{"id":209,"date":"2016-06-01T12:02:57","date_gmt":"2016-06-01T12:02:57","guid":{"rendered":"https:\/\/blogs.mathworks.com\/headlines\/?p=209"},"modified":"2016-07-25T10:22:47","modified_gmt":"2016-07-25T10:22:47","slug":"using-matlab-to-catch-athletes-who-cheat","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/headlines\/2016\/06\/01\/using-matlab-to-catch-athletes-who-cheat\/","title":{"rendered":"Using MATLAB to catch athletes who cheat"},"content":{"rendered":"

The difference between\u00a0a gold and a silver<\/a>\u00a0medal in many Olympic races is only a fraction of a second. This pushes athletes, and the networks that support them, to look for ways to get even the slightest edge, often through unethical approaches.<\/p>\n

On May 17th<\/sup>, The Washington Post<\/a> <\/em>reported that 31 Olympic athletes recently tested positive in doping reanalysis. The International Olympic Committee specifically retested samples from\u00a0454\u00a0athletes from the Beijing 2008 Olympics who\u00a0are considered likely to compete in the upcoming Rio 2016 Olympics.<\/p>\n

The 31 athletes who\u00a0failed the reanalysis hail from 12 different countries and competed in six sports<\/a>. Last week, 14 athletes from Russia were identified,\u00a0including ten who\u00a0had\u00a0medaled<\/a> in the Beijing Olympics. These athletes could be stripped of their medals<\/a> in addition to being banned from future Olympic competitions.<\/p>\n

\"843022.TIF\"<\/p>\n

Using MATLAB to catch athletes who cheat<\/strong><\/span><\/h2>\n

To avoid discovery, athletes have moved away from heavy steroid use. Now, synthetic hormones and erythropoietin<\/a>\u00a0(EPO) are more common. EPO allows the blood to have a greater carrying capacity for oxygen. Some athletes take small amounts\u2014known as \u201cmicrodosing\u201d\u2014to evade detection and still get the benefits.<\/a><\/p>\n

EPO is nearly impossible to detect in normal blood and urine tests, and synthetic hormones masquerade as regular hormones in athletes. In the case of microdosing, detection of minuscule amounts may not be enough to prove the use of doping in athletes, since \u201cnormal\u201d varies on a person-by-person basis.<\/p>\n

Researchers from World Anti-Doping Agency (WADA) have turned to Bayesian statistics<\/a> to find changes that deviate from the \u201cnormal\u201d for any given athlete.<\/p>\n

\u201cThey soon realized that, even if they couldn\u2019t detect the drugs themselves, they could often detect the effects of the drugs\u2014changes in the levels and ratios of young and mature red blood cells, for example,\u201d says Alex Hutchinson for The New Yorker<\/em><\/a>. \u201cEven better, this approach could flag athletes who were turning to so-called autologous blood doping, extracting and later reinjecting their own blood without actually using any illicit substances.\u201d<\/p>\n

A Bayesian solution looks at previous results to predict normal results going forward, and can flag those results that fall outside the expected range. In order to do this, testing centers create an athlete biological passport (ABP): a record in which results of doping tests are collected over a period of time and used to calculate thresholds of suspicious results for each individual athlete.<\/p>\n