An intelligent clustering framework for substitute recommendation and player selection
Pages 16409 - 16441
Abstract
Player selection is an important aspect of team-based sports such as cricket. Various situations, like players getting injured, rested, or falling under disciplinary action, etc., are common in cricket, and in those circumstances, the proper substitution of players is very important. We present an innovative knowledge-based intelligent framework for substitute suggestions by employing various clustering techniques like DBSCAN, Spectral clustering. We compared it with the substitution made in the real-time team selection process and obtained a large similarity between that and the recommendations generated using Spectral clustering. We have also compared our proposed results with existing state-of-the-art works in our experimental setup, where they have used the K-means clustering technique. The results highlighted that Spectral clustering is the best choice for substitute recommendations among the mentioned clustering techniques. We also present an intelligent framework for team selection and apply various similarity measures like Euclidean distance, Cosine Similarity, Manhattan distance, and Pearson Correlation Coefficient to find the most accurate combination of players. The recommendations obtained from the Pearson Correlation Coefficient have a maximum accuracy of 77.50% with a high F-measure (0.77) and present different directions for the team line-up formation.
References
[1]
Das NR, Ghosh S, Mukherjee I, and Paul G Adoption of a ranking based indexing method for the cricket teams Expert Syst Appl 2023 213 118796
[2]
Sankaran S Comparing pay versus performance of IPL bowlers: an application of cluster analysis Int J Perform Anal Sport 2014 14 1 174-187
[3]
Radhakrishnan S, Velambur C, Mahesh K (2018) V Score-a data analytical versatility metric for cricket. In: 2018 International conference on advances in computing, communications and informatics (ICACCI), pp 1569–1573.
[4]
Janani R and Vijayarani S Text document clustering using Spectral Clustering algorithm with Particle Swarm Optimization Expert Syst Appl 2019 134 192-200
[5]
McNamara DJ, Gabbett TJ, and Naughton G Assessment of workload and its effects on performance and injury in elite cricket fast bowlers Sports Med 2017 47 3 503-515
[6]
Kimber AC and Hansford AR A statistical analysis of batting in cricket J R Stat Soc A Stat Soc 1993 156 3 443-455
[7]
Damodaran U Stochastic dominance and analysis of ODI batting performance: The Indian Cricket Team, 1989–2005 J Sports Sci Med 2006 5 4 503
[8]
Mukherjee S Quantifying individual performance in Cricket-A network analysis of batsmen and bowlers Physica A: Statistical Mechanics and its Applications 2014 393 624-637
[9]
Das NR, Priya R, Mukherjee I, Paul G (2021) Modified Hedonic based price prediction model for players in IPL auction. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp 1–7.
[10]
Gupta K An integrated batting performance analytics model for women’s cricket using Principal Component Analysis and Gini scores Decis Analyt J 2022 4 100109
[11]
Roy TJ, Mahmood MA, Mohanta A, Roy D, Jyoti JT, Ghosh PK (2022) A machine learning approach to analyze the performance of Bangladesh Cricket in T20. In: 2022 International conference on innovations in science, engineering and technology (ICISET), pp 129–134.
[12]
Iyer SR and Sharda R Prediction of athletes performance using neural networks: an application in cricket team selection Expert Syst Appl 2009 36 3 5510-5522
[13]
Sathya SS, Jamal MS (2009) Applying genetic algorithm to select an optimal cricket team. In: Proceedings of the international conference on advances in computing, communication and control, pp 43–47.
[14]
Jayanth SB, Anthony A, Abhilasha G, Shaik N, and Srinivasa G A team recommendation system and outcome prediction for the game of cricket J Sports Analyt 2018 4 4 263-273
[15]
Manage AB, Scariano SM, and Hallum CR Performance analysis of T20-World Cup Cricket 2012 Sri Lankan J Appl Stat 2013 14 1 1-12
[16]
Ahmad H, Daud A, Wang L, Hong H, Dawood H, and Yang Y Prediction of rising stars in the game of cricket IEEE Access 2017 5 4104-4124
[17]
Hatharasinghe MM, Poravi G (2019) Data mining and machine learning in cricket match outcome prediction: missing links. In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), pp 1–4.
[18]
Bailey M and Clarke SR Predicting the match outcome in one day international cricket matches, while the game is in progress J Sports Sci Med 2006 5 4 480
[19]
Kapadia K, Abdel-Jaber H, Thabtah F, Hadi W (2020) Sport analytics for cricket game results using machine learning: an experimental study. Appl Comput Informat 18(3/4):256-266.
[20]
Singh T, Singla V, Bhatia P (2015) Score and winning prediction in cricket through data mining. In: 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), pp 60–66.
[21]
Wickramasinghe I Applications of machine learning in cricket: a systematic review Mach Learn Appl 2022 10 100435
[22]
Pathak N and Wadhwa H Applications of modern classification techniques to predict the outcome of ODI cricket Proced Comput Sci 2016 87 55-60
[23]
Kumar J, Kumar R, Kumar P (2018) Outcome prediction of ODI cricket matches using decision trees and MLP networks. In: 2018 1st International Conference on Secure Cyber Computing and Communication (ICSCCC), pp 343–347.
[24]
ESPNcricinfo (2021) https://www.espncricinfo.com/
[25]
Von Luxburg U A tutorial on spectral clustering Stat Comput 2007 17 4 395-416
[26]
Schubert E, Sander J, Ester M, Kriegel HP, and Xu X DBSCAN revisited, revisited: why and how you should (still) use DBSCAN ACM Trans Database Syst (TODS) 2017 42 3 1-21
[27]
Yang Y, Qian C, Li H, Gao Y, Wu J, Liu CJ, Zhao S (2022) An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning. J Supercomput 78:19566-19604.
[28]
Chang CC, Chou JS, and Chen TS An efficient computation of Euclidean distances using approximated look-up table IEEE Trans Circuits Syst Video Technol 2000 10 4 594-599
[29]
Wang D, Lu H, and Bo C Visual tracking via weighted local cosine similarity IEEE Trans Cybern 2014 45 9 1838-1850
[30]
Faisal M, Zamzami E, et al. Comparative analysis of inter-centroid k-means performance using Euclidean distance, Canberra distance and Manhattan distance J Phys: Confer Ser 2020 1566 1 012112
[31]
Zhou H, Deng Z, Xia Y, and Fu M A new sampling method in particle filter based on Pearson correlation coefficient Neurocomputing 2016 216 208-215
[32]
[33]
[34]
[35]
Cricbuzz (2021) https://www.cricbuzz.com/
[36]
[37]
[38]
Patel KA et al. An Efficient and scalable density-based clustering algorithm for normalize data Proced Comput Sci 2016 92 136-141
[39]
Li H, Liu J, Liu RW, Xiong N, Wu K, and Kim TH A dimensionality reduction-based multi-step clustering method for robust vessel trajectory analysis Sensors 2017 17 8 1792
[40]
Bansal S, Baliyan N (2019) Evaluation of collaborative filtering based recommender systems against segment-based shilling attacks. In: 2019 International Conference on Computing, Power and Communication Technologies (GUCON), pp 110–114
Recommendations
Recommendation algorithm based on improved spectral clustering and transfer learning
Collaborative filtering (CF) recommendation has made great success in solving information overload. However, CF has some disadvantages such as cold start, data sparseness, low operation efficiency and knowledge cannot transfer between multiple rating ...
Study on multi-center fuzzy C-means algorithm based on transitive closure and spectral clustering
Fuzzy C-means (FCM) clustering has been widely used successfully in many real-world applications. However, the FCM algorithm is sensitive to the initial prototypes, and it cannot handle non-traditional curved clusters. In this paper, a multi-center ...
Bagging-based spectral clustering ensemble selection
Traditional clustering ensemble methods combine all obtained clustering results at hand. However, we can often achieve a better clustering solution if only parts of the clustering results available are combined. In this paper, we generalize the ...
Comments
Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 27 April 2023
Accepted: 14 April 2023
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 08 Mar 2025