Using K-means Clustering to Create Training Groups for Elite American Football Student-athletes Based on Game Demands

Zachary Shelly, Reuben F. Burch, Wenmeng Tian, Lesley Strawderman, Anthony Piroli, Corey Bichey


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.


United States Football, Cluster Analysis, Wearable Electronic Devices, Physical Conditioning, Athletes

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Bangsbo, J. (2014). Physiological Demands of Football. Sports Science Exchange, 27(125), 1–6.

Beernaerts, J., de Baets, B., Lenoir, M., & van de Weghe, N. (2020). Spatial movement pattern recognition in soccer based on relative player movements. PLoS ONE, 15(1), 1–17.

Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., … Cable, N. T. (2017). Monitoring Athlete Training Loads: Consensus Statement. International Journal of Sports Physiology and Perfromance, 12(s2), 161–170.

Burch, R. F. (2019). Technology Arms Race: A Story About Wearables, Athletics, and Trust. NEXUS: A Magazine by NSPARC at Mississippi State University, Spring, 2–7.

Cheng, G., Zhang, Z., Kyebambe, M. N., & Kimbugwe, N. (2016). Predicting the outcome of NBA playoffs based on the maximum entropy principle. Entropy, 18(12), 1–16.

Creasey, S. (2015). Wearable technology will up the game for sports data analytics. Computer Weekly.

Fullagar, H. H. K., McCunn, R., & Murray, A. (2017). Updated review of the applied physiology of American college football: Physical demands, strength and conditioning, nutrition, and injury characteristics of america’s favorite game. International Journal of Sports Physiology and Performance, 12(10), 1396–1403.

Gómez, M. Á., Lago, C., Gómez, M. T., & Furley, P. (2019). Analysis of elite soccer players’ performance before and after signing a new contract. PLoS ONE, 14(1), 1–15.

Gustavo, D. (2008). College Football: The History and Evolution of the Spread Offense. Bleacher Report. Retrieved from

Hanuska, A., Chandramohan, B., Bellamy, L., Burke, P., Ramanathan, R., & Balakrishnan, V. (2017). Smart Clothing Market Analysis. University of California Berkeley, 1–47.

Hynes, G., O’Grady, M., & O’Hare, G. (2013). Towards Accessible Technologies for Coaching. International Journal of Sports Science & Coaching, 8(1), 105–114.

Izquierdo, D. G., Ceballos, I. D., Ramírez Molina, M. J., Vallejo, E. N., & Díaz, J. D. (2019). Risk for eating disorders in “high”- And “low”-risk sports and football (soccer): A profile analysis with clustering techniques. Revista de Psicologia Del Deporte, 28(4), 117–126.

Julien, C. (2020a). What is IMA? Catapult.

Julien, C. (2020b). What is PlayerLoad? Catapult. Retrieved from

Kriegel, H. P., Kröger, P., Sander, J., & Zimek, A. (2011). Density-based clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(3), 231–240.

Lee, P., Chen, R., & Lakshman, V. (2016). Predicting Offensive Play Types in the National Football League. Stanford University, 1–5.

Lindsey, J. (2006). BCS race already is getting ugly. Pittsburgh Post-Gazette, 42. Retrieved from

Louvet, B., & Campo, M. (2020). Do high emotional intelligent soccer referees better cope with competitive stressors? Movement and Sports Sciences - Science et Motricite, 105(3), 17–26.

Luczak, T., Burch, R., Lewis, E., Chander, H., & Ball, J. (2019). State-of-the-art review of athletic wearable technology : What 113 strength and conditioning coaches and athletic trainers from the USA said about technology in sports. International Journal of Sports Science and Coaching, 15(1), 26–40.

NFL Combine Results. (2019). 2019 NFL Combine Results - 32 Years of NFL Scouting Combine Data. Nflcombineresults.Com. Retrieved from

Olson, C. F. (1995). Parallel algorithms for hierarchical clustering. Parallel Computing, 21(8), 1313–1325.

Open Data Science. (2018). Three Popular Clustering Methods and When to Use Each. Medium. Retrieved from

Park, L. A. F., Scott, D., & Lovell, R. (2019). Velocity zone classification in elite women’s football: where do we draw the lines? Science and Medicine in Football, 3(1), 21–28.

Pincivero, D. M., & Bompa, T. O. (1997). A physiological review of American football. Sports Medicine. Springer International Publishing.

Popovych, I., Zavatskyi, V., Tsiuniak, O., Nosov, P., Zinchenko, S., Mateichuk, V., … Blynova, O. (2020). Research on the types of pre-game expectations in the athletes of sports games. Journal of Physical Education and Sport, 20(1), 43–52.

Reid, B., Schreiber, K., Shawhan, J., Stewart, E., Burch, R., & Reimann, W. (2020). Reaction time assessment for coaching defensive players in NCAA division 1 American football: A comprehensive literature review. International Journal of Industrial Ergonomics, 77, 102942 (1-10).

Rollins, B. (2018). The rise of “11 personnel” in the NFL. PFF. Retrieved from

Sarmento, H., Peralta, M., Harper, L., Vaz, V., & Marques, A. (2018). Achievement goals and self-determination in adult football players – A cluster analysis. Kinesiology, 50(1), 43–51.

Schofield, M. (2014). Understanding Football Offensive Personnel Packages. Inside the Pylon. Retrieved from

Shirkhorshidi, A. S., Aghabozorgi, S., & Ying Wah, T. (2015). A Comparison study on similarity and dissimilarity measures in clustering continuous data. PLoS ONE, 10(12), e0144059.

Sierer, S. P., Battaglini, C. L., Mihalik, J. P., Shields, E. W., & Tomasini, N. T. (2008). The national football league combine: Performance differences between drafted and nondrafted players entering the 2004 and 2005 drafts. Journal of Strength and Conditioning Research, 22(1), 6–12.

Singh, A. (2013). K-means with Three different Distance Metrics. International Journal of Computer Applications, 67(10), 13–17.

Steinbach, P. (2013). Tracking Technology Revolutionizes Athlete Training. Athletic Business. Retrieved from

Valovich McLeod, T. C., Decoster, L. C., Loud, K. J., Micheli, L. J., Parker, J. T., Sandrey, M. A., & White, C. (2011). National athletic trainers’ association position statement: Prevention of pediatric overuse injuries. Journal of Athletic Training, 46(2), 206–220.

Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In lcml (Vol. 1, pp. 577–584).

Ward, P. A., Ramsden, S., Coutts, A. J., Hulton, A. T., & Drust, B. (2018). Positional differences in running and nonrunning activities during elite american football training. The Journal of Strength & Conditioning Research, 32(7), 2072–2084.

Wellman, A. D., Coad, S. C., Flynn, P. J., Climstein, M., & McLellan, C. P. (2017). Movement demands and perceived wellness associated with preseason training camp in NCAA Division I college football players. The Journal of Strength & Conditioning Research, 31(10), 2704–2718.

Wright, P. M., Smart, D. L., & McMahan, G. C. (1995). Matches Between Human Resources and Strategy Among Ncaa Basketball Teams. Academy of Management Journal, 38(4), 1052–1074.



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