1
School of Business, Woxsen University, Hyderabad, Telangana 502345, India
(Gandaakhila@woxsen.edu.in)
2
Professor of Artificial Intelligence, School of Business, Woxsen University, Hyderabad, Telangana 502345, India
(Hemachandran.k@woxsen.edu.in)
3
Associate Professor of Analytics, Department of Decision Sciences, Robert Willumstad School of Business, Adelphi University, 11530, New York, USA
( jjaramillo@adelphi.edu)
Abstract :
The purpose of the present article is to highlight the outcomes of Indian premier league cricket match utilizing a managed taking in come nearer from a team-based point of view. The methodology consists of prescriptive and descriptive models. Descriptive model focuses mainly on two aspects they are, it describes data and statistics of the previous information. i.e., batting, balling or allrounder and It predicts past matches of IPL. Predictive model predicts ranking and winning percentage of the team. The two models show the measurements of winning level of the group Winner that the user has selected. This paper predicts the result through which technique match has highest result. The dataset consists of two groups that is the toss outcome, venue date, which tells about of the counterpart for all matches. Since the nature impact can't be expected in the game, 109 matches which were either finished by downpour or draw/tie, have been taken out from the dataset. The dataset is partitioned into two sections to be specific the test information and the train information.The readiness dataset contains the 70% of the information from our dataset and the test dataset contains 30% of the information from our dataset. There were all out of 3500 coordinates in getting ready dataset and 1500 matches. This paper has been researched earlier by different scholars like Pathak and Wadwa, Munir etl ,and many other scholars. This viewpoint discusses the application of INDIAN PREMIER LEAGUE Matches held in different states. Gives the score of batsman and bowler with the help of machine learning techniques. Focuses on predicted analysis which is predicted by applying with various AI strategies to the real outcome actual result and gives the percentage of predicted result.
Keywords :
Sports Analytics , Cricket , Data Science , Machine Learning , Prediction.
References :
[1] Richard O. Duda, Peter.E. Hart, David G. Stork (2001) Pattern Classification. https://cds.cern.ch/record/683166/files/0471056693_TOC.pdf
[2] Akhil Nimmagadda, Nidamanuri Venkata Kalyan, Manigandla Venkatesh, Nuthi Naga Sai Teja, & Chavali Gopi Raju, C.G., (2018). Cricket Score and winning Prediction using Data Mining.V.3, DOI:V313-1230/30.03.2018.
https://www.ijarnd.com/manuscript/cricket-score-and-winning-prediction-using-data-mining/
[3] Neeraj Pathak & Hardik Wadhwa, (2016). Applications of Modern Classification Techniques to Predict the Outcome of ODI Cricket. In Procedia Computer Science. V.87,pp.55-60. DOI: 10.1016/j.procs.2016.05.126. https://www.researchgate.net/publication/303848376_Applications_of_Modern_Classification_Techniques_to_Predict_the_Outcome_of_ODI_Cricket
[4] D. Böhning(1992) Multinomial logistic regression algorithm.. http://www.ism.ac.jp/editsec/aism/pdf/044_1_0197.pdf
[5] Madan Jhanwar, Vikram Pudi, (2016). Predicting the Outcome of ODI Cricket Matches: A Team Composition Based Approach., Conference: Machine Learning and Data Mining for Sports Analytics, ECML-PKDD'16
[6] Stylianos Kampakis, William Thomas, W., (2015). Using machine learning to predict the outcome of English county twenty over cricket Matches.
[7] Ron Kohavi, George H. John. (1997) Wrappers for feature subset selection. Artificial Intelligence, Volume 97, Issues 1–2, December 1997, Pages 273-324.
[8] J.M, Keller, M.R, Gray, J.A. Givens (1985) A fuzzy k-closest Neighbor Algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Volume: SMC-15, Issue: 4, pp: 580 - 585.
[9] W. McKinney. (2012) Python for information investigation: Data fighting with Pandas, NumPy, and I Python.
[10] Mark A. Hall (1999) Correlation-based component choice for Machine Learning..
[11] Breiman, L. Random Forests. Machine Learning 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324
[12] Frank, E., Wang, Y., Inglis, S. et al. Using Model Trees for Classification. Machine Learning 32, 63–76 (1998). https://doi.org/10.1023/A:1007421302149
[13] Rory P.Bunker,Fadi Thabtah(2019) A machine learning framework for predicting sports Results. Applied Computing and Informatics, Volume 15, Issue 1, January 2019, Pages 27-33
[14] Munir M.Qazzaz, William Winlow, (2015). Predicting result of T20 cricket match.DOI:17-11-2015.
| Style | # |
|---|---|
| MLA | Gande Akhila, Hemachandran K, Juan R Jaramillo. "Indian Premier League Using Different Aspects of Machine Learning Algorithms." Journal of Cognitive Human-Computer Interaction, Vol. 1, No. 1, 2021 ,PP. 01-07 (Doi : https://doi.org/10.54216/JCHCI.010101) |
| APA | Gande Akhila, Hemachandran K, Juan R Jaramillo. (2021). Indian Premier League Using Different Aspects of Machine Learning Algorithms. Journal of Journal of Cognitive Human-Computer Interaction, 1 ( 1 ), 01-07 (Doi : https://doi.org/10.54216/JCHCI.010101) |
| Chicago | Gande Akhila, Hemachandran K, Juan R Jaramillo. "Indian Premier League Using Different Aspects of Machine Learning Algorithms." Journal of Journal of Cognitive Human-Computer Interaction, 1 no. 1 (2021): 01-07 (Doi : https://doi.org/10.54216/JCHCI.010101) |
| Harvard | Gande Akhila, Hemachandran K, Juan R Jaramillo. (2021). Indian Premier League Using Different Aspects of Machine Learning Algorithms. Journal of Journal of Cognitive Human-Computer Interaction, 1 ( 1 ), 01-07 (Doi : https://doi.org/10.54216/JCHCI.010101) |
| Vancouver | Gande Akhila, Hemachandran K, Juan R Jaramillo. Indian Premier League Using Different Aspects of Machine Learning Algorithms. Journal of Journal of Cognitive Human-Computer Interaction, (2021); 1 ( 1 ): 01-07 (Doi : https://doi.org/10.54216/JCHCI.010101) |
| IEEE | Gande Akhila, Hemachandran K, Juan R Jaramillo, Indian Premier League Using Different Aspects of Machine Learning Algorithms, Journal of Journal of Cognitive Human-Computer Interaction, Vol. 1 , No. 1 , (2021) : 01-07 (Doi : https://doi.org/10.54216/JCHCI.010101) |