Abstract
Background: American football and the athletes that participate have continually evolved since the sport’s inception. The fluidity of the sport, as well as the growth of the body of knowledge pertaining to American football, requires evolving training techniques. While performance data is being garnered at very high rates by elite level sports organizations, the limiting factor to the value of data can be the limited known uses for the data. Objective: This study introduces a technique that can be used in tandem with data collected from wearable technology to better inform training decisions. Method: The K-means clustering technique was used to group athletes from two seasons worth of data from an NCAA Division 1 American football team that is in the “Power 5.” The data was obtained using Catapult Sports OPTIMEYE S5 TM in games played against only other “Power 5” programs. This data was then used to create average game demands of each student-athlete, which was then used to create training groups based upon individual game demands as previously mentioned. Results: The resultant groupings from the single-season analyses of seasons one and two showed results that were similar to traditional groupings used for training in American football, which worked as validation of the results, while also offering insights on individuals that may need to consider training in a non-traditional group based upon their game demands. Conclusion: This technique can be brought to `athletic training and be useful in any organization that is dealing with training multitudes of athletes.
Highlights
Problem Identification: The original Three Groupings and Evidence of Change in American FootballIn 1997, Pincivero and Bompa recognize that, “A basic understanding of the physiological systems utilized in the sport of football is necessary in order to develop optimal training programmes geared for preparation as well as the requirements of individual field positions.” They recognized position-specific demands will aid in optimal training when they identified three player categories: linemen, backs and receivers, and linebackers
The assumption was made that the system or offensive playstyle did not change at the particular Division 1 program that the data was recorded from, but because of the nature of collegiate football there is a high turnover rate amongst student athlete personnel
Other studies looking at sports, such as basketball and the National Basketball Association (NBA), used k-means clustering to attempt to predict the outcome of games (Cheng, Zhang, Kyebambe, & Kimbugwe, 2016) and, while the authors of this study believe that the Catapult data did show some predictive capability based the researchers’ extreme familiarity with the student-athletes and the coaching staff, this was still not the intent of this particular study
Summary
Problem Identification: The original Three Groupings and Evidence of Change in American FootballIn 1997, Pincivero and Bompa recognize that, “A basic understanding of the physiological systems utilized in the sport of football is necessary in order to develop optimal training programmes geared for preparation as well as the requirements of individual field positions.” They recognized position-specific demands will aid in optimal training when they identified three player categories: linemen, backs and receivers, and linebackers. Pincivero and Bompa lay out the differences in size, body composition, strength, speed, and endurance as well as demands specific to their role during the game (Pincivero & Bompa, 1997) These classifications are similar to the training groups that are observed in collegiate football strength and conditioning circles today, often being referred to as “bigs, skills, and big-skills” (Sierer, Battaglini, Mihalik, Shields, & Tomasini, 2008). The data was obtained using Catapult Sports OPTIMEYE S5 TM in games played against only other “Power 5” programs This data was used to create average game demands of each student-athlete, which was used to create training groups based upon individual game demands as previously mentioned. Conclusion: This technique can be brought toathletic training and be useful in any organization that is dealing with training multitudes of athletes
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