Abstract
Millions of people around the globe are fond of cricket. With such a big fan base, teams are sure to be competitive. Teams usually try to get their players to play at their full potential. Like many other sports, cricket also has a large amount of data available per match. Data collected from these matches can be used to provide insights on why a specific match resulted in a certain outcome. Data related to team performance and individual player statistics can be retrieved from each match. Analysis of these data helps the team improve their player performance. However, sometimes factors like venue, toss winning, and ranking can also affect the result of a match. In this paper, we design and implement sports data mining approaches to predict match results. In particular, we focus on cricket. Evaluation on real-life T20 International World Cup data—which examine and analyze factors like venue, toss winning, team ranking, and matches won against a specific opponent to predict the winner of a given match—demonstrates the practicality and effectiveness of our sport data mining and prediction approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dhaouadi, A., Bousselmi, K., Monnet, S., Gammoudi, M.M., Hammoudi, S.: A multi-layer modeling for the generation of new architectures for big data warehousing. In: Barolli, L., Hussain, F., Enokido, T. (eds.) AINA 2022. LNNS, vol. 450, pp. 204–218. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99587-4_18
Di Martino, B., D’Angelo, S., Esposito, A., Lupi, P.: Anomalous witnesses and registrations detection in the Italian justice system based on big data and machine learning techniques. In: Barolli, L., Hussain, F., Enokido, T. (eds.) AINA 2022. LNNS, vol. 451, pp. 183–192. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99619-2_18
Fung, D.L.X., et al.: Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19. J. Transl. Med. 19(1), 1–18 (2021)
Leung, C.K., et al.: Explainable data analytics for disease and healthcare informatics. In: IDEAS 2021, pp. 12:1–12:12 (2021)
Liu, Q., et al.: A two-dimensional sparse matrix profile DenseNet for COVID-19 diagnosis using chest CT images. IEEE Access 8, 213718–213728 (2020)
Souza, J., Leung, C.K., Cuzzocrea, A.: An innovative big data predictive analytics framework over hybrid big data sources with an application for disease analytics. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds.) AINA 2020. AISC, vol. 1151, pp. 669–680. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44041-1_59
Leung, C.K., et al.: Explainable artificial intelligence for data science on customer churn. In: IEEE DSAA 2021, pp. 235–244 (2021)
Anderson-Grégoire, I.M., et al.: A big data science solution for analytics on moving objects. In: Barolli, L., Woungang, I., Enokido, T. (eds.) AINA 2021. LNNS, vol. 226, pp. 133–145. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75075-6_11
Barkwell, K.E., et al.: Big data visualisation and visual analytics for music data mining. In: IV 2018, pp. 235–240 (2018)
Cabusas, R.M., Epp, B.N., Gouge, J.M., Kaufmann, T.N., Leung, C.K., Tully, J.R.A.: Mining for fake news. In: Barolli, L., Hussain, F., Enokido, T. (eds.) AINA 2022. LNNS, vol. 450, pp. 154–166. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99587-4_14
Cameron, J.J., et al.: Finding strong groups of friends among friends in social networks. In: IEEE DASC 2011, pp. 824–831 (2011)
Leung, C.K., Jiang, F., Poon, T.W., Crevier, P.É.: Big data analytics of social network data: who cares most about you on Facebook? In: Moshirpour, M., Far, B., Alhajj, R. (eds.) Highlighting the Importance of Big Data Management and Analysis for Various Applications. SBD, vol. 27, pp. 1–15. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-60255-4_1
Leung, C.K., et al.: Personalized DeepInf: enhanced social influence prediction with deep learning and transfer learning. In: IEEE BigData 2019, pp. 2871–2880 (2019)
Isichei, B.C., et al.: Sports data management, mining, and visualization. In: Barolli, L., Hussain, F., Enokido, T. (eds.) AINA 2022. LNNS, vol. 450, pp. 141–153. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99587-4_13
Balbin, P.P.F., et al.: Predictive analytics on open big data for supporting smart transportation services. Procedia Comput. Sci. 176, 3009–3018 (2020)
Leung, C.K., Braun, P., Hoi, C.S.H., Souza, J., Cuzzocrea, A.: Urban analytics of big transportation data for supporting smart cities. In: Ordonez, C., Song, I.-Y., Anderst-Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DaWaK 2019. LNCS, vol. 11708, pp. 24–33. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27520-4_3
Han, J., et al.: Data Mining: Concepts and Techniques, 4th edn. Morgan Kaufmann, San Francisco (2022)
Leung, C.K., et al.: Distributed uncertain data mining for frequent patterns satisfying anti-monotonic constraints. In: IEEE AINA Workshops 2014, pp. 1–6 (2014)
Leung, C.K.-S., Hayduk, Y.: Mining frequent patterns from uncertain data with MapReduce for big data analytics. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013. LNCS, vol. 7825, pp. 440–455. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37487-6_33
Rahman, M.M., et al.: Mining weighted frequent sequences in uncertain databases. Inf. Sci. 479, 76–100 (2019)
Schumaker, R.P.: Sports Data Mining. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-6730-5
Steyaert, M., et al.: Sensor-based performance monitoring in track cycling. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds.) Machine Learning and Data Mining for Sports Analytics, MLSA 2021. Communications in Computer and Information Science, vol. 1571, pp. 167–177. Springer, Cham (2021). https://doi.org/10.1007/978-3-031-02044-5_14
Jacquelin, N., et al.: Detecting swimmers in unconstrained videos with few training data. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds.) Machine Learning and Data Mining for Sports Analytics, MLSA 2021. CCIS, vol. 1571, pp. 145–154. Springer, Cham (2021). https://doi.org/10.1007/978-3-031-02044-5_12
Moura, H.D., et al.: Low cost player tracking in field hockey. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds.) Machine Learning and Data Mining for Sports Analytics, MLSA 2021. CCIS, vol. 1571, pp. 103–115. Springer, Cham (2021). https://doi.org/10.1007/978-3-031-02044-5_9
Leung, C.K., Joseph, K.W.: Sports data mining: predicting results for the college football games. Procedia Comput. Sci. 35, 710–719 (2014)
Behera, S.R., Saradhi, V.V.: Learning strength and weakness rules of cricket players using association rule mining. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds.) Machine Learning and Data Mining for Sports Analytics, MLSA 2021. CCIS, vol. 1571, pp. 79–92. Springer, Cham (2021). https://doi.org/10.1007/978-3-031-02044-5_7
Tirtho, D., et al.: Cricketer’s tournament-wise performance prediction and squad selection using machine learning and multi-objective optimization. Appl. Soft Comput. 129, 109526:1–109526:14 (2022)
Vetukuri, V.S., et al.: A multi-aspect analysis and prediction scheme for cricket matches in standard T-20 format. Int. J. Knowl.-Based Intell. Eng. Syst. 23(3), 149–154 (2019)
Gupta, A., Muthiah, S.B.: Learning cricket strokes from spatial and motion visual word sequences. Multimedia Tools Appl. 82(1), 1237–1259 (2023)
Raval, K.R., Goyani, M.M.: A survey on event detection based video summarization for cricket. Multimedia Tools Appl. 81(20), 29253–29281 (2022)
Vetukuri, V.S., et al.: Generic model for automated player selection for cricket teams using recurrent neural networks. Evol. Intel. 14(2), 971–978 (2021)
Longmore, A., et al.: Cricket. In: Encyclopedia Britannica (2021). https://www.britannica.com/sports/cricket-sport
Acknowledgements
This work is partially supported by NSERC (Canada) and University of Manitoba.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Anuraj, A. et al. (2023). Sports Data Mining for Cricket Match Prediction. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol 655. Springer, Cham. https://doi.org/10.1007/978-3-031-28694-0_63
Download citation
DOI: https://doi.org/10.1007/978-3-031-28694-0_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28693-3
Online ISBN: 978-3-031-28694-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)