Physical Therapy Haven

In Michael Lewis’s book, Moneyball: The Art of Winning an Unfair Game, Lewis describes the Oakland Athletics’ 2003 amazing playoff run in which evidence-based statistics called Sabermetrics (B. James, C. Wright) were used to aid decisions made in drafting prospects and signing free agent players. The team’s general manager, Billy Beane, caught a lot of flack for his use of this method, but his belief paid off. Twelve years later, this strategy has transcended into almost every major professional sport, making improbable, lesser-known athletes perform at a championship level TOGETHER.

Although it is an invaluable tool that helps bring together players who can increase opportunities to score against particular pitchers, teams, fields, etc.—in ways that have been shown to aide in the form of wins versus losses—Sabermetrics does not account for variables of the human body. For instance, past injuries and the ability to physically adapt new movements in response to those injuries can and should be factored into mapping out an athlete’s longevity and career. As you can see, there are many variables on the individual level that can contribute to a team’s success that are not addressed in classic Sabermetrics.

A new formula that applies this human element has to be created to look at injury risk and the potential of a player to make a return on an investment despite past injuries. But where would we start?

My argument for physical/functional tests and measures (those of which are performed in the physical therapy realm) need to be included to predict the value of a player and if they are worth the $50 million dollar price tag their agent is asking.

There is someone out there trying to do the same: Stan Conte, the LA Dodgers’ senior director of medical services, is continually modifying his own formula, as seen here in ESPN’s August 2012 edition (http://espn.go.com/mlb/story/_/id/7603159/dodgers-injury-guru-stan-conte-wants-end-dl-espn-magazine). Conte explores assessing injury risk with variables such as age, position, tenure, and past injuries. But injury risk is just one component, and if I were him, I would have tough decisions to make during trade deadlines without additional functional and resiliency measures.

Take quarterback Peyton Manning’s trade post-cervical neck fusion as an example. Everyone who passed up on Manning due to the injury, including the Indianapolis Colts, was eating their words this past NFL season as Manning led the Denver Broncos to the Super Bowl. Manning, however, is one of a few elite athletes who has the ability to bounce back after a severe injury. What makes him so different from the Dodgers’ Matt Kemp or Lakers’ Kobe Bryant? (Please read my past articles and predictions about their injuries—Search Keywords “Kemp” or “Kobe” on www.thenoticeca.com). Imagine if there was a way to assess Manning before the Colts dealt him away. Imagine further that someone on the Broncos’ end KNEW he was a worthy deal despite the injury. Is there a system that can help determine this and help teams win while limiting liability with an aging and injured athlete?

What if there was a formula/system?

1) …That can predict the probability an athlete will blow out a shoulder or elbow, tear their ACL, or hurt their backs and be out for the remainder of the season or, worse yet, the rest of a 5 year multimillion dollar contract? This knowledge can be used to avoid bad drafts (e.g., local stars Josh Hamilton, Albert Pujols) or leveraged in the beginning of contract negotiations.

2) …That can predict the probability an injured or aging athlete can be restored to their past homerun smashing form? This knowledge can be used to acquire forsaken, but high ‘caliber’ athletes at a discount.

3) …That can identify physical weaknesses in players already on a team’s roster? This knowledge can be used to find optimal ways to rehabilitate (on the owner’s side) or strategically exploit vulnerabilities (on the opponent’s side).

This is all possible with what I have created: The Kinematic Sports Analysis System (KSAS).

Next week’s Part 2 of “The Next Moneyball” will delve into the origins and scientific basis of my system before I explain the technical components and their application.

 We can and we will get better TOGETHER,

 Dr. Justin C Lin