Sport Analytics A Systematic Review
Resumo
Ciência de Dados é uma das áreas da Tecnologia de Informação em maior desenvolvimento, sendo usada nos mais diversos ramos, impactando fortemente a competitividade das empresas e a forma como as pessoas interagem. O seu uso nos esportes é designado como Sport Analytics, sendo usado para entender padrões, definir táticas/treinamentos e prever resultados. Este artigo procura resumir as pesquisas que estão sendo realizadas neste campo, bem como entender o conhecimento gerado e como ele está sendo aplicado.
Palavras-chave:
Sport Analytics, Ciência de Dados, Inteligência Artificial, Aprendizado de Máquinas, Desempenho dos Atletas
Referências
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Altini, M. and Amft, O. (2018). Estimating Running Performance Combining Noninvasive Physiological Measurements and Training Patterns in Free-Living. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018-July:2845–2848.
Anand, A., Sharma, M., Srivastava, R., Kaligounder, L., and Prakash, D. (2018). Wearable motion sensor based analysis of swing sports. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, 2018January:261–267.
Aoki, R. Y. S., Assuncao, R. M., and de Melo, P. O. S. (2017). Luck is Hard to Beat: The Difficulty of Sports Prediction. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, pages 1367–1376, New York, NY, USA. ACM.
Budgen, D. and Brereton, P. (2006). Performing systematic literature reviews in software engineering. In Proceeding of the 28th international conference on Software engineering - ICSE ’06, ICSE ’06, page 1051, New York, New York, USA. ACM Press.
Cintia, P., Giannotti, F., Pappalardo, L., Pedreschi, D., and Malvaldi, M. (2015). The harsh rule of the goals: Data-driven performance indicators for football teams. Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015.
Cokins, B. G., Degrange, W., Chambal, S., and Walker, R. (2017). Sports analytics taxonomy, V1.0. ORMS-Today, informs, 43(3):1–9.
Costa, I., Pires, C., and Marinho, L. (2017). Capı́tulo 2 Sports Analytics: Mudando o Jogo, chapter 2, pages 30–62. Sociedade Brasileira de Computação - SBC. de Leeuw, A.-W., Meerhoff, L. A., and Knobbe, A. (2018). Effects of Pacing Properties on Performance in Long-Distance Running. Big Data, 6(4):248–261.
de Smet, D., Verleysen, M., and Francaux, M. (2017). Running Race Times Prediction and Runner Performances Comparison using a Matrix Factorization Approach. In Proceedings of the 5th International Congress on Sport Sciences Research and Technology Support, number icSPORTS, pages 96–101. SCITEPRESS - Science and Technology Publications.
Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12):64– 73.
Dubbs, A. (2018). Statistics-free sports prediction. Model Assisted Statistics and Applications, 13(2):173–181.
Fernandez, J., Medina, D., Gomez, A., Arias, M., and Gavalda, R. (2016). From Training to Match Performance: A Predictive and Explanatory Study on Novel Tracking Data. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), number October, pages 136–143. IEEE.
Gu, W., Foster, K., Shang, J., and Wei, L. (2019). A game-predicting expert system using big data and machine learning. Expert Systems with Applications, 130:293–305.
Hamdad, L., Benatchba, K., Belkham, F., and Cherairi, N. (2018). Basketball Analytics. Data Mining for Acquiring Performances. pages 13–24. Springer, Cham.
Hayashi, C. (1998). What is Data Science ? Fundamental Concepts and a Heuristic Example. In What is Data Science ? Fundamental Concepts and a Heuristic Example, pages 40–51. Springer, Tokyo.
Jelinek, H. F., Kelarev, A., Robinson, D. J., Stranieri, A., and Cornforth, D. J. (2014). Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football. Applied Soft Computing Journal, 14(PART A):81–87.
Kasiri-Bidhendi, S., Fookes, C., Morgan, S., Martin, D. T., and Sridharan, S. (2015). Combat sports analytics: Boxing punch classification using overhead depthimagery. Proceedings - International Conference on Image Processing, ICIP, 2015December:4545–4549.
Kautz, T., Groh, B. H., Hannink, J., Jensen, U., Strubberg, H., and Eskofier, B. M. (2017). Activity recognition in beach volleyball using a Deep Convolutional Neural Network: Leveraging the potential of Deep Learning in sports. Data Mining and Knowledge Discovery, 31(6):1678–1705.
Kempe, M., Goes, F. R., and Lemmink, K. A. (2018). Smart data scouting in professional soccer: Evaluating passing performance based on position tracking data. Proceedings - IEEE 14th International Conference on eScience, e-Science 2018, pages 409–410.
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Moatamed, B., Darabi, S., Gwak, M., Kachuee, M., Metoyer, C., Linn, M., and Sarrafzadeh, M. (2017). Sport analytics platform for athletic readiness assessment. 2017 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2017, 2017Decem:156–159.
Nagarajan, R. and Li, L. (2018). Optimizing NBA player selection strategies based on salary and statistics analysis. Proceedings - 2017 IEEE 15th International Conference on Dependable, 2018-January:1076–1083.
Naglah, A., Khalifa, F., Mahmoud, A., Ghazal, M., Jones, P., Murray, T., Elmaghraby, A. S., and El-baz, A. (2018). Athlete-Customized Injury Prediction using Training Load Statistical Records and Machine Learning. In 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), number d, pages 459–464. IEEE.
O’Donoghue, J., Roantree, M., Cullen, B., Moyna, N., Sullivan, C. O., and McCarren, A. (2015). Anomaly and event detection for unsupervised athlete performance data. CEUR Workshop Proceedings, 1458(October):205–217.
Op De Beéck, T., Meert, W., Schütte, K., Vanwanseele, B., and Davis, J. (2018). Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’18, pages 606–615, New York, NY, USA. ACM.
Pariath, R., Shah, S., Surve, A., and Mittal, J. (2018). Player Performance Prediction in Football Game. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), number Iceca, pages 1148–1153. IEEE.
Rossi, A., Perri, E., Trecroci, A., Savino, M., Alberti, G., and Iaia, F. M. (2017). GPS data reflect players’ internal load in soccer. IEEE International Conference on Data Mining Workshops, ICDMW, 2017-November:890–893.
Ruiz, H., Power, P., Wei, X., and Lucey, P. (2017). ”The Leicester City Fairytale?”: Utilizing New Soccer Analytics Tools to Compare Performance in the 15/16 & 16/17 EPL Seasons. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1991–2000.
UE (2018). Study on the Economic Impact of Sport through Sport Satellite Accounts. Number April.
Value, I. and Team, E. (2015). Sports Analytics & Risk monitoring based on Hana Platform. International SoC Design Conference (ISOCC), pages 221–222.
Young, C. M., Luo, W., Gastin, P., Tran, J., and Dwyer, D. B. (2019). The relationship between match performance indicators and outcome in Australian Football. Journal of Science and Medicine in Sport, 22(4):467–471.
Altini, M. and Amft, O. (2018). Estimating Running Performance Combining Noninvasive Physiological Measurements and Training Patterns in Free-Living. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018-July:2845–2848.
Anand, A., Sharma, M., Srivastava, R., Kaligounder, L., and Prakash, D. (2018). Wearable motion sensor based analysis of swing sports. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, 2018January:261–267.
Aoki, R. Y. S., Assuncao, R. M., and de Melo, P. O. S. (2017). Luck is Hard to Beat: The Difficulty of Sports Prediction. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, pages 1367–1376, New York, NY, USA. ACM.
Budgen, D. and Brereton, P. (2006). Performing systematic literature reviews in software engineering. In Proceeding of the 28th international conference on Software engineering - ICSE ’06, ICSE ’06, page 1051, New York, New York, USA. ACM Press.
Cintia, P., Giannotti, F., Pappalardo, L., Pedreschi, D., and Malvaldi, M. (2015). The harsh rule of the goals: Data-driven performance indicators for football teams. Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015.
Cokins, B. G., Degrange, W., Chambal, S., and Walker, R. (2017). Sports analytics taxonomy, V1.0. ORMS-Today, informs, 43(3):1–9.
Costa, I., Pires, C., and Marinho, L. (2017). Capı́tulo 2 Sports Analytics: Mudando o Jogo, chapter 2, pages 30–62. Sociedade Brasileira de Computação - SBC. de Leeuw, A.-W., Meerhoff, L. A., and Knobbe, A. (2018). Effects of Pacing Properties on Performance in Long-Distance Running. Big Data, 6(4):248–261.
de Smet, D., Verleysen, M., and Francaux, M. (2017). Running Race Times Prediction and Runner Performances Comparison using a Matrix Factorization Approach. In Proceedings of the 5th International Congress on Sport Sciences Research and Technology Support, number icSPORTS, pages 96–101. SCITEPRESS - Science and Technology Publications.
Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12):64– 73.
Dubbs, A. (2018). Statistics-free sports prediction. Model Assisted Statistics and Applications, 13(2):173–181.
Fernandez, J., Medina, D., Gomez, A., Arias, M., and Gavalda, R. (2016). From Training to Match Performance: A Predictive and Explanatory Study on Novel Tracking Data. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), number October, pages 136–143. IEEE.
Gu, W., Foster, K., Shang, J., and Wei, L. (2019). A game-predicting expert system using big data and machine learning. Expert Systems with Applications, 130:293–305.
Hamdad, L., Benatchba, K., Belkham, F., and Cherairi, N. (2018). Basketball Analytics. Data Mining for Acquiring Performances. pages 13–24. Springer, Cham.
Hayashi, C. (1998). What is Data Science ? Fundamental Concepts and a Heuristic Example. In What is Data Science ? Fundamental Concepts and a Heuristic Example, pages 40–51. Springer, Tokyo.
Jelinek, H. F., Kelarev, A., Robinson, D. J., Stranieri, A., and Cornforth, D. J. (2014). Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football. Applied Soft Computing Journal, 14(PART A):81–87.
Kasiri-Bidhendi, S., Fookes, C., Morgan, S., Martin, D. T., and Sridharan, S. (2015). Combat sports analytics: Boxing punch classification using overhead depthimagery. Proceedings - International Conference on Image Processing, ICIP, 2015December:4545–4549.
Kautz, T., Groh, B. H., Hannink, J., Jensen, U., Strubberg, H., and Eskofier, B. M. (2017). Activity recognition in beach volleyball using a Deep Convolutional Neural Network: Leveraging the potential of Deep Learning in sports. Data Mining and Knowledge Discovery, 31(6):1678–1705.
Kempe, M., Goes, F. R., and Lemmink, K. A. (2018). Smart data scouting in professional soccer: Evaluating passing performance based on position tracking data. Proceedings - IEEE 14th International Conference on eScience, e-Science 2018, pages 409–410.
Kitchenham, B. and Charters, S. (2007). issue: EBSE 2007-001. Technical report, 2(3).
Moatamed, B., Darabi, S., Gwak, M., Kachuee, M., Metoyer, C., Linn, M., and Sarrafzadeh, M. (2017). Sport analytics platform for athletic readiness assessment. 2017 IEEE Healthcare Innovations and Point of Care Technologies, HI-POCT 2017, 2017Decem:156–159.
Nagarajan, R. and Li, L. (2018). Optimizing NBA player selection strategies based on salary and statistics analysis. Proceedings - 2017 IEEE 15th International Conference on Dependable, 2018-January:1076–1083.
Naglah, A., Khalifa, F., Mahmoud, A., Ghazal, M., Jones, P., Murray, T., Elmaghraby, A. S., and El-baz, A. (2018). Athlete-Customized Injury Prediction using Training Load Statistical Records and Machine Learning. In 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), number d, pages 459–464. IEEE.
O’Donoghue, J., Roantree, M., Cullen, B., Moyna, N., Sullivan, C. O., and McCarren, A. (2015). Anomaly and event detection for unsupervised athlete performance data. CEUR Workshop Proceedings, 1458(October):205–217.
Op De Beéck, T., Meert, W., Schütte, K., Vanwanseele, B., and Davis, J. (2018). Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’18, pages 606–615, New York, NY, USA. ACM.
Pariath, R., Shah, S., Surve, A., and Mittal, J. (2018). Player Performance Prediction in Football Game. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), number Iceca, pages 1148–1153. IEEE.
Rossi, A., Perri, E., Trecroci, A., Savino, M., Alberti, G., and Iaia, F. M. (2017). GPS data reflect players’ internal load in soccer. IEEE International Conference on Data Mining Workshops, ICDMW, 2017-November:890–893.
Ruiz, H., Power, P., Wei, X., and Lucey, P. (2017). ”The Leicester City Fairytale?”: Utilizing New Soccer Analytics Tools to Compare Performance in the 15/16 & 16/17 EPL Seasons. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1991–2000.
UE (2018). Study on the Economic Impact of Sport through Sport Satellite Accounts. Number April.
Value, I. and Team, E. (2015). Sports Analytics & Risk monitoring based on Hana Platform. International SoC Design Conference (ISOCC), pages 221–222.
Young, C. M., Luo, W., Gastin, P., Tran, J., and Dwyer, D. B. (2019). The relationship between match performance indicators and outcome in Australian Football. Journal of Science and Medicine in Sport, 22(4):467–471.
Publicado
15/10/2019
Como Citar
BOGO, Jair; OLIVEIRA, Janine; LIMA, Elivelton; MELLO, Rafael; FURTADO, Ana.
Sport Analytics A Systematic Review. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 670-681.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9324.
