Dangerous studying: statistics on college sports injuries

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Pavel Prikhodko, Ph.D. Machine Learning

Many people name their own reasons why college time is the best time of your life — but even the best time of your life can be dangerous. In this post, we’ll examine the average annual estimates on the number of injuries in sports competitions and practices by gender (based on 25 championship sports in the United States). From 2009 to 2014, the average annual number of sports injuries for both genders combined amounted to 210,674 (76,176 occurred in competition and 134,498 in practice).

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During the winter season from 2009 to 2014, basketball caused more injuries among male athletes than any other sport — an average of 16,607 men suffered basketball injuries, with 6,259 occurring during competition and 10,349 during practice. Basketball was also quite dangerous for women, causing an average of 10,858 injuries (4,084 in competition and 6,774 in practice). The second most dangerous college sport for men and the most dangerous sport for women was indoor track, which (on average) caused 8,328 injuries among men and 11,519 injuries among women.

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The fall season brings different sports and different types of injuries to college students. The five-year average amounted to 47,199 injuries in men’s college football during the fall season (19,982 for competition and 27,217 for practice). The average number of injuries in men’s soccer amounted to 13,435, with 6,458 occurring during competition and 6,977 during practice.

Football is often seen as a men’s sport, but women play too — during the fall season from 2009 to 2014, an average of 15,113 football injuries were recorded among women (7,434 in competition and 7,679 in practice). Women’s volleyball caused around 8,961 injuries with 2,372 injuries coming from competition and 6,589 injuries coming from practice, while the average number of injuries occurring during women’s cross country amounted to 5,723 (735 in competition and 4,989 in practice).

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About Pavel Prikhodko

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Pavel Prikhodko, Ph.D. Machine Learning

Pavel has worked for many years as a researcher and developer on a wide range of applications (varying from mechanics and manufacturing to social data, finance and advertising), building predictive systems and trying to find stories that data can tell.

In his free time, he enjoys being with his family.

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