Intelligent sports prediction analysis system based on improved Gaussian fuzzy algorithm

Men, Yanhua

doi:10.1016/j.aej.2021.08.084

Cited by 15 publications

(6 citation statements)

References 22 publications

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“…For enhancing the intelligent sports' prediction and analysis, Men designed an algorithm for sports action recognition based on the traditional high-speed model, which compares the detected and recognized action model with that in the standard database, and then determines the accuracy; and uses the statistical analysis and multifactor analysis method to predict the results of the sports competition. The experiment outcomes verify the model effectiveness [ 11 ]. These studies rely on computer vision technology to complete the recognition of sports movements and the analysis of training techniques, which is an auxiliary optimization of sports training from the perspective of action technology.…”

Section: Related Workmentioning

confidence: 84%

Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

Jing,

Wang,

Suo

2024

Heliyon

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Section: Related Workmentioning

confidence: 84%

Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

Jing,

Wang,

Suo

2024

Heliyon

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“…The study enriches our understanding of IPL match dynamics and provides actionable insights for team management, betting markets, and cricket enthusiasts. By identifying key factors that influence winning chances, teams can optimize strategies, fine-tune player selections, and enhance their competitive edge (Men, 2022;Turhan & Canpolat, 2023). The study's combination of data-driven analysis and advanced modeling techniques contributes to the ongoing evolution of cricket analytics and promotes greater engagement with the sport, ultimately enhancing the fan experience and decision-making processes in the IPL (Šuštaršič et al, 2022;Gu et al, 2023).…”

Section: Discussionmentioning

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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“…Outlier detection played a crucial role in identifying extreme values within key metrics, such as matches played, batting and bowling averages, catches taken, and performance predictions, ensuring data integrity. Data preprocessing was crucial to ensure data accuracy and involved deduplication and the detection and removal of outliers [3,24,25]. The dataset was split into training and testing sets to assess model performance, and key performance indicators were selected as features for prediction.…”

Section: Methodsmentioning

confidence: 99%

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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Intelligent sports prediction analysis system based on improved Gaussian fuzzy algorithm

Cited by 15 publications

References 22 publications

Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

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

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

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