What athletes don't know: how to squat

February 7, 2011 • Training

A funny thing happens when you walk into a weight room with an NCAA Division III athlete. It's kind of like witnessing a "Best of Bro-science" compilation, and by "best of" I really mean stuff so awful that you pray for short-term amnesia.

It isn't that what's happening is so aesthetically upsetting that you'd rather watch someone mop up a paint spill with facial tissue. Yes, a lot of it is hard to watch, but what really makes it uncomfortable is that many of them are 100% certain that they know what they're doing.

One of the freshmen said, "I think, by now, everyone pretty much knows how to lift." The irony gave me concussion-like symptoms.

The infamous ball squat.
At least I didn't have to slap anyone for doing this.

During my year coaching NCAA baseball players, I saw countless problems. Among them were some ugly rows, a shocking lack of pull-ups, the improper use of unstable surfaces, and a neglect of soft tissue work. The biggest problem, of course, was -- with the exception of a few athletes -- extremely poor barbell work.

For the most part, if anyone was doing barbell work, he wasn't doing anything but back squats, and calling them "half squats" would have been more accurate. Depth is easily the most prevalent problem with squats.

Poor depth results in quad dominance because of poor activation of the abductors, adductors, hamstrings, and glutes. Because no one wants to back off to a load that their weak posterior chains can handle, poor depth is the toughest problem to correct.

On top of that, poor depth is typically accompanied by excessive ankle dorsiflexion. Instead of bending at the hips, the athlete's knees track forward to allow for more knee flexion. This action moves the barbell closer to the ground, but does not improve the depth of a squat. In this position -- above parallel with ankle dorsiflexion -- the anterior part of the knee faces unnecessary sheer force which may cause pain and may eventually lead to injury.

Next to depth, the most common issue with squats is knee position. Apparently, someone is out there teaching young athletes to squat with a wide stance and feet facing forward. (I have a vague recollection of being taught that myself while in high school.) As the legs bend into the squat and approach 90° of knee flexion, this stance creates unnecessary valgus stress on the medial collateral ligament, which is really good if you're also into grinding your lateral menisci. Such a position suffers many of the same muscle activation problems as poor depth and causes a great deal more pain. (This is true for any flat-footed, standing position where the knees wind up medial to the feet.)

On top of these issues there are chunks of bro-science to deal with: counting that quarter-depth squat as a completed rep so you can tell people "I squatted 400 lbs", coaching cues like "look at the ceiling", and thinking that the Smith Machine is just as good as a barbell.

It takes about 10 minutes to teach someone the correct way to squat, but it takes quite a bit of practice to get it right. Anyone willing to do the work in the first place should be willing to do the work correctly. I'm not going to re-invent the wheel here by breaking the squat down piece by piece and telling you how to do it, but I'm also not going to leave you hanging.

Starting Strength, 2nd Edition, cover.

Mark Rippetoe and Lon Kilgore collaborated on one of the most popular strength training books of all time. It's called Starting Strength. You may have heard of it.

This book is the strength training bible for anyone that hasn't mastered the basic barbell lifts (squat, deadlift, press, clean). It tells you everything you need to know to do these exercises the right way.

If you're serious about strength training, you owe it to yourself to make sure you know what you're doing, and if you're a competitive athlete, there's no reason you shouldn't be serious about strength training.


Measuring pitch variability through PITCHf/x

January 3, 2011 • Analysis

For a while, I've been wondering what can be measured and analyzed using PITCHf/x data that hasn't already been measured and analyzed. A few things crossed my mind, but the most interesting thought was about the degree of variability of a pitcher's pitches.

It would be relatively easy to measure how much variability a pitcher has in velocity and movement if all things were equal. Of course, they aren't.

The two biggest problems for analyzing this type of variability are, as I see them, pitch type identification and park-to-park measurement error. Variability would mean little if half of a pitcher's "two-seam fastballs" are actually change-ups. Variability also runs into problems when parks like Kansas City -- whose radar gun readings are notoriously high -- are included in a data set with other ballparks.

Fortunately, if we only look at a single ballpark -- usually the pitcher's home ballpark because it has the greatest sample size -- park-to-park measurement error should be less of a factor. Without some form of park-to-park normalization, though, interpark comparisons shouldn't necessarily be taken at face value.

Additionally, 2010 saw a huge improvement in pitch type identification. While it still isn't 100% accurate, it is close enough on many pitchers to give me confidence while playing around with my ideas.

I haven't really dug into the numbers yet, but I will be looking to see if variability within a pitch type helps or hurts a pitcher. My gut feeling is that the number itself won't have much meaning.

To calculate the variability, I plan to capture the 95% window using two measurements of the standard deviation in both directions from the mean. By definition, this eliminates the outliers, but it will take some study to determine if that's really the measurement to use.

It may be beneficial to use a pythagorrean measure to find the variability for pitch movement; however, this would not appropriately model pitches that have greater variability vertically than horizontally (and vice versa, of course).

Look for a follow-up after I play around with this idea.


Thinking about run values

December 30, 2010 • Analysis

For some time, I've been looking for a way to appropriately integrate run values into the PITCHf/x database. I have read articles at Beyond the Boxscore, Inside The Book, and Cubs f/x, but I am no closer to getting what I want. Unfortunately, I lack the resources and time to find the answers myself.

Many tables have been published with run expectancies for the 12 ball/strike count states for various time periods. Tables have also been published for the 24 base/out states. Because the two tables contain different representations of the same data, there's no way to combine them. What I would like to see -- and I'm sure this makes me a sadist -- is a run expectancy table for the 288 ball/strike/base/out states.

Yes, that's one hell of a matrix to process, but there are two thoughts that seem to be the beginning of arguments against the two relatively simple approaches:

  • The thought against only using the 12 ball/strike count states table: a first-pitch strike in a bases loaded, no out situation has to effect the run expectancy more than a first-pitch strike in a bases empty, two out situation, right?
  • The thought against only using the 24 base/out states table: an 0-2 single with a runner on first base has to effect the run expectancy more than an 3-0 single with a runner on first base, right?

Admittedly, I don't have the knowledge or skills necessary to issue either of those thoughts as facts, so I have posed them as questions. It seems logical, though, doesn't it?

I think an appropriate time period for the analysis to cover is 1998-present -- since the last expansion.

Does anyone know if anyone has tackled this subject, successfully or otherwise? Is this covered in a book that I have not yet read -- possibly even one that I have read?

Consider this an open call for help in this matter.

[UPDATE: Tom Tango finally calculated the 288 states after the 2018 season, but his website was not working when I tried to grab the link.]


2010 Texas Rangers Win-Curve Revisited

December 28, 2010 • Analysis

In 2009, I published a win-curve that predicted Texas Rangers attendance for a given win level. The Rangers won 87 games, and my win-curve predicted 27,958 attendees per game for that win level. Actual attendance was only 27,641. The difference was 317, only a 1.15% difference.

This season, I updated my data and published another win-curve. The yellow dot on the graph marks the 2009 attendance level, and the red dot marks the 2009 win level.

Texas Rangers, Wins vs Estimated Attendance, 2010
2010 Attendance Prediction. For a full description, read the original article (link above).

In 2010, the Texas Rangers won 90 games. My win-curve predicted an average home attendance of 31,202. According to ESPN's numbers, the actual average home attendance was 30,928.

The difference of 274 attendees per game translates to only a 0.89% overshoot. The model was more accurate this year than last year.

As the season approaches, I will update the data and issue a new prediction.


A collection of thoughts on Stephen Strasburg

September 8, 2010 • Analysis

Yeah, I'm late to the party on this one, but I wanted to share some of what has been written in the blogosphere about Stephen Strasburg's elbow injury.

To start this post off, here are two quotes from my March 2009 analysis of his mechanics after watching him pitch against TCU:

His flexed elbow moves well behind his back and reaches shoulder height before the ball. From there, he must forcefully externally rotate his arm to get the ball to driveline height. This causes late forearm turnover and increases the valgus torque that occurs during reverse forearm bounce. This is a risk factor for his ulnar collateral ligament.

Strasburg has some of the common flaws of traditional pitching mechanics and carries with him the associated risks. These risks will almost certainly not affect his draft status because it could be 10 years before anything goes wrong.

The second paragraph is included to give context for my analysis.

Around the same time as my analysis, Kyle Boddy (then writing for Driveline Mechanics - the now-defunct SBN blog) compared Strasburg's mechanics to those of Pedro Martinez and Mark Prior. The three pitchers demonstrated striking mechanical similarities.

Notably, Pedro Martinez pitched relatively injury free for most of his career until his age 34 season, the one exception being rather severe shoulder inflammation in 2001.

Mark Prior, of course, was not as lucky. After initially injuring his shoulder in a baserunning collision, Prior suffered from a string of elbow and shoulder injuries. Some people blame the collision for his problems, and while it seems like a possibilty, it is impossible to know for sure.

After Strasburg's injury, Kyle wrote two articles concerning Strasburg and elbow injuries in general.

His first article (Elbow Injuries and What Causes Them (Stephen Strasburg Bonus Content!)) is a lengthy discussion of how horizontal shoulder abduction -- referred to as "scap loading" or "scapular loading" by some -- leads to increased horizontal adduction velocities that increase valgus stress in the elbow. He notes that while this clearly can't be labeled as the sole contributor to Strasburg's injury, it certainly played a role.

Kyle's second article (Strasburg, The Inverted W, and Pitching Mechanics) attacks some misconceptions and naysaying about the reputation of the inverted W position. In his discussion, he brings it back to Mark Prior by comparing Prior's peak horizontal shoulder abduction position to Strasburg's peak horizontal shoulder abduction position.

Finally, Eric Cressey offered his thoughts -- The Skinny on Stephen Strasburg’s Injury. Much of the article explains how important the health of the anterior forearm musculature (flexor-pronator mass) is in helping take valgus stress in the UCL. He briefly tackles overall tissue quality and links back to the great series he wrote on elbow pain.

Cressey puts some of the blame on the inverted W, but he is quick to mention that mechanical quirks like that aren't always a sign of impending injury.

A lot of people subscribe to the idea that a pitcher "only has so many bullets" in his arm. Cressey quotes J.P. Ricciardi and seems to agree with him. The idea is hard to argue with, since "so many bullets" could be 1,000 or 1,000,000 or even 1,000,000,000.

As a stand-alone theory, it leaves a lot to be desired, and leads to a series of questions:

  • How many bullets do I have?
  • What's the best way to conserve my bullets?
  • Can I get more bullets? If so, how?

With a boiled-down, unexplained idea like this, people are likely to misapply it by any number of means. That could include keeping strict pitch counts to protect the arm but still pitching year-round without rest. Alternatively, some people may wind up thinking that there's nothing they can do to extend the life of their arms and then neglect appropriate strength and conditioning.

Cressey, however, applies idea very well in a brief discussion of how to save those bullets. If you haven't read his thoughts, you should.

I have some of my own thoughts to share about Strasburg, but it may take me some time to pull them all together. Stay tuned.