2019
Journal article
Open Access
A public data set of spatio-temporal match events in soccer competitions
Pappalardo L., Cintia P., Rossi A., Massucco E., Ferragina P., Pedreschi D., Giannotti F.
Medical research Football analytics Library and Information Sciences Data science Information Systems Data Descriptor Statistics Information technology Computer Science Applications Education Network analysis Big data Statistics and Probability Sports Analytics Interdisciplinary studies Probability and Uncertainty Soccer analytics
Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.
Source: Scientific data 6 (2019): 236. doi:10.1038/s41597-019-0247-7
Publisher: Nature Publishing Group, London, Regno Unito
Bornn, L, Cervone, LD, Fernández, J. Soccer analytics: Unravelling the complexity of “the beautiful game”. Significance. 2018; 15: 26-29
2.Anderson, C. & Sally, D. The Numbers Game: Why Everything You Know About Football is Wrong. Penguin Books (2013)
Reep, C, Benjamin, B. Skill and Chance in Association Football. Journal of the Royal Statistical Society. 1968; 131: 581-585
4.Sykes J. & Paine N. How One Man’s Bad Math Helped Ruin Decades Of English Soccer. Five Thirty Eight (2016)
5.Gudmundsson, J., Butte, A. J. & Horton, M. Spatio-Temporal Analysis of Team Sports. ACM Computing Surveys 50(2), 22:1-22:34 (2017).
6.Decroos, T., Van Haaren, J. & Davis, J. Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18), 223–232 (2018).
7.Cintia, P., Pappalardo, L., Pedreschi, D., Giannotti, F. & Malvaldi, M., 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), 1–10 (2015).
8.Cintia, P., Rinzivillo, S. & Pappalardo, L. Network-based Measures for Predicting the Outcomes of Football Games. Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA), 46–54 (2015).
9.Brooks, J., Kerr, M. & Guttag, J. Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16), 49–55 (2016).
10.Bornn, L. & Fernendez, J. Wide Open Spaces: A statistical technique for measuring space creation in professional soccer. MIT Sloan Sports Analytics Conference 2018 (2018).
11.Wei, X., Sha, L., Lucey, P., Morgan, S. & Sridharan, S. Large-Scale Analysis of Formations in Soccer. Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 1–8 (2013).
Rossi, A. Effective injury forecasting in soccer with GPS training data and machine learning. PloS One. 2018; 13 (7): 1-15
13.Pappalardo, L. et al. PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach. ACM Transactions on Intelligent Systems and Technology (TIST), 10(5), 59:1-59:27 (2018).
14.Duch, J., Waitzman, J. S. & Amaral, L. A. N. Quantifying the Performance of Individual Players in a Team Activity. PlosOne 5(6), 1–7 (2010)
15.Bialkowski, A. et al. Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data. Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM), 725–730 (2014).
Buldú, JM. Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Frontiers in Psychology. 2018; 9: 1664-1078
17.Yucesoy, B. & Barabasi, A.-L. Untangling performance from success. EPJ Data Science 5(1), 1–17 (2016).
18.Cintia, P., Pappalardo, L. & Pedreschi, D. “Engine Matters”: A First Large Scale Data Driven Study on Cyclists’ Performance. Proceedings of the 13th IEEE International Conference on Data Mining Workshops, 147–153 (2013).
19.Pappalardo, L. & Cintia, P. Quantifying the relation between performance and success in soccer. Advances in Complex Systems 21(3) (2017).
20.Pappalardo, L., Cintia, P., Pedreschi, D., Giannotti, F. & Barabasi, A.-L. Human Perception of Performance. Preprint at, http://arxiv.org/abs/1712.02224 (2017).
Pappalardo, L, Massucco, E. figshare. 2019
22.Link, D. & Hoerning, M. Individual ball possession in soccer. PLoS One 12(7), 1–15 (2017).
23.Armatas, V., Yiannakos, A., Papadopoulou, S. & Skoufas, D. Evaluation of goals scored in top ranking soccer matches: Greek “Superleague” 2006-07. Serbian Journal of Sports Sciences 3(1), 39–43 (2009).
24.Alberti, G., Iaia, F. M., Arcelli, E., Cavaggioni, L. & Rampinini, E. Goal scoring patterns in major European soccer leagues. Sport Sciences for Health 9(3), 151–153 (2013).
25.Unkelbach, C. & Memmert, D. Game Management, Context Effects, and Calibration: The Case of Yellow Cards in Soccer Journal of Sport and Exercise Psychology 1, 95–109 (2008).
26.Link, D. & Weber, H. Using individual ball possession as a performance indicator in soccer. Workshop on Large-Scale Sports Analytics (2015)
27.Gama, J. et al. Network analysis and intra-team activity in attacking phases of professional football. International Journal of Performance Analysis in Sport 14(3), 692–708 (2014).
Passos, P. Network analysis and intra-team activity in attacking phases of professional football-networks as a novel tool for studying team ball sports as complex social systems. Journal of Science and Medicine in Sport. 2011; 14 (2): 170-176
Clemente, FM, Martins, FML, Kalamares, D, Wong, DP, Mendes, RS. General network analysis of national soccer teams in FIFA World Cup 2014. International Journal of Performance Analysis in Sport. 2015; 15: 80-96
30.Wang, Q., Zhu, H., Hu, W., Shen, Z. & Yao, Y. Discerning tactical patterns for professional soccer teams: an enhanced topic model with applications. 21 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015).
31.Rein, R. & Memmert, D. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus 5(1) (2016).
1. Bornn, L., Cervone, L. D. & Fernández, J. Soccer analytics: Unravelling the complexity of “the beautiful game”. Signicfiance 15, 26-29 (2018).
2. Anderson, C. & Sally, D. eTh Numbers Game: Why Everything You Know About Football is Wrong. Penguin Books (2013)
3. Reep, C. & Benjamin, B. Skill and Chance in Association Football. Journal of the Royal Statistical Society 131, 581-585 (1968).
4. Sykes J. & Paine N. How One Man's Bad Math Helped Ruin Decades Of English Soccer. Five iThrty Eight (2016)
5. Gudmundsson, J., Butte, A. J. & Horton, M. Spatio-Temporal Analysis of Team Sports. ACM Computing Surveys 50(2), 22:1-22:34 (2017).
6. Decroos, T., Van Haaren, J. & Davis, J. Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18), 223-232 (2018).
7. Cintia, P., Pappalardo, L., Pedreschi, D., Giannotti, F. & Malvaldi, M., 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), 1-10 (2015).
8. Cintia, P., Rinzivillo, S. & Pappalardo, L. Network-based Measures for Predicting the Outcomes of Football Games. Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA), 46-54 (2015).
9. Brooks, J., Kerr, M. & Guttag, J. Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16), 49-55 (2016).
10. Bornn, L. & Fernendez, J. Wide Open Spaces: A statistical technique for measuring space creation in professional soccer. MIT Sloan Sports Analytics Conference 2018 (2018).
11. Wei, X., Sha, L., Lucey, P., Morgan, S. & Sridharan, S. Large-Scale Analysis of Formations in Soccer. Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 1-8 (2013).
12. Rossi, A. et al. Eefctive injury forecasting in soccer with GPS training data and machine learning. PloS One 13(7), 1-15 (2018).
13. Pappalardo, L. et al. PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach. ACM Transactions on Intelligent Systems and Technology (TIST), 10(5), 59:1-59:27 (2018).
14. Duch, J., Waitzman, J. S. & Amaral, L. A. N. Quantifying the Performance of Individual Players in a Team Activity. PlosOne 5(6), 1-7 (2010)
15. Bialkowski, A. et al. Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data. Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM), 725-730 (2014).
16. Buldú, J. M. et al. Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Frontiers in Psychology 9, 1664-1078 (2018).
17. Yucesoy, B. & Barabasi, A.-L. Untangling performance from success. EPJ Data Science 5(1), 1-17 (2016).
18. Cintia, P., Pappalardo, L. & Pedreschi, D. “Engine Matters”: A First Large Scale Data Driven Study on Cyclists' Performance. Proceedings of the 13th IEEE International Conference on Data Mining Workshops, 147-153 (2013).
19. Pappalardo, L. & Cintia, P. Quantifying the relation between performance and success in soccer. Advances in Complex Systems 21(3) (2017).
20. Pappalardo, L., Cintia, P., Pedreschi, D., Giannotti, F. & Barabasi, A.-L. Human Perception of Performance. Preprint at, http://arxiv. org/abs/1712.02224 (2017).
21. Pappalardo, L. & Massucco, E. Soccer match event dataset. gfishare , https://doi.org/10.6084/m9.figshare.c.4415000.v2 (2019).
22. Link, D. & Hoerning, M. Individual ball possession in soccer. PLoS One 12(7), 1-15 (2017).
23. Armatas, V., Yiannakos, A., Papadopoulou, S. & Skoufas, D. Evaluation of goals scored in top ranking soccer matches: Greek “Superleague” 2006-07. Serbian Journal of Sports Sciences 3(1), 39-43 (2009).
24. Alberti, G., Iaia, F. M., Arcelli, E., Cavaggioni, L. & Rampinini, E. Goal scoring patterns in major European soccer leagues. Sport Sciences for Health 9(3), 151-153 (2013).
25. Unkelbach, C. & Memmert, D. Game Management, Context Eefcts, and Calibration: eTh Case of Yellow Cards in Soccer Journal of Sport and Exercise Psychology 1, 95-109 (2008).
26. Link, D. & Weber, H. Using individual ball possession as a performance indicator in soccer. Workshop on Large-Scale Sports Analytics (2015)
27. Gama, J. et al. Network analysis and intra-team activity in attacking phases of professional football. International Journal of Performance Analysis in Sport 14(3), 692-708 (2014).
28. Passos, P. et al. Networks as a novel tool for studying team ball sports as complex social systems. Journal of Science and Medicine in Sport 14(2), 170-176 (2011).
29. Clemente, F. M., Martins, F. M. L., Kalamares, D., Wong, D. P. & Mendes, R. S. General network analysis of national soccer teams in FIFA World Cup 2014. International Journal of Performance Analysis in Sport 15, 80-96 (2015).
30. Wang, Q., Zhu, H., Hu, W., Shen, Z. & Yao, Y. Discerning tactical patterns for professional soccer teams: an enhanced topic model with applications. 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015).
31. Rein, R. & Memmert, D. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus 5(1) (2016).
2.Anderson, C. & Sally, D. The Numbers Game: Why Everything You Know About Football is Wrong. Penguin Books (2013)
Reep, C, Benjamin, B. Skill and Chance in Association Football. Journal of the Royal Statistical Society. 1968; 131: 581-585
4.Sykes J. & Paine N. How One Man’s Bad Math Helped Ruin Decades Of English Soccer. Five Thirty Eight (2016)
5.Gudmundsson, J., Butte, A. J. & Horton, M. Spatio-Temporal Analysis of Team Sports. ACM Computing Surveys 50(2), 22:1-22:34 (2017).
6.Decroos, T., Van Haaren, J. & Davis, J. Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18), 223–232 (2018).
7.Cintia, P., Pappalardo, L., Pedreschi, D., Giannotti, F. & Malvaldi, M., 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), 1–10 (2015).
8.Cintia, P., Rinzivillo, S. & Pappalardo, L. Network-based Measures for Predicting the Outcomes of Football Games. Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA), 46–54 (2015).
9.Brooks, J., Kerr, M. & Guttag, J. Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16), 49–55 (2016).
10.Bornn, L. & Fernendez, J. Wide Open Spaces: A statistical technique for measuring space creation in professional soccer. MIT Sloan Sports Analytics Conference 2018 (2018).
11.Wei, X., Sha, L., Lucey, P., Morgan, S. & Sridharan, S. Large-Scale Analysis of Formations in Soccer. Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 1–8 (2013).
Rossi, A. Effective injury forecasting in soccer with GPS training data and machine learning. PloS One. 2018; 13 (7): 1-15
13.Pappalardo, L. et al. PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach. ACM Transactions on Intelligent Systems and Technology (TIST), 10(5), 59:1-59:27 (2018).
14.Duch, J., Waitzman, J. S. & Amaral, L. A. N. Quantifying the Performance of Individual Players in a Team Activity. PlosOne 5(6), 1–7 (2010)
15.Bialkowski, A. et al. Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data. Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM), 725–730 (2014).
Buldú, JM. Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Frontiers in Psychology. 2018; 9: 1664-1078
17.Yucesoy, B. & Barabasi, A.-L. Untangling performance from success. EPJ Data Science 5(1), 1–17 (2016).
18.Cintia, P., Pappalardo, L. & Pedreschi, D. “Engine Matters”: A First Large Scale Data Driven Study on Cyclists’ Performance. Proceedings of the 13th IEEE International Conference on Data Mining Workshops, 147–153 (2013).
19.Pappalardo, L. & Cintia, P. Quantifying the relation between performance and success in soccer. Advances in Complex Systems 21(3) (2017).
20.Pappalardo, L., Cintia, P., Pedreschi, D., Giannotti, F. & Barabasi, A.-L. Human Perception of Performance. Preprint at, http://arxiv.org/abs/1712.02224 (2017).
Pappalardo, L, Massucco, E. figshare. 2019
22.Link, D. & Hoerning, M. Individual ball possession in soccer. PLoS One 12(7), 1–15 (2017).
23.Armatas, V., Yiannakos, A., Papadopoulou, S. & Skoufas, D. Evaluation of goals scored in top ranking soccer matches: Greek “Superleague” 2006-07. Serbian Journal of Sports Sciences 3(1), 39–43 (2009).
24.Alberti, G., Iaia, F. M., Arcelli, E., Cavaggioni, L. & Rampinini, E. Goal scoring patterns in major European soccer leagues. Sport Sciences for Health 9(3), 151–153 (2013).
25.Unkelbach, C. & Memmert, D. Game Management, Context Effects, and Calibration: The Case of Yellow Cards in Soccer Journal of Sport and Exercise Psychology 1, 95–109 (2008).
26.Link, D. & Weber, H. Using individual ball possession as a performance indicator in soccer. Workshop on Large-Scale Sports Analytics (2015)
27.Gama, J. et al. Network analysis and intra-team activity in attacking phases of professional football. International Journal of Performance Analysis in Sport 14(3), 692–708 (2014).
Passos, P. Network analysis and intra-team activity in attacking phases of professional football-networks as a novel tool for studying team ball sports as complex social systems. Journal of Science and Medicine in Sport. 2011; 14 (2): 170-176
Clemente, FM, Martins, FML, Kalamares, D, Wong, DP, Mendes, RS. General network analysis of national soccer teams in FIFA World Cup 2014. International Journal of Performance Analysis in Sport. 2015; 15: 80-96
30.Wang, Q., Zhu, H., Hu, W., Shen, Z. & Yao, Y. Discerning tactical patterns for professional soccer teams: an enhanced topic model with applications. 21 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015).
31.Rein, R. & Memmert, D. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus 5(1) (2016).
1. Bornn, L., Cervone, L. D. & Fernández, J. Soccer analytics: Unravelling the complexity of “the beautiful game”. Signicfiance 15, 26-29 (2018).
2. Anderson, C. & Sally, D. eTh Numbers Game: Why Everything You Know About Football is Wrong. Penguin Books (2013)
3. Reep, C. & Benjamin, B. Skill and Chance in Association Football. Journal of the Royal Statistical Society 131, 581-585 (1968).
4. Sykes J. & Paine N. How One Man's Bad Math Helped Ruin Decades Of English Soccer. Five iThrty Eight (2016)
5. Gudmundsson, J., Butte, A. J. & Horton, M. Spatio-Temporal Analysis of Team Sports. ACM Computing Surveys 50(2), 22:1-22:34 (2017).
6. Decroos, T., Van Haaren, J. & Davis, J. Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18), 223-232 (2018).
7. Cintia, P., Pappalardo, L., Pedreschi, D., Giannotti, F. & Malvaldi, M., 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), 1-10 (2015).
8. Cintia, P., Rinzivillo, S. & Pappalardo, L. Network-based Measures for Predicting the Outcomes of Football Games. Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA), 46-54 (2015).
9. Brooks, J., Kerr, M. & Guttag, J. Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16), 49-55 (2016).
10. Bornn, L. & Fernendez, J. Wide Open Spaces: A statistical technique for measuring space creation in professional soccer. MIT Sloan Sports Analytics Conference 2018 (2018).
11. Wei, X., Sha, L., Lucey, P., Morgan, S. & Sridharan, S. Large-Scale Analysis of Formations in Soccer. Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 1-8 (2013).
12. Rossi, A. et al. Eefctive injury forecasting in soccer with GPS training data and machine learning. PloS One 13(7), 1-15 (2018).
13. Pappalardo, L. et al. PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach. ACM Transactions on Intelligent Systems and Technology (TIST), 10(5), 59:1-59:27 (2018).
14. Duch, J., Waitzman, J. S. & Amaral, L. A. N. Quantifying the Performance of Individual Players in a Team Activity. PlosOne 5(6), 1-7 (2010)
15. Bialkowski, A. et al. Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data. Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM), 725-730 (2014).
16. Buldú, J. M. et al. Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Frontiers in Psychology 9, 1664-1078 (2018).
17. Yucesoy, B. & Barabasi, A.-L. Untangling performance from success. EPJ Data Science 5(1), 1-17 (2016).
18. Cintia, P., Pappalardo, L. & Pedreschi, D. “Engine Matters”: A First Large Scale Data Driven Study on Cyclists' Performance. Proceedings of the 13th IEEE International Conference on Data Mining Workshops, 147-153 (2013).
19. Pappalardo, L. & Cintia, P. Quantifying the relation between performance and success in soccer. Advances in Complex Systems 21(3) (2017).
20. Pappalardo, L., Cintia, P., Pedreschi, D., Giannotti, F. & Barabasi, A.-L. Human Perception of Performance. Preprint at, http://arxiv. org/abs/1712.02224 (2017).
21. Pappalardo, L. & Massucco, E. Soccer match event dataset. gfishare , https://doi.org/10.6084/m9.figshare.c.4415000.v2 (2019).
22. Link, D. & Hoerning, M. Individual ball possession in soccer. PLoS One 12(7), 1-15 (2017).
23. Armatas, V., Yiannakos, A., Papadopoulou, S. & Skoufas, D. Evaluation of goals scored in top ranking soccer matches: Greek “Superleague” 2006-07. Serbian Journal of Sports Sciences 3(1), 39-43 (2009).
24. Alberti, G., Iaia, F. M., Arcelli, E., Cavaggioni, L. & Rampinini, E. Goal scoring patterns in major European soccer leagues. Sport Sciences for Health 9(3), 151-153 (2013).
25. Unkelbach, C. & Memmert, D. Game Management, Context Eefcts, and Calibration: eTh Case of Yellow Cards in Soccer Journal of Sport and Exercise Psychology 1, 95-109 (2008).
26. Link, D. & Weber, H. Using individual ball possession as a performance indicator in soccer. Workshop on Large-Scale Sports Analytics (2015)
27. Gama, J. et al. Network analysis and intra-team activity in attacking phases of professional football. International Journal of Performance Analysis in Sport 14(3), 692-708 (2014).
28. Passos, P. et al. Networks as a novel tool for studying team ball sports as complex social systems. Journal of Science and Medicine in Sport 14(2), 170-176 (2011).
29. Clemente, F. M., Martins, F. M. L., Kalamares, D., Wong, D. P. & Mendes, R. S. General network analysis of national soccer teams in FIFA World Cup 2014. International Journal of Performance Analysis in Sport 15, 80-96 (2015).
30. Wang, Q., Zhu, H., Hu, W., Shen, Z. & Yao, Y. Discerning tactical patterns for professional soccer teams: an enhanced topic model with applications. 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015).
31. Rein, R. & Memmert, D. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus 5(1) (2016).
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BibTeX entry
@article{oai:it.cnr:prodotti:412387,
title = {A public data set of spatio-temporal match events in soccer competitions},
author = {Pappalardo L. and Cintia P. and Rossi A. and Massucco E. and Ferragina P. and Pedreschi D. and Giannotti F.},
publisher = {Nature Publishing Group, London, Regno Unito},
doi = {10.1038/s41597-019-0247-7},
journal = {Scientific data},
volume = {6},
pages = {236},
year = {2019}
}
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