DeLauter versus Florida State’s Parker Messick and Bryce Hubbart was one of the most anticipated matchups of Opening Weekend. The two lefties tied the Cape League’s top prospect up in knots over the first two games, striking out DeLauter in all five plate appearances. After showing advanced plate coverage and discipline on-Cape, DeLauter was reduced to searching and feeling, looking lost at the plate.
He’s always had the odd back foot finish1, but his hips were noisier, diving out early to throw off his usual balance and timing. There were still glimpses of the player seen in Orleans. The physicality and bat speed are apparent, his speed and ease of action were there, and he found barrels when he did make contact. There was even a moment of self-deprecation shared with teammates after his first hit Sunday, breaking a 1-12 start.
Even so, for a lightly recruited, mid-major bat with a short track record looking to answer questions about his hit tool in his draft spring, the initial showing left us with more questions instead.
DeLauter’s profile compares well to past looks of Kyle Lewis. Nearly identical as power over hit, physical college outfielders with history of quality performance. After DeLauter’s rocky start, a look at Lewis’ draft spring can be instructive. For simplicity’s sake, I’ll gloss over other factors and say their competition is equal. Overall, Lewis slashed .395/.535/.731 in 2016. Say DeLauter is in the ballpark of Lewis’s output at the end of the year -- or exceeds it, as he’s responded with a Pablo Sanchez-esque .609/.710/1.238 line in 7 games since -- and looks as he did on-Cape versus draft quality arms. Is he not still a top 5-10 overall pick?
High octane lefties may always be an issue, but that’s not unlike any other risk from a lefty bat, and he can contribute in game with other tools. If convicted in the bat to mash against all other foes, DeLauter can still be your guy.
N.C. State’s freshman phenom Tommy White took the college baseball world by storm with nine home runs in his first eight games, including three in his debut. Give credit where it is due, but there is cause to slow the hype train. Eight of the nine were located middle to middle away, mid-thigh or higher, with the one other a lefty slider down and in at the knees. Quality of competition should also be considered. Evansville, High Point, Longwood, and Quinnipiac don’t measure up to the ACC or, arguably, what White faced at IMG Academy.
As I noted last week, more advanced stuff, especially hard inside, will provide a proper test. Wednesday at Campbell was a bit of a preview. They challenged him inside, resulting in a strikeout, a jammed flare pop-out to the second baseman, and a soft groundout to the second baseman. Northeastern's Cam Schlitter, a Cape alum, also attacked him inside on Friday night and induced four harmless groundouts.
This was just two games, but we’ve seen other freshmen come out blazing (Beer, Schwarz, Baker, Shewmake, Skoug, et al) to varied pro success. With two more years until draft-eligibility, there’s plenty of time to make adjustments.
First impressions can often sway an overall view of a prospect. The key is not to be beholden to a hot streak or a cold spell, but to turn in their true talent just right.
1 My theory is the finish is a product of how hard DeLauter drives his back hip. Druw Jones does so at times in a similar fashion.
While looking through Tampa Bay's 2021 position player stats, six players stood out as exceptional values. Randy Arozarena, Brandon Lowe, Joey Wendle, Mike Zunino, Wander Franco, and Willy Adames each produced in excess of 3.5 bWAR with a salary under $3 million.
Using that as a baseline for the rest of the league, I found that there were only 27 such Elite Value position players for the 2021 season. The Rays' six put them at the top, and the only other team with as many as three was the Cardinals. 13 teams didn't even have one Elite Value position player.
[Note: Willy Adames is counted for both Tampa Bay and Milwaukee (not shown). While he, specifically, was markedly more valuable for Milwaukee, counting him and similar players on both teams is sufficient for this article.]
Over the past 10 full seasons, only 14 out of 300 teams had at least 3 Elite Value position players. The 2018 Dodgers had 4 and are the only other team with more than 3: Cody Bellinger, Max Muncy, Kiké Hernández, and Chris Taylor.
Expanding the criteria to explore its arbitrary nature, I also looked at position players that produced at least 2 bWAR with a salary below $4 million. Of the 71 such High Value position players in 2021, the Rays had 9. The next closest team, again, was the Cardinals with 5.
Over the past ten years, a team has had at least six High Value position players just six times, and a whopping four of them were the Rays. The only team to exceed six such players was -- surprise! -- the 2021 Rays with nine.
By either measure, the Rays 2021 season was very special in terms of position player value. How were these Elite Value players acquired?
Randy Arozarena (2020)
Randy Arozarena loudly introduced himself during the 2020 MLB Playoffs when he seemingly came out of nowhere to hit 10 home runs in 20 games with a ridiculous 1.273 OPS. The Rays acquired Arozarena ahead of the 2020 season from the Cardinals along with Jose Martinez and a draft pick in exchange for Matt Liberatore, Edgardo Rodriguez, and a draft pick. Arozarena wasn't able to maintain that insane homerun pace in 2021, but he was named the American League Rookie of the Year.
Brandon Lowe (2015)
Lowe is the only player on this list that was drafted by the Rays. A third-round pick in 2015 (87th overall) out of the University of Maryland, Lowe has comfortably exceeded expectations. Of the 1,215 players drafted in 2015, only Alex Bregman (2nd overall), Andrew Benintendi (7th overall), Walker Buehler (24th Overall), and Paul DeJong (131st overall) have more career bWAR so far.
Joey Wendle (2017)
The Rays acquired Wendle in 2017 for a player to be named later (Jonah Heim). The following season, he broke out with 4.9 WAR, tying for 21st in MLB! After a lackluster 2019, Wendle was an Elite Value again in 2020 and 2021. This offseason he was traded to Miami for Kameron Misner (2019 1st round pick).
Mike Zunino (2019)
In 2019 the Rays traded Mallex Smith and Jake Fraley to the Mariners for Mike Zunino, Guillermo Heredia, and Michael Plassmeyer. In his first 2 years with the Rays, Zunino struggled offensively and had a combined -0.3 bWAR. Following the 2020 season, the Rays re-signed him for $2 million (with a club option for 2022) and were rewarded with a 3.8 WAR season, the 3rd most for a catcher in 2021.
Wander Franco (2017)
You won't see Wander Franco’s name on this list again after he signed an 11-year, $182 million contract. He originally signed as an international free agent as a 16-year old. Consistently atop prospects lists since he signed, Franco has the potential to be one of the best players in the game. He played in just 70 games during his rookie season, but his 3.5 WAR in less than half a season is nothing short of remarkable. Only seven hitters in 2021 had a lower strikeout rate (min 300 PA’s), and of those seven, only Yuli Gurriel had a higher OPS+.
Willy Adames (2014)
Before Franco took over at shortstop, the position was manned by Willy Adames. The Rays acquired Adames along with Drew Smyly in the 2014 David Price trade. Adames was having a good season in low-A but entered the year ranked 30th on Baseball America's Tigers top 30 prospects list. A 2018 rookie, Adames had his first Elite Value season in 2019 and was on-pace for another in the shortened 2020 season. His production declined in 2021 before he was traded to Milwaukee, where his production sky-rocketed to 3.5 WAR in only 99 games.
These six players were acquired in different ways across seven seasons. Lowe (draft) and Franco (international free agent) are home-grown talents, while Arozarena, Wendle, Zunino, and Adames were all acquired via trade.
With a committment to competing at a high level despite historically low player payrolls, it's important for the Rays organization to find all-star production from inexpensive players. Having this many in a single season suggests quite a bit of luck, but the Rays' historical success with Elite Value production shows there is more to it than just luck.
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.
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.
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.
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.
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.
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.
BONUS: DATA CONSIDERATIONS
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.
PRODUCING THE VIDEO
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.