How can we use math to help us understand sports?

There are some interesting debates raging within the sports community about the ways in which statistics can help us understand athletic performance and the value of different players to a team. These statistics also are used to evaluate what are effective strategies and which are not.

Though some of the debate is about whether we should or should not rely on these statistical models, there are some interesting differences among those models themselves. Each model relies on different assumptions and maps the reality of the game differently. Sometimes, as in the case discussed in the article below, different models give us wildly different answers about a player’s value. Which is correct? What does this case tell us about the ability and limits of using math to understand reality? Is it possible to resolve this debate?

http://www.espn.com/mlb/story/_/id/18114272/miller-going-war-mystery-robbie-ray

How does the quote below apply to this case?

“A man with a watch knows what time it is. A man with two watches is never sure.”

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Implicit bias and the NFL draft Teams don’t recognize how unconscious attitudes about race affect which players they select

“Even in an industry where minority workers sometimes appear to be favored for highly desirable jobs,” the two concluded, “employers may still fall prey to symbolic discrimination, relying on deeply embedded stereotypes about minority groups during the interview process.”

In N.F.L., Deeply Flawed Concussion Research and Ties to Big Tobacco

This article connects to some interesting TOK issues. Clearly we can discuss the ethics, or lack of ethics, in the NFL’s manipulation of data to disprove conclusions that undermine its business.

This also illustrates how math can help us understand and possibly prove complex issues like the connection between football and health issues like concussions and CTE.  Rather than observing or intuiting a causal relationship between two phenomenon, we have to use math along with the methods of proof in the natural sciences to establish truth and construct knowledge. By misrepresenting data, one might reach incorrect conclusions, which seems to have been the case here.

http://www.nytimes.com/2016/03/25/sports/football/nfl-concussion-research-tobacco.html

A second article about how flawed data undermines our ability to construct knowledge.

“Researchers primed to believe that the NFL has concussions under control, a data set that’s missing important information, and publication in a journal edited by a consultant to the NFL — it looks more like an attempt to create evidence for a predetermined message than good science. But even if we throw out these studies, we can’t yet conclude that football inevitably leads to lasting brain damage.”

http://fivethirtyeight.com/features/the-nfls-shoddy-science-means-we-know-even-less-about-concussions/

N.F.L. Announcers Are Bad at Math, Too

What does this article tell us about people’s motivation to take “correct” actions? What happens when math says one thing but our emotions tell us another? What if the agreed upon consensus correct answer is in fact wrong?

“It’s not that coaches don’t know the math — rather, it seems they don’t want to be criticized. If a coach does the expected and sends out the punt unit on fourth down, and then his team goes on to lose, players are blamed for the defeat. If the coach orders a conversion attempt that fails, the coach is blamed for subsequent defeat.”

http://www.nytimes.com/2015/09/30/upshot/nfl-announcers-are-bad-at-math-too.html?hp&action=click&pgtype=Homepage&module=mini-moth®ion=top-stories-below&WT.nav=top-stories-below&_r=0

Fears that stats trump hoops acumen

“There is a growing concern that the rise in the popularity of basketball analytics (such as player efficiency rating and true shooting percentage) has led to more stat-based personnel hires rather than ex-players becoming general managers.”

http://espn.go.com/nba/story/_/id/10673335/some-executives-fear-reliance-stats-keeping-ex-nba-players-front-offices