The Great and Powerful Oz has spoken – Effective Velocity Pitch Sequencing is of No Value        Time to Pull Back the Curtain

Driveline ‘Scientific’ Study
In a recent blog article, Driveline Baseball did a “study”, at least their version of a study, of Effective Velocity or at least their version of “Effective Velocity” and shockingly, found no value in its pitch sequencing concepts. We’ll explore the evolution of Effective Velocity (Ev) and the massive amount of physical testing and MLB data that supports the actual  science then you decide how ‘effective’ Effective Velocity is.

As a result of this critique from Driveline  and seeing how far away they are from any new advances in the next frontier of pitch sequencing, I am announcing the release of the Ev Liquid Analytics Pitching Membership.  I was planning a more exclusive release but you will see why I am opening it up to the public instead as you read further into this very long article.  It will be a video membership with multiple weekly messages plus deep dives into the proof behind Ev.  This will include the Ev Liquid Analytics Pitch Sequencing video course to be delivered at the end of the third week of the deep dive messages to get you caught up with what you need to know to truly implement the only scientific pitch sequencing methodology.

I have also taken my advanced hitting programs off the market temporarily, again, you will see why as you read on.  I’ll still have the Time Training Level 1 & 2 hitting programs available.  For the immediate future, I am focusing on my latest ‘Hypothesis’ about the state of the game of baseball and the frantic search for the next frontier in pitch sequencing.

Effective Velocity Successful History – Ignore These Trivial Facts

  • The top pitching coach of the top pitching team in MLB currently, endorses Ev and used it to go from worst to 1st in virtually every meaningful pitching category. That franchise also won their first World Series along the way.
  • A different Ev minded MLB pitching coach took his staff from worst to near 1st in pitching in 2 consecutive years with 2 different MLB teams, both near the bottom to start. This might lead one to believe that any MLB team’s pitching staff could get significantly better when they implement Ev.
  • The Ev Microscope predicted virtually every Hard Contact of the 2017 Postseason and Carlos Pena presented the predictions before the first pitch on MLB Now.  After the World Series, Carlos showed the results of the hard contact predictions and 2 categories were right on the money and the other 2 within a couple of percentage points.
  • As a client, Carlos Pena lead the AL in HRs (tied) missing a month of the season.  Using timing based Ev game plans his K’s went down 25% during that period, BA up 25%, hit an HR every 8.5 at bats, his Well Hit Avg. was .369, a full 100 points higher than his BA. This is similar to the 2004 Ev Inside Edge study that showed a significant hard hit ball avg. higher than BA league wide.
  • 41 year Raul Ibanez helped the Yanks down the stretch with some very key homers and the following year hit over 20 HRs before the All Star Break with a little swing efficiency adjustment back to his earlier, more efficient self, along with a  simple Ev mindset change.   The swing efficiency advice was quite opposite of what you’ll see is the trending methodology of today.
  • The MLB hitting coach of the top offensive team in MLB in 2018 used Ev timing based concepts to dissect pitching plans of opponents on their way to a World Series championship. He told us on MLB Now in an interview that he gives a lot of credit for their game planning to Ev and gave me the nickname “The Father of Timing”.  This hitting coach also helped test and prove the existence of the effects of changing reactionary speed through location changes.
  • Names like Zito, Oswalt, Verlander, Cole, Morton, Keuchel and many, many others have turned their careers around implementing Ev sequencing concepts and true ‘Ev Pitch Design’ makeovers to one degree or another.
  • Dr. Tom House, first to be introduced to Ev and who coached Nolan Ryan, Randy Johnson and a long list of others, including a who’s who of NFL quarterbacks including Tom Brady. Tom, working with Bobby Valentine, introduced Ev Pitch Tunnels to Japanese and Korean national pitchers in 2005. Keep in mind that Korea & Japan were the number 1 and number 3 pitching teams in the first World Baseball Classic, shocking international baseball. Zero experts picked either team to be even close to the world’s international pitching leaders but Ev Tunnels were a huge part of their unlikely success and are still featured prominently with pitchers entering the MLB through Japan and Korea. By the way, Bobby Valentine won his first Japanese title that year also.
  • Ron Wolforth who is ‘the’ early innovator of modern velocity training and who is a very strong Ev advocate. His academy has produced hundreds of drafted pitchers, rehabilitating many pitchers back to MLB form and created seminars for coaches to get the education to advance pitching to the level of physical development it is today.
  • This year’s National NCAA Softball Champion UCLA and the University of Oklahoma was in the finals this year and won 3 of the last 6 NCAA Championships, all using Ev pitch calling. Oklahoma’s TEAM ERA for the year was 1.06 and UCLA’s TEAM ERA 1.39 with the Back to Back Collegiate Player of the Year Rachel Garcia, who had an ERA of 1.1 leading the way.  These teams accomplished this, while pitching at the equivalent of Coors Field every game. NCAA collegiate level fences are the same distance as when these players were 10 years old and with many fields equidistance all around, meaning little or no added distance to center field, making homeruns to any field a constant threat.
  • Oklahoma softball, while leading the nation in about 10 categories including ERA was also the best offensive team in the country. They lead NCAA in HRs and BA utilizing Ev hitting concepts.  We’ll discuss hitting coach JT Gasso’s uses of Ev hitting concepts later.
  • Rachel Garcia, NCAA Back to Back Player of the Year also hits and with her instructor added 11 MPH of Exit Velocity before her first Player of the Year season.  The increase was not over months but rather minutes and again, flies in the face of today’s swing efficiency model.  Todd Budke, Rachel’s hitting coach, was a USA Men’s National Softball captain and considered the best hitter in the sport for a decade facing pitch speeds up to 84-86 MPH from 46 feet.  I’ll let you do the reactionary equivalency math for that.  I’ll leave it at the highest velocity MLB pitchers would be considered ‘low velo’ guys in that league.
  • The Hitting Is A Guess video with Jay Bell, was the first to use Exit Velocity and Launch Angle as evidence to reverse engineer the swing movements that measured swing efficiency. It probably doesn’t mean anything that we added 10 MPH of Exit Velocity that day to Jay’s swing; he was a two time All Star and 17 year MLB veteran and that should not be possible. We took his ball flight from a minus Launch Angle average to the equivalent of a high line drive or a ‘Barrel’, yesterday’s invention but in today’s vernacular. This was not done over months of training but rather all in the same day proving it was a matter of swing efficiency. The fact that the year was 2000, almost 2 decades ago and we did all this “in a cave with a box of scraps” as Obidiah Stane would say.  Ev changed the hitting game using caveman tools to diagnose complex swing movements to improve swing efficiency and get significant immediate results. Immediate results are only possible through improving swing efficiency.
  • The Hitting is a Guess video introduced the concepts of underload/overload bats in training hitters, plyometric hitting concepts of heavy ball and dynamic balance and the ‘Self Leveling’ training atmosphere that ‘some’ in the industry have ’emulated’.  Hitting Is A Guess introduced precision in measuring swing output that lead to the discovery of Ev and virtually all of the modern hitting and pitch deception metrics being used today.
  • The tilt axis, spin characteristics, arm angle, wrist angle and other elements that help us understand the mechanics of the movement of pitches  and the original Ev Pitch Design concept came from Downright Filthy Pitching Book 3  in 2005-6 and not originally from the Driveline lab.  Barry Zito was the first to have an Ev Pitch Design makeover before the 2005 season.
  • The US Patent that was granted for “The Analysis of a Pitched Ball” that prohibits others from using any computer medium that uses release point data, measures speed with any device or method for capturing speed, gathers other key pitch-flight data and then adjusts the speed to better measure reactionary time for training, scouting and testing players.

Dan Aucoin & Driveline – “In spite of the belief that EV theory can provide pitchers with an ultimate sequence for success, the analyses above find very little indication that EV theory is effective at the MLB level.”

More from Dan – While disappointing, we find it important to acknowledge that pitch sequencing will likely always be an art-form to some extent, and that Perry Husband’s EV theory has helped pave the way for some of the more important qualitative discoveries surrounding pitch sequencing in the past decade. As a result, we do not view this analysis as a step backward in terms of what we know about pitch sequencing; instead, we view it as an important stepping stone in disseminating theory from fact and art from science.”

Ev – Perry Husband – After those words of inspiration, please allow me to bow out for those more important quantitative discoveries surrounding pitch sequencing in the past decade…..just kidding, pitch sequencing just got my full attention……let’s find out why Dan & Driveline believe ‘pitch sequencing’ is destined to be more of an ‘art form’ while I believe Ev Liquid Analytics Pitch Sequencing  is an ‘Exact Measurable Science’.  When you understand fully what Ev is and understand that every element has been tested in the lab, in real life and in the databases, you will believe as I do.

Anecdotal Evidence Aside – Implementing the ‘Scientific Method’
Driveline and the analytic world refer to these game changing silly facts of winning titles and what not as ‘anecdotal’, so we’ll assume for the moment, none of these things mentioned matter at all in the discussion of whether Ev Pitch Sequencing is effective or not. Let’s turn to the ‘scientific method’ for determining true and undeniable facts.

Let’s analyze the hard facts about Effective Velocity, using Driveline’s recent “study”, where they concluded that Ev pitch sequencing simply was not very effective. I was weirdly optomistic to see this study, even though I had to hear about it on the mean streets of Twitter, without even being tagged in the article release (insert emoji with a sad face for being left out).

Though the study was done without my permission as owner of that Intellectual Property, I can only assume they were not aware.   That little oversight aside for now, I was sure Driveline would have looked at Ev the same way it was discovered, by testing every element one at a time in their lab with hard evidence that points to absolute scientific facts and then using MLB data to back the science or disprove it.

Surely, Driveline didn’t simply pick out a few portions of ‘their idea’ of Ev and do a data based study alone…………..right? Even if they did use data alone, at least they bothered to get the definition of Effective Velocity correct and use only data relevant to put it to a fair test?  Or at the very least, did they repeat the same study that was done in 2003-4, when the only source for MLB pitch data was Inside Edge, containing almost 5 million pitches?

Driveline, being the self-proclaimed data driven science leaders of baseball, would have certainly done the due diligence of following the ‘Scientific Method’, you know  (1) gather data (2) guess what the data tells us – I mean, create a Hypothesis (3) test the Hypothesis by breaking down each of the elements that can be tested (4) re-test each of the elements of the Hypothesis and if the results are repeatable, you can now call it a scientific fact.  This repeatability has been referred to as the ‘cornerstone’ of science. Data studies alone would represent only the first step in this process.  Well, technically, you could count the step where they guess what the data means, as getting to step 2.  So did Driveline use the scientific method before publicly labeling Ev as ineffective?

Step 2 is sadly where my expectation of a fair evaluation and Driveline’s scientific methodology both went out the window. I was sure they would not draw misleading conclusions and share them with the public without testing each and every one of the elements involved thoroughly, as I did with Ev during the original discovery process of more than a decade.  I tested pitch recognition skills, reactionary abilities of hitters to see if they were capable of reacting to all speed pitches, tested the effects of location adjusted speed differentials, did a 2nd version of the location adjusted speed differential test with a current  MLB hitting coach, and then did a 12,000 plus at bat controlled experiment just to validate the visual and reactionary findings.

This was all done before ever going to the MLB databases to see if the test results made sense according to MLB data.  What did Driveline use as their foundational research materials?  They used Xan Barksdale’s completely incorrect interpretation of Ev’s Time Unit zone graphic to come up with their calculations for EvMPH………..from a Youtube video.

The below graphic was taken from the Downright Filthy Pitching Book 1 that was intended to show the Time Units, which both Xan and Dan Aucoin of Driveline mistook for MPH speed equivalents.  They also neglected to research that every speed has its own EvMPH location adjusted speed value because 100 MPH pitches move through space just a little quicker than 70 MPH curveballs.

Above is the ‘actual’ Ev Zone chart for Time Units (TU) that are appropriate for each zone box.  A TU is basically how long it takes a pitch to travel 18 inches at a given speed.  So this chart changes every 1 MPH and it is grossly wrong to assume the numbers represent MPH rather than TUs.  This is the Intellectual Property of Ev and it is patent and copyright protected.   

Shouldn’t a ‘science based’ organization use something remotely close to the true definitions of Ev, rather than base their fundamental assumptions on a prop graphic taken out of context for an online YouTube video?  I do think that Xan was giving a positive review of Ev, although the speed adjustments were off, I appreciated the shout out.   However, he is not an Ev spokesperson nor does he fully grasp that crucial fundamental EvMPH value and it appears neither does Dan Aucoin & Driveline.

Ev Analysis Background – Inside Edge Pitch Database Study
The next step after thoroughly testing all the elements of Hitters’ Attention was to compare it to MLB data.  The field testing was done with controlled simulated at bats using Ev Efficient and non Ev Efficient sequences from multiple common pitcher types and done with 5 different control hitter groups, one group  each day and the pitcher type faced all groups for the entire week.  This insured that we saw the same pitcher type versus all 5 control groups to compare and contrast.

The goal with the MLB data from the 2003-4 season was to see if there were similarities to the testing and the at bat results we found.   Would applying ‘location adjusted speed’ to pitches show that hitters are actually hitting pitches that have a common reaction time, regardless of pitch type?  For example, fastballs away and sliders in would have similar, if not exactly the same ‘True Reactionary Speed’ or EvMPH and would likely be hit hard equally or close.  If these showed to be hit hard on the same EvMPH, it would be positive proof supporting the test results.

One of the elements of the hypothesis was that faster off speed pitches, such as some changeups, split finger or cut fastballs that were slightly mislocated, were somewhat ‘accidentally’ running into hitter’s bats.  Meaning, hitters swinging at what they thought was a fastball but was an off speed pitch that had moved into the path of the bat which was heading to the fastball contact point.

Ev Crossovers are pitches whose actual reactionary speed has crossed over into the same speed as another pitch type or a common speed range.  If you have ever listened to MLB hitter interviews, you have heard them say their approach is to work off the fastball.  To quote Ron Wolforth, “when you’re a hammer, the whole world is a nail”.  To hitters, all pitches are FBs.  Virtually all interviews mention that because it is what hitters base their game plans around and as long as pitchers cooperate with a lot of FBs and pitches within 6 EvMPH, this works great.  This is why MLB, although very late to the party – 15 or so years) finally cut the FB use to closer to 50%, like the Downright Filthy Books advised about 5 years before the Pitch f/x database existed.

A prevailing idea that is still taught to this day at the highest level of the game, is to time the fastball away and ‘react’ to the fastball in.  This idea is really a game planning strategy mixed with a physical swing mechanic combined.  It is describing a ‘swing-adjust’ approach (much more as you read on) to dealing with the speed changes prevalant in MLB games.  This very cliche notion is why I began the exploration of Ev.

It seemed virtually impossible for hitters to react within the time it takes to recognize pitches, adding in  the time to swing versus all of the huge speed differentials that pitchers could possibly create.  So I began testing that ‘hypothesis’………… element at a time for a decade.  The first was the idea that speed is very relative.  Einstein, describing his theory of relativity, asked us to look at the speed of a train based on where we were standing.  On a hillside the train was creeping.  Next to the tracks as the train zoomed past, it was moving very fast.  Inside the train, time seemed to be standing somewhat still.  The speed of the pitched ball depends greatly on many different variables that EvMPH measures.

Clearing Up the Definition of Ev Efficient Sequences
There is one Ev element from the original books that has evolved over the first few years.  Early on, pitches with large speed spreads but not in an Ev Pitch Tunnel, were called Not At Risk (although ill advised more than 10% – in the book).  These would include pitches like curveballs that were much slower than FBs but could not share an Ev Tunnel if they were in the zone.  These types of off speed pitches tend to ‘Freeze’ hitters due to the shock of how ‘out of the zone’ they look just after release.  Freezing can be good but pitches easy to identify are soon adjusted to.  These pitch types are now included in the At Risk category of ‘No Ev Tunnel’ in the rule of 50/20/20/10 (much more later on this).

It became evident that too many of these, (even though that was stated and the Ev advocates are all well aware), would result in early identification and would be hit hard.  I have since updated the definition accordingly but not done a new book edition at present.  The primary change is that now the dangerously easy to identify pitches are called At Risk.  Carlos Pena spelled  out the Ev Efficient definition very clearly on the MLB Network in multiple essays on the subject.  Ev Efficient Pitch Sequences are pitches thrown using the maximum deception with 6 EvMPH between pitches.  That is the simple version but there are many layers to the Ev Onion and Ev Liquid Analytics is the most advanced use of analytics with the optimum Ev Pitch Design to eliminate patterns and create the maximum deception to attack hitters at their absolute most vulnerable areas at the speed of the game.

Above is Carlos Pena on MLB Network illustrating how different pitches looked out of the graphic pitcher’s hand when they did not have an Ev Tunnel.  The top arc of the illustration represents a pitch with ‘No Ev Tunnel’.

There simply isn’t time for hitters to react at 100/100 and cover all the speeds possible……………without help from the pitcher.  Meaning, when pitchers stop ‘hitting bats’ and making pitches easier to identify and time with  inefficient sequences, hitters are forced to time pitches on their own.  They are simply not as good at it as Driveline would have your think with their 11 inch plate coverage theory and multiple studies negating the effects of Ev on reaction time.  My early testing showed hitters could handle about 5 EvMPH of speed changes at or near 100/100.  MLB data showed that it was about 6 EvMPH that they reacted within at their highest level.   The swing-adjust method is suited for pitch speeds that clump together in one target rich environment with close proximity and close EvMPH.  That target rich environment is virtually non existent with Ev Efficient Sequences.  When the random pitch sequencing goes away, so does the accidental contact to a large degree, which is staggeringly higher than you know.  This Inside Edge Ev Analysis was designed to find out.

Ev Analysis Study
Inside Edge created query software to look at the statistics produced with pitches from 60 EvMPH to 100 EvMPH.  After updating the computer database with accurate EvMPH adjustments, every individual speed was queried separately with 5 basic stats that were the closest to ‘timing based’ as possible for the times.  I chose Batting Average (BA), Well Hit Average (WH Avg), Swing & Miss % (Miss %), Home Run % (HR %) and Groundball % (GB %) but not for the same reasons that analytics uses it for.  Ev looks at groundballs for what they truly represent, early contact, which helps pinpoint when hitters are nearing perfect on time contact.

  • Dan Aucoin & Driveline – Results: We found little evidence supporting EV Theory’s claims using 2015-2018 MLB pitch-level data
    Ev – I found a lot of evidence both in the Inside Edge database in 2004 as well as the Statcast database from 2014 to 2019.

Above is an individual speed query of 90 EvMPH from the special Ev Analysis software that Inside Edge created for the original study. This software gave me the ability to get results on these ‘timing based’ stats for each individual EvMPH speed. To get the ’true reactionary speed’ of pitches, the correct location adjustments were added in. This was all pitch types that equaled 90 EvMPH ONLY.  It could have been a FB at 95 MPH located down and away or an 88 MPH cutter located in an area where it added 2 EvMPH or a slider at 86 MPH located in an area that equaled 90 EvMPH. 

All pitch speeds from 60 EvMPH to 100 EvMPH were run individually and then results were put into a spreadsheet to compare. There were only 360,000 pitches in their almost 5 million pitch database that had exact pitch speeds with exact locations, pitch type, result etc…….  TV telecasts did not always show pitch velocities at that time, which was the only method for gathering pitch data.  Some of the other studies done by pitch type included the entire database without speeds but with all other results of location etc….. We will dive deep into those studies later for those participating in the membership.

This was done in this fashion to show that it was the ‘true reactionary timing’ of each and every speed pitch that was causing hard contact.  Turns out that 90 EvMPH pitches produced the highest peak of all the key stats but that was many pitch types in many locations of the zone, but with the same  EvMPH.  This is hard to wrap your mind around at first until you add the timing of the bat path, which we will explore shortly.

  • Dan Aucoin & Driveline – Results: Regardless of the EV estimates used, it is unlikely that EV sequencing theory is supported at the MLB level
  • Ev – Seems that when you get the Ev estimates from the source rather than a YouTube video from someone using the Time Units as EvMPH, they come out exactly as described.

Above is the section of my Ev study of the Inside Edge database that shows the speed range that hitters are most geared to with the epicenter at 90 EvMPH. Notice the Well Hit Avg. is .330 with the BA at .285 at the peak. That is eerily similar to Carlos Pena’s trend in 2009 of a Well Hit Average 100 points higher than his BA as he got better and better at 100/100 contact, with the difference being he was doing it on purpose while much of this data is Ev Crossovers being hit hard.  

This shows the peak happening when hitters were getting closer to their best timing using their ‘A’ swing (100/100), which is also why the HR % peaks there.  Swing and Miss% is the lowest when hitters are on time, which peaks at 90 EvMPH.  In 2004, 90 EvMPH is the most geared to reactionary speed…….period.  Now that Exit Velocity is available, it is even more clear when you look through the right lens but the peak may be higher due to velocity increases.  I would love to be a fly on the wall when all the computers in the game try this experiment (in violation of over half the nearly 90 claims of the Ev Patents of course) and find that Ev was right out of the gate. 

This also shows the fairly uniform decline on either side of the peak, indicating the hitters’ timing is getting worse because they are less ‘on time’ in both directions.  The Well Hit Avg. and Swing and Miss % are the most telling because they are the most reliant on timing, Well Hit Average being the 2004 equivalent to Exit Velocity. This study certainly shows Hitters’ Attention has a peak and a speed range that hitters perform best within.  Without the lab testing supporting the findings however, this would only be a hypothesis using data alone.  Good thing we had both and now we have all that anecdotal evidence of teams and players having success to support the ‘science’

There were other peaks in this data study due to certain pitch speeds being hit hard, such as off speed pitches at common velocities that are easily identified. Pitchers tend to throw curveballs at similar speeds league wide and when in the zone, visually ‘pop’ over the fastball line at a common speed. Easily identifiable off speed pitches tend to be hit hard so that shows up as a peak in the data in weird places, but it still makes sense through an Ev lens.  This was one of Driveline’s attempts at disproving the + or – 6 EvMPH portion of Ev, a misunderstood element taken out of context.  Yes hitters can handle much larger than 6 EvMPH spreads but primarily when the pitches are inefficient such as we just described.

Inside Edge Database – Ev Analysis
It is very important to note that without the proper speed adjustments (Time Units – TUs) for each pitch speed, adjusted based on elevation and lateral location, movement etc…. the database would simply show a mix of random results not related to Ev……a giant mixed puzzle.  Like the Griswold’s wad of Chrismas lights and the data studies that Dan & Driveline ended up with.  The Ev Analysis showed very clearly that Hitters’ Attention exists and pitches are hit hardest at the peak and there is a 6 EvMPH speed range that hitters produce the best results within.

Random pitch sequences not based on timing and Ev Deception is like knowing how the chess pieces move but no idea about strategy.  You make some randomly bad/OK/good/great/awful moves but you have no idea which is which so you can’t repeat the great and are doomed to repeat the awful and hope that your opponent is doing the same.  You can even use the awful move and think that it’s great if you don’t know the EvMPH.  Pitchers are doomed to throw into a barrel unless they understand how pitch speed and barrel times match up.  This random sequencing method makes it virtually impossible to predict the outcomes because it is about random luck of the draw.  Only EvMPH can decipher why the hard contact continues to happen in the same exact ways.  Welcome to the world of historical analytics with Dan & Driveline telling us how the horsie moves.

Studies done using all pitch types/speeds/locations without using EvMPH, tend to show the stats for hard contact gravitating toward the middle of the zone. This is why basically every hot zone shown, features the same general area as hot.  The BA studies I did in 2004 show the highest is in the middle/middle box and decreases away from the center fairly evenly and also consistent year to year.  This is due to the mixing of pitch types/speeds/locations that blur into the same reaction times,  what is referred to as Ev Crossover Speeds. Another interesting note is the same boxes in the zone tend to produce the same results every year which is another indication of Hitters’ Attention.  If Driveline was correct the results  would be random and almost never have the same results in the same boxes.

Dan Aucoin & Driveline – “Instead, we observe that batters would need to possess nearly 6 feet of plate coverage to be able to hold reaction time equal for all three pitches! More specifically, if we assume that the point of contact for a 90 mph fastball down the middle is 23 inches from the back edge of home, this would mean that an 86 mph pitch would have to be hit 68.6 inches in front of the back edge of home, on average, whereas a 94 mph would have to be hit 1.9 inches behind the back edge of home, on average. For comparison, our newer estimates assume that batters would only need roughly 11 inches of plate coverage to hit those same three pitches at a similar reaction time.” 

Ev –  Obviously Dan & Driveline has misinterpreted the concept of EvMPH  or the idea that the same swing path is going to pass through multiple contact points as it goes through the hitting area reaching each one at different times.  If you were to place a ball on a tee at bat #42 below and one at bat #35, the same swing would hit them both at different times but the same swing path.  Now imagine one pitch slower and inside at 42 and the other faster and away to arrive at 35 at ‘relative times’ during the swing.  EvMPH is the decoder ring to avoid the Ev Crossovers running into bats every night.

This is a great pic borrowed from the internet that shows relative time extremely well.  There are very roughly 42 bats leading up to the contact shown and many more bats after.  The swing takes about .13 to .15 seconds from about the time the bat starts down into the zone to contact, but which contact?  It takes close to .15 seconds to get beyond 42 and about .13 second right at bat 42 and roughly .11 seconds at bat 35. 

If you line up the baseballs as pictured, there are about 210 baseballs from 54 feet (release point of many pitchers but it varies quite a bit) to bat 42.  If we say the ball pictured is a RHP slider at 87 MPH it would get to bat 42 at approximately .43 seconds.  If a second pitch was in the same Ev Pitch Tunnel but moved arm side from the RHP to bat 35 at 90 MPH also arriving at about .43 seconds.  The faster pitch doesn’t lose EvMPH speed  and the slower one doesn’t really gain speed, but ‘Effectively’ (thus the name) they might as well.  All pitches lose speed on the way to the plate but in the EvMPH sense is what that meant.  Add in a third pitch at 94 MPH and have it be a sinker with arm side movement also, it would ‘run into’ a bat somewhere between bat 28 to 30 earlier in the same bat path. 

You can think of EvMPH time as subtracting bats or adding or subtracing baseballs, speed change or time change or whatever works for you.  Either way, there are a lot of possible contact points and EvMPH deciphers which pitches will run into which bats at which locations at a given speed.  The theory of relativity for pitching, if you will, however no longer a theory. 

There are a few bats on either side of perfect that work pretty well but still only one at 100/100 contact point.  This means the point when the hitter is at 100% of his mechanical efficiency and 100% on time.  Every contact is either at 100/100 or early or late to one degree or another.  EvMPH are measured from that 100/100 perfect contact point.

When pitches share a common EvMPH, such as the combo described above, they would all be considered 90 EvMPH for example.  With the Ev Analysis study from Inside Edge, all pitches that could possibly equal 90 EvMPH would be used in that query with the selected set of stats.  This is why the numbers line up so perfectly with the reactionary test results (and not Driveline’s atrocious Ev copy), experimental at bat results and all the studies I have ever done, including the Ev Analysis Inside Edge Study in 2004.

Note that the example pitches fit neatly because they are in the exact bat path shown.   Others would have different elevations or lateral locations, they would be in the ‘general space proximity’  at the same time the bat is passing by and there are a lot of accidental contact and/or hitters adjusting to the small space differences.  This would be akin to the catcher being crossed up but the glove is right there to still make the catch, although awkwardly.  This is why you see so many slight hesitations in hitter’s swings as they adjust to these small space differences.

When the bat is already headed into  the general area, the hitter adjustment is smaller.  They think they are adjusting to 9 MPH Initial Velocity speed differentials but in essence they are adjusting to 0 MPH speed differentials just slight spacial adjustments.  How do we know this?  All of it was tested in the 12,000 at bat experiment using both sets of speed differentials ahead of time before ever looking at the database info.  Put the same 9 MPH speed differentials in an Ev Pitch Tunnel and watch the same hitter fail to hit either pitch hard.  We see that every day on Twitter with the Ev Tunnel overlays, which Carlos and I were doing in 2009 to uncover some of the Ev Illusions pitchers use.  I was using Ev Tunnel overlays from about 2001-2 when discovering them and their effectiveness, Twitter wasn’t even a thing just yet.

Consequently, low FBs usually have a much lower swing and miss and higher Exit Velocity and BA because they are headed into bats virtually all the time.  Oh and because the paths are similar, it yields a lot of homers too.  There are about 20+ reasons why the low and/or away FB is the most At Risk pitch.  FB down and/or away is the single most ‘Hanging’ pitch of all.  One bat path, 3 different Initial Velocity (IV) speed pitches, all being crushed at the same EvMPH.  Conversely, Ev Liquid Analytics Pitch Sequencing takes a multitude of factors and simplifies into a flowing, ever changing Ev Pitch Design that customizes every pitcher into the very best version of him or herself, avoiding accidental contact like the plague.

Driveline says that when spin is considered low, they advise locating down with the FB, the single most At Risk pitch in the game.  Spin rates mean very little in this example unless they are enough to change the timing.  Some spins do change timing but those are also figured into EvMPH.  A slider moves more than normal, it goes into a new ‘time zone’.  That’s how Carlos Pena could turn around a Justin Verlander high spin FB at 96 to 100 MPH at the top of the zone for a homer when only about 8 lefties in JV’s career had ever done that.  If I remember correctly, it was about 8,000 FBs.  Don’t quote me on that but trust it was rare.

  • Dan Aucoin & Driveline – Results: We find it highly unlikely that a batter perceives a ~10 mph spread in velocity due to varying contact points by locations –
  • Ev – Below there is a 3 swing overlay of a JMU hitter in a fall game with all 3 bats in front of the plate all at similar but slightly different points in their path.  The hitter is roughly 8-10 EvMPH late to the 100/100 contact point where his bat (one of 3) is now out in front of home plate for the highest FB.   The high FB was ‘percieved’ to be about 88 EvMPH which is about 10 EvMPH late.  He is only about 2-3 MPH early on the changeup.The range between changeup and FB  is about 12-13 EvMPH between them.  Notice that there are no baseballs in the area where the Driveline 11 inch bat path around the front edge of home plate.  That 11 inch area is where pitchers featuring Ev Crossovers go to get hit hard by marginal hitting plans.  Just as the airport’s time is the only one that counts for flight time, so is EvMPH for contact time.  You are there at 100/100 or you are early or late.  EvMPH tell you how much.  This side view shows the bat well in front of the batters box and the high FB very near the back of the box, rough estimate of 6 feet, maybe slightly shorter.The metric is exact, the measuring tools are not quite perfect for this just yet.  We can’t be certain exactly where in space these baseballs or bats are but it is very near 10 EvMPH of time due to the space.  Also, please stop calling it ‘Perceived’ velocity, unless you are able to read minds you have absolutely no idea what the hitter percieves unless he swings and we have a way to measure the distances away from 100/100 he is.  Until then, this pic is the best we can do right now.  I have seen futuristic 3D tech versions of this can track the ball at all points but using it for adjusted reactionary speed is Ev IP.

This young hitter for JMU has just been introduced to an Ev Efficient sequence.  This is the exact opposite of ‘hitting bats’.  Ev Efficient sequences aviod bats that are poorly timed with poor or no strategy.   It takes 72 baseballs to swing the bat and he is late by 24 baseballs or 6 ft or about 9-10 MPH late in this picture depending on the speed of the pitch, exact place in space etc…….  His bat is out front where he needed to be for 100/100 or close and the ball is a very long distance behind that point.  Dan & Driveline can tell us that EvMPH are off but this picture and my patent screams otherwise. 

The EvMPH basic estimates are conservative and keep in mind that the patent is written so the other variables of individual pitchers and hitters can eventually get measured for absolute precision.  It astounds me that a science focused baseball world and a researcher for Driveline (who pride themselves as baseball scientists) is baffled by simple time and distance measurments.  The perfect contact does exist for every location at every speed (test it with Exit Velocity) and the ball is traveling at a speed someplace in space in relation to where it was released and the bat’s place in space.  The swing is cheated or efficient and the timing is perfect or late or early by X amount.  In this case, X = EvMPH.        

Thanks to JMU pitching coach, Jimmy Jackson who shared an overlay with 3 Ev Efficient pitches from a fall game  in one at bat.  The ball above the hitter’s ankle is a FB he was late to.  The ball above that one is another more elevated FB he is also obviously late to.  The ball in front of home plate is a changeup that he was early to, missing beyond the end of the bat.  All the swings had close to the same timings but the EvMPH of the pitches did not match.

This combo of pitches made him early and late due to Ev Pitch Tunnels and Ev Speed Differentials, the opposite of hitting bats and the opposite of most all of Dan & Driveline’s conclusions from their study.  You can see the HUGE spacial difference in each of these pitches. The FBs were in locations that were adding EvMPH and the changeup was losing EvMPH but out of the same basic Ev Pitch Tunnel.

Instead of coming together like Ev Crossovers in the previous example, they were moving apart creating larger Ev speed spreads, later pitch recogniton, earlier decision time and a vast amount of other Ev Liquid Analytics sequencing advantages.  In Driveline’s review of Ev, they found it ‘unlikely’ that these factors would matter at all, which I find offensive and laughable at the same time.  Driveline and analytics doesn’t think timing matters all that much, based on their conclusions and Ev is 100/100 about timing.

To be continued………………….this study had to be broken up in multiple parts because there is  a boatload more eye opening studies using actual science to compare to the findings of Dan & Driveline.  Think of it as the first of the free content coming soon along with the premium content in the membership.

Available now……….the first 100 members  will get a 50% discount on the yearly premium content membership.

When pitchers develop great stuff, they are ‘Nasty’.  When they learn to pitch, they become ‘Filthy’.  It isn’t about ‘better stuff’………….it’s about using your ‘stuff better’.                Ev Liquid Analytics