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Department of Health Education, Health Aspects of Sports, The Pennsylvania State University, University Park, Pennsylvania
The purpose of this investigation was to examine risk patterns of concussion in college football. Multivariate models were used to interpret the data. Specifically, log-linear modeling and analysis techniques were incor porated into the investigation. An average of 49 college teams were studied over the 8 year period 1975 to 1982. This represented over 36,000 athlete-seasons and 395 team-seasons. The data selected were limited to 1,005 game-related concussions. The general hy potheses tested were the null hypothesis that the vari ables of team (offense and defense), player position, situation (rushing and passing), and activity (block and tackle) had no effect on the occurrence of these game- related concussions.
It was found that concussions were a persistent and regular but relatively infrequent type of injury in college football. Concussions accounted for 75% of the total number of injuries on or about the head. The injuries were examined relative to player position, situation, and activity using a log-linear modeling technique, with in teractions among the variables also established.
The contribution of each variable was not always equal or completely interactive. Generally, the highest risk of concussion was associated with offensive and defensive players involved in a block on a rushing play. Specifically, running backs demonstrated the highest risk of concussion, regardless of activity. The lowest risk was for offensive linemen and quarterbacks while blocking on any type of play.
On defense, the secondary exhibited the highest risk of concussion while being blocked on a running play. Similarly, linemen experienced their greatest risk while being blocked on plays run inside the tackle. The small est risk of concussion was demonstrated by lineback ers on passing plays.
This investigation demonstrated the viability of mul tivariate analysis of sport injury data and confirmed the utility of the log-linear analysis of nominal data.
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