Cricket Insight Hub: Data Analysis and Best 11 Team Generation. by IRJET Journal

Cricket Insight Hub: Data Analysis and Best 11 Team Generation.

Rohan Kanse1, Omkar Gawde2, Nabhangi Mhatre3, Niraj Raut4, Dr. Mahendra Pawar5

1. Student, Dept. of COMP Engineering, VPPCOE & VA, Maharashtra, India

2. Student, Dept. of COMP Engineering, VPPCOE & VA, Maharashtra, India

3. Student, Dept. of COMP Engineering, VPPCOE & VA, Maharashtra, India

4. Student, Dept. of COMP Engineering, VPPCOE & VA, Maharashtra, India

5. Associate Professor, Dept. of COMP Engineering, VPPCOE & VA, Maharashtra, India ***

Abstract - The Cricket Insight Hub is a comprehensive data analysis platform designed to provide in-depth statistical insights into cricket matches and players. Utilizing cutting-edge analytics and machine learning techniques, this hub aims to offer a detailed analysis of player performance, team strategies, and match outcomes. This platform gathers vast amounts of data from various cricket matches, including player statistics, match results, pitch conditions, and historical performances. Through advanced algorithms and predictive models, it generates actionable insights that aid in strategic decision-making for team selection and match strategies. One of the key features of the Cricket Insight Hub is its Best 11 Team Generation tool. This tool utilizes historical data, player performance metrics, and situational analysis to suggest the most optimal line-up for a given match, considering factors such as player form, opponent strengths, and match conditions. It takes into account various parameters to recommend the best combination of players that maximizes the team's chances of success. Furthermore, the platform offers visual representations of data through graphs, charts, and heat maps, making it easier for coaches, analysts, and fans to interpret and understand the trends and patterns in cricket matches. The Cricket Insight Hub is an invaluable resource for cricket teams, coaches, and enthusiasts, providing them with the necessary tools to make informed decisions based on data-driven insights. By amalgamating the power of data analytics and cricket expertise, this hub aims to revolutionize the way cricket is understood and strategized, ultimately enhancing the performance and success of teams in the sport.

Key Words: Data Analysis, Team Selection, Machine Learning in Cricket, Player Statistics, Player Performance Prediction, Cricket Analytics

KeyAbbreviations:

CIH:Cricket Insight Hub

DA:Data Analysis

TS:Team Selection

MLC:Machine Learning inCricket

DDD:Data-drivenDecision-making

1.INTRODUCTION

Welcome to the Cricket Insight Hub, your gateway to a world of data-driven analysis and team strategy in the realm of cricket. This innovative platform is tailored for enthusiasts, analysts, and fans seeking comprehensive insights into the game through meticulous data analysis and the creation of the best possible playing elevens. Withinthishub,weharnessthepowerofstatistics,match performances, player attributes, and historical data to unraveltheintricaciesofthegame.Fromexaminingplayer form to delving into team dynamics, our focus is on providingadeepunderstandingofcricketthroughadatacentriclens.Ourprimefocusliesingeneratingtheoptimal playing eleven teams. Through a blend of statistical algorithms, historical performance metrics, and a profound understanding of the game's dynamics, we craft teams designed for success in various formats of cricket. These 'best 11' line-ups are not just based on individual prowess but also on the synergy and compatibility between players, ensuring a well-balanced and effective team composition. Whether you're an ardent fan seeking to understand the nuances behind every win, a coach looking for data-backed insights to strategize better, or a fantasy league enthusiast aiming for the perfect team selection, the Cricket Insight Hub is your destination. Join us in this journey through the depths of cricket analytics, where numbers and insights converge to enhance your appreciation and comprehension of this beautiful sport. Explore,analyse,andexperiencethegameinawholenew dimension with us. Welcome to the Cricket Insight Hub wheredatameetstheexcitementofthegame

2. MOTIVATION

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

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providingadeepunderstandingofcricketthroughadatacentriclens.Ourprimefocusliesingeneratingtheoptimal playing eleven teams. Through a blend of statistical algorithms, historical performance metrics, and a profound understanding of the game's dynamics, we craft teams designed for success in various formats of cricket. These 'best 11' line-ups are not just based on individual prowess but also on the synergy and compatibility between players, ensuring a well-balanced and effective team composition. Whether you're an ardent fan seeking to understand the nuances behind every win, a coach looking for data-backed insights to strategize better, or a fantasy league enthusiast aiming for the perfect team selection, the Cricket Insight Hub is your destination. Join us in this journey through the depths of cricket analytics, where numbers and insights converge to enhance your appreciation and comprehension of this beautiful sport. Explore,analyze,andexperiencethegameinawholenew dimension with us. Welcome to the Cricket Insight Hub wheredatameetstheexcitementofthegame.

3. OBJECTIVES

 Todevelopadatacollectionmechanismtogather comprehensiveIPL-specific data,including player statisticsandmatchresults.

 To perform data pre-processing and cleansing to ensuredataaccuracyandconsistency.

 To implement data analysis algorithms to evaluate player performances based on IPLspecificmetrics.

 TodevelopreliablepredictionmodelsusingRidge Regression for predicting various performance metricssuchasnextruns,nextballs,nextwickets, andnextrunsgiven.

 Togenerateanoptimalteamof11playersforany given IPL match based on the predicted player performances. The team should align with the team composition rules in Dream11 and aim to provide the user with a team that can yield maximumprofit.

 To evaluate the effectiveness of the proposed system in predicting optimal teams and its potential benefits for users in fantasy cricket platformslikeDream11.

4. LITERATURE SURVEY

In the realm of cricket analytics, where data-driven insights redefine strategies and outcomes, a plethora of researchendeavorsilluminatethepathtowardspredictive precisionandstrategicacumen.

In their seminal work, [1] embark on a comparative journey through machine learning algorithms to predict the winning team in the Indian Premier League (IPL). Employing logistic regression, random forest regression, k-Nearest Neighbour, Support Vector Machine, Naïve Bayes, and Decision Tree algorithms, they unveil the predictive potential residing within the data. Following suit,[2]proposea predictivemodel forIPL matchresults, unveiling the supremacy of the Random Forest algorithm with an accuracy soaring at 88.10%. Their exploration delves deep into the realm of SVM, RFC, and Logistic Regression,illuminatingpathwaystopredictiveprowess.

Venturingfurther,[3]presentamachinelearningodyssey, navigating through Decision Trees, Random Forests, and Naïve Bayes classifiers to unveil the secrets of cricket match outcome prediction. Their journey underscores the fusion of data and algorithms in shaping predictive destinies. In a pioneering experimental endeavor, [4] unravel the mysteries of cricket game results through the lens of machine learning. Armed with Decision Trees, Random Forests, and Naïve Bayes classifiers, they dissect the intricacies of match predictions, laying the groundwork for a data-driven revolution in cricket analytics. Turning the spotlight onto team dynamics, [5] delveintotheworldofdataanalysistopredicttheoptimal cricket team. Their voyage through Principal Component Analysis and K-Means Clustering unveils the essence of teamsynergyandcomposition.Shiftinggears,[6]propose a data mining expedition to classify cricket players based onperformance. Leveraging K-meansclustering, Principal Component Analysis, and Support Vector Machine techniques, they delineate the nuances of player categorization, reshaping the landscape of talent identification. In the pursuit of individual excellence, [7] embark on a regression-driven quest to predict batsmen performance. Through the prism of Linear Regression, Ridge Regression, Lasso Regression, and Elastic Net Regression, they decipher the intricacies of batting prowess, unveiling pathways to predictive precision. In therealmoffantasycricket,[8]unveiltheTOPSISmethod as a beacon for selecting the optimal Dream-11 team in T20 cricket. Their methodology offers a structured approach to team selection, blending mathematical rigor with strategic insight. Delving deeper into talent identification,[9]embarkonafeatureselectionodysseyto identify cricketing talent. Through Principal Component Analysis, Recursive Feature Elimination, and Correlationbased Feature Selection, they unveil the essence of talent identification, reshaping the contours of cricketing excellence. In the pursuit of bowling excellence, [10] harness publicly available data to predict bowling performance. Their data-driven approach offers insights into the factors shaping bowling prowess, laying the groundwork for predictive precision in cricket analytics. Thus, through a tapestry of research endeavors, the journey towards predictive precision and strategic

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excellence in cricket analytics unfolds, illuminating pathways to data-driven enlightenment in the realm of cricketingstrategyandperformance.

5. PROPOSED SOLUTION

5.1 Objective

The objective of the proposed system is to develop an application that predicts the best 11 players for Indian Premier League (IPL) fantasy teams based on historical playerperformancedata.

5.2 Overview

The proposed system will leverage data scraping from "cricmetric.com" using Selenium WebDriver to gather player statistics. It will then employ predictive modelling techniques, specifically Ridge regression, to forecast playerperformancefortheupcomingIPLmatches.Finally, it will generate a recommended team of 11 players for fantasycricketusers.

5.3 Data Acquisition:

The proposed system involves the acquisition of cricket player data from the website "cricmetric.com" using Selenium WebDriver. This data includes comprehensive statistics and performance metrics for each player participatingintheIndianPremierLeague(IPL).Thedata is collected in real-time to ensure its relevance and accuracy.

5.4 Data Pre-processing and Transformation:

Upon collection, the acquired data undergoes preprocessing and transformation steps to ensure its suitabilityforpredictive modelling.Thisincludescleaning the data to handle missing values, outliers, and inconsistencies. Additionally, feature engineering techniques are employed to extract relevant features and enhancethepredictivepowerofthemodels.

5.5 Predictive Modelling:

The system utilizes Ridge regression algorithms to develop predictive models for various aspects of cricket performance, including predicting next runs, next balls, next wickets, and next runs given. These models are trainedonhistoricalplayerperformancedataandarefinetuned to capture subtle patterns and trends in player behaviour

5.6 Player Performance Prediction:

Based on the latest match data and the trained predictive models, the system generates player performance predictions for the upcoming matches. These predictions estimate the performance of each player in terms of runs

scored,ballsfaced,wicketstaken,andrunsconceded.The predictions provide valuable insights into the expected performanceofindividualplayersinthenextmatch.

5.7 Team Generation:

Using the player performance predictions, the system generatesanoptimalteamof11playersforfantasycricket platforms like Dream11. Players are categorized into different roles such as batsmen, wicket-keeper, bowler, and all-rounder, based on their predicted performance andhistoricalstatistics.Theteamcompositioniscarefully selected to maximize the team's potential for success and profitabilityinfantasycricketleagues.

5.8 User Interface:

The proposed system features a user-friendly interface that allows users to easily input their preferences and view the generated team recommendations. The interface provides interactive features for exploring player statistics, comparing players, and adjusting team compositions based on user preferences and strategic considerations.

5.9 Deployment and Integration:

The system is deployed on a scalable and robust cloud infrastructure to ensure reliability and availability. Integration with popular fantasy cricket platforms like Dream11 allows users to seamlessly incorporate the generated team recommendations into their fantasy cricketcontests.Regularupdatesandmaintenanceensure the system remains up-to-date with the latest match data andperformancetrends.

6. METHODOLOGY

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6.1 Data Scraping

The data collection process begins with the extraction of playerdatafromthewebsite“cricmetric.com”.Weutilized Selenium WebDriver, a powerful tool for automating web browsers,toscrapetherequireddata.Thistoolallowedus to navigate the website programmatically and extract the necessary data fields for each player. The data fields include various performance metrics that provide a comprehensiveviewofeachplayer’scapabilities.

6.2 Data Preprocessing and Transformation

Once the data was collected, it underwent several preprocessing and transformation steps. The preprocessing phase involved handling missing values, which were either filled using appropriate strategies or the corresponding data entries were removed. The transformation phase involved normalizing numerical data to ensure that all features have the same scale, and encoding categorical data into a format that can be understood by our machine learning models. These steps were crucial in preparing the data for the subsequent modelingphase.

6.3 Prediction Models

The prediction models were developed using Ridge Regression, a type of linear regression that includes a regularization term. This algorithm was chosen due to its ability to prevent overfitting, thereby improving the generalization of the model. The models were trained to predict various performance metrics, including next runs, next balls, next wickets, and next runs given. The training process involved tuning various hyperparameters to optimize the model’s performance. The models’ performance was validated using appropriate validation techniquestoensuretheirreliability.

6.4 Team Generation

The final part of the methodology involves generating the optimal team of 11 players based on the predicted player performances. We developed an algorithm that categorizes players by their roles (batsmen, wicketkeepers, bowlers, and all-rounders) and selects the best playersforeachrole.Thealgorithmtakesintoaccountthe team composition rules in Dream11 and aims to provide theuserwithateamthatcanyieldmaximumprofit.

Inconclusion,ourmethodologyinvolvedacombinationof data scraping, preprocessing, machine learning, and optimization techniques to predict the optimal IPL team for fantasy cricket platforms. The methods used were chosencarefullytoensurethereliabilityandeffectiveness oftheproposedsystem.

7. ARCHITECTURE / FRAMEWORK

8. CONCLUSION

The "Cricket Insight Hub" project aims to bridge the gap betweendataanalyticsandIPLteamselection.Bycreating anintelligentapplicationthatgeneratesthebestIPLteams based on data driven insights, we hope to revolutionize team selection strategies and empower cricket enthusiasts, team selectors, and fantasy cricket players to makeinformedandstrategicdecisions.TheCricketInsight Hub integrates diverse cricket data sources, employing robust data analysis and statistical models for player evaluation and opposition assessment. It features an intuitiveinterfacewithdynamicvisualizationstofacilitate data interpretation for coaches and selectors. This comprehensive system dynamically generates optimal team compositions for varying match scenarios, incorporating real-time updates, player scouting, and external tool integration. Stringent security measures safeguard sensitive player information, while continuous feedback andcollaboration withcricket expertsrefinethe system. The Hub serves as a multifaceted platform for data-drivenanalysisandthegenerationofthebestcricket teams

9. Acknowledgement

We take this opportunity to express our earnest appreciation to Dr. Mahendra Pawar for her important direction in this extended report without which the venture report would not have been completed. We are exceptionallymuchthankfultoherforheruntiringhelpin thisreportandshehasbeenempoweringusindispensing withthemistakes.Thereporthasbeencreatedasaresult of her profitable exhortation. We are moreover thankful for the participation, important recommendations rendered by Dr.Rais Mulla, as a head of the computer division and all other instructing staff of the computer office. Weare enormouslyobligated totheadministration

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of our college and express appreciation and much appreciation to our vital Dr.Alam Shaikh, for planning sharp intrigue and giving fundamental offices in completingtheventurereport.Weareappreciativetoour classmates and companions for their back and being such a greatcompany. Weamplifyourappreciation toanalysts andresearcherswhosepapersandthesehadbeenutilized inourclassreport.

10. REFERENCES

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[6] A. Balasundaram, S. Ashokkumar, D. Jayashree and S. Magesh Kumar, "Data mining based Classification of Players in Game of Cricket," 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2020, pp. 271-275, doi: 10.1109/ICOSEC49089.2020.9215413.

[7] G. Bhatambarekar and S. Rai, "Batsmen Performance Prediction Using Regression Models," 2022 International Conferenceon Artificial Intelligenceand DataEngineering(AIDE),Karkala,India,2022,pp.8085,doi:10.1109/AIDE57180.2022.10060071.

[8] Himagireesh, C., P.V., P., B., B & Taj, T. (2023). Selection of dream-11 players in T20 cricket by using TOPSIS method.Management Science Letters , 13(4), 257-264.

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[14] Pashte, Rupali D., Rais Mulla, and Mahendra Pawar. "Blur Detection in Video Stream using Filtering Algorithm." International Journal of Science, Engineering and Technology Research (IJSETR) 5.1 (2017):296-299.

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[16] Rais Allauddin Mulla, Satish Saini, “MACHINE LEARNING BASED FRAMEWORK FOR MAKING ADAPTIVE STOCK MARKET INDEX PREDICTION SYSTEM”,Editorial,vol.53, no.12, pp. 243–263, Dec. 2021.

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