A forecasting system for batsmen playing one-day internation cricket | |
| Author | Wijayarathna, Peruma H.S.S. |
| Call Number | AIT RSPR no.IM-17-10 |
| Subject(s) | Decision-making--Data processing Big data--Analysis Cricket--Data processing |
| Note | A research submitted in partial fulfillment of the requirements for the degree of Master of Science in Information Management, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Research studies project report ; no. IM-17-10 |
| Abstract | The growth of the information and the decision-making systems are currently in vogue in the global environment. The emerging areas such as Big Data and Cloud computing are playing a major technological role in handling large data volume and for subsequent analyzing. When the data volume is high, it is important to analyze correct data attributes using different tools and technologies. The concept of data mining is a famous way of collecting, preprocessing and analyzing data, accordingly, in a supervised or unsupervised way. Therefore, machine learning algorithms are being used to manage large data volumes to make better decisions. This research work was focused to develop a model to forecast performance of batsmen playing in one-day international cricket tournaments. After carefully analyzing the current trends and technologies, the model was developed, based , according to the study, among 28 factors, such as Age, Matches won or lost, whether Batting or chasing, Batting position and Bowling style. They were ,inter alia, identified as significant in affecting the performance behavior of batsmen. To develop the model many data mining tools, such as SPSS, Math Lab, WEKA, Orange, R and Python, were identified as suitable. WEKA tool was, anyhow, employed for this research study because it is an open source and is easy to use, compared to others. The study found out, after the development of the model that, there is a correlation between age and the batting position for performance average run per match for a particular player. |
| Year | 2017 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Sumanta Guha; |
| Examination Committee(s) | Dailey, Matthew N.;Vatcharaporn Esichaiku; |
| Scholarship Donor(s) | Technical Education Development Project Asian Development Bank; |
| Degree | Research studies project report (M. Sc.) - Asian Institute of Technology, 2017 |