Data Mining and Machine Learning in Cricket Match Outcome Prediction: Missing Links

Hatharasinghe, Manuka Maduranga; Poravi, Guhanathan

doi:10.1109/i2ct45611.2019.9033698

Cited by 16 publications

(4 citation statements)

References 8 publications

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“…They employed machine learning algorithms to classify the runs and wickets into different ranges, with the Random Forest algorithm outperforming other algorithms. Nigel Rodrigues et al [10] predicted the traits of batsmen and bowlers in current matches, assisting in player selection for upcoming matches by leveraging past performance data and employing Multiple Random Forest Regression. Wright [11] predicted possible cricket match fixtures by considering various factors such as venue, teams, and the number of holidays between matches.…”

Section: IImentioning

confidence: 99%

“…They employed a metaheuristic procedure called Subcost-Guided Simulated Annealing (SGSA) to progress from initial to final solutions. Maduranga et al [12] predicted match outcomes using data mining algorithms and offered solutions based on the approaches used by other authors. Shetty et al [13] predicted player capabilities based on factors such as the ground, pitch type, and opposition team, utilizing machine learning techniques.…”

Section: IImentioning

confidence: 99%

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Analyzing Key Factors for Second Innings Triumph in IPL Using Machine Learning

et al. 2023

IJRASET

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bstract: The Indian Premier League (IPL) has emerged as one of the most captivating and celebrated cricket tournaments worldwide. With its unique blend of sporting excellence, entertainment, and fierce competition, the IPL has captured the imagination of millions of cricket enthusiasts. In this era of data-driven decision-making, harnessing the power of statistical analysis and predictive modelling can provide valuable insights into the dynamics and outcomes of IPL matches. This research paper aims to explore on the analysis of factors contributing to a team's success in the second innings of IPL matches using exploratory data analysis (EDA) and logistic regression modelling. The EDA section will uncover patterns and correlations among variables such as team composition, batting order, run rate, power-play performance, bowling strategies, fielding efficiency, and match conditions. Logistic regression modelling will be applied to develop a predictive model that forecasts the likelihood of a team's victory in the second innings based on the identified factors. The model will be trained, validated, and evaluated using historical data.

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Section: IImentioning

confidence: 99%

Section: IImentioning

confidence: 99%

Analyzing Key Factors for Second Innings Triumph in IPL Using Machine Learning

et al. 2023

IJRASET

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“…The authors constructed a dataset from different websites and obtained the accuracy of prediction as 75% for CSK, 80% for RR, 55% for DD, 75% for RCB, 80% for MI, 80% for SRH, 75% for KXIP, and 84% for KKR. In 2019, Hatharasinghe et al [20] stated that the utilization of computing intelligence in analyzing and modeling the game of cricket is a popular and promising research area. Throughout the year's different approaches have been adopted and the results obtained are neither clear nor documented because of differences in approaches.…”

Section: State Of Artmentioning

confidence: 99%

Analyzing and Exploring the Impact of Big Data Analytics in Sports Sector

Kaur¹,

Kaur²,

Jagdev

2021

SN COMPUT. SCI.

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Big data are the first-ever use of data and statistics enabling us to make effective decisions in professional sports. The primary focus of the research paper is to evaluate how the use of big data has benefited sports. The research work conducted in the paper focuses on extracting information from the constructed database as per user requirements. Two different databases have been constructed by gathering data from espncricinfo.com. The first database consists of the records of batsmen comprising 16 attributes relevant to the international career of the batsmen. The second database contains records of bowlers involving 18 attributes relevant to the international career of the bowlers. The research paper encompasses a detailed flowchart and an algorithm detailing the systematic approach followed to mine required data from the database. The research has been conducted focusing to come up with a user-friendly tool that would offer optimal results via simplified scripts and queries in tabular and visualization form. The research enables any cricket fan or a layman to mine the information in which he/she is interested. The research paper elaborates on the use of the Hortonworks Hue 2.2.0 framework in analyzing the gathered data and extracting information via appropriate tools of the framework as per need. The screenshots included in the research work enable the general masses to have insight into the workflow of the conducted research and to gain a better understanding of performing different functions on different tools. As compared to past research, the research work does not make use of any statistical tools which are difficult to understand and work upon. The constructed database is made available to the framework and the queries and scripts are formatted to get results in tabular and graphical form. The research work enables coaches and instructors to select the best batsmen and outstanding bowlers to construct a strong team via mining the relevant databases and come out with the best outcome. The research work even enables general masses to build their teams after analyzing the past performance of different batsmen and bowlers and construct their teams and participate in fantasy gaming platforms like Dream11, MyTeam11, Howzat, etc. The research paper illustrates the work conducted on sentiment analysis using Python and Java programming languages to predict the popularity of batsmen or bowlers via word cloud to get a deep insight into the public opinion regarding upcoming tournaments based on past performances of the players and the teams. Keywords Big data • Batsman • Bowler • Data mining • Sports • Hadoop • Sentiment analysisThis article is part of the topical collection "Applications of Cloud Computing, Data Analytics and Building Secure Networks" guest edited by Rajnish Sharma, Pao-Ann Hsiung and Sagar Juneja.

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“…The author also investigated their model's performance and find that model has achived an accuracy score of 75 percent. The most recent data mining and machine learning techniques for predicting cricket match outcomes are reviewed by (Hatharasinghe & Poravi, 2019), who also point out any gaps in the body of existing knowledge. They suggest a new research agenda that focuses on creating new algorithms, enhancing data quality, forecasting results for several classes, and forecasting at various phases of the match.…”

Section: Introductionmentioning

confidence: 99%

Performance Assessment of the Machine Learning Algorithms for First Inning Score Prediction in Cricket

Bhattacharjee,

Bhattacharjee

2023

Preprint

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This study presents a comprehensive performance assessment of various machine learning algorithms employed for the prediction of first inning scores in cricket matches. Cricket, a sport characterized by diverse formats like IPL, T20 Interna-tionals (T20Is), and One Day Internationals (ODIs), poses a unique challenge for score prediction due to its dynamic nature and format-specific nuances. We evaluate a range of machine learning models, including regression techniques, decision trees, and ensemble methods, across these cricket formats. Our analysis considers the significance of feature engineering, encompassing player statistics, pitch conditions, weather, and venue-specific factors, in enhancing predictive accuracy. Results indicate that the effectiveness of machine learning models varies across formats, with XGBoost regression performance in IPL and T20Is is better , whereas ridge regression performed well in ODIs. This research contributes valuable insights for cricket analysts, teams, and enthusiasts seeking to leverage machine learning for informed first inning score predictions, thereby enhancing their understanding of the intricate dynamics within the game.

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Data Mining and Machine Learning in Cricket Match Outcome Prediction: Missing Links

Cited by 16 publications

References 8 publications

Analyzing Key Factors for Second Innings Triumph in IPL Using Machine Learning

Analyzing Key Factors for Second Innings Triumph in IPL Using Machine Learning

Analyzing and Exploring the Impact of Big Data Analytics in Sports Sector

Performance Assessment of the Machine Learning Algorithms for First Inning Score Prediction in Cricket

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