Cricket Players Performance Prediction and Evaluation Using Machine Learning Algorithms

Sumathi, M.; Prabu, S.; Rajkamal, M

doi:10.1109/icnwc57852.2023.10127503

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…These individual Health, sport, rehabilitation Health, sport, rehabilitation Здоров'я, спорт, реабілітація Здоров'я, спорт, реабілітація Здоровье, спорт, реабилитация Здоровье, спорт, реабилитация 2024 10(1) 16 predictions provide actionable insights for team managers and selectors. They can make data-driven decisions when choosing players, improving the team's competitiveness on the world stage [5]. While previous research has primarily focused on team outcomes, this study contributes by demonstrating the application of machine learning to predict individual player performances, enriching the field of cricket analytics [6,26,27].…”

Section: Discussionmentioning

confidence: 90%

“…In this study, we conducted a thorough comparison of three machine learning models such as, Random Forest (RF), Support Vector Regression (SVR), and XGBoost (XGB), to predict the performance probabilities of Indian cricket players in the ODI Cricket World Cup 2023 [5,6]. The results highlighted significant differences in their predictive accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, individual player performances take on immense importance in determining a team's success [4]. Cricket enthusiasts and analysts have long recognized the significance of predicting player performance, as it has implications not only for team selection but also for informing cricketing strategies, engaging fans, and enriching the understanding of the broader community [5]. Accurate predictions serve as a tool for teams to optimize their lineups, adapt their strategy, and improve their chances of clinching the coveted World Cup trophy.…”

Section: Introductionmentioning

confidence: 99%

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Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023

Sanjaykumar,

Udaichi,

Rajendiran

et al. 2024

HSR

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Background and purpose Cricket, a globally renowned bat and ball sport, is the second most popular sport worldwide. The objective of the study is to utilize machine learning algorithms to predict the performance probabilities of Indian cricket players participating in the ODI Cricket World Cup 2023. Furthermore, we aim to assess and compare the predictive precision of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost. Materials and Methods Data collection centered on Indian One Day International cricket statistics, encompassing matches played, batting and bowling averages, catches taken, and performance predictions. We sourced this data from reputable platforms such as ESPNcricinfo and the International Cricket Council website. Our performance prediction utilized of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost. Comparative analysis was conducted, evaluating these models through essential metrics including Mean Squared Error, Root Mean Squared Error, Mean absolute Error, and R-squared. Results The comparative analysis revealed that the XGBoost model consistently outperformed the others. It exhibited lower errors with the lowest Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error, signifying greater predictive accuracy. XGBoost achieved the highest R-squared value, indicating a robust relationship between predictions and actual performance probabilities. Random Forest produced satisfactory results but fell short of XGBoost's accuracy, while Support Vector Regression displayed less accurate predictions across all metrics. Conclusions This research demonstrates the superior predictive ability of the XGBoost model in the performance probabilities of Indian cricket players in the ODI Cricket World Cup. The practical implications underscore the significance of data-driven insights for team selection and strategy.

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023

Sanjaykumar,

Udaichi,

Rajendiran

et al. 2024

HSR

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“…In response to this surge, the significance of employing advanced predictive modeling techniques cannot be overstated (Kapadia et al, 2022). This research endeavors to explore the application of Support Vector Regression (SVR) with distinct kernel models, namely ANOVA, Bessel function, and Inverse MultiQuadratic, to predict the player's performance of women's premier league cricket in the upcoming 2024 season (Sumathi et al, 2023;Aburas et al, 2018). Cricket, as a sport, is influenced by a multitude of factors, ranging from individual player form to team dynamics and external conditions.…”

Section: Introductionmentioning

confidence: 99%

Using Support Vector Regression Kernel Models for Cricket Performance Prediction in the Womens Premier League 2024

Lakshmi,

Sanjaykumar,

Dharuman

et al. 2024

Teor. metod. fiz. vihov.

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Background. The interest in women’s premier league cricket has caused the need for advanced analytics to understand the multifaceted dynamics of the sport. Study Purpose. This study aimed to contribute to sports analytics by assessing the efficacy of Support Vector Regression (SVR) kernel models in predicting the most valuable player. Such research methods as ANOVA, Bessel function, and Inverse MultiQuadratic kernel application have been deliberately chosen for their diverse mathematical approaches, aligning with the nuanced intricacies of women's premier league cricket. Materials and methods. Player performance was analyzed by using the following study methods: ANOVA, Bessel function and Inverse MultiQuadratic kernel application. The data, sourced from espncricinfo.com and the International Cricket Council, includes essential metrics for five teams. Rigorous preprocessing techniques, such as imputation and outlier removal, enhance data reliability, ensuring robust predictive models. Results. The application of the Inverse MultiQuadratic kernel exhibits exceptional predictive performance, surpassing ANOVA and Bessel function models. The kernels radial basis function proves effective in capturing the intricate dynamics of women’s premier league cricket. The findings underscore the suitability of kernel method for predicting standout performers in the Womenʼs Premier League 2024 season. Conclusions. The study revealed the dynamic interplay between sports analytics and machine learning in women's premier league cricket. The application of the Inverse MultiQuadratic kernel stands out as the most effective model, providing key insights into player predictions. This emphasizes the continual integration of advanced analytical techniques to enhance our understanding of the evolving landscape of women’s premier league cricket. As the sport gains prominence on the global stage, such analytical endeavors become imperative for strategic decision-making and sustained growth.

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“…Central to the IPL's allure is the element of unpredictability, where every match is a battleground where teams vie for victory, driven by a potent mix of skill, strategy, and determination. In this dynamic and ever-evolving landscape, the ability to forecast winning chances emerges as a tantalizing challenge, offering valuable insights into the intricate dynamics that shape match outcomes Sumathi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Predicting Team Success in the Indian Premier League Cricket 2024 Season Using Random Forest Analysis

Sanjaykumar,

Natarajan,

Lakshmi

et al. 2024

Teor. metod. fiz. vihov.

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Background. Random Forest is a popular machine learning algorithm used for classification and regression tasks. The study purpose is to investigate the use of Random Forest machine learning to predict the winning chances of teams in the 2024 Indian Premier League (IPL) season. Objectives. By analyzing comprehensive player statistics, including matches played, batting and bowling averages, as well as fielding contributions, the study aims to understand the factors that influence team success in T20 cricket and provide actionable insights for team management, betting markets, and cricket enthusiasts. Material and methods. The study involved 10 cricket teams competing in the IPL 2024 season. Player statistics and match outcomes data from previous IPL seasons were collected and cleaned, with missing values addressed through imputation. The features were based on player statistics, including both aggregate measures and calculated metrics. A Random Forest is chosen as the machine learning model, trained using preprocessed data, with features derived from player statistics as input and match outcomes as the target variable. The dataset was split into training and validation sets, using methodologies such as cross-validation to ensure a robust model performance. Results. The Random Forest model demonstrates strong predictive performance, with a low Mean Squared Error (MSE) of 8.2174, Root Mean Squared Error (RMSE) of 2.8666, and a high R-Squared value of 0.9173, indicating approximately 91.73% variance explained in the data. Chennai Super Kings emerge as frontrunners with a predicted performance percentage of 83.4%, while teams like Punjab Kings and Lucknow Super Giants show lower predicted performance percentages, suggesting potential areas for its improvement. Conclusions. This study reveals the effectiveness of Random Forest machine learning in forecasting IPL match outcomes based on player statistics. It provides valuable insights into team dynamics and offers actionable recommendations for team management and cricket enthusiasts. The findings enrich our understanding of IPL match dynamics, contribute to the evolution of cricket analytics, and promote greater engagement with sport, ultimately enhancing the fan experience in the IPL.

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Cricket Players Performance Prediction and Evaluation Using Machine Learning Algorithms

Cited by 14 publications

References 15 publications

Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023

Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023

Using Support Vector Regression Kernel Models for Cricket Performance Prediction in the Womens Premier League 2024

Predicting Team Success in the Indian Premier League Cricket 2024 Season Using Random Forest Analysis

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