Assessment with pitchLogic Data

Most younger athletes don't come in quite this advanced, but let's discuss the good and the bad. 

The picture below shows data for this individuals fastball, change-up, and slider. The last picture in the bottom right shows the movement plot for each pitch. 

The first thing to notice is the velocity on the fastball. With this being a high school freshman.....this is really good! Given the velocity, we look at the vertical and horizontal movement. Because he throws harder, gravity isn't going to have as long to affect the ball, which means the 19 inches of vertical movement is really good. He's going to get carry/life on his fastball! This is indicated by the red dot on the bottom right plot. 

Moving on to his change-up. One thing I generally don't see from young pitchers is a good change-up. Typically the patterns are similar to their fastballs, or they'll slow down their arm/body to take velocity off. For too many kids force circle change grips and can't produce good movement (unless cutting the ball is what they're after). What we look for here is trying to kill vertical movement. As you can see in comparison to the fastball, he has 9 inches less of vertical movement. That, by itself, is great, but the additional horizontal movement makes it that much better. Indicated by the green dot on the bottom right plot. 

Now, before we move on the slider, there's one more important piece for the change-up. Look at the difference in velocity. While the pitch itself has great characteristics, the velocity gap between his fastball and change-up are too spread out. This will lead to increase movement due to gravity having more time to impact the ball (albeit, just slightly), but it also means he's significantly slowing down arm speed. Against younger hitters, this won't matter much, but moving forward, we want to sell that a fastball is coming. 

On to the slider (yellow dot)......with the life he gets on his fastball, this is the money pitch! While it's going to be good against high school hitters, that's not the goal. The spin is OK and the movement is OK. The problem here, again, is that it's too slow. It's curveball speed with slider spin. So the first thing it has to do, is pop up out of the hand due to lack of velocity. Again, this is fine against high school hitters. Against the better competition, we don't want that. As we work to throw this pitch with more velocity, the spin should pick up, leading to later and sharper break. 

This is a guy to keep an eye on moving forward!

Workload Management: Going Beyond Acute:Chronic Workload

      Workload Management: Going Beyond Acute:Chronic Workload

      Many experiments end up as "failed" projects.  Many don't answer the initial question you may have set out to answer and sometimes they provide results contrary to preconceived notions.  On occasion, you'll stumble upon something significant by accident! This summer, we (Ryan Aruanno, Cam Earles & myself) stumbled upon one of those!

      Now, take this with a grain of salt because it didn't actually lead to anything......yet.  It lead to very interesting data that has turned into more questions and more discussions about what we saw.  Let's backtrack a bit and talk about the idea of workload management. We know that pitch counts aren't the greatest indicator, but they do provide some sense of accountability.  In recent years, discussions about Acute:Chronic Workloads have gained in popularity to offer a much better approach to managing workloads.  I don't want to dive into the ins and outs of Acute:Chronic Ratios (ACR), but here's a great link for anyone who wants to learn more on that topic. The only thing I want to point out about ACR, is that studies show that once that number gets above 1.3, the likelihood of injury goes up significantly. 

     This past summer, we implemented a bunch of things to collect data on in hopes of finding significant correlations.  A bunch of these involved a Wii Board.  One of which was to record shoulder force production. We measured Lying Y's & T's and Standing ER's (we had 1 more that we ended up taking out).  The initial thought was that measuring shoulder forces could give insight into velocity.  That was somewhat present but that's not the topic of this blog.  What it did, was lead to the thought that maybe there would be correlations with individuals producing more shoulder force and gaining velocity.  Results weren't quite as good with that, but it led us to an interesting though: "Could it possibly provide insight into recovery?"

      Now let's finally discuss the actual experiment! Using those 3 tests (Lying Y's, T's & Standing ER's), we measured dominant and non-dominant arms Pre & Post Throwing on most training days inside the facility.  Each athlete performed isometric holds while pushing into the Wii Boards as hard as possible for 2-3 seconds each.  2 reps were performed for each test and the average was recorded. For Recovery purposes, we didn't pay attention to the total numbers for each, but rather, we looked at the difference between dominant and non-dominant arm for each test.  Meaning that if an individual recorded a higher number with his dominant side than his non-dominant side, the number for that particular test would read as a positive number.  If the dominant was lower than the non-dominant, the number would read as a negative number.  

      As it worked out, there was 1 pitcher that we had using Motus Throw to monitor arm speed on a daily basis, so we decided to record ACR and Chronic Workload every day for him alongside of his Shoulder Force data!  Keep in mind the 1.3 number I mentioned previously because it's about to be a big part of this discussion.  Using this particular individual's data, we were able to get some very insightful information!  Below is a chart with his data.  *Note: this particular individual threw on days he didn't come to the facility also. He did use Motus Throw for all of his throwing, even away from the facility*  

ACR	Chronic WL	Date	Time After Pitching	ER Difference	Front Y Difference	Front T Difference
0.88	3.9	25-Jun	Pre Throw	2	0.7	0.85
1.35	4.6	25-Jun	Post Throw	-0.65	0.3	1.85
1.12	4.2	27-Jun	Pre Throw	1.85	0.8	-0.1
1.3	4.5	27-Jun	Post Throw	0.15	-1.45	0.3
0.58	4.6	9-Jul	Pre Throw	0.7	0.85	-0.25
0.93	5.1	9-Jul	Post Throw	1.8	0.7	1.6
		11-Jul	Pre Throw	-0.5	-0.65	0.95
0.86	7.7	11-Jul	Post Throw		0.3	0
0.91	4.6	16-Jul	Pre Throw	2.15	0.75	0.65
1.14	5	16-Jul	Post Throw	1.35	0.55	0.75
1.08	5	18-Jul	Pre Throw	0.5	1.6	1.35
1.28	5.4	18-Jul	Post Throw	-1.25	-2.3	-1.95
1.42	5.1	19-Jul	Pre Throw	1.5	-0.8	2.8
1.48	5.2	19-Jul	Post Throw	-0.1	-2.7	-0.3
0.71	5.4	6-Aug	Pre Throw	0.6	-0.5	2.7
		6-Aug	Post Throw	-1.45	0.1	2.7
0.93	5.9	8-Aug	Pre Throw	-1	-1.25	2.35
1.06	6.2	8-Aug	Post Throw	0.55	-2.2	-0.75
1.1	6.2	9-Aug	Pre Throw	-1.9	-3.4	-1.75
1.16	6.5	9-Aug	Post Throw	-0.5	-4.05	-0.7
		22-Aug	Post Throw	-1	-3.8	-2.9

       

      Remember that 1.3 ACR number??? Take a look at ACR on July 19th on the chart above.  That's the first time this athlete gets to a 1.4.  If you go back before this date, you'll notice that there are some negative numbers, but they are always post-throwing with one exception.  He came in on July 19th with an ACR of 1.42 and a negative Front Y score (which is the data point we paid extra attention to for this individual).  So at this point, it appears that ACR is a good indicator of fatigue!  But now let's go beyond that......

      Let's look at what happens AFTER July 19th.  For starters, there's a long period of time before we record data again.  That's because this athlete and myself went on vacation at different times during this period.  But let's pick back up with August 6th and note that this athlete took 1 week off from throwing while he was on vacation. Other than that, he was playing catch while using Motus Throw.  On August 6th, which is 2 1/2 weeks after his ACR went to 1.4, he still had a pre-throw Y Score of -0.5.  That's not a big difference, but it's still negative, nonetheless. Keep in mind, to this point, the Y Test has been the only test that's shown a negative Pre-Throw score so far.  

      Since ACR was in a good range now and the fact this athlete was trying to make a college roster for the first time, we wanted to return to a normal throwing routine.  However, as you should be able to clearly see, his Shoulder Force numbers were rapidly going the wrong direction.  We tried to pick and choose when he pushed since he was trying to make a team, but it wasn't an easy task seeing those numbers not return to a normal level.  This athlete ended up hurting his arm several weeks after getting back to college.  

      What does all of this mean and did we miss anything?  For starters, even though we were following ACR guidelines, I believe if we look back at Post Throwing on July 18th, there was something insightful that was reason for concern.  Although ACR was 1.28 Post-Throwing, the Shoulder Force Numbers were significantly down. While it was normal for this individual's Post-Throw Numbers to be slightly down, these were very significant.  The next day he didn't throw much at all and again, a significant drop.  What probably should have happened at this point was a period of low intensity throwing to give the arm a chance to recover.  At the time when Shoulder Forces were constantly low, his velocity readings during bullpens were constantly increasing.  So even though ACR was back in a normal range and velocity was increasing, the Shoulder Force Tests were providing data to suggest fatigue. 

      Maybe it takes a significant amount of time to recover once going above that 1.3 threshold. Maybe building chronic workload could've helped create more resiliency once high intensity throwing began.  Maybe high intensity throwing requires additional testing, such as what we did here, to get a full scope of fatigue.  While Motus does a great job of measuring ACR in terms of workload on the elbow, maybe tests like this provide insight into not only the arm, but the Central Nervous System.  What if a pitcher isn't well rested?  What if a pitcher is taxed from a hard lift the day before?  I'm not sure if anything is definitive, but I do know that this experiment provided insight to suggest that ACR matters!  To start easily tracking ACR, Motus provides a great, affordable product! 

      

      

Force Plate Data and the Dangers of Generalized Data

      FORCE PLATE DATA

&

THE DANGERS OF GENERALIZED DATA

      One of the easiest, and at the same time hardest things about using a lot of technology, is making sense of the data.  Reading studies that others have put out is fairly straightforward.  They've done the work and published the results.  You can then use those results to help shape how you go about training athletes. However, the one thing I've found is that when results get published, they tend to be generalizations. While generalizations can be useful, they often don't mean much for the individual. 

      Over the last few months, I've looked into Ground Forces quite a bit.  I've seen studies that suggests that there's not much correlation between Ground Forces and Pitching Velocity.  However, with a lot of discussion surrounding the significance of the front leg's ability to stabilize recently, some believe that front leg forces are very important.  

     With the help of a Wii Balance Board and the installation of a program to measure force and rate of force (http://www.rehabtools.org/strength.html ), I had to dive in an experiment for myself.  For the experiment, 7 pitchers were used and they each performed the same drill while Force and Rate of Force (RFD) were tested on each leg.  Each guy threw approximately 5 pitches (more throws were added when mis-reads occured) and averages were taken for each.  Force is measured in kg and RFD is measured in kg/sec.  When all was said and done, the results yielded results that were somewhat expected based off of other studies I had seen.  

Velo	BL Force	BL RFD
79.533	81	280.567
78.56	160.46	1087
74.85	79.8	251.25
80.85	135.025	679.325
79.2667	77.8333	275.7
77.9	101.75	621.3
76.35	108.7	679.5
Correlation with Velo	0.180611864	0.046609374

Velo	FL Force	FL RFD
77.1	137.667	865.4
78.08	141.68	867.22
75.55	129.65	1075.75
79.3667	174.9	1496.87
80.333	80.2	664.433
75.95	99.475	743.975
74.06667	113.3	803.3667
Correlation with Velo	0.1023267	0.197671198

      As you can see, all 4 of these correlations are very weak. That doesn't necessarily mean that the results aren't useful.  Look at the 2 different guys highlighted above in red & yellow.  They have a pretty similar max indoor mound velocity (90.8 vs 89.9). They both generated pretty similar velocity, force and rate of force with the back leg for this test.  The front leg tells a much different story.  One stabilizes fairly well (red).  The other doesn't (yellow).  Now to dive even deeper into how this data isn't a waste and show an example of the dangers of generalizations, let's take out one of individual data sets from above (highlighted in grey).  

Velo	BL Force	BL RFD
75.2	144	921.5
77.5	167.8	1180.9
82.7	167.4	1247.8
78.5	165.2	1075.5
78.9	157.9	1009.3
Correlation with Velo	0.687653072	0.770633418

Velo	FL Force	FL RFD
71.9	138.5	737.2
78.8	148.3	815.1
80.8	143.2	1034.5
77.7	142.9	744.5
81.2	135.5	1004.8
Correlation with Velo	0.1201869	0.793923004

     
      This is where the dangers of generalizations come into play.  Looking at the average correlation's previously, it would've been easy to conclude that there is no correlation between ground forces and velocity.  As you can see in this example, ground force data may be very important for this individual.  I'm hesitant to say it definitely is because these are limited data sets.  Since it's just 5 throws, the trend may not hold up over hundreds of throws.  The data could become more similar to the generalization.  However, if the trend were to stabilize with a correlation around a 0.45, there's still something here.  Everyone generates velocity differently.  It's up to those working with pitcher's to figure out how and then use it to help the individual develop! 

      Some things I want to look into further on this subject are how these numbers look when adjusting for the individual's bodyweight and whether or not looking into the differences between back and front leg forces can paint a picture of how well the athlete transfers energy!  For a fun experiment, 5 of these 7 guys have hit 89+ mph in games.  The other 2 are low 80's.  Try to figure out which 2 are low 80's from this data!!

      To conclude, there are benefits to generalizations.  They just don't necessarily mean anything for the individual athlete.  While ground force data may not yield significant correlations with velocity, the individual data could help paint the picture of how the individual generates velocity.  Everything can be useful.  It just may take a lot more digging than you hope for!  

Does Distance Running Negatively Impact Pitchers?

This is a study from 2008 on conditioning style and the effects on power output for a collegiate pitching staff.      The researchers divided the pitching staff into 2 groups of 8 over the course of a season. They had both groups do everything the same, except for conditioning (running portion of their strength and conditioning workouts).  One group was a sprint group while the other was an endurance group.  The sprint group ran 3 days per week and did 10-30 sprints at 15-60 meters with 10-60 second rest between sets.  The endurance group jogged or cycled (stationary bike riding) 3 or 4 days per week from anywhere between 20-60 minutes.      The results of this study may be very surprising to a quite a few people.  The results found that over the course of the season, the endurance group's peak power output dropped  by an average of 39.5 watts. The sprint group's peak power output increased  by an average of 210.6 watts.  *I'm not sure exactly what they measured for peak power output**      Pitching is all about power so why would we want to lose power by running distance?  If you enjoy running distance then by all means go for it.  But if you do it because you think it's best or because  someone is telling you to, it may be in your best interest to stop running distance primarily.  It will be much more beneficial for you to run sprints and gain power.      There are a lot of people out there that think the way to build up your endurance to pitch is by distance running.  We never move continuously for 30 minutes at a time while pitching so why we would train that way?  At most it will take a couple of seconds from start to finish during a pitch and then you will have at least 10 seconds of rest before your next pitch.  So sprinting, or really acceleration starts (10 yards or so) is much closer to emulating the energy system used while pitching.  So if you're spending your time  running long distances (unless you enjoy it), you may actually be decreasing performance.

Diving Deeper Into Spin Rate

Diving Deeper Into Spin Rate

         

          By now, most are aware of what spin rate is in its simplest form.  Many are becoming aware of the impacts of spin rate and many are seeking this information when evaluating players.  Naturally, this leads into the question of how spin rates can be improved.  Thus far, we know that certain substances being used on the ball or fingers can affect spin drastically.  Subtle changes to positioning or grip can also have small impacts.  The thought has been, for the most part, that your spin is your spin and there’s not a lot you can do about it. 

          A few months ago, I was given the opportunity to record and collect data using K Motion technology.  A 4 sensor set up that records angular velocities on pelvis, torso, upper arm and hand speeds. It also spits out charts on sequencing and plays the motion back in 3D.  After the data was collected, it was time to look through the data. 

          At first glance, I was surprised at the results.  What I expected to see was not true in a general sense.  This led to a lot of questions on my end.  My biggest concern initially was pertaining to velocity. I tried to find correlations with each of the peak speeds and velocity, but nothing really existed when comparing athletes.  Since nothing existed there, I started to think that the differences between angular velocities may be more telling.  That led me to thinking about Spin Rate. 

   
       I figured that since we don’t know much about what impacts spin, I’d start looking there because that could be highly beneficial if any correlations existed.  Before I get into what I found, I want to point out that when looking at Spin Rate’s, it’s important to take velocity into consideration.  To normalize Spin Rate, the use of Bauer Units are needed.   

    
       Before getting into what I found, I have to explain how I calculated Bauer Units.  A big mistake on my part was not collecting spin data along with the angular velocities.  To compensate for that, I took the peak fastball velocity and peak fastball spin rate recorded in the lab for each individual.  So for this study, Bauer Units = Peak Fastball Spin Rate/ Peak Fastball Velocity.

     
     For the study, 6 Collegiate or D1 High School commits were used.  Each threw between 6-10 fastballs a piece.  Average angular velocities were recorded for each.  Correlations are calculated at the bottom. You can see the chart here (wouldn't copy right when I tried directly on here). 

        
      I included correlations for Bauer Units, Velocity and Total Spin Rate below each column.  I did that to show that they can look very different and I think it may be more impactful for the correlations with Bauer Units.  As you can see, there are some strong correlations here.  More important is the right side, in my opinion.  I’m going to leave this sort of open-ended so that you can draw your own conclusions from this.  Keep in mind that this was a very limited test since there were only 6 pitchers tested.  My hopes are that more testing like this will be done and we can see if these correlations hold up with more input.  If so, we may be on the road to figuring out how spin rate is generated and maybe even ways to increase is.  Of course, like everything else, nothing is exact when looking at these sorts of things. 

*Per usual, I must make a side note that I’m home with a 4 year old and 1 year old so writing can get tough going back and forth. Please excuse any grammatical errors! If it seems like my mind was all over the place and unorganized, you’re probably right.*

Using K Vest to Compare Rotational Velocities During The Pitching Motion

Using K Vest to Compare Rotational Velocities During The Pitching Motion

          There are many aspects of the throwing motion that get talked about as being impactful to throwing velocity.  One such thing that has caught my attention lately is the idea of rotational velocities.  Clearly, baseball is a rotational sport and requires very fast rotational movements.  I was given access to a K-Vest for a few weeks so I wanted to collect a bunch of data to see if rotational velocities alone could give insight into throwing velocity. 

          K-Vest uses 4 sensors to capture data: a vest with a sensor on the upper back, a belt with a sensor on the tail bone, an upper arm strap with a sensor just above the elbow & a lower arm strap with a sensor on the wrist.  Rotational velocities of each show up as Pelvis, Torso, Upper Arm and Hand.  The measurements for each are Degrees Per Second. 

          I collected data on 10 different pitchers ranging in age from 10-21 years old.  2 are current college pitchers, 1 is an incoming college freshman, 3 are High School D1 commits, 2 are current High School Freshman and 2 are youth level players.  Each threw between 6&8 pitches while using the K-Vest.  Below are the average results for each pitcher, along with their peak indoor mound velocity and a ranking (1-10) in each individual category.

Current Level	Peak Indoor Velo (MPH)	Avg Pelvis Speed (deg/sec)	Avg Trunk Speed (deg/sec)	Avg Upper Arm Speed (deg/sec)	Avg Hand Speed (deg/sec)
D3 Junior (D1 Transfer)	90.7 (1)	835 (2)	1,220.2 (5)	1,357.8 (7)	2,768 (8)
HS Sr D1 Commit	90.3 (2)	598.7 (8)	944.3 (9)	1,489.5 (5)	2,953.8 (3)
D1 Junior	88.4 (3)	612.7 (7)	1,273  (4)	1,623.7 (3)	2,926.7 (5)
HS Sr D1 Commit	82.1 (4)	840.8 (1)	1,238.5 (3)	1,347.3 (8)	2,504 (9)
D3 Freshman	81.3 (5)	743 (6)	1,404.8 (2)	1,553.8 (4)	2,975.3 (2)
HS Sr D1 Commit	77.7 (6)	596.4 (9)	1,208.6 (7)	1,218.7 (10)	2,790 (7)
HS Freshman	71.9 (7)	795.2 (3)	1,149.2 (8)	1,447.5 (6)	2,855.5 (6)
HS Freshman	69.1 (8)	794 (4)	1,415.8 (1)	1,739.6 (2)	2,403.2 (10)
Youth Level	60 (9)	514.5 (10)	805.2 (10)	1,281.6 (9)	2,982.3 (1)
Youth Level	54.2 (10)	793.5 (5)	1,217.8 (6)	1,886.5 (1)	2,943.5 (4)

The chart below shows the correlation (R Value) between velocity and each rotational speed.  
     

Velocity vs Pelvis Speed	Velocity vs Trunk Speed	Velocity vs Upper Arm Speed	Velocity vs Hand Speed
.0136	.1683	-.3419	-.0513

Wrap Up

          From the charts above, it’s fair to say that there is no correlation between any of the 4 rotational speeds being directly linked to velocity.   

There are several thoughts that I have after conducting this test.  The K-Vest calculates K Angle, which is the difference between pelvis and trunk within the playback animation.  This should allow for actual numbers on shoulder/hip separation. I will go back and calculate each throws maximum K Value and compare that to throwing velocity.  That being said, the idea of shoulder/hip separation isn’t as cut and dry as just shoulder/hip separation at any part of the throwing motion.  The timing of it should be very important.  Also, the amount of bend in the trunk will impact shoulder/hip separation.  The K Vest also measures that, but I’m not certain on how to calculate it into a separation number.  When looking at other studies and trying to compare their findings with the data from the K Vest, I don’t know exactly how the measurements themselves differ.  This could lead to errors in comparison due to different ways the measurements are taken. I also don’t have anything to test its accuracy against. Even though there were no correlations to velocity, the K Vest has huge potential benefits for pitchers due to its ability to take the guesswork out of numerous angles throughout the entire delivery. 

Questions to Ponder

1.    Could the differences from metric to metric (pelvis to trunk, trunk to upper arm, upper arm to hand) give us real insight as to where guys are efficiently gaining their current velo and where they have inefficiencies?

2.    Does weight play a role in all of this?  If 2 guys have similar trunk rotational speeds but one weighs 200 pounds while the other weighs 150 pounds, will the 200 pound guy benefit more from that rotational speed?

3.    If there was a way to determine force into the ground while maximum rotational velocities were occurring, how much better would that insight give us into ball velocity?

4.    If we know when maximum negative trunk rotation occurs, would the K Value at that moment have a strong correlation with velocity?