about as far as I want to take things myself. I'm sure there's plenty more to dig into, but it will have to be one of you that does it. If you have questions or comments, feel free to reach out to @texasleaguers on Twitter or use my contact form to send me an email.

Feel free to download, copy, and reuse the data in the spreadsheet, but please credit me, the blog, or the website if you publish any analysis related to the data contained therein.

Pitch Movement, Part IV: Tunnel Vision

Trip Somers • June 25, 2020 • Training

About a week ago, I was inspired to add another entry to the Pitch Movement series. The inspiration was this pair of tweets:

What's particularly great about the videos from Hughes is that the shots are aligned nearly perfectly with the initial trajectory of his pitches and give a great view of the pitch's initial spin (when not overlayed with other pitches). The stationary perspective also lends itself incredibly well to release comparisons and pitch tunneling overlays.

Smith's tweet seemed like a pretty obvious signal to me, so I reached out to Hughes to provide the details for a blog post aimed at helping other pitchers produce their own pitch trajectory overlay videos. He did not disappoint.

What follows is a blogified version of the notes he provided. Those source notes are the result of a collaborative effort by Smith, Hughes, Connor Hinchliffe (@conhinch), and Andrew Smith (@roo1776).


The basics of the setup are not hard to understand: get a high-speed video camea, put it on a tall tripod, put the tripod in the right spot, and aim it at the center of the strike zone.

To select an appropriate camera, you need to figure out what you already have, your desired frame rate, and your budget. Hughes uses the Sony RX100 VI digital camera and records at 960 frames per second (fps), but that isn't likely in your budget unless you've played in the bigs, too.

240 fps – roughly 8x slow-motion – might be enough for your needs, and if it is, the GoPro HERO8 might be a better fit for your budget.

Before you go out and buy a camera, check out what your phone or existing digital camera is capable of. You might be surprised. For example, almost every iPhone model can easily record at 120 fps, but certain iPhone 8 and newer models that default to 120 fps for slow motion are capable of 240 fps at Full HD 1080p resolution with a small settings adjustment.

At 240 fps, a video of a 90 MPH pitch from release to the plate is about 3.2 seconds in length. I think most applications of slow-motion video would be fine in the 400-500 fps range which would produce a 6-7 second video. Unfortunately, there aren't many mid-range high-speed cameras out there. 120 fps and 240 fps options are aplenty, but above that, you're looking at 960+ fps cameras that are all quite pricey.

240 fps should provide decent but not ideal results, and with the right editing tools, the playback speed can be adjusted to slow it down – at the cost, of course, of fine detail. If there's another argument to make for lower frame rates, it's that higher frame rates produce longer videos that require extra storage and take longer to process.

Once you've got your camera, you're going to need to get a tall tripod. Compared to the cost of the camera, this won't be expensive, but your standard $25 basic model won't cut it. Since you will need it to look down at the strike zone through your release point, it will need to be pretty tall or elevated on a sturdy surface.

With the camera on the tripod, you'll need to work out the exact location to get the shot lined up with the pitch trajectory. You will need to take some test shots, so I recommend using the standard frame rate during this step. When you find the exact right spot and height, mark it or measure it to save time spent setting up for your next session.

Hughes has a pretty low 5' 6" vertical release height, and after adjusting for breathing room – you won't want the camera crowding you or getting knocked over by a stray limb – his ideal camera height is 7' 1". He also warns that windy days can be trouble for tall tripods.

The tripod will be wide to your arm-side to create the correct angle for the video and, again, far enough away from you so that neither your arm swing nor your back leg hit it. The final position is going to seem far away and really high, but that's where it needs to be to line up the tunnel.


Touch the camera as little as possible once it is in the recording position. If possible, only touch it to start and stop recording. Additional touching runs the risk of unintentional camera movement. If you're feeling fancy, you might be able to find a remote control or trigger to start and stop recording so you don't have to touch the camera at all. If you go the GoPro route, some of their models accept voice commands.

Extremely flexible and/or aggressive pitchers may struggle with the back foot blocking the camera during follow-through. There isn't a good way to deal with this, unfortunately. The clearest option is toning down the follow-through, but that could lead to misleading results due to altered mechanics.

Hughes advises that if you are planning to match up your video with data from a Rapsodo or other tracking device, you will want to take notes on each recorded pitch. In his words:

I typically scribble 4 things on a piece of paper after each recorded pitch. “Pitch type, video #, rapsodo #, release (x,y)”. I include release because it helps me see which pitches will likely tunnel well in an overlay. Also, the goal is to release every pitch from the exact same place. If I’m not at my best release height I can make adjustments to get back to it.

Lighting Note: Hughes also warns that if you are outdoors, bright sunlight may reflect too powerfully off the white baseball leather and create too much glare to be able to see the seams clearly.


Hughes provided some additional notes to improve data accuracy and consistency:

Use a new baseball if you want a true read on how your pitch is moving. If you want to compare how different grips move, use the same unscuffed baseball each time you throw a new grip. I typically warm up with my batch of scuffed baseballs, but when it comes time to record, I use my new baseballs.


There are a lot of options for editing, combining, and overlaying video clips. This is not going to be a full tutorial, but it may provide you with a couple of ideas you hadn't thought before. You should research video editing options for yourself and figure out what works best for you.

For Hughes, who records at 960 fps, he only records a couple of pitches per bullpen due to file size and how long it takes to write the massive video files to storage. The files can be upwards of 1 GB!

You can experiment with on-camera editors, but Hughes offers that trimming and editing on-camera may result in reduced video quality in the form of a lower effective frame rate. To avoid this, he uploads the original files to Dropbox where he keeps a video archive of his recorded pitches along with notes.

The file is then downloaded to his phone (iPhone 11 Pro Max) via Dropbox – the file has now gone from camera to Dropbox to phone – where he loads it into an app called Fused where he creates his overlays. At this point, video quality is greatly reduced but remains more than high enough to visualize release, movement, and how different pitches and locations play well together to create tunnels.

If you own a quality laptop or desktop, you can likely find some free or low cost video editing software, such as Blender or iMovie, that may produce higher quality videos or may simply feel more comfortable to you while editing.

Taiki Green produced a good tutorial for doing this with iMovie a while back:

You will have to experiment to find out what works best for you, but your end result should be a pretty cool, pretty valuable tool for analyzing your pitch tunnels.

Scouting Grades: Power

Trip Somers • June 22, 2020 • Scouting

It's been four months since I published the first entry in the Scouting Grades series, and it's fair to say that motivation to continue blogging about baseball has been limited in that time. With the 2020 draft in the rearview, I found myself thinking about scouting grades again.

If you haven't already, it's probably a good time to review the post on Speed. Now, let's talk about POWER.

Power wins games. Power puts butts in seats. No other tool has a showcase -- the home run derby -- that exists specifically to elevate and celebrate a specific tool. It should come as no surprise that power also draws big signing bonuses and buys prospects more time to develop.

In this context, development most often means translating raw power into game power, the actual production of runs. That development generally takes the form of increased plate discipline which is not limited to swinging at fewer balls but also includes learning and taking advantage of pitch locations that really allow the hitter to get to that power.

Raw Power vs Game Power

Before player- and ball-tracking technology, raw power grades were determined by watching a hitter unload on baseballs -- usually during batting practice, a scout's best opportunity to see a lot of swings in a short amount of time. Raw power was essentially how hard a player could hit the ball, frequently evidenced by distance.

[Note: batting practice is not the only input, obviously. Game swings matter more and commonly result in harder contact than batting practice swings. However, scouts will almost always see fewer game swings than BP swings, and plenty of hitters will show you what they've got during BP.]

The Raw Power Scouting Scale (roughly):

  • 20 – Even with a favorable gale force wind, the ball probably isn't going over the fence.
  • 30 – A stiff breeze should do it.
  • 40 – Doesn't need help, but you don't know it's over the fence until it is.
  • 50 – Hey, there's some pop!
  • 60 – Stadium vendors and people on the concourse should pay attention.
  • 70 – The ball hits weird parts of the stadium, and occasionally leaves to check out other places.
  • 80 – The ball has a greater than zero chance of escaping Earth's atmosphere.

Game power is an assessment of how often a player is going to tap into that raw power against MLB pitching. There's no escaping the subjectivity required for a game power grade since it relies on, among other things, the player's future hit tool and future raw power which are both projected by the scout.

This produces an unbelievably wide spectrum of projections and results. There are guys you've never even heard of with top shelf, 80-grade raw power that projected to hit approximately 0.000 at the highest level. Then you've got freaks like Frank Thomas and Miguel Cabrera that are just as likely to win a big league batting title as they are to win a homerun crown.

The Game Power Scouting Scale (roughly, thrown into disarray by the new baseball; not funny):

  • 20 – 5 homeruns in a single season would be incredible.
  • 30 – ~5 homeruns per season.
  • 40 – ~11 homeruns per season.
  • 50 – ~18 homeruns per season.
  • 60 – ~26 homeruns per season.
  • 70 – ~35 homeruns per season.
  • 80 – Homerun crown contender.

Of the obvious variables that limit raw power in games -- pitch recognition, plate discipline, swing length, and others which will be discussed in the Hitting post -- there is one in particular that can limit a hitter with plus hitting and plus raw power to mediocre or even below average production: launch angle.

Exit Velocity & Launch Angle

Three names immediately jump to mind when I think about how a low launch angle stifles power: Nick Markakis, Eric Hosmer, and Yandy Diaz. That's three big dudes with big raw power, decent batting averages, and just not very many homeruns. Depending on your age, you may recall that, as prospects, Markakis and Hosmer were fairly widely believed to be potential perennial 40-homerun hitters. Diaz, who arrived just in time for launch angle to become a hot button issue, has been frequently noted as a guy that hits the ball as hard as anyone else in the game.

Last season, all three were in the Top 50 in Hard Hit % (95+ MPH) -- Hosmer #32, Diaz #43, Markakis #48 -- just ahead of Mike Trout (#49) and Fernando Tatis, Jr (#50).

Last Season, all three were in the Top 60 in Average Exit Velocity -- Diaz #20, Markakis #30, Hosmer #57 -- ahead of Anthony Rendon (#64). Trout checked in at #51. (Stats according to StatCast via Baseball Savant.)

In 469 plate appearances, Markakis hit 9 homeruns. In 667 plate appearances, Hosmer hit 22 homeruns. In 347 plate appearances, Diaz hit 14 homeruns. That's 45 homeruns in just under 1,500 plate appearances.

In 600 plate appearances, Trout hit... 45 homeruns.

Given 2.5x the number of plate appearances, three hitters that are ostensibly better at hitting the ball hard than Mike Trout combined to produce the exact same number of homeruns as Mike Trout alone.

The key difference (and admittedly not the only difference) is launch angle.

Eric Homser had a 2.1° average launch angle. Yandy Diaz had a 5.7° average launch angle. Nick Markakis had a 7.3° average launch angle.

The average launch angle for all MLB batted balls was 11.2°.

Mike Trout had a 22.2° average launch angle.

The point of all of this is to illustrate that translating raw power into game power requires hitting the ball in the air. A player can hit 100 MPH ground balls all day, but it won't result in power production.

Context and Projection

As scouting evolves thanks to the ever expanding use of technology, the scout's role will increasingly be to provide context for objective data rather than authoring almost entirely subjective reports. With the proliferation of Trackman, HitTrax, and similar systems, for example, it's a matter of course to have objective data for a player's exit velocity and launch angle. A scout provides little of use by assigning a 20-80 grade for present raw power.

A player's future raw power, on the other hand, is a contextual grade usually backed by an assessment of physical projection. Physical projection offers insight into a player's potential through continued growth and added speed/strength. An 18-year-old that could still grow a couple of inches and has never lifted a day in his life could get a 2-grade bump, while a 23-year-old with a mature build generally gets none.

When it comes to game power, the hit tool is the big separator. It is easily the most complex tool grade and is frequently broken into several sub-grades, and the realization of hitting potential often relies on additional external factors that are not easy for a scout to assess. That really is a topic for a future post, but a few quick examples will illustrate the type of context scouts should be chasing.

The path of the barrel, including its overall length and depth, plays a key role for both power and hitting. While a long, flat path might lead to a lot of overall contact, it isn't likely to lead to much hard contact. A shorter path on plane with the pitch has a better chance of producing hard contact, and being on plane with the pitch has the added benefit of an increased launch angle.

The length of the swing ties into timing which has downstream effects on pitch recognition and plate discipline. All other things equal, a hitter with a longer path to contact has to start to swing earlier, giving the hitter less time to see the ball before launching a swing. Less time to see the ball means worse recognition which will lead to bad swings and bad takes.

When timing is more easily disrupted, the contact point "moves" to different parts of the swing. Generally speaking, if the swing is late, the ball is more likely to be popped up because the point of contact has "moved" deeper where the barrel hasn't come back up to the anticipated point of contact yet. If the swing is early -- particularly on slower pitches that drop more -- the ball is more likely to be hit on the ground because the point of contact has "moved" out front where the barrel has already risen past the anticipated point of contact.

These issues are amplified more in swings that are further from the plane of the pitch.

Projecting a future game power grade can seem a lot like witchcraft, but it boils down to just a couple of questions:

  • Will the player's raw power increase or decrease?
  • How will the player hit against top-level pitching?
  • Will the player hit the ball in the air?

At this point, projecting a player falls into the gap between scouting and player development. Amateur scouts can get to know a guy well enough to have a good idea of the player's development potential ahead of the draft, but the same can't be always be said for pro scouts who are watching another ball club's players. This development gap is a complex topic and will hopefully be the focus of a future post.

Pitch Movement, Part III: The River of Seams

Trip Somers • April 2, 2020 • Analysis

Part I in this series covered the basics of the Magnus effect and how pitch spin creates movement. Part II covered some interesting research being done by Barton Smith to explain non-Magnus seam effects.

At his blog,, Smith has logged research results as he's worked through several experiments aimed at analyzing and describing a baseball's aerodynamic wake as it relates to the position of the baseball's seams. As the posts stacked up and more impactful conclusions could be drawn, he realized he needed a way to describe seam orientation.

A few emails later, he and I had worked out the basics of a simple system, and a couple of days after that, we co-authored the first post about describing seam orientation.

Based on a few subsequent conversations and work done to produce visualizations, we realized that describing seam orientation wasn't the only missing piece needed to completely describe a pitch's spin.

In this post, I'm going to discuss the system as it is currently implemented by the pitch spin modeler. (You may want to have that open in another tab while you continue reading.)

Pitch Spin Modeler
A preview of the modeler, for those that don't care to click the link above.

Describing spin basics

There are already three extremely common and well understood components of spin description. There is no reason to mess with them really.

"Spin rate" is a simple measure of how fast the pitch is spinning, typically in rotations per minute (RPM) though you may see a different unit of measure in a math-heavy analytics post or article.

"Tilt" is the two-dimensional representation of the direction of the Magnus effect created by the spin. For example, a fastball with pure backspin has a tilt of 12:00 because the force created by the Magnus effect points straight up which is 12:00 on a clock face.

This clock-face-based description was popularized by Rapsodo and widely welcomed as a solution to the rather constant confusion that resulted from trying to communicate spin axis as an angle. (Astute readers will notice that, while tilt doesn't directly describe the spin axis, it does do so indirectly via the right-hand rule.)

"Spin efficiency" is commonly used to represent the percentage of spin that contributes to the Magnus effect. A lower efficiency means there is more gyro spin. A less common term for this is "Active Spin", though it is still represented as a percentage. In the pitch spin modeler, I have labeled it "Active Spin" under the Efficiency header.

UPDATE (4/16/2020): To more accurately reflect the role of gyro spin in pitch movement, the "Efficiency" header has been renamed "Magnus Efficiency", and the "Active Spin" slider has been converted to "Gyro Angle" with the efficiency percentage following in parentheses.

Improving the basics: gyro spin is missing something

On the pitch spin modeler you'll see another setting under Efficiency called "Gyro Pole". This setting indicates which of the two spin axis poles is "responsible" for the efficiency percentage. Technically speaking, both spin axis poles are equally responsible since one can't move without the other, but for now "responsible" means that the particular pole is forward on the leading surface of the ball.

Generally speaking, RHPs will have a "negative" gyro angle, and LHPs will have a "positive" angle. So... Why not establish a positive pole and a negative pole? Why not just use positive and negative angles? My short answer is this: for something that sounds so simple, it is rather confusing and non-descriptive when it comes to practicality.

Take, for example, a curveball with 6:00 tilt thrown by a RHP with 80% spin efficiency thanks to a gyro angle of 36.9°. Should that angle be positive or negative? What if a LHP threw it? Take another example: a fastball with pure side spin. If the bottom pole is forward, is that positive or negative? Is the answer to that question the same for both a RHP and a LHP?

Identifying the specific pole on the same clock face as the tilt is easy and perfectly descriptive. The two poles will always be at +3 hours and -3 hours, respectively, from the pitch's tilt. The pitch modeler already identifies them for you, so you simply select which pole should be angled forward!

77.1% Active Spin @ 9:00
77.1% Active Spin @ 3:00
12:00 Tilt with 77.1% Active Spin at opposing poles. Left: 9:00. Right: 3:00.

Back to seam orientation

This one was a little tricky. The clock face was already taken, and this really can't be solved by a two-dimensional model anyway. The general baseball audience probably doesn't understand Euler angles or quaternions. (I barely do, and I coded a pitch spin modeler that depends on them!)

As far as I could determine -- with the help of Barton Smith and Tom Tango -- there were really only two options for eloquently describing seam orientation: (1) a coordinate system that uses latitude and longitude like a globe and (2) a pair of angles that tell you how to turn the ball.

The coordinate system has its advantages. It's well known. It's fairly commonly understood in general terms. It's precise, and it's specific. It also has some important disadvantages. Though it's fairly commonly understood in general terms, I'd venture to say that players and coaches don't have a lot of practical experience using it every day. What would an orientation of (34, -117) actually mean even if they could find that spot on the ball?

So I decided to push forward with what seems like the simplest possible approach: rotate the ball from the top, rotate the ball from the front. Both rotations, for descriptive purposes, are clockwise for positive and counter-clockwise for negative.

Orientation redundancy and pitch grips

Due to the pattern of the seams on the baseball, there are a lot of different ways to grip the baseball to create identical spins. For example, from the origin position, a 180° Top rotation gives you a different look but produces the same spin as the 0° origin! Likewise, -90° and +90° both produce a standard four-seam spin.

Top: -90 degrees.
Top: +90 degrees.
Two different orientations that produce the same spin. Left: -90° Top rotation. Right: +90° Top rotation.

The original release of the pitch spin modeler restricted Top rotation to a range of -90° to +90° to avoid confusion regarding unique spin models, but I quickly felt that it unnecessarily prevented useful visualization of two-seam grips that use vertical seams.

With it extended to a full 180° in both directions, you can use the tool to model any grip you can think of!

Altogether now

The pitch spin modeler has 6 variable inputs that are used to completely describe a baseball's spin. That is twice as many as were widely used just a few weeks ago. Take any one of those 6 inputs away, and you create a blind spot in the pitch description.

With all 6 given, it becomes a matter of math to determine where movement-critical seams are located. Once the math is ready, this will fantastically complicate both the standard constant acceleration model and the more advanced Nathan model for pitch movement that do not currently account for seam-based wake effects.

The next entry in this series will discuss currently available tracking technologies and what they can and can't tell you about a pitch. (Don't hold your breath, though. It's going to take some time because there will be a lot to verify ahead of posting. There will probably be several unrelated posts between now and then.)