Cricket Squad Analysis Using Multiple Random Forest Regression

Rodrigues, Nigel; Sequeira, Nelson; Rodrigues, S.; Shrivastava, Varsha

doi:10.1109/icait47043.2019.8987367

Cited by 17 publications

(3 citation statements)

References 4 publications

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“…In this study, we employed two widely recognized predictive models, RF and MLR, commonly utilized in the field of sports analytics for their efficacy in predictive modeling [44,45]. Our evaluation, utilizing R2 values and MSE as performance metrics for each exercise, aligns with prior studies in sports analytics [46,47], indicating the widespread pursuit of effective predictive models in various athletic disciplines [48,49]. While our study focuses on weightlifting exercises within CF, it's important to recognize the broader context of predictive modeling in sports.…”

Section: Discussionmentioning

confidence: 71%

Exploring CrossFit Performance Prediction and Analysis via Extensive Data and Machine Learning

Lim,

Song

2023

Preprint

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(1) Background: The analysis of athletic performance has always aroused great interest from sport scientist. This study utilized machine learning methods to build predictive models using a comprehensive CrossFit (CF) dataset, aiming to reveal valuable insights into the factors influencing performance and emerging trends.; (2) Methods: The study used Random Forest (RF) and Multiple Linear Regression (MLR) models to predict performance in four key weightlifting exercises within CF: clean &amp; jerk, snatch, back squat, and deadlift. Performance was evaluated using R-squared (R2) values and Mean Squared Error (MSE). Feature importance analysis was conducted using RF, XGBoost, and AdaBoost models.; (3) Results: The RF model excelled in deadlift performance prediction (R2 = 0.80), while the MLR model demonstrated remarkable accuracy in clean &amp; jerk (R2 = 0.93). Across exercises, clean &amp; jerk consistently emerged as a crucial predictor. The feature importance analysis revealed intricate relationships among exercises, with gender significantly impacting deadlift performance.; (4) Conclusions: This research advances our understanding of performance prediction in CF through machine learning techniques. It provides actionable insights for practitioners, optimize performance, and demonstrates the potential for future advancements in data-driven sports analytics.

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Section: Discussionmentioning

confidence: 71%

Exploring CrossFit Performance Prediction and Analysis via Extensive Data and Machine Learning

Lim,

Song

2023

Preprint

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“…Barot et al [7] predicted match outcomes based on factors such as the toss and venue. Kaluarachchi et al [8] predicted match outcomes using the Naïve Bayes classifier, considering home ground, match time, match type, winning the toss, and batting first. Passi et al [9] addressed two classification problems: predicting player performance based on runs and the number of wickets.…”

Section: IImentioning

confidence: 99%

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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“…The Random Forest algorithm outperforms better than other algorithms. Nigel Rodrigues et al [10], predicted the value of the traits of the batsmen and the bowlers in the current match. This would help in selecting the players for the upcoming matches by using past performances of a player against a specific opposition team by using Multiple Random Forest Regression.…”

Section: Literature Surveymentioning

confidence: 99%

Prediction of IPL Match Outcome Using Machine Learning Techniques

Srikantaiah¹,

Khetan²,

Kumar³

et al. 2021

Atlantis Highlights in Computer Sciences

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India's most popular sport is cricket and is played across all over the nation in different formats like T20, ODI, and Test. The Indian Premier League (IPL) is a national cricket match where players are drawn from regional teams of India, National Team and also from international team. Many factors like live streaming, radio, TV broadcast made this league as popular among cricket fans. The prediction of the outcome of the IPL matches is very important for online traders and sponsors. We can predict the match between two teams based on various factors like team composition, batting and bowling averages of each player in the team, and the team's success in their previous matches, in addition to traditional factors such as toss, venue, and day-night, the probability of winning by batting first at a specified match venue against a specific team. In this paper, we have proposed a model for predicting outcome of the IPL matches using Machine learning Algorithms namely SVM, Random Forest Classifier (RFC), Logistic Regression and K-Nearest Neighbor. Experimental results showed that the Random Forest algorithm outperforms other algorithms with an accuracy of 88.10%.

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Cricket Squad Analysis Using Multiple Random Forest Regression

Cited by 17 publications

References 4 publications

Exploring CrossFit Performance Prediction and Analysis via Extensive Data and Machine Learning

Exploring CrossFit Performance Prediction and Analysis via Extensive Data and Machine Learning

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

Prediction of IPL Match Outcome Using Machine Learning Techniques

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