A Study on Impact of Team Composition and Optimal Parameters Required to Predict Result of Cricket Match

Ishi, Manoj S.; Patil, J. B.

doi:10.1007/978-981-15-2071-6_32

Cited by 3 publications

(6 citation statements)

References 18 publications

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“…Overall, the redundant and irrelevant features are removed with feature selection methods. The benefits of feature selection methods are accuracy of models is improved with less training time [1,9,15,17].…”

Section: Ensemble Methods and Artificial Neural Networkmentioning

confidence: 99%

“…Sports analytics is a technique used to explore essential data and investigate past data to get fundamental knowledge for proper decision-making. The extraction of comprehensive statistical information in sport has allowed it to apply data mining (DM) techniques for manufacturing predictive models on large datasets [1][2][3]. Cricket is a top-rated game.…”

Section: Introductionmentioning

confidence: 99%

“…Cricket is a top-rated game. Cricket is reasonably significant within the statistical science community, but this game's unpredictable and unstable nature makes it more challenging to use in standard probability models [1,4,5]. Uncertainty is the feature of cricket, and it increases as the game becomes shorter.…”

Section: Introductionmentioning

confidence: 99%

“…The main goal is to define the key factors that influence the game's outcome and pick the best machine learning model that matches this data with the best results. It is essential to keep in mind that all the primary factors that may influence the match outcome need to be chosen to design the best model [1,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…The sports analysis show is broadcasted before the match, during innings break, and after the completion of match for the data analysis. They try to predict the possible result of cricket matches using historical information [1,13,14].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Winner Prediction in One Day International Cricket Matches Using Machine Learning Framework: An Ensemble Approach

Ishi¹,

Patil²,

Patil³

et al. 2022

INDJCSE

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Forecasting output of any sports match is in massive demand for the sports industry. Avoiding defeat is the eventual aim of any sports game. Cricket is one of the well-known sports in the world. Cricket is a sport of uncertainty; the state of match gets changed on each ball of the game. Due to that, winner prediction in cricket is becoming a challenging task. In this paper, the focus is given to the prediction of victory in one day international game of cricket using machine learning. In this work, 128 features are considered for the implementation. With these feature sets, three models are proposed based on battingbowling strength of the team, run-scoring pattern for the team, and overall team strength. The concept of ensemble algorithm using voting and stacking classifier is used for prediction with machine learning algorithms. The feature selection methods are used for this work to remove irrelevant or redundant features. The investigation of the prediction model is performed using accuracy, precision, F1-score, recall value. The Logistic Regression and Support Vector Machine give better results than other models with an accuracy of 96.30% for predicting the winner of the ODI match.

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Section: Ensemble Methods and Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Winner Prediction in One Day International Cricket Matches Using Machine Learning Framework: An Ensemble Approach

Ishi¹,

Patil²,

Patil³

et al. 2022

INDJCSE

View full text Add to dashboard Cite

show abstract

An efficient team prediction for one day international matches using a hybrid approach of CS-PSO and machine learning algorithms

Ishi¹,

Patil²,

Patil³

2022

Array

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Player Performance Predictive Analysis in Cricket Using Machine Learning

Bharadwaj,

Saxena,

Kumar

et al. 2024

RIA

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Player performance is the most critical parameter for a match's outcome. The selection of a certain set of players according to various parameters, including consistency, Form, performance against the particular opponent, performance in the specific venue, the tournament in which the match is being played, the pressure of the type of match, etc., elevates the probability of a team winning the game. The following research aims to analyze and predict the player's performance based on the player's performance parameters. The problem is segmented into two parts, i.e., batting performance and bowling performance. The problem is presumed to be a classification problem. Runs scored, and wickets taken are classified in distinct ranges. Naïve Bayes', Decision Tree, Random Forest, and Support Vector Machine (SVM) are the algorithms used in the research. Random Forest and Decision Tree were almost identical and, hence, the most accurate for the result.

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A Study on Impact of Team Composition and Optimal Parameters Required to Predict Result of Cricket Match

Cited by 3 publications

References 18 publications

Winner Prediction in One Day International Cricket Matches Using Machine Learning Framework: An Ensemble Approach

Winner Prediction in One Day International Cricket Matches Using Machine Learning Framework: An Ensemble Approach

An efficient team prediction for one day international matches using a hybrid approach of CS-PSO and machine learning algorithms

Player Performance Predictive Analysis in Cricket Using Machine Learning

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