Sports Science Data Protocol
DOI:
https://doi.org/10.17140/SEMOJ-5-174Keywords:
Sports science, Data protocol, Sports performance, Sports scientists, Load, Professional athletes, Sports teamsAbstract
Purpose
At the most elite level, even an increase of 1% improvement can make the difference between winning and losing. Sports scientists can help athletes gain insights that can be the differentiating factor. The purpose of this protocol is to delineate the process from the identification of key performance indicators to the presentation of the findings for sports scientists. It is designed to provide chronological steps in efforts to mitigate barriers of collecting data and tracking players as well as to help gain athlete buy-in to sports science by helping them maximize their performance.
Study Design
The competitive nature of professional sports leads to players, their agents, and teams to seek the expertise in sports performance areas such as those from athletic trainers, strength and conditioning coaches, physical therapists, nutritionists, and sports psychologists. However, much of the knowledge provided by these entities typically remain in silos. Thus, the whole picture of the athlete’s performance enhancement mechanism is not elucidated. This is where the significance of a sports scientist ensues, with the ability to integrate the data from each of the sports performance areas with the objective of obtaining a complete and comprehensive picture of the athlete.
Data Collection
This area of collecting data and monitoring athletes is becoming mainstream. As such professional sports leagues have started to implement privacy rules and regulations on the protection of athlete biometric data. It is important to be aware of the rules related to wearable technology and athlete biometric data as well as how to go about the process of collecting data from professional athletes. That is why it is important that care be taken and a protocol be followed to ensure the integrity of data collection
in the field of sports science.
Data Processing and Analyses
Data mining is the extraction of data for the purpose of discovering meaningful patterns, normalization, and the choosing of
statistical models that can help in making data driven decisions. The interpretation and presentation of the data can have a large
impact on the decisions. As such, a protocol should be adhered to.
Conclusion
The following protocol will serve as a guide on how to collect data efficiently and successfully.
References
Nevill A, Atkinson G, Hughes M. Twenty-five years of sport performance research in the Journal of Sports Sciences. J Sports Sci. 2008; 26(4): 413-426. doi: 10.1080/02640410701714589
Barnes M, Cousens L, MacLean J. From silos to synergies: A network perspective of the Canadian sport system. International Journal of Sport Management and Marketing. 2007; 2(5-6): 555-571. doi: 10.1504/IJSMM.2007.013967
McDuff DR, Garvin M. Working with sports organiza-tions and teams. Int Rev Psychiatry. 2006; 28(6): 595-605. doi: 10.1080/09540261.2016.1212820
Reade I, Rodgers W, Hall N. Knowledge transfer: How do Research Protocol | Volume 5 | Number 2| Martin L high performance coaches access the knowledge of sport sci-entists?. Int J Sports Sci Coaching. 2008; 3(3): 319-334. doi: 10.1260/174795408786238470
Miles KH, Clark B, Fowler PM, Miller J, Pumpa KL. Sleep prac-tices implemented by team sport coaches and sports science sup-port staff: A potential avenue to improve athlete sleep?. J Sci Med Sport. 2019; 22(7): 748-752. doi: 10.1016/j.jsams.2019.01.008
Martin L. Sports Performance Measurement and Analytics: The Science of Assessing Performance, Predicting Future Outcomes, Interpreting Statisti-cal Models, and Evaluating the Market Value of Athletes. New Jersey, USA: FT Press; 2016.
Robertson S, Bartlett JD, Gastin PB. Red, amber, or green? Ath-lete monitoring in team sport: the need for decision-support sys-tems. Int J Sports Physiol Perform. 2017; 12(Suppl 2): S273-S279. doi: 10.1123/ijspp.2016-0541
Sands W. Communicating with coaches: Envisioning data. In: Applied Proceedings: Acrobatics. Perth, WA, USA: School of Biomedical and Sports Science; 2005: 11-20.
Atkinson G, Nevill AM. Selected issues in the design and analysis of sport performance research. J Sports Sci. 2001; 19(10): 811-827. doi: 10.1080/026404101317015447
Morrow JR. Measurement and Evaluation in Human Performance, 5th ed. Champaign, IL, USA: Human Kinetics; 2015.
Impellizzeri FM, Marcora SM. Test validation in sport physiol-ogy: lessons learned from clinimetrics. Int J Sports Physiol Perform. 2009; 4(2): 269-277. doi: 10.1123/ijspp.4.2.269
Saw AE, Main LC, Gastin PB. Monitoring athletes through self-report: factors influencing implementation. J Sports Sci Med. 2015; 14(1): 137-146.
Burgess DJ. The research doesn’t always apply: Practical so-lutions to evidence-based training-load monitoring in elite team sports. Int J Sports Physiol Perform. 2017; 12(s2): S2136-S2141. doi: 10.1123/ijspp.2016-0608
Hopkins WG. Measures of reliability in sports medicine and science. Sports Med. 2000; 30(1): 1-15. doi: 10.2165/00007256- 200030010-00001
Düking P, Hotho A, Holmberg HC, Fuss FK, Sperlich B. Com-parison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technolo-gies. Front Physiol. 2016; 7: 71. doi: 10.3389/fphys.2016.00071
Johnston RJ, Watsford ML, Kelly SJ, Pine MJ, Spurrs RW. Va-lidity and interunit reliability of 10 Hz and 15 Hz GPS units for as-sessing athlete movement demands. J Strength Cond Res. 2014; 28(6): 1649-1655. doi: 10.1519/JSC.0000000000000323
McGraw KO, Wong SP. Forming inferences about some intra-class correlation coefficients. Psychol Methods. 1996; 1: 30-46. doi: 10.1037/1082-989X.1.1.30
Shrout PE, Fleiss JL. Intraclass correlations: Uses in assessing rater reliability. Psychol Bull. 1979; 86: 420-428. doi: 10.1037//0033- 2909.86.2.420
Atkinson G, Nevill AM. Statistical methods for assessing mea-surement error (reliability) in variables relevant to sports medi-cine. Sports Med. 1998; 26(4): 217-238. doi: 10.2165/00007256- 199826040-00002
Li L, Zeng L, Lin ZJ, Cazzell M, Liu H. Tutorial on use of intraclass correlation coefficients for assessing intertest reliability and its application in functional near-infrared spectroscopy–based brain imaging. J Biomed Opt. 2015; 20(5): 050801. doi: 10.1117/1. JBO.20.5.050801
Zych N. Collection and ownership of minor league athlete ac-tivity biometric data by major league baseball franchises. DePaul J. Sports L. 2018; 14: 129.
Osborne B. Legal and ethical implications of athletes’ biomet-ric data collection in professional sport. Marq. Sports L. Rev. 2017; 28: 37.
Gale K. The sports industry’s new power play: Athlete biomet-ric data domination. Who owns it and what may be done with it. Ariz. St. U. Sports & Ent. LJ. 2016; 6: 7. Submit your article to this journal | https://openventio.org/submit-manuscript/
Lowens B, Motti VG, Caine K. Wearable privacy: Skeletons in the data closet. Paper presente at: 2017 IEEE International Conference on Healthcare Informatics (ICHI); 2017; Park City, UT, USA; doi: 10.1109/ICHI.2017.29
Akenhead R, Nassis GP. Training load and player monitoring in high-level football: Current practice and perceptions. Int J Sports Physiol Perform. 2016; 11(5): 587-593. doi: 10.1123/ijspp.2015-0331
Thornton HR, Delaney JA, Duthie GM, Dascombe BJ. Devel-oping athlete monitoring systems in team sports: Data analysis and visualization. Int J Sports Physiol Perform. 2019; 14(6): 698-705. doi: 10.1123/ijspp.2018-0169
Stein M, Janetzko H, Seebacher D, et al. How to make sense of team sport data: From acquisition to data modeling and research aspects. Data. 2017; 2(1): 2.
De Beéck TO. Data science approaches for the analysis and interpretation of training load data of athletes. 2019.
Karlsson D, Timpka T, Jacobsson J, et al. Electronic data cap-ture on athletes’ pre-participation health and in-competition injury and illness at major sports championships: An extended usability study in athletics. Health Informatics Journal. 2018; 24(2): 136-145. doi: 10.1177/1460458216661861
Durkin TM. Health data privacy and security in the age of wearable tech: Privacy and security concerns for the NFLPA and WHOOP. J. High Tech. L. 2018; 19: 279.
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All content published in Sports and Exercise Medicine (SEM) is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows anyone to copy, distribute, transmit, and adapt the work — including for commercial purposes — provided appropriate credit is given to the original author(s) and source.
