Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization
- Title
- Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization
- Creator
- Suguna R.; Praveen Kumar Y.; Suriya Prakash J.; Neethu P.S.; Kiran S.
- Description
- Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket. 2023 IEEE.
- Source
- 2023 IEEE 3rd Mysore Sub Section International Conference, MysuruCon 2023
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Cricket analytics; Cricket prediction; Machine Learning; Player Classification; Regression and Classification; Score prediction
- Coverage
- Suguna R., Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology, Department of Computer Science and Engineering, Chennai, India; Praveen Kumar Y., Vidhya Jyothi Institute of Technology, Department of Computer Science and Engineering, Hyderabad, India; Suriya Prakash J., JAIN (Deemed-to-be-University), Department of Computer Science and Engineering, Bengaluru, India; Neethu P.S., CHRIST (Deemed to Be University), School of Engineering and Technology, Bengaluru, India; Kiran S., Nitte Meenakshi Institute of Technology, Department of Mathematics, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835034035-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Suguna R.; Praveen Kumar Y.; Suriya Prakash J.; Neethu P.S.; Kiran S., “Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19741.