Indian Premier League Research Articles

Competing against former teammates predicts team victory

The small but growing body of research on team vs. team competition focuses on predicting the winner based on multilevel factors, including the team's strength and prior relations among team members within a team. Our research demonstrates the significance and power of prior relations among members between competing teams in predicting the outcome of a contest. Leveraging data over 8 seasons of the Indian Premier League (IPL), we demonstrate the effects of competing against former teammates on a team's victory in IPL matches. If two teams, A and B, are competing in a match, and nA players from A are former teammates of players on B and nB players from B are former teammates of players on A, then if team A has smaller values of nA, it will have a competitive advantage over Team B with a higher value of nB. We call the magnitude of the difference of nA and nB the “ecosystem” factor in predicting performance. Using regression and stochastic network models, we find that the ecosystem factor significantly impacts the outcome of a match. Our findings have implications for franchise owners. While recruiting a player, franchise owners should not rely solely on the player's ability but also leverage the rivalry between former teammates.

Exploring the impact of Qatar’s soft power sponsorship in the Indian Premier League: a social identity perspective

PurposeDrawing upon the Social Identity Theory, this research aims to investigate the effectiveness of Qatar’s soft power sponsorships among the Indian Premier League (IPL) fans in India. We explore the relationship between team identification, team-sponsor fit, and purchase intention of sports fans, while also examining the moderating effect of sponsor brand awareness in the context of the IPL.Design/methodology/approachUsing a quantitative research design, we conducted both offline and online surveys from sports fans. The proposed model was examined using Confirmatory Factor Analysis and Partial Least Square-Structural Equation Modelling to test the study’s hypotheses.FindingsThe results reveal a positive and significant association between team identification and team-sponsor fit, and team-sponsor fit and purchase intention of sponsor brands. Further, brand awareness moderates the relationship between team-sponsor fit and purchase intention of sponsor brands, but not on the team identification and team-sponsor fit relationship.Research limitations/implicationsThis research adds to the paradigm of social identity theory and soft power sponsorship research. By providing insights into fans’ predisposition towards the team sponsors, the study will help managers design effective brand promotion strategies.Originality/valueThis research is the first to explore the effectiveness of soft-power sponsorships in the Indian mega-sports landscap. It is also the first to explore the moderating effect of sponsor brand awareness on sponsorship effectiveness, which is determined through the purchase intentions of the sponsor brand.

Book review: Vidya Subramanian, Speeding up Sport: Technology and the Indian Premier League

Vidya Subramanian, Speeding up Sport: Technology and the Indian Premier League (Oxford: Oxford University Press, 2022), vii + 142 pp.

Statistical and Exploratory Data Analysis on Indian Premier League

The Indian Premier League (IPL) is a prominent professional cricket league in India that commenced in 2008, featuring eight teams representing different cities. With a massive following, particularly among Indian fans, IPL data analysis holds significant importance. This research delves into a comprehensive statistical and exploratory analysis of the IPL dataset, aimed at extracting meaningful insights for informed decision-making within the league. By critically evaluating limitations and challenges, this study offers strategic recommendations to enhance the overall IPL experience. With a focus on teams, players, matches, and umpires, the analysis encompasses over 20 key investigations. It seeks to unveil performance disparities among teams and players, identify patterns in IPL matches, and assess the league's impact on the selection of Indian cricketers for the national team. Moreover, the research presents valuable findings that could optimize the player selection process. The insights gleaned from this extensive IPL dataset analysis can serve as a valuable tool to boost team dynamics and individual player performance.

IPL Score Forecaster: Using Machine Learning to Predict First Innings Scores

Abstract: This paper introduces a novel application of linear regression to predict first-innings scores in the Indian Premier League (IPL), aiming to enhance analytical capabilities and strategic planning in IPL cricket. Leveraging a comprehensive dataset of historical match data, including vital factors like venue, team order, overs played, and wickets have fallen, the study utilizes meticulous preprocessing and feature selection techniques. The model undergoes training and evaluation using a split dataset, with performance assessed through metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Successful model training enables predictions for upcoming IPL matches, providing valuable insights for teams to make informed strategic decisions. The findings highlight the efficacy of linear regression in forecasting first innings scores, offering teams a potential competitive advantage in the IPL. Furthermore, the study underscores the critical role of cricket analytics in modern strategic planning, emphasizing the significance of data-driven approaches in cricket management.

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

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.

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

Navigating Choices: A Study on Consumer Perception on Brands and their Marketing During Indian Premier League (IPL)

This particular research deeps dive into understanding of how brands and their marketing make a huge impact in particular in such a way that improves their brand value when it comes to sponsorship and other advertising placements during the Indian Premier League (BCCI’s flagship cricket mega event) This study further tries to understand the relationship between the product placements of brands that have been associated with either IPL teams (Or) have been official partners for the mega event. Enhancing the amount of sale projections and ROI related to the brands will also be equally discussed as it is of the most important parameter to be understood. IPL happens once in every year which is during the March-May window of ICC’s FTP (Future Tour Programme) and it is during this time there is peak rush for tickets at various stadiums and restaurants with huge screening facilities, the brands get associated there too. Some of the brands have been having long term association and some have just made inroads that is well thought of understanding the market dynamics.

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

Symbolic Interactionism and the Metamorphosis of Sports Brands: Indian Premier League’s Journey From Niche to Mass Cool

This study delves into the evolution of the Indian Premier League, the world’s second-largest sports league, charting its transition from a niche to a mass cool brand. Through the lens of symbolic interactionism, it scrutinizes 13 years of the Indian Premier League’s promotional videos with 18 in-depth interviews of cultural intermediaries. The research spotlights the nuanced process of brand coolness within the Indian Premier League, demonstrating how the league’s brand identity undergoes a deliberate reconstruction, blending both traditional and modern sociocultural elements. A pivotal “boost cool” phase is identified, signaling a strategic shift to expand the brand’s appeal to a wider audience. This research contributes significantly to brand coolness literature by conceptualizing a brand coolness life cycle, charting a path from uncool, through niche and boost cool, to mass cool, and eventually to uncool or reinvention, highlighting “boost cool” as a critical transitional stage.

A comparative analysis between the batting performances of the Indian and foreign batters in the Indian premier league: A study based on empirical evidences

A comparative analysis between the batting performances of the Indian and foreign batters in the Indian premier league: A study based on empirical evidences

Deep Learning Algorithms for Sports Data Analysis and Prediction in Sports Competitions

Abstract Sports data analysis and prediction are essential for gaining a competitive advantage in today’s sports. Artificial Neural Networks (ANNs) have shown promising outcomes in several disciplines, including sports analytics. Sports data is dynamic and complex, making it difficult for standard ANNs to identify minute patterns in it. We introduce a new Puzzle-Optimized Artificial Neural Network (PO-ANN) in this work, which is intended for sports data processing and prediction. The PO-ANN is optimized using a puzzle-inspired method to enhance the network’s ability to identify and comprehend complex patterns in the data. The technique constantly modifies the weights and network topology, enabling the model to better react to the shifting dynamics of sports competitions. The Indian Premier League provided the dataset, which consists of 950 matches and 20 variables (IPL). We implemented our proposed PO-ANN and forecast accuracy in sports data analysis and prediction using Python. We performed a comparison analysis between our suggested PO-ANN approach and other existing methods, using numerous metrics, including MSE, MAE, and MAPE. The suggested POANN technique produced better outcomes than the previous approaches.

Transcendence beyond sport: examining the dynamic power of sport human brand on national pride

PurposeDrawing upon social judgment theory, the research examines whether changes in psychological involvement with a sport human brand owing to their sporting success can generate spillover effects on people's national pride, a proxy for the collective level of well-being and whether the individual's behavioral engagement in sport spectating will moderate this relationship.Design/methodology/approachLeveraging a quasi-natural experiment grounded in the 2021 Indian Premier League championship, the authors solicit responses from 296 representative individuals residing in India twice using a two-wave panel design. The authors conducted confirmatory factor analysis, repeated measures T-test, latent change score analysis, and structural regression analysis to examine the relationship between psychological involvement with sport human brand and national pride.FindingsThe results demonstrate a positive association between psychological involvement with sport human brand and national pride. Further, the findings indicate that an increase in psychological involvement with sport human brand was associated with an increase in national pride due to the successful athletic endeavor involving the sport human brand. However, the relationship between psychological involvement with sport human brand and national pride was invariant irrespective of patrons' spectatorship behavior.Originality/valueThis research demonstrates that sport human brands possess transformative soft power that extends their prerogative cultural identity personified by their athletic ability and success, allowing them to shape public sentiments of national pride via their profound influence through and beyond the complex network of brand ecosystems.

Determining the playing 11 based on opposition squad: An IPL illustration

Indian Premier League (IPL) is the most popular T20 domestic sporting league globally. Player selection is crucial in winning the competitive IPL tournament. Thus, team management select 11 players for each match from a team’s squad of 15 to 25 players. Different player statistics are analysed to select the best playing 11 for each match. This study attempts an approach where the on-field player performance is used to determine the playing-11. A player’s on-field performance in a match is computed as a single metric considering a player’s attributes against every player present in the opposition squad. For this computation, past ball-by-ball data is cleaned and mined to generate data containing player-vs-player performance attributes. Next, the various performance attributes for a player-vs-player combination is converted into a player’s performance rating by computing a weighted score of the performance attributes. Finally, an optimisation model is proposed and developed to determine the best playing-11 using the computed performance ratings. The developed optimisation model suggests the playing-11 that maximises the possibility of winning against a given opponent. The proposed procedure to determine the playing-11 for an IPL match is demonstrated using past data from 2008-20. The demonstration indicates that for matches in the league stage, the suggested playing-11 by model and the actual playing-11 have a ∼7% similarity across all teams. The remaining ∼3% are different from those selected in the actual team. Nevertheless, this difference approximately yields a ∼ Indian Premier League (IPL) is the most popular T20 domestic sporting league globally. Player selection is crucial in winning the competitive IPL tournament. Thus, team management select 11 players for each match from a team’s squad of 15 to 25 players. Different player statistics are analysed to select the best playing 11 for each match. This study attempts an approach where the on-field player performance is used to determine the playing-11. A player’s on-field performance in a match is computed as a single metric considering a player’s attributes against every player present in the opposition squad. For this computation, past ball-by-ball data is cleaned and mined to generate data containing player-vs-player performance attributes. Next, the various performance attributes for a player-vs-player combination is converted into a player’s performance rating by computing a weighted score of the performance attributes. Finally, an optimisation model is proposed and developed to determine the best playing-11 using the computed performance ratings. The developed optimisation model suggests the playing-11 that maximises the possibility of winning against a given opponent. The proposed procedure to determine the playing-11 for an IPL match is demonstrated using past data from 2008-20. The demonstration indicates that for matches in the league stage, the suggested playing-11 by model and the actual playing-11 have a ∼7% similarity across all teams. The remaining ∼3% are different from those selected in the actual team. Nevertheless, this difference approximately yields a ∼13.32% increase in performance rating compared to the existing team.3.32% increase in performance rating compared to the existing team.

Analysis and Prediction of Winning Team and Player Performance in Franchise Cricket using Machine Learning

Sports, especially cricket, produces an enormous amount of statistical data that has led to the evolution of the game. Unlike other sports, cricket involves numerous variables ranging from pitch conditions, weather, boundary length, and several other factors that make each game unique. These variables make every match have its own significance, making cricket an ever-evolving sport that continues to captivate its audience. Over the years, cricket has witnessed a massive transformation with the integration of analytics. The use of data analytics has changed the way players and coaches approach the game. It has brought a new dimension to the game and has opened several new avenues for analyzing and interpreting the game. The Indian Premier League (IPL) has acted as a catalyst in showcasing the potential of cricket to the world and has bridged the gap between different cultures and audiences. In the present-day IPL, every statistic available is being used due to the high level of competitiveness of the tournament. Teams use data analytics to understand their strengths and weaknesses, analyze their opponents' gameplay, and devise strategies to win matches. These analytics provide valuable insights into various aspects of the game, such as player performance, team strategies, and game trends, among others. This paper is a sincere effort to uncover hidden insights in IPL by utilizing data from previous seasons. The vast amount of data available offers a treasure of formation that can help us to understand the nuances of the game and make informed decisions. By analyzing this data, we can identify patterns and trends, assess player performance, and make informed decisions that can impact the outcome of a match.

IPL Score Prediction & Analysis

Cricket, particularly the Indian Premier League (IPL), is known for its unpredictability. In this context, this project tackles the challenge of predicting the total score of an inning in IPL matches using machine learning techniques. By leveraging historical match data, team dynamics, player statistics, and environmental variables, a predictive model is constructed. The system not only delivers score predictions but also offers insights into the critical factors influencing these predictions. The goal is to empower cricket enthusiasts, teams, and broadcasters with a tool that enhances their understanding of match dynamics and aids in making informed predictions. This project represents an exciting intersection of sports, data science, and predictive analytics, with the potential to reshape how cricket fans and professionals engage with the IPL.

Need for Pandemic Preparedness Teaching during the Medical Curriculum.

When we talk about 20-20 it goes without saying that we are talking about Indian Premier League matches. But when we talk about 2020, we get goosebumps even remembering the memories of the coronavirus disease 2019 (COVID-19) pandemic! It was just about that time when the National Medical Commission (NMC) rolled out the Competency-based Medical Education (CBME) curriculum for the first Bachelor of Medicine, Bachelor of Surgery (MBBS) batch admitted in the 2019-2020 academic year. Who knew that the timetable so well planned would go for a toss due to the global pandemic? It was indeed a learning lesson for all. The word pandemic is derived from the Greek words pan = all and demos = people. An epidemic that affects large geographic areas across the entire world at the same time is called a pandemic. Most of the pandemics are caused by viruses, for example, influenza, acquired immune deficiency syndrome, and now COVID-19. Susceptible individuals get affected, they transmit the organism to many individuals in a short time. International travel spreads the disease globally in a short time and a pandemic is declared. Hence, it is the need of the hour to sensitize the medical students by incorporating the pandemic preparedness module in the medical curriculum. How to cite this article: Londhey V. Need for Pandemic Preparedness Teaching during the Medical Curriculum. J Assoc Physicians India 2023;71(10):11-12.

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

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.

LEVERAGING MOCK IPL AUCTION PERFORMANCE TO ENHANCE BUSINESS VOCABULARY IN LANGUAGE LEARNING

This research study examines the impact of using mock IPL (Indian Premier League) auctions as a teaching tool in language learning, with particular focus on business vocabulary development and understanding of business concepts among undergraduate business administration students. The study included 45 participants who met specific inclusion and eligibility criteria. It uses a research design that includes a mock IPL auction performance evaluation conducted before and after the intervention. Assessment methods include pre- and performance tests that assess speaking skills, vocabulary quizzes that measure business-related vocabulary development, and surveys that gather qualitative feedback on participants' experiences. Findings reveal significant improvements in speaking skills, significant improvements in business-related vocabulary, and positive qualitative perceptions of the IPL simulation. This study supports the hypothesis that exposing language learners to mock IPL auction performance contributes not only to improved vocabulary but also to a deeper understanding of business concepts. This highlights the potential synergies between the IPL bidding environment and language education, emphasizing the value of experiential learning in language acquisition and business-related learning.

Gender and remuneration in professional franchise cricket: analysing outcomes of the 2023 Indian Premier League auctions

The amount that players are paid in professional cricket is mostly derived from salary not, as in golf and tennis, from prize money and outside endorsements. Consequently, their pay is dependent on the remuneration system in operation which, in most cases, are administered set by their employers. Such prices embody severe gender inequalities with, for example, the lowest paid man being paid more than the highest paid woman in England’s iconic Hundred competition. In contrast, in the Women’s Premier League (WPL) and the Indian Premier League (IPL) auctions, prices are determined by the market with each player’s pay being determined by the highest bidder. From a detailed analysis of data from these two auctions for December 2022 (IPL) and March 2023, I show that the market system goes some way towards ameliorating the gender inequalities embedded in administered prices. After decomposing inequality in sale prices, I show that the main driver of inequality under these auctions was not gender but whether the candidates were domestic or overseas players.