2018
Journal article
Open Access
Effective injury forecasting in soccer with GPS training data and machine learning
Rossi A., Pappalardo L., Cintia P., Iaia F. M., Fernandez J., Medina D.
Machine Learning (stat.ML) Research and Analysis Methods Mathematics Statistics (Mathematics) Soccer Analytics Machine Learning Physical Activity Data Science Sports Science FOS: Computer and information sciences Applied Data Science Mathematical and Statistical Techniques Kinematics H.2.8 Management Engineering Sports Analytics Behavior Injury Prediction Multidisciplinary Research Article Sports Statistics - Machine Learning Decision Analysis Artificial Intelligence Classical Mechanics 62-07 Medicine and Health Sciences Decision Tree Learning Injury Forecasting Computer and Information Sciences Statistical Methods Forecasting Physics Physical Sciences Engineering and Technology Biology and Life Sciences Recreation Public and Occupational Health Statistics - Applications Games Decision Trees Applications (stat.AP)
Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.
Source: PloS one 13 (2018): 1–15. doi:10.1371/journal.pone.0201264
Publisher: Public Library of Science, San Francisco, CA , Stati Uniti d'America
1. Hagglund M, Walden M, Magnusson H, Kristenson H, Bengtsson H and Exstrand J. Injuries a ect team performance negatively in professional football: an 11-year follow-up of the UEFA Champions League injury study. British Journal of Sports Medicine, doi: 10.1136/bjsports-2013-092215, 2013.
2. Hurley OA. Impact of Player Injuries on Teams' Mental States, and Subsequent Performances, at the Rugby World Cup 2015. Frontiers in Psychology 7:807, doi: 10.3389/fpsyg.2016.00807, 2016.
3. Lehmann EE, Schulze GG. What Does it Take to be a Star? { The Role of Performance and the Media for German Soccer Players. Applied Economics Quarterly 54:1, pp. 59-70, doi: 10.3790/aeq.54.1.59, 2008.
4. Fernandez-Cuevas I., Gomez-Carmona P, Sillero-Quintana M, Noya-Salces J, ArnaizLastras J, Pastor-Barron A. Economic costs estimation of soccer injuries in rst and second Spanish division professional teams. 15th Annual Congress of the European College of Sport Sciences ECSS, 23th 26th june. 2010.
5. Gudmundsoon H, Horton M. Spatio-Temporal Analysis of Team Sports - A Survey. CoRR: abs/1602.06994, 2016.
6. Stein M., Janetzko H., Seebacher D., Jager A., Nagel M., Holsch, J., Grossniklaus M. How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects. Data, 2:1, 2, doi:10.3390/data2010002, 2017.
7. Cintia P., Pappalardo L., Pedreschi D. Engine Matters: A First Large Scale Data Driven Study on Cyclists' Performance. 13th IEEE International Conference on Data Mining Workshops, pp. 147{153, doi:10.1109/ICDMW.2013.41, 2013.
8. Cintia P., Coscia M., Pappalardo L. The Haka network: Evaluating rugby team performance with dynamic graph analysis. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1095{1102, doi: 10.1109/ASONAM.2016.7752377, 2016
9. Cintia P., Pappalardo L., Pedreschi D., Giannotti F., Malvaldi M. The harsh rule of the goals: Data-driven performance indicators for football teams. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1{10, doi:10.1109/DSAA.2015.7344823, 2015
10. Pappalardo L., Cintia P. Quantifying the relation between performance and success in soccer. CoRR: abs/1705.00885, 2017.
11. Fernandez J, Medina D, Gomez A, Arias M, Gavalda R. From Training to Match Performance: A Predictive and Explanatory Study on Novel Tracking Data. 16th IEEE International Conference on Data Mining Workshops, doi:10.1109/ICDMW.2016.0027, 2016
12. Rossi A., Savino M., Perri E., Aliberti G., Trecroci A., Iaia M. Characterization of in-season elite football trainings by GPS features: The Identity Card of a Short-Term Football Training Cycle. 16th IEEE International Conference on Data Mining Workshops, pp. 160-166, doi: 10.1109/ICDMW.2016.0030, 2016.
13. Brink MS1, Visscher C, Arends S, Zwerver J, Post WJ, Lemmink KA. Monitoring stress and recovery: new insights for the prevention of injuries and illnesses in elite youth soccer players. Br J Sports Med. 2010;44: 809-15.
14. Ehrmann FE, Duncan CS, Sindhusake D, Franzsen WN, Greene DA. GPS and injury prevention in professional soccer. J Strength Cond Res. 2015;30:306-307.
15. Venturelli M, Schena F, Zanolla L, Bishop D. Injury risk factors in young soccer players detected by a multivariate survival model. Journal of Science and Medicine in Sport. 2011;14:293298.
16. Kirkendall D.T., Dvorak J. E ective Injury Prevention in Soccer. The physician and sportsmedicine, 38:1, doi: http://dx.doi.org/10.3810/psm.2010.04.1772, 2010.
17. Hagglund M, Walden M, Bahr R, Ekstrand J. Methods for epidemiological study of injuries to professional football players: developing the UEFA model. British Journal of Sports Medicine, 39:6, pp. 340{346, doi:10.1136/bjsm.2005.018267, 2005.
18. FIFA circular no. 1494 of 8 July 2015, http://bit.ly/2pBmhK7.
19. Gabbett TJ. Reductions in pre-season training loads reduce training injury rates in rugby league players. British Journal of Sports Medicine. 2004;38: 743749.
20. Gabbett TJ, Jenkins DG. Relationship between training load and injury in professional rugby league players. Journal of Science and Medicine in Sport. 2011;14: 204209.
21. Gabbett TJ. In uence of training and match intensity on injuries in rugby league. Journal of Sports Sciences. 2004;22(5):409-417.
22. Gabbett TJ. The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes. The Journal of Strength & Conditioning Research. 2010;24(10):2593-2603.
23. Gabbett TJ, Domrow N. Relationships between training load, injury, and tness in sub-elite collision sport athletes. Journal of Sports Sciences. 2007;25(13):1507-1519.
24. Anderson L, Triplett-McBride T, Foster C, Doberstein S, Brice G. Impact of training patterns on incidence of illness and injury during a women's collegiate basketball season. The Journal of Strength & Conditioning Research. 2003; 17: 734738.
25. Gabbett TJ, Ullah S. Relationship between running loads and soft-tissue injury in elite team sport athletes. J Strength Cond Res. 2012;26: 953960.
26. Rogalski B, Dawson B, Heasman J, Gabbett TJ. Training and game loads and injury risk in elite Australian footballers. J Sci Med Sport. 2013;16: 499503.
27. Gabbett TJ. The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016; bjsports-2015-095788.
28. Hulin BT, Gabbett TJ, Blanch P, Chapman P, Bailey D, Orchard JV. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med. 2014;48:708-712.
29. Murray NB, Gabbett TJ, Townshend AD, Blanch P. Calculation acute:chronic workload ratios using exponential weighted moving averages provides a more sensitive indicator of injury likelihood than rolling averages. Br J Sports Med. 2016; bjsports-2016-097152.
30. Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sports Exerc. 1998;30:11641168.
31. Talukder H, Vincent T, Foster G, Hu C, Huerta J, Kumar A, et al. Preventing in-game injuries for NBA players. MIT Sloan Analytics Conference. Boston; 2016.
32. Buchheit M, Al Haddad H, Simpson BM, Palazzi D, Bourdon PC, Di Salvo V, MendezVillanueva A. Monitoring accelerations with GPS in football: time to slow down? International Journal of sports physiology and performance, 9 (3), pp. 442-445, doi: 10.1123/ijspp.2013-0187, 2014.
33. Medina D, Pons E, Gomez A, Guitart M, Martin A, Vazquez-Guerrero J, Camenforte I, Carles B, Font R. Are There Potential Safety Issues Concerning the Safe Usage of Electronic Personal Tracking Devices? The Experience of a Multi-sport Elite Club. International Journal of sports physiology and performance, 4, pp. 1-12, doi: 10.1123/ijspp.2016-0368, 2017.
34. Duncan MJ, Badland HM, Mummery WK. Applying GPS to enhance understanding of transport-related physical activity. Journal of Science and Medicine in Sport. 2009;12: 549556.
35. Hulin BT, Gabbett TJ, Lawson DW, Caputi P, Sampson JA. The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players Br J Sports Med. 2016;50:231-236.
36. Bowen L., Gross AS, Gimpel M, Li FX. Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. British Journal of Sports Medicine, 51, 452-459, doi:10.1136/bjsports-2015-095820, 2017.
37. Menaspa P. Are rolling averages a good way to assess training load for injury prevention? British Journal of Sports Medicine, 51, doi:http://dx.doi.org/10.1136/bjsports-2016- 096131, 618-619, 2017.
38. Fuller CW, Ekstrand J, Junge A, Andersen TE, Bahr R, Dvorak J, Hagglund M, McCrory P, Meeuwisse WH. Consensus statement on injury de nitions and data collection procedures in studies of football (soccer) injuries. British Journal of Sports Medicine, 40, pp. 193-201, doi:http://dx.doi.org/10.1136/bjsm.2005.025270, 2006.
39. Di Prampero PE, Fusi S, Sepulcri L, Morin JB, Belli A, Antonutto G. Sprint running: a new energetic approach. J Exp Biol. 2005;208: 28092816.
40. Gaudino P, Iaia FM, Alberti G, Strudwick AJ, Atkinson G, Gregson W. Monitoring training in elite soccer players: systematic bias between running speed and metabolic power data. Int J Sports Med. 2013;34: 963968.
41. James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning. New York, NY: Springer New York; 2013.
42. Guyon I, Weston J, Barnhill S, Vapnik V. Gene Selection for Cancer Classi cation Using Support Vector Machines. Machine Learning 46, 2002, 10.1023/A:1012487302797.
43. Li J., Cheng K., Wang S., Morstatter F., Trevino R., Jiliang T., Huan L. Feature Selection: A Data Perspective. arXiv:1601.07996, 2016
44. Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. Springer series in Statistics, volume 1, Springer, Berlin, 2001.
45. Hastie TJ and Tibshirani RJ and Friedman JH The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics, isbn: 978-0-387-84857-0, 2009
46. Lowry CA, Woodall WH, Champ CW, Rigdon SE. A Multivariate Exponentially Weighted Moving Average Control Chart. Technometrics. Technometrics. 1992;34: 4653.
47. Lucas JM, Saccucci MS. Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements. Technometrics. 1990;32: 112.
1 Hägglund M, Waldén M, Magnusson H, Kristenson H, Bengtsson H, Exstrand J. Injuries affect team performance negatively in professional football: an 11-year follow-up of the UEFA Champions League injury study. British Journal of Sports Medicine, 10.1136/bjsports-2013-092215, 2013 23645832
2 Hurley OA. Impact of Player Injuries on Teams’ Mental States, and Subsequent Performances, at the Rugby World Cup 2015. Frontiers in Psychology 7:807, 10.3389/fpsyg.2016.00807 27375511
3 Lehmann EE, Schulze GG. What Does it Take to be a Star?–The Role of Performance and the Media for German Soccer Players. Applied Economics Quarterly 54:1, pp. 59–70, 10.3790/aeq.54.1.59, 2008.
4 Fernández-Cuevas I., Gomez-Carmona P, Sillero-Quintana M, Noya-Salces J, Arnaiz-Lastras J, Pastor-Barrón A. Economic costs estimation of soccer injuries in first and second Spanish division professional teams. 15th Annual Congress of the European College of Sport Sciences ECSS, 23th 26th june. 2010.
5 Gudmundsson H, Horton M. Spatio-Temporal Analysis of Team Sports, ACM Computing Surveys 50, 2, Article 22 (4 2017), 34 pages. 10.1145/3054132.
6 Stein M, Janetzko H, Seebacher D, Jäger A, Nagel M, Hölsch J, et al How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects. Data, 2:1, 2, 10.3390/data2010002, 2017.
7 Rossi A, Savino M, Perri E, Aliberti G, Trecroci A, Iaia M. Characterization of in-season elite football trainings by GPS features: The Identity Card of a Short-Term Football Training Cycle. 16th IEEE International Conference on Data Mining Workshops, pp. 160–166, 10.1109/ICDMW.2016.0030, 2016
8 Pappalardo L, Cintia P. Quantifying the relation between performance and success in soccer, Advances in Complex Systems, 20 (4), 10.1142/S021952591750014X, 2017.
9 Cintia P, Pappalardo L, Pedreschi D, Giannotti F, Malvaldi M. The harsh rule of the goals: data-driven performance indicators for football teams, In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA’2015), 10.1109/DSAA.2015.7344823, 2015.
10 Brink MS, Visscher C, Arends S, Zwerver J, Post WJ, Lemmink KA. Monitoring stress and recovery: new insights for the prevention of injuries and illnesses in elite youth soccer players. Br J Sports Med. 2010;44: 809–15. 10.1136/bjsm.2009.069476 20511621
11 Ehrmann FE, Duncan CS, Sindhusake D, Franzsen WN, Greene DA. GPS and injury prevention in professional soccer. J Strength Cond Res. 2015;30:306–307. 10.1519/JSC.0000000000001093 26200191
12 Venturelli M, Schena F, Zanolla L, Bishop D. Injury risk factors in young soccer players detected by a multivariate survival model. Journal of Science and Medicine in Sport. 2011;14:293–298. 10.1016/j.jsams.2011.02.013 21474378
13 Kirkendall DT, Dvorak J. Effective Injury Prevention in Soccer. The physician and sports medicine, 38:1, 10.3810/psm.2010.04.1772, 2010.
14 Gabbett TJ. The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes. The Journal of Strength & Conditioning Research. 2010;24(10):2593–2603. 10.1519/JSC.0b013e3181f19da4 20847703
15 Gabbett TJ, Ullah S. Relationship between running loads and soft-tissue injury in elite team sport athletes. J Strength Cond Res. 2012;26: 953–960. 10.1519/JSC.0b013e3182302023 22323001
16 Rogalski B, Dawson B, Heasman J, Gabbett TJ. Training and game loads and injury risk in elite Australian footballers. J Sci Med Sport. 2013;16: 499–503. 10.1016/j.jsams.2012.12.004 23333045
17 Gabbett TJ. The training-injury prevention paradox: should athletes be training smarter and harder?Br J Sports Med. 2016 10.1136/bjsports-2015-095788 26758673
18 Anderson L, Triplett-McBride T, Foster C, Doberstein S, Brice G. Impact of training patterns on incidence of illness and injury during a women’s collegiate basketball season. The Journal of Strength & Conditioning Research. 2003; 17: 734–738. 10.1519/00124278-200311000-00018 14636112
19 Gabbett TJ. Reductions in pre-season training loads reduce training injury rates in rugby league players. British Journal of Sports Medicine. 2004;38: 74–749. 10.1136/bjsm.2003.005181 14751951
20 Hulin BT, Gabbett TJ, Blanch P, Chapman P, Bailey D, Orchard JV. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med. 2014;48:708–712. 10.1136/bjsports-2013-092524 23962877
21 Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sports Exerc. 1998;30:1164–1168. 10.1097/00005768-199807000-00023 9662690
22 Talukder H, Vincent T, Foster G, Hu C, Huerta J, Kumar A, et al. Preventing in-game injuries for NBA players. MIT Sloan Analytics Conference. Boston; 2016.
23 Kampakis S. Predictive modeling of football injuries, Phd Thesis, University College London, 2016
24 Hagglund M, Walden M, Bahr R, Ekstrand J. Methods for epidemiological study of injuries to professional football players: developing the UEFA model. British Journal of Sports Medicine, 39:6, 340–346, 10.1136/bjsm.2005.018267, 2005 15911603
25 Duncan MJ, Badland HM, Mummery WK. Applying GPS to enhance understanding of transport-related physical activity. Journal of Science and Medicine in Sport. 2009;12: 549–556. 10.1016/j.jsams.2008.10.010 19237315
26 Murray NB, Gabbett TJ, Townshend AD, Blanch P. Calculation acute:chronic workload ratios using exponential weighted moving average s provides a more sensitive indicator of injury likelihood than rolling averages. Br J Sports Med. 2016 10.1136/bjsports-2016-097152 28003238
27 Guyon I, Weston J, Barnhill S, Vapnik V. Gene Selection for Cancer Classification Using Support Vector Machines. Machine Learning 46, 2002 10.1023/A:1012487302797
28 James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning New York, NY: Springer New York; 2013.
29 He H, Bai Y, Garcia EA, Li S. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. 2008 IEEE International Joint Conference on Neural Networks.
30 Kazemitabar J, Amini A, Bloniarz A, Talwalkar A. Variable Importance using Decision Trees. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.
2. Hurley OA. Impact of Player Injuries on Teams' Mental States, and Subsequent Performances, at the Rugby World Cup 2015. Frontiers in Psychology 7:807, doi: 10.3389/fpsyg.2016.00807, 2016.
3. Lehmann EE, Schulze GG. What Does it Take to be a Star? { The Role of Performance and the Media for German Soccer Players. Applied Economics Quarterly 54:1, pp. 59-70, doi: 10.3790/aeq.54.1.59, 2008.
4. Fernandez-Cuevas I., Gomez-Carmona P, Sillero-Quintana M, Noya-Salces J, ArnaizLastras J, Pastor-Barron A. Economic costs estimation of soccer injuries in rst and second Spanish division professional teams. 15th Annual Congress of the European College of Sport Sciences ECSS, 23th 26th june. 2010.
5. Gudmundsoon H, Horton M. Spatio-Temporal Analysis of Team Sports - A Survey. CoRR: abs/1602.06994, 2016.
6. Stein M., Janetzko H., Seebacher D., Jager A., Nagel M., Holsch, J., Grossniklaus M. How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects. Data, 2:1, 2, doi:10.3390/data2010002, 2017.
7. Cintia P., Pappalardo L., Pedreschi D. Engine Matters: A First Large Scale Data Driven Study on Cyclists' Performance. 13th IEEE International Conference on Data Mining Workshops, pp. 147{153, doi:10.1109/ICDMW.2013.41, 2013.
8. Cintia P., Coscia M., Pappalardo L. The Haka network: Evaluating rugby team performance with dynamic graph analysis. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1095{1102, doi: 10.1109/ASONAM.2016.7752377, 2016
9. Cintia P., Pappalardo L., Pedreschi D., Giannotti F., Malvaldi M. The harsh rule of the goals: Data-driven performance indicators for football teams. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 1{10, doi:10.1109/DSAA.2015.7344823, 2015
10. Pappalardo L., Cintia P. Quantifying the relation between performance and success in soccer. CoRR: abs/1705.00885, 2017.
11. Fernandez J, Medina D, Gomez A, Arias M, Gavalda R. From Training to Match Performance: A Predictive and Explanatory Study on Novel Tracking Data. 16th IEEE International Conference on Data Mining Workshops, doi:10.1109/ICDMW.2016.0027, 2016
12. Rossi A., Savino M., Perri E., Aliberti G., Trecroci A., Iaia M. Characterization of in-season elite football trainings by GPS features: The Identity Card of a Short-Term Football Training Cycle. 16th IEEE International Conference on Data Mining Workshops, pp. 160-166, doi: 10.1109/ICDMW.2016.0030, 2016.
13. Brink MS1, Visscher C, Arends S, Zwerver J, Post WJ, Lemmink KA. Monitoring stress and recovery: new insights for the prevention of injuries and illnesses in elite youth soccer players. Br J Sports Med. 2010;44: 809-15.
14. Ehrmann FE, Duncan CS, Sindhusake D, Franzsen WN, Greene DA. GPS and injury prevention in professional soccer. J Strength Cond Res. 2015;30:306-307.
15. Venturelli M, Schena F, Zanolla L, Bishop D. Injury risk factors in young soccer players detected by a multivariate survival model. Journal of Science and Medicine in Sport. 2011;14:293298.
16. Kirkendall D.T., Dvorak J. E ective Injury Prevention in Soccer. The physician and sportsmedicine, 38:1, doi: http://dx.doi.org/10.3810/psm.2010.04.1772, 2010.
17. Hagglund M, Walden M, Bahr R, Ekstrand J. Methods for epidemiological study of injuries to professional football players: developing the UEFA model. British Journal of Sports Medicine, 39:6, pp. 340{346, doi:10.1136/bjsm.2005.018267, 2005.
18. FIFA circular no. 1494 of 8 July 2015, http://bit.ly/2pBmhK7.
19. Gabbett TJ. Reductions in pre-season training loads reduce training injury rates in rugby league players. British Journal of Sports Medicine. 2004;38: 743749.
20. Gabbett TJ, Jenkins DG. Relationship between training load and injury in professional rugby league players. Journal of Science and Medicine in Sport. 2011;14: 204209.
21. Gabbett TJ. In uence of training and match intensity on injuries in rugby league. Journal of Sports Sciences. 2004;22(5):409-417.
22. Gabbett TJ. The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes. The Journal of Strength & Conditioning Research. 2010;24(10):2593-2603.
23. Gabbett TJ, Domrow N. Relationships between training load, injury, and tness in sub-elite collision sport athletes. Journal of Sports Sciences. 2007;25(13):1507-1519.
24. Anderson L, Triplett-McBride T, Foster C, Doberstein S, Brice G. Impact of training patterns on incidence of illness and injury during a women's collegiate basketball season. The Journal of Strength & Conditioning Research. 2003; 17: 734738.
25. Gabbett TJ, Ullah S. Relationship between running loads and soft-tissue injury in elite team sport athletes. J Strength Cond Res. 2012;26: 953960.
26. Rogalski B, Dawson B, Heasman J, Gabbett TJ. Training and game loads and injury risk in elite Australian footballers. J Sci Med Sport. 2013;16: 499503.
27. Gabbett TJ. The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016; bjsports-2015-095788.
28. Hulin BT, Gabbett TJ, Blanch P, Chapman P, Bailey D, Orchard JV. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med. 2014;48:708-712.
29. Murray NB, Gabbett TJ, Townshend AD, Blanch P. Calculation acute:chronic workload ratios using exponential weighted moving averages provides a more sensitive indicator of injury likelihood than rolling averages. Br J Sports Med. 2016; bjsports-2016-097152.
30. Foster C. Monitoring training in athletes with reference to overtraining syndrome. Med Sci Sports Exerc. 1998;30:11641168.
31. Talukder H, Vincent T, Foster G, Hu C, Huerta J, Kumar A, et al. Preventing in-game injuries for NBA players. MIT Sloan Analytics Conference. Boston; 2016.
32. Buchheit M, Al Haddad H, Simpson BM, Palazzi D, Bourdon PC, Di Salvo V, MendezVillanueva A. Monitoring accelerations with GPS in football: time to slow down? International Journal of sports physiology and performance, 9 (3), pp. 442-445, doi: 10.1123/ijspp.2013-0187, 2014.
33. Medina D, Pons E, Gomez A, Guitart M, Martin A, Vazquez-Guerrero J, Camenforte I, Carles B, Font R. Are There Potential Safety Issues Concerning the Safe Usage of Electronic Personal Tracking Devices? The Experience of a Multi-sport Elite Club. International Journal of sports physiology and performance, 4, pp. 1-12, doi: 10.1123/ijspp.2016-0368, 2017.
34. Duncan MJ, Badland HM, Mummery WK. Applying GPS to enhance understanding of transport-related physical activity. Journal of Science and Medicine in Sport. 2009;12: 549556.
35. Hulin BT, Gabbett TJ, Lawson DW, Caputi P, Sampson JA. The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players Br J Sports Med. 2016;50:231-236.
36. Bowen L., Gross AS, Gimpel M, Li FX. Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. British Journal of Sports Medicine, 51, 452-459, doi:10.1136/bjsports-2015-095820, 2017.
37. Menaspa P. Are rolling averages a good way to assess training load for injury prevention? British Journal of Sports Medicine, 51, doi:http://dx.doi.org/10.1136/bjsports-2016- 096131, 618-619, 2017.
38. Fuller CW, Ekstrand J, Junge A, Andersen TE, Bahr R, Dvorak J, Hagglund M, McCrory P, Meeuwisse WH. Consensus statement on injury de nitions and data collection procedures in studies of football (soccer) injuries. British Journal of Sports Medicine, 40, pp. 193-201, doi:http://dx.doi.org/10.1136/bjsm.2005.025270, 2006.
39. Di Prampero PE, Fusi S, Sepulcri L, Morin JB, Belli A, Antonutto G. Sprint running: a new energetic approach. J Exp Biol. 2005;208: 28092816.
40. Gaudino P, Iaia FM, Alberti G, Strudwick AJ, Atkinson G, Gregson W. Monitoring training in elite soccer players: systematic bias between running speed and metabolic power data. Int J Sports Med. 2013;34: 963968.
41. James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning. New York, NY: Springer New York; 2013.
42. Guyon I, Weston J, Barnhill S, Vapnik V. Gene Selection for Cancer Classi cation Using Support Vector Machines. Machine Learning 46, 2002, 10.1023/A:1012487302797.
43. Li J., Cheng K., Wang S., Morstatter F., Trevino R., Jiliang T., Huan L. Feature Selection: A Data Perspective. arXiv:1601.07996, 2016
44. Friedman J, Hastie T, Tibshirani R. The elements of statistical learning. Springer series in Statistics, volume 1, Springer, Berlin, 2001.
45. Hastie TJ and Tibshirani RJ and Friedman JH The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics, isbn: 978-0-387-84857-0, 2009
46. Lowry CA, Woodall WH, Champ CW, Rigdon SE. A Multivariate Exponentially Weighted Moving Average Control Chart. Technometrics. Technometrics. 1992;34: 4653.
47. Lucas JM, Saccucci MS. Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements. Technometrics. 1990;32: 112.
1 Hägglund M, Waldén M, Magnusson H, Kristenson H, Bengtsson H, Exstrand J. Injuries affect team performance negatively in professional football: an 11-year follow-up of the UEFA Champions League injury study. British Journal of Sports Medicine, 10.1136/bjsports-2013-092215, 2013 23645832
2 Hurley OA. Impact of Player Injuries on Teams’ Mental States, and Subsequent Performances, at the Rugby World Cup 2015. Frontiers in Psychology 7:807, 10.3389/fpsyg.2016.00807 27375511
3 Lehmann EE, Schulze GG. What Does it Take to be a Star?–The Role of Performance and the Media for German Soccer Players. Applied Economics Quarterly 54:1, pp. 59–70, 10.3790/aeq.54.1.59, 2008.
4 Fernández-Cuevas I., Gomez-Carmona P, Sillero-Quintana M, Noya-Salces J, Arnaiz-Lastras J, Pastor-Barrón A. Economic costs estimation of soccer injuries in first and second Spanish division professional teams. 15th Annual Congress of the European College of Sport Sciences ECSS, 23th 26th june. 2010.
5 Gudmundsson H, Horton M. Spatio-Temporal Analysis of Team Sports, ACM Computing Surveys 50, 2, Article 22 (4 2017), 34 pages. 10.1145/3054132.
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BibTeX entry
@article{oai:it.cnr:prodotti:401270,
title = {Effective injury forecasting in soccer with GPS training data and machine learning},
author = {Rossi A. and Pappalardo L. and Cintia P. and Iaia F. M. and Fernandez J. and Medina D.},
publisher = {Public Library of Science, San Francisco, CA , Stati Uniti d'America},
doi = {10.1371/journal.pone.0201264 and 10.48550/arxiv.1705.08079},
journal = {PloS one},
volume = {13},
pages = {1–15},
year = {2018}
}
0000-0002-8085-9338CNR ExploRA
ISTI Repository
DOI
ISTI Repository
DOI
10.1371/journal.pone.0201264
10.48550/arxiv.1705.08079
