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Thursday, March 19, 2015

7 Charts that Show the State of Youth Sports in the US and Why it Matters | The Aspen Institute




The declining participation numbers are the most disturbing chart in this article, IMO. More important than Wins and Losses to a youth sports coaches record should be the number of kids who return to the sport in the following years versus drop-outs (See: Why Johnny Hates Sports). Your retention rate should matter more than your W-L percentage.

from Aspen Idea Blog:
7 Charts that Show the State of Youth Sports in the US and Why it Matters | The Aspen Institute:





7 Charts that Show the State of Youth Sports in the US and Why it Matters


By Alice Lee

In the words of US Olympic Committee member Anita DeFrantz, “Sport is a birthright.” From combatting the growing obesity epidemic to promoting camaraderie and confidence, sport plays a pivotal role in helping kids become healthy on all levels —physical, social, emotional, and cognitive. 
Unfortunately, the number of kids participating in sports is decreasing. According to the Sports & Fitness Industry Association (SFIA), the number of kids that played a team sport on a regular basis decreased from 44.5 percent in 2008 to 40 percent in 2013. To reverse this continuing trend and to make youth sport a national imperative, Project Play at the Aspen Institute aims to make sport more accessible and engaging to all youth across the country. Below is an infographic overview of how we can change the current state of sports. 
The Current Sports Problem
In the time period from 2008 to 2013, sports participation and fitness have significantly dropped. Nearly 3 million fewer children have played basketball, soccer, track and field, baseball, football, and softball, and less than 1 in 3 children between the ages of 6-12 participated in a high-calorie-burning sport or fitness activity three times a week, according to SFIA data.
Household income is one major indicator of sports participation. In urban or poorer areas, schools often provide fewer sports options and opportunities for their students than suburban or more affluent areas. Additionally, youth from homes in the lowest income bracket ($25,000 or less) are at least half as likely to participate in sports such as football, lacrosse, and swimming than youth from wealthier households ($100,000 or greater). Simply put, families that can afford more can allow their kids to play more.
With the decrease in sports participation, the current public health status of the US is likely to become more precarious. Lack of activity is closely linked to obesity, and today obesity is one of the biggest problems plaguing the US. Currently, the US is the country with the highest number of obese youth among 15 of its peer countries. For children ages 5 to 17, nearly 40 percent of girls and 35 percent of boys are obese.
Sports participation will certainly combat the growing obesity epidemic, but youth sports also provide a number of other important benefits. In a study done by University of Illinois researcher Dr. Chuck Hillman, physical activity was shown to activate the brain: After children went on a 20-minute walk, MRI scans of their brains showed the highest amount of neuro-electric activity (shown in red below). 
Research has also shown that sports provide compounding benefits for active children. When children enter sports at an early age, they experience many lifelong benefits: they are one-tenth as likely to become obese, 15 percent more likely to go to college, and they are more likely to be productive adults than children who do not play sports.

Solutions to Building Athletes for Life
To increase sports participation, Project Play outlines eight strategies so that children from all backgrounds are able and eager to get into the game. Read the report to learn more about these possible solutions. 
RELATED CONTENT:
A New Vision, Platform for Youth Sports in America
'via Blog this'

I just did my baseball and softball re-certification for umpiring HS sports and in addition to the concussion protocols, there is now a section on arm injuries. This is now a focus because some of the numbers the IHSA cited were truly alarming:

  • 45% of pitchers under 12 years old experience chronic elbow pain
  • UCL reconstruction ie: Tommy John surgeries have increased over 700% in the last decade for adolescent pitchers. 
Those are stunning numbers. They also offered some worthwhile recommendations to turn the tide. 
  • pitchers should have somewhere between 2-4 months off from pitching competition, and I would add to that, preferably playing another sport in order to prevent mental burnout.
  • Less than 100 inning pitched per year. ( note: and to me, 100 IP seems like a lot )
  • Pitchers should play any other position except catcher. ( coaches may be reluctant to implement that since they want a solid arm behind the plate to combat steals )
Some of the commentary I hear on the radio or read in periodicals is simply abysmal as well, so I feel for concerned parents trying to sift through some of the garbage and get good information for their kids. 

Collegiate Baseball has been running a series of articles from a coach who links the rise in Tommy John surgeries to the beginning of the PED era, even going so far as to pinpoint 1994 as the exact year that both problems began to run in the wrong direction across the baseball landscape. IMO, a 
stunning misunderstanding of  cause and effect, correlation and causality.

Hey, why not? I look at the chart below, I see a rise in TJ surgeries beginning around 1994 and I say to myself, "Hey self, isn't that about the time the steroid era began in baseball?" Then I grabs me my pocket calculator, I put 2 and 2 together and I come up with 5. However, I do continue to stand behind my observation that every time the rooster crows, the sun rises. Therefore, the rooster caused the sun to rise.  



I thought the consensus and prevailing explanation was that pitchers were not partaking of the juice, so to speak, therefore they were being cheated by hitters who were. I can tell you from being involved in strength training and conditioning somewhat that pitchers were reluctant to weight train, which leads to them being less likely to have a need for muscle building or weight training, The balls only five ounce for crying out loud. Therefore, weight training most likely would have little or no causal effect on the rise in Tommy John injuries. Especially among adolescents and pre-adolescents where weight training would almost have no effect. And yet we see a rise in surgeries migrating to the lower ages.

"We confuse coincidence with correlation and correlation with causality". Can I get an amen on that?
When we confuse coincidence with correlation and correlation with causality we end up with spurious correlations or relationships just as sure as when we assume we make and a$$ out of 
ourselves. Here it's just good comedy, in the sports arena it's tragedy. 

Odd Couple - "My Strife in Court" (Assume Scene)



Again, I sympathize with parents. There are a lot of pseudo-experts out there and the stakes are high: your children's health and well-being or a chance at a college scholarship? So lets not do that A$$UME stuff  anymore, OK?

This stuff is too important to keep screwing up.
    1. Spurious correlation is often a result of a third factor that is not apparent at the time of examination. Spurious comes from the Latin word spurious, which means illegitimate or false.
    In statistics, a spurious relationship (not to be confused with spurious correlation) is a mathematical relationship in which two events or variables have no direct causal connection, yet it may be wrongly inferred that they do, due to either coincidence or the presence of a certain third, unseen factor (referred to as a "common response variable," "confounding factor," or "lurking variable"). Suppose there is found to be a correlation between A and B. Aside from coincidence, there are three possible relationships:
    Where A is present, B is observed. (A causes B.)
    Where B is present, A is observed. (B causes A.)
    OR
    Where C is present, both A and B are observed. (C causes both A and B.)


You would think by now that we could say unequivocally what causes what. But the question of cause, which has haunted science and philosophy from their earliest days, still dogs our heels for numerous reasons. Humans are evolutionarily predisposed to see patterns and psychologically inclined to gather information that supports pre-existing views, a trait known as confirmation bias. We confuse coincidence with correlation and correlation with causality.
For A to cause B, we tend to say that, at a minimum, A must precede B, the two must covary (vary together), and no competing explanation can better explain the covariance of A and B. Taken alone, however, these three requirements cannot prove cause; they are, as philosophers say, necessary but not sufficient. In any case, not everyone agrees with them.
Speaking of philosophers, David Hume argued that causation doesn't exist in any provable sense. Karl Popper and the Falsificationists maintained that we cannot prove a relationship, only disprove it, which explains why statistical analyses do not try to prove a correlation; instead, they pull a double negative and disprove that the data are uncorrelated, a process known as rejecting the null hypothesis.
With such considerations in mind, scientists must carefully design and control their experiments to weed out bias, circular reasoning, self-fulfilling prophecies and hidden variables. They must respect the requirements and limitations of the methods used, draw from representative samples where possible, and not overstate their results.

Ready to read about 10 instances where that wasn't so easy?
Merriam-Webster defines them each as:
  • Correlation: a relation existing between phenomena or things or between mathematical or statistical variables which tend to vary, be associated, or occur together in a way not expected on the basis of chance alone.
  • Causation: the act or process of causing.
  • Coincidence: the occurrence of events that happen at the same time by accident but seem to have some connection.
The difference then is that correlation doesn't make the claim that one event causes the other, just that they occur together statistically in a way that wouldn't be expected based on random chance. One can view this as similar to consistent coincidence.
Causation, on the other hand, claims that two or more events are tied together directly. And coincidence, as we are all likely aware, occurs when two events happen at the same time but aren't at all related.
Let's put this into real-world examples.
  • Correlation: If you eat three square meals every day promptly at 8 a.m., 12:30 p.m., and 6 p.m., there will be a sizable period of time twice per year where your dinner time will correlate to the sun setting. An outside observer for this fixed duration may easily claim that like Pavlov's dog, your hunger for dinner is caused by the setting of the sun. Obviously this isn't true, but for this period the two events correlate.
  • Causation: If you're walking down the street, texting all the way and walk face-first into a lamp post, you will get a bruise. While obviously texting doesn't cause facial bruises (though in this instance there is a correlation), the event of striking one's face against a hard object is the direct cause of the bruise. Thus, this is an example of causation.
  • Coincidence: If you're sitting in a coffee shop and say hello to your friend and at exact the same time someone's phone rings, this is a coincidence. The mere sound of your voice doesn't inspire the ringing of phones and statistically one wouldn't expect the event to occur together outside of random chance.
It's very important to understand and remember the difference between the three and to question data based on an understanding of this difference. In fact, below I've included a link to an article on "spurious correlation" (which the meal-time situation noted above is an example of), but for now these definitions will work well.
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