Cricket Match Research Articles

A pressure index for the team batting second in T20I cricket

In sports, players usually undergo pressure situations in order to meet certain performance standards set by themselves or others. During the course of any competitive sporting event, players are often motivated to take calculated risks in attempts to secure a victory or to showcase their performance capabilities. However, quantifying the pressure experienced by players at any given stage of a game is quite a challenging and imposing task. This study discusses a method to compute the pressure experienced by batters when pursuing a target in a T20I cricket match. Diligent and extensive exploration of psychological pressures motivates us to formulate a differential equation, based on a set of reasonable assumptions, whose solution gives rise to a useful pressure index formula for quantifying the level of pressure experienced by the batters of the team batting second, i.e., the team chasing the target. This index takes into account two key assumptions: first, the rate of change of pressure on the batters at a given stage of the game depends on the pressure at that stage; and, second, the required run rate commensurate with available resources remaining at that stage of the game. The available data are used to fine-tune the parameter of the model. The applicability of the proposed pressure index is illustrated using data for T20I matches played by the major test playing countries during the years 2017, 2018, 2019, and 2021. Additionally, this pressure index can be used not only to predict the probability of winning at any given stage of the second innings, but also to quantify the contribution of each batter, taking into account the pressure situation in which they score runs.

Prediction of Heteroscedastic Sports Data By using Regression and Various Machine Learning Models

This study investigates the impact of extra deliveries—wide’s, no balls, byes, and leg byes—on T20I cricket match outcomes. It aims to find out whether these extras have a significant effect on the result and which type contributes the most runs. Using heteroscedastic data from the previous three T20I World Cups (20212024), the analysis underlines the importance of reducing extras to boost a team's chances of winning. OLS assumes that the error component has constant variance; when this assumption is violated, heteroscedasticity arises, resulting in incorrect predictions and conclusions. This study employs several types of machine learning techniques to increase prediction accuracy for heteroscedastic data. We applied machine learning models such as Decision Tree, Random Forest, Gradient Boosting, and K Nearest Neighbor, as well as Linear Regression, to predict values in test datasets. Machine learning models typically outperformed linear regression, resulting in decreased sum of squares errors, with some models producing predictions that were very close to observed values. This study shows that machine learning works well with heteroscedastic data. DOI: https://doi.org/10.52783/pst.836

Predicting Cricket match Outcomes: Sentiment Analysis on Commentary

Predicting Cricket match Outcomes: Sentiment Analysis on Commentary

Predicting the Winner of a Twenty20 International Cricket Match: Classification and Explainable Machine Learning Approach

We present a supervised machine learning approach to predict the winner of a Twenty20 (T20) international match. The prediction dynamically changes as the match progresses. We also use explainable machine learning techniques (SHAP scores) to understand the importance of various features in making the decision at various stages of the T20 match. We present results on a dataset of 808 men's T20 international matches. The dynamic accuracy increases from about 55% in the initial stages of the T20 match to a maximum of about 85% in the final stages of the match (with an overall accuracy of about 63% in innings 1 and 74% in innings 2). SHAP scores reveal that team strength is an important feature in making the prediction in initial stages of the match; however, in the final stages, match situation plays the dominant role in the decision making process. Our work may help team coaches and captains to assess their chances of winning and/or chart a course towards winning in the ongoing T20 match, as well as be useful for sports analytics and gambling websites and apps.

Identify Cricket Shots using Machine Learning

Cricket shot detection is a game-changing technology that offers deep insights into player performance and match data, completely changing the way the sport is played. The main elements and importance of cricket shot detection systems are explored in this abstract. Using computer vision and machine learning techniques, the system examines video footage of cricket matches to accurately detect and classify every shot made by batsmen. Shot types (such as cover drive, pull, or leg glance), shot trajectories, and success rates are among the important data it retrieves. Numerous stakeholders in the cricketing ecosystem find diverse uses for cricket shot detection. It provides coaches and professional athletes with an unmatched post-match analysis tool that helps with strategic planning and performance enhancement.The method is used by team analysts to create winning strategies by gaining insight into opponents' shot patterns. Shot detection provides compelling visualizations and real-time shot labels in the broadcasting domain, enhancing the viewing experience. While talent scouts and cricket organizations use technology to find players and nurture talent, cricket enthusiasts profit from comprehensive shot data. This abstract highlight cricket shot detection's potential and adaptability, highlighting how it can revolutionize the cricket industry. Technology keeps improving the game, empowering players, and enthralling spectators with a deeper comprehension of the sport. Several sports have received a lot of attention and popularity recently. Many people were pining for live sports action during the height of the recent outbreak because there were no sporting activities. With millions of devoted fans who watch the games with emotion, cricket is one of the most respected sports in India. Enticed by the game, fans frequently conduct in-depth evaluations of certain players, focusing on their skills and shot choices. A greater number of people are interested in assessing players' performances in order to make wise choices for fantasy teams, especially in light of the popularity of fantasy leagues and related services. Automation presents a potential solution to the significantly time-consuming and manual process of detecting cricket batters' shots. In order to accomplish its goals, this study uses deep learning in the form of Convolutional Neural Networks (CNNs) to present an efficient method for evaluating cricket strokes

Cricket Score Predictor using Machine Learning

In cricket, particularly the twenty20 format is most watched and loved by the people, where no one can guess who will win the match until the last ball of the last over. In India, The Indian Premier League (IPL) started in 2008 and now it is the most popular T20 league in the world. So we decided to develop a machine learning model for predicting the outcome of its matches. Winning in a Cricket Match depends on many key factors like a home ground advantage, past performances on that ground, records at the same venue, the overall experience of the players, record with a particular opposition, and the overall current form of the team and also the individual player. This paper briefs about the key factors that affect the result of the cricket match and the regression model that best fits this data and gives the best predictions. In these the score prediction includes linear regression, lasso regression and ridge regression whereas in winning prediction SVC classifier, decision tree classifier and random forest classifier are used. The model used the supervised machine learning algorithm to predict the winning. Random Forest Classifier used for good accuracy and the stable accuracy so that desired predicted output is accurate

Bluetooth Enabled LCD Cricket Scoreboard

This abstract centres on designing and developing a Bluetooth-enabled LCD cricket scoreboard with a primary focus on reducing delay time during score display, aiming to significantly enhance the cricket match experience for players, spectators, and officials. The scoreboard seamlessly integrates Bluetooth technology with LCDs to ensure real-time transmission and instant display of match statistics, scores, player information, and game progress. The system incorporates Arduino-based hardware for LCD control and Bluetooth communication, complemented by a dedicated mobile application functioning as an intuitive interface for scoreboard management. Through the mobile app, users can wirelessly interact with the scoreboard, thereby reducing delays by enabling swift updates of match data, configuring display preferences, and ensuring rapid data exchange between the app and the LCD scoreboard. The emphasis on reducing delay time in score presentation underscores the commitment to delivering timely and accurate information, contributing to an enriched viewing experience for cricket enthusiasts, sports venues, educational institutions, and community cricket events. This represents an innovative approach, leveraging Bluetooth connectivity and LCD technology to minimize delays and provide comprehensive, real-time match information, thereby modernizing and augmenting traditional cricket scoreboards.

Cricket data analytics: Forecasting T20 match winners through machine learning

In the ever-evolving world of cricket, the T20 format has captured the imaginations of fans worldwide, intensifying the anticipation for match outcomes with each passing delivery. This study explores the realm of predictive analytics, leveraging the power of machine learning to alleviate the suspense by forecasting T20 cricket match winners before the first ball is bowled. Drawing on a rich dataset encompassing factors such as past team performance and rankings, a diverse ensemble of predictive models, including logistic regression, support vector machine (SVM), random forest, decision tree, and XGBoost, is meticulously employed. Among these, the random forest Classifier emerges as the standout performer, boasting an impressive prediction accuracy rate of 84.06%. To assess the real-world applicability of our predictive framework, a post-case study is conducted, focusing on the high-stakes World Cup T20 matches of 2022, where England emerges as the triumphant team. The dataset underpinning this study is meticulously curated from ESPN CricInfo, ensuring the robustness of our analysis. Moreover, this paper extends its contribution by offering a comprehensive comparative analysis, scrutinizing performance metrics such as accuracy, precision, recall, and the F1-score across benchmark machine learning models for cricket match prediction. This in-depth evaluation not only validates the efficacy of our models but also sheds light on their superior execution time and statistical robustness, further bolstering their utility in the realm of cricket outcome forecasting.

Bluetooth Enabled LCD Cricket Scoreboard

This abstract centres on designing and developing a Bluetooth-enabled LCD cricket scoreboard with a primary focus on reducing delay time during score display, aiming to significantly enhance the cricket match experience for players, spectators, and officials. The scoreboard seamlessly integrates Bluetooth technology with LCDs to ensure real-time transmission and instant display of match statistics, scores, player information, and game progress. The system incorporates Arduino-based hardware for LCD control and Bluetooth communication, complemented by a dedicated mobile application functioning as an intuitive interface for scoreboard management. Through the mobile app, users can wirelessly interact with the scoreboard, thereby reducing delays by enabling swift updates of match data, configuring display preferences, and ensuring rapid data exchange between the app and the LCD scoreboard. The emphasis on reducing delay time in score presentation underscores the commitment to delivering timely and accurate information, contributing to an enriched viewing experience for cricket enthusiasts, sports venues, educational institutions, and community cricket events. This represents an innovative approach, leveraging Bluetooth connectivity and LCD technology to minimize delays and provide comprehensive, real-time match information, thereby modernizing and augmenting traditional cricket scoreboards.

Difficult at dusk? Illuminating the debate on cricket ball visibility

ObjectivesInvestigate the visibility of new and old red, white and pink cricket balls under lighting and background conditions experienced during a day–night cricket match. DesignWe modelled the luminance contrast signals available for a typical observer for a ball against backgrounds in a professional cricket ground, at different times of day. MethodsSpectral reflectance (light reflected as a function of wavelength) was derived from laboratory measurements of new and old red, white and pink balls. We also gathered spectral measurements from backgrounds (pitch, grass, sightscreens, crowd, sky) and spectral illuminance during a day–night match (natural afternoon light, through dusk to night under floodlights) from Lord's Cricket Ground (London, UK). The luminance contrast of the ball relative to the background was calculated for each combination of ball, time of day, and background surface. ResultsOld red and old pink balls may offer little or no contrast against the grass, pitch and crowd. New pink balls can also be of low contrast against the crowd at dusk, as can pink and white balls (of any age) against the sky at dusk. ConclusionsReports of difficulties with visibility of the pink ball are supported by our data. However, our modelling also shows that difficulties with visibility may also be expected under certain circumstances for red and white balls. The variable conditions in a cricket ground and the changing colour of an ageing ball make maintaining good visibility of the ball a challenge when playing day–night matches.

Utilizing Machine Learning for Comprehensive Analysis and Predictive Modelling of IPL-T20 Cricket Matches

Objective : Current study intends to develop a predictive model for Indian Premier League (IPL) cricket match results using machine learning techniques. In order to provide a precise framework that allows for the prediction of IPL match outcomes, it aims to examine player statistics, match dynamics, and historical data. Method : SVM, Random Forest, Logistic Regression, Decision Tree, and KNN models were used in this study to predict player performance on any given day. Form, fitness, and previous results were among the historical player data that were used as characteristics. Each model preceded through training and testing phases, with accuracy, precision, and recall metrics evaluated to determine the most effective algorithm for forecasting player performance. Findings : Final studies indicated that relative team strength of competitor teams, recent form of players, and opponent pairings are distinguishing features for predicting the performance of both players and teams on any given day. The multi-machine learning approach-based model that was constructed demonstrated an accuracy of 0.71, further indicating improved performance for the given challenge. Modelling team strength is similar to modelling individual player batting and bowling performances, which is the cornerstone of our approach. Novelty : This paper was designed based on a novel approach leveraging combinatorial machine learning methods. This has been found to demonstrate unprecedented performance improvement in predicting a player’s performance on a given day. Additionally, the presented approach may prove valuable in opening new avenues to advance machine learning applications in sports analytics by addressing the limitations of existing methods. Keywords: Machine Learning, Sports analytics, SVM, Random Forest, KNN

Winner prediction in an ongoing one day international cricket match

Cricket is a team sport with an intricate set of rules, where players specialize in multiple skills such as batting, bowling, and fielding. Playing conditions and home advantage also impact the game. Thus, it is quite challenging to build an accurate quantitative model for the game. In this paper, we provide a data driven approach to predict the winner of a cricket match. We divide the ongoing match into various states and provide a prediction for each state using supervised machine learning models. We employ dynamic features that account for the current match situation, together with the static features like team strength, winner of the toss, and the home advantage. We also use SHAP scores—an explainable AI technique—to interpret the proposed prediction model. We use ball-by-ball data from 1359 men’s one day international cricket matches played between January 2004 to January 2022 to present our results. We achieved the best in-play prediction accuracy of about 85% . SHAP scores reveal that during initial phases of the match, the model treats static features like team strength more important than others, in making the predictions. But as the match progresses, dynamic features capturing the current match situation become exceedingly important. Our work may be useful in preparing tools for in-play winner prediction for live cricket matches that can be used in websites and mobile applications covering the sport, in providing analytics during live television commentary, and in legal betting platforms.

Bowler and coach experiential knowledge of new ball swing bowling in elite cricket

ABSTRACT Swing bowling can influence the outcome of cricket matches, but technique characteristics and coaching practices have not been investigated at an elite level. This study aimed to provide insight into the perceived technique parameters, coaching practices and variables contributing to conventional new ball swing bowling in elite cricket. Six Australian Test match fast bowlers and six Australian international and national-level coaches were interviewed. A reflexive thematic analysis of interview transcripts generated themes associated with swing bowling. Most bowlers reported their technique allows them to naturally create either inswing or outswing, with technique variations used to create swing in the opposite direction. To increase delivery effectiveness, bowlers and coaches recommended pitching the ball closer to the batter in length and varying release positions along the crease. Coaches recommended making individualised technique adjustments, but suggested all bowlers could benefit from maintaining balance and forward momentum to create a consistent release position in repeated deliveries. This study could inform training strategies to alter techniques and improve swing bowling performance. Future research should investigate the physical qualities of fast bowlers and use biomechanical analyses to provide a deeper understanding of swing bowling.

Statistic analysis of IPL match score and winner inning wise using machine learning algorithms

This study explains the statistical analysis of cricket match score prediction using machine learning. According to recent changes in data science and sports, the use of sports-based machine learning and data mining shows the importance of process in outcome performance and prediction. The scope of this research paper is to evaluate current measurements used in the previous work to understand the estimation the ways used to model and analyze data and characterize the variables that govern performance using statistical methods. Actually, this research article will present a reliable statistical tool for data analysis using machine learning algorithms. At present, sports organizations produce enough statistical information on every player, team, match, and season for particular related sports. The first sports researchers were thought to be experts, coaches, team managers, and analysts. Sports organizations want to do statistical analysis of player from their previous data stored on their database using different data mining and machine learning algorithms. Sports data helps coaches and managers in many ways, such as predicting results, analyzing player performance, and skills, and evaluating strategies. Forecasts help managers and organizations make decisions to win teams and competitions. The current evaluation of research shows that primary studies of data mining systems can predict outcomes and evaluate the strengths and weaknesses of each system. Statistical analyses are made for each match for result predictions. Although in many respects this application is very limited. These are prime factors which important to examine machine learning algorithms in these situations to see if the application can give the nearest results in analysis. This research aims to give solutions that will help to make predictions more accurate and precise than previous methods, using more accurate data and machine learning.

A review and reassessment of the aerodynamics of cricket ball swing

This paper reviews 70 years of cricket ball swing literature and reassesses the results in light of new measurements. A comparison of ball tracking data with published experimental results shows that current understanding does not explain the behaviour observed in professional cricket matches. Descriptions of cricket ball boundary layer aerodynamics are updated with new results which show that the seam acts more like a series of vortex generators than a trip and that there is a laminar separation bubble on the seam side of the ball. Previous results for reverse swing are consolidated and compared with new data, showing that different magnitudes of swing occur depending on the condition of both sides of the ball. Variation in pressure and temperature should be included when the Reynolds number, a non-dimensional ball speed, is calculated, while humidity can be neglected. Studies on the effect of wind speed and direction are summarised and results considering the effect of free-stream turbulence are compared with new measurements. Throughout the paper, recommendations for future work are suggested. These include quantitative study of the effect of surface defects and roughness, an assessment of whether atmospheric turbulence can affect swing and investigation into the effect of backspin on swing.

ROLE OF MEDIA IN PROMOTING TOURISM THROUGH SPORTS IN PAKISTAN

Pakistan enjoys weather conditions which are considered appropriate for both sport and tourism. With its diverse landscapes and historical cultural heritage, there are countless opportunities for sports enthusiasts and adventurers from all around the world. Media is vital to creating and marketing sports tourism in Pakistan. By showcasing the beauty of Pakistan's natural landscapes, historical heritage and places, and diverse culture through various media platforms, the country can easily attract a global audience of sports fans. For instance, coverage of sporting events, such as cricket matches, polo tournaments, and jeep rallies, provides a unique glimpse into the country's vibrant sporting culture and natural beauty. Through attractive visuals and compelling narratives, the media has the ability to effectively highlight Pakistan as a desirable sports tourism destination. Consequently, the arrival of tourists will increase, and economic development will be promoted. Therefore, this research focuses on role of media in promoting tourism through sports in Pakistan. This study uses qualitative research technique i.e., interview method. Sample size of 20 respondents has been drawn by using purposive sampling technique comprising of sports journalists, and economic stake holders. Additionally, thematic analysis was applied to further analyze data collected on the basis of themes and sub-themes.

Social Media a Concern for Foreign Relationships with Neighbor Countries

Indian Sub-continent, especially Bangladesh, India, and Pakistan were divided by Islamic ideology. Bangladesh gained independence from West Pakistan in 1971 with the unbelievable support of the neighboring country India. The present article attempts to find out about the recent time controversy through social media between these countries. In the 2023 Men’s World Cup Cricket Final match India vs Australia held on November 19, 2023, Australia won by 6 Wickets. Here Bangladeshi young cricket lovers enjoyed supporting Team Australia but Indians could not take it positively. They think Bangladesh as a Muslim country avoids Hindus and does not respect and love them. However, the study reveals that 83% disagree that Islam is related to support. 59.50% voted that a player’s behavior is responsible for the support of India and only 3% think that religion may affect supporting the cricket matches. Finally, an effective recommendation has been structured for resolving the crisis.

Cricket Win Prediction using Machine Learning

Abstract: Cricket, being one of the most popular sports worldwide, has attracted significant interest in developing accurate win prediction models. With the advent of machine learning techniques, researchers have leveraged the power of data-driven algorithms to predict cricket match outcomes. This research paper aims to improve cricket win prediction model by using XGBoost machine learning algorithm. Feature importance analysis is conducted to identify the most influential factors contributing to match outcomes. The dataset is divided into training and test sets, and the models are evaluated on both datasets to measure their generalization performance. The findings demonstrate the potential of machine learning techniques in accurately forecasting cricket match outcomes, enabling stakeholders to make informed decisions in the dynamic and unpredictable domain of cricket

An unusual case of thrombotic storm in an amateur cricketer—a case report

BackgroundThrombotic storm is a series of acute to subacute thrombotic events that evolve over a few days to weeks and result in progressive thromboses at multiple sites. There is often a predisposing event to thromboses, such as trauma or infections. Prompt initiation of anti-coagulation can prove life-saving in such patients.Case reportWe describe a previously healthy young male who developed thromboses of the right axillary, brachial, radial, and ulnar arteries while bowling in a cricket match. A few hours later, he developed a stroke involving the right anterior and middle cerebral arteries. His thrombophilia workup was significant for elevated homocysteine levels. Although he had a delayed presentation to our hospital, he was treated with anticoagulation and given a trial of thromboembolectomy, which failed and he had to ultimately undergo a right below-elbow guillotine amputation.ConclusionThrombotic storm should be recognized promptly in the Emergency Department and timely anticoagulation should be initiated.

Ultimate cricket experience: Dynamic web app for a real-time scoring system in university cricket

Cricket, a globally popular team sport, involves eleven players and encompasses both batting and bowling skills, with the objective of scoring runs and dismissing the opposition's batsmen. The sport has complex rules and demands skill, technique, and teamwork. Cricket enjoys a significant following worldwide and has produced iconic players. It has evolved with shorter formats and tournaments, attracting a global audience. The " Ultimate Cricket Experience: Dynamic Web App for Real-Time Scoring System in University Cricket” project aims to develop a web application specifically designed for university cricket sports. The main goal of the project is to develop a dynamic web application that provides real-time cricket scorecards for cricket enthusiasts, offering an immersive and engaging experience across all active cricket matches. By providing a user-friendly platform that makes it simple for users to stay up to date with live scores, statistics, and important match highlights, this program strives to satisfy the ardent love of cricket fans. The strategic implementation of the MERN stack, combining MongoDB, Express.js, React.js, and Node.js, ensures a robust, scalable, and modern solution for developing the real-time scorecard system. This combination of technologies offers a robust and scalable solution for developing A Real-time Cricket Scorecard System. The key features of the web application include a live scorecard that provides real-time updates on ongoing matches, and comprehensive match details such as team information, player statistics, and match schedules. The platform also offers university sports news, sports event information, and other relevant details to keep users informed and engaged with the cricketing activities within the university.