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Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review
João Gustavo Claudino, Daniel de Oliveira Capanema, Thiago Vieira de Souza and 3 more
Sports Medicine - Open · 2019 · Review · CC BY
Abstract
Background. The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using.
Methods. Systematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes.
Results. Fifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI.
Conclusions. The results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods.
Electronic supplementary material. The online version of this article (10.1186/s40798-019-0202-3) contains supplementary material, which is available to authorized users.
References 103
- 1.
Russell S, Norvig P. Artificial Intelligence: a modern approach. 3rd ed. New Jersey: Pearson; 2016.
Not in our library
- 2.
Witten IH, Frank E, Hall MA, et al. Data Mining: practical Machine Learning tools and techniques. 4th ed. San Francisco: Morgan Kaufmann; 2017.
Not in our library
- 3.
Zaki MJ, Meira Jr, W. Data Mining and analysis: fundamental concepts and algorithms. 1st ed. Cambridge: Cambridge University Press; 2014.
Not in our library
- 4.
Passfield L, Hopker JG. A mine of information: can sports analytics provide wisdom from your data? Int J Sports Physiol Perform. 2017;12(7):851-855.
Not in our library10.1123/ijspp.2016-0644
- 5.
- 6.
Blei DM, Smyth P. Science and data science. Proc Natl Acad Sci USA. 2017;201702076.
Not in our library10.1073/pnas.1702076114
- 7.
Bunker RP, Thabtah F. A machine learning framework for sport result prediction. Appl Comput Info. 2019;15(1):27-33.
Not in our library
- 8.
Israni ST, Verghese A. Humanizing artificial intelligence. JAMA. 2019;321(1):29-30.
Not in our library10.1001/jama.2018.19398
- 9.
Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320(21):2199-2200.
Not in our library10.1001/jama.2018.17163
- 10.
Baker M. Data science: industry allure. Nature. 2015;520(7546):253-255.
Not in our library10.1038/nj7546-253a
- 11.
Jordan M. I., Mitchell T. M. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255-260.
Not in our library10.1126/science.aaa8415
- 12.
McCall A, Davison M, Carling C, et al. Can off-field ‘brains’ provide a competitive advantage in professional football? Br J Sports Med. 2016;50(12):710-712.
Not in our library10.1136/bjsports-2015-095807
- 13.
Nassis GP. Leadership in science and medicine: can you see the gap? Sci Med Football. 2017;1(3):195-196.
Not in our library
- 14.
Borresen J, Lambert MI. The quantification of training load, the training response and the effect on performance. Sports Med. 2009;39(9):779-795.
Not in our library10.2165/11317780-000000000-00000
- 15.
López-Valenciano A, Ayala F, Puerta JM, et al. A preventive model for muscle injuries: a novel approach based on learning algorithms. Med Sci Sports Exerc. 2018;50(5):915-927.
Not in our library10.1249/mss.0000000000001535
- 16.
Novatchkov H, Baca A. Artificial intelligence in sports on the example of weight training. J Sports Sci Med. 2013;12(1):27-37.
Not in our library
- 17.
- 18.
Saw AE, Main LC, Gastin PB. Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. Br J Sports Med. 2016;50(5):281-291.
Not in our library10.1136/bjsports-2015-094758
- 19.
Horsley T, Dingwall O, Sampson M. Checking reference lists to find additional studies for systematic reviews. Cochrane Database Syst Rev. 2011;10(8):MR000026.
Not in our library10.1002/14651858.mr000026.pub2
- 20.
Li C. Predict the neural network mathematical model of basketball team scores based on improved BP algorithm. BioTechnol Indian J. 2013;8(5):628-633.
Not in our library
- 21.
Lu G. Evaluation model of young basketball players’ physical quality and basic technique based on RBF neural network. BioTechnol Indian J. 2013;8(9):1193-1198.
Not in our library
- 22.
Wu L. The participating team’s technical analysis of women’s basketball in the 30th Olympic Games based on neural network. J Chem Pharma Res. 2013;5(11):152-158.
Not in our library
- 23.
Zhang Q. Prediction based on basketball competition video athlete behaviors and on-line RBF neural network application. BioTechnol Indian J. 2014;10(4):764-769.
Not in our library
- 24.
Kempe M, Grunz A, Memmert D. Detecting tactical patterns in basketball: comparison of merge self-organising maps and dynamic controlled neural networks. Eur J Sport Sci. 2015;15(4):249-255.
Not in our library10.1080/17461391.2014.933882
- 25.
Bianchi F, Facchinetti T, Zuccolotto P. Role revolution: towards a new meaning of positions in basketball. Electron J Appl Stat Anal. 2017;10(3):712-734.
Not in our library
- 26.
Tilp M, Schrapf N. Analysis of tactical defensive behavior in team handball by means of artificial neural networks. IFAC-PapersOnLine. 2015;28(1):784-785.
Not in our library
- 27.
Hassan A, Schrapf N, Ramadan W, et al. Evaluation of tactical training in team handball by means of artificial neural networks. J Sports Sci. 2017;35(7):642-647.
Not in our library10.1080/02640414.2016.1183804
- 28.
Hassan A, Schrapf N, Tilp M. The prediction of action positions in team handball by non-linear hybrid neural networks. Int J Perform Anal Sport. 2017;17(3):293-302.
Not in our library
- 29.
Schrapf N, Alsaied S, Tilp M. Tactical interaction of offensive and defensive teams in team handball analysed by artificial neural networks. Math Comput Model Dyn Syst. 2017;23(4):363-371.
Not in our library
- 30.
Abdullah MR, Maliki ABHM, Musa RM, et al. Intelligent prediction of soccer technical skill on youth soccer player’s relative performance using multivariate analysis and artificial neural network techniques. Int J Adv Sci Eng Inf Technol. 2016;6(5):668-674.
Not in our library
- 31.
Strnad D, Nerat A, Kohek Š. Neural network models for group behavior prediction: a case of soccer match attendance. Neural Comput App. 2017;28(2):287-300.
Not in our library
- 32.
Park YJ, Kim HS, Kim D, et al. A deep learning-based sports player evaluation model based on game statistics and news articles. Knowledge Syst. 2017;138:15-26.
Not in our library
- 33.
Tümer AE, Koçer S. Prediction of team league’s rankings in volleyball by artificial neural network method. Int J Perform Anal Sport. 2017;17(3):202-211.
Not in our library
- 34.
Croft H, Lamb P, Middlemas S. The application of self-organising maps to performance analysis data in rugby union. Int J Perform Anal Sport. 2015;15(3):1037-1046.
Not in our library
- 35.
Fuster-Parra P, Garcia-Mas A, Cantallops J, et al. Ranking features on psychological dynamics of cooperative team work through Bayesian networks. Symmetry. 2016;8(5):34.
Not in our library
- 36.
Link D, Hoernig M. Individual ball possession in soccer. PLoS One. 2017;12(7):e0179953.
Not in our library10.1371/journal.pone.0179953
- 37.
Healey G. Learning, visualizing, and assessing a model for the intrinsic value of a batted ball. IEEE Access. 2017;5:13811-13822.
Not in our library
- 38.
Jelinek HF, Kelarev A, Robinson DJ, et al. Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football. Appl Soft Comput J. 2014;14:81-87.
Not in our library
- 39.
Robertson S, Back N, Bartlett JD. Explaining match outcome in elite Australian Rules football using team performance indicators. J Sports Sci. 2016;34(7):637-644.
Not in our library10.1080/02640414.2015.1066026
- 40.
Woods CT, Veale J, Fransen J, et al. Classification of playing position in elite junior Australian football using technical skill indicators. J Sports Sci. 2018;36(1):97-103.
Not in our library10.1080/02640414.2017.1282621
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