Stat Regression Project

Analysis of ACL iaj_uries

Introduction

For this project, we wanted to use a dataset that caught our attention, and considering

we are athletes here at Truman, it was obvious that we would choose something sports-related.

Our project investigates ACL (Anterior Cruciate Ligament) injuries amongst high school athletes

across the United States. These injuries are widely known as serious and gruesome in the

sports' world. An ACL injury is ranked as the second most common injury an athlete can

endure.

We gathered this information from a dataset located in the Journal of Athletic Training.

The dataset was derived from an injury surveillance study which took place over the span of two

calendar years; dating back to 2007/2008 and 2011/2012. It exemplifies the number of ACL

injury rates (per 100,000 Athlete Exposures) for high school athletes in their particular sports.

These sports consist of Men's Football, Men's Soccer, Women's Soccer, Women's Volleyball,

Men's Basketball, Women's Basketball, Men's Wrestling, Men's Baseball, and Women's Softball

(referred to as Women's "Baseball"). The dataset also shows the number of ACL injuries that

took place in a competitive atmosphere (game-orientated setting) compared to a practice setting

as well as the total number of competitions and practices that took place. We chose to

manipulate this data and find out the ACL injury rate for athletes in the competitive and practice

settings of their sports. We did this by taking the number of ACL injuries for each sport in

practice and divided that by the total exposed athletes in practice for the perspective sport. This

was repeated, but instead we used the data for competition instead of practice. The explanatory

variables we included for our models consist of: Competition (Practice = 0, or Competition = 1),

Gender (Male = 0, or Female = 1 ), and Contact (Contact Sport = 1, or Non-Contact Sport= 0).