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Posts Tagged ‘Athletes’

Want to Win Olympic Medals? Fix Your Economy First

Steven Perlberg of Business Insider quotes a private research note by ConvergEx’s Nick Colas on the correlation between Olympic success and economic strength. “The Winter Olympics are a useful backdrop for case studies on the relationship between athletic performance and economic progress in emerging markets around the world,” writes Colas. “We’ve analyzed the medal count by country since the inaugural Winter Games in 1924, and indeed the results show that athletes rarely make it to the podium until their respective countries experience economic progress and stability.”  A few case studies from Colas’s note:

  • Japan’s Winter Olympic performance history tells a post-WWII recovery story.  The country competed in three Winter Games (1928, 1932 and 1936) before it won its first medal – silver – in 1956.  Japanese athletes didn’t earn any additional medals until the 1972 games, which the country hosted, and have been consistently making an appearance on the podium since 1980.  Japan won its first medal when it was taking off as an emerging economy and getting its economic act together following WWII.  Industrialism in the country picked up rapidly following the war, and the Olympic medal consistency coincided with the consumption boom in the 1980s. 


Predicting the Winter Olympics with Economics

How many medals will U.S. athletes win at the Sochi Winter Olympics?

To answer this question, one might want to think about the abilities of the athletes involved in each competition.  And then use that information to forecast who is going to win each event.

Of course, that approach requires knowledge of the athletes involved in a wide variety of sports.  Furthermore, even if you knew how to measure ability, you would also have to figure out some way to forecast each athletes’ performance.

In a recent paper by Madeleine Andreff and Wladimir Andreff — “Economic Prediction of Medal Wins at the 2014 Winter Olympics” (PDF) — an approach advocated by a number of sports economists is employed. 


How Much Tax Are Athletes Willing to Pay?

The Laffer Curve is a unicorn-y concept that seeks to explain the rate of taxation at which revenues will fall because earners either move away or decide to earn less (or cheat more, I guess).

If I were a tax scholar interested in this concept, I would be taking a good, hard look at the current behavior of top-tier professional athletes. Boxing is particularly interesting because it allows a participant to choose where he performs. If you are a pro golfer or tennis player, you might be inclined to skip a particular event because of a tax situation, but you generally need to play where the event is happening. A top-ranked boxer, meanwhile, can fight where he gets the best deal.

Which is why it’s interesting to read that Manny Pacquiao will probably never fight in New York — primarily, says promoter Bob Arum, because of the taxes he’d have to pay.


How to Fix College Coaching?

Rutgers University fired Mike Rice – the head basketball coach – last Wednesday. This firing came about after ESPN released a video that showed Rice abusing his players. Such a video had already been seen by Rice’s boss at Rutgers in November, but until the video was shown to the public, Rutgers did not feel compelled to fire Rice.

Former NBA player Paul Shirley (author of Can I Keep My Jersey?) observed the following about the Rutgers case in a recent interview at HuffPost Live (around 13:30):

The thing that people don’t want to hear, but which is true, is that this is probably closer to the norm than not. 

Shirley goes on to note that he doesn’t think many coaches are actually hitting players. But he does note that coaches do tend to have a certain approach in conveying information to players (an approach Shirley describes in the interview).

Is this general approach to coaching effective?  To date, I am not aware of any study of the effectiveness of college coaching.  A study I co-authored with Mike Leeds, Eva Marikova Leeds, and Mike Mondello and published in the International Journal of Sport Finance (full PDF here) looked at 62 NBA coaches across thirty years of data. Across this sample, only 14 coaches were found to have a statistically significant and positive impact on player performance. So most NBA coaches do not appear to make their players more productive.


The Sharapova Effect

A recent paper (full PDF here) by Young Hoon Lee and Seung Chan Ahn makes a clever point about occupations in which people are paid for a main activity and a secondary area where success depends on productivity in the main activity.  If success in the latter also depends on some other characteristic, people who are well-endowed with that characteristic will invest more in the skills needed to be productive in the main activity: the incentives created by that synergy will spill over to earnings in the main activity. 

Their example is the Ladies Professional Golf Association (LPGA).  Better-looking golfers get lower scores (perform better) — but only going from average-lookers to the best-looking. Below the average, there’s no effect of differences in looks on tournament scores.  That makes sense — you probably won’t get more endorsement opportunities if you’re average-looking instead of bad-looking.  Although not golf, one might call this the Sharapova Effect. Are there other labor markets, or other activities, in which a similarly unusual synergy exists??


How Much Do Football Wins Pay Off for a College?

An NBER paper by Michael L. Anderson looks into the how a university’s football performance affects its academic performance:

Spending on big-time college athletics is often justified on the grounds that athletic success attracts students and raises donations. Testing this claim has proven difficult because success is not randomly assigned. We exploit data on bookmaker spreads to estimate the probability of winning each game for college football teams. We then condition on these probabilities using a propensity score design to estimate the effects of winning on donations, applications, and enrollment. The resulting estimates represent causal effects under the assumption that, conditional on bookmaker spreads, winning is uncorrelated with potential outcomes. Two complications arise in our design. First, team wins evolve dynamically throughout the season. Second, winning a game early in the season reveals that a team is better than anticipated and thus increases expected season wins by more than one-for-one. We address these complications by combining an instrumental variables-type estimator with the propensity score design. We find that winning reduces acceptance rates and increases donations, applications, academic reputation, in-state enrollment, and incoming SAT scores.


How to Make a Better Athlete

Olympic athletes have become increasingly reliant on scientists as advisers. A Wired article by Mark McClusky explores the efforts of sports scientists to improve athletic performance as gains have become harder to achieve. The Australian Institute of Sport is leading the charge; its success is best-demonstrated by an example from the skeleton, a sledding event that was recently reintroduced as an Olympic event:

They determined that one significant predictor of success had nothing to do with the sled itself or even the skill of the pilot. The faster a competitor pushed the sled through the 30-meter start zone before jumping on it, the better they performed. So researchers set up a national testing campaign, looking for women with backgrounds in competitive sports who excelled at the 30-meter sprint. They also evaluated candidates to see how well they responded to feedback and coaching. Eventually, they picked a group of 10 athletes—including track sprinters, a water skier, and several surf lifesavers, an Australian sport that requires sprinting through sand.


The Consequences of Athletes in Bikinis

What do girls think when they see their favorite soccer start posing in Sports Illustrated in a bikini instead of a soccer jersey?  A new study, summarized by the BPS Research Digest, surveyed girls after they viewed five images of either “female athletes in a sporting context in their full sporting attire,” “female athletes in a sexualized context,” or “bikini-clad magazine models given random names.” Here’s the BPS Digest:

The key finding is that the girls and undergrads who viewed the sexualized athlete images tended to say they admired or were jealous of the athletes’ bodies, they commented on the athletes’ sexiness, and they evaluated their own bodies negatively. Some also said they found the images inappropriate. The participants who viewed the bikini-clad glamour models responded similarly, except they rarely commented on the inappropriateness of the images, as if they’d come to accept the portrayal of women in that way…


FREAK-est Links

This week, the European debt crisis explained with lego, why American mobility causes uniformity, a new way of cheating in college, why we make drunken mistakes, an interactive map of the history of war, and why pro athletes are giving themselves frost bite.


College Athletes and Sudden Cardiac Death: Why Do Male Basketball Players Have Such a High Risk?

There’s an interesting story in today’s Wall Street Journal, by Katherine Hobson about a new method some cardiologists have come up with to better diagnose life-threatening heart conditions among student athletes. Apparently, since the hearts of well-conditioned athletes sometimes put out more electrical voltage than average, their ECG’s can often look like that of someone with a heart problem. This has led to an underestimation of the risks that sudden cardiac death (SCD) poses to student athletes, according to the study, even though it’s their leading medical cause of death during exercise. The findings were published this month in the American Heart Association journal Circulation. You can read the abstract here.
What really caught my eye though was an info-graphic the WSJ ran next to the story. Using data from Circulation, the graphic depicts the overall rates of SCD, from high to low, per year among NCAA college athletes, broken out by different sports.