IPL Visualization and Prediction Using HBase

Singh, Shubhra; Kaur, Parmeet

doi:10.1016/j.procs.2017.11.454

Cited by 20 publications

(4 citation statements)

References 2 publications

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“…In 2017, Singh et al [16] stated that the task of managing and extracting the data has become difficult with each match been played. The authors presented a data visualization and prediction tool utilizing HBase as an open-source, non-relational, and distributed tool to store and maintain data related to Indian Premier League (IPL).…”

Section: State Of Artmentioning

confidence: 99%

Analyzing and Exploring the Impact of Big Data Analytics in Sports Sector

Kaur¹,

Kaur²,

Jagdev

2021

SN COMPUT. SCI.

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Big data are the first-ever use of data and statistics enabling us to make effective decisions in professional sports. The primary focus of the research paper is to evaluate how the use of big data has benefited sports. The research work conducted in the paper focuses on extracting information from the constructed database as per user requirements. Two different databases have been constructed by gathering data from espncricinfo.com. The first database consists of the records of batsmen comprising 16 attributes relevant to the international career of the batsmen. The second database contains records of bowlers involving 18 attributes relevant to the international career of the bowlers. The research paper encompasses a detailed flowchart and an algorithm detailing the systematic approach followed to mine required data from the database. The research has been conducted focusing to come up with a user-friendly tool that would offer optimal results via simplified scripts and queries in tabular and visualization form. The research enables any cricket fan or a layman to mine the information in which he/she is interested. The research paper elaborates on the use of the Hortonworks Hue 2.2.0 framework in analyzing the gathered data and extracting information via appropriate tools of the framework as per need. The screenshots included in the research work enable the general masses to have insight into the workflow of the conducted research and to gain a better understanding of performing different functions on different tools. As compared to past research, the research work does not make use of any statistical tools which are difficult to understand and work upon. The constructed database is made available to the framework and the queries and scripts are formatted to get results in tabular and graphical form. The research work enables coaches and instructors to select the best batsmen and outstanding bowlers to construct a strong team via mining the relevant databases and come out with the best outcome. The research work even enables general masses to build their teams after analyzing the past performance of different batsmen and bowlers and construct their teams and participate in fantasy gaming platforms like Dream11, MyTeam11, Howzat, etc. The research paper illustrates the work conducted on sentiment analysis using Python and Java programming languages to predict the popularity of batsmen or bowlers via word cloud to get a deep insight into the public opinion regarding upcoming tournaments based on past performances of the players and the teams. Keywords Big data • Batsman • Bowler • Data mining • Sports • Hadoop • Sentiment analysisThis article is part of the topical collection "Applications of Cloud Computing, Data Analytics and Building Secure Networks" guest edited by Rajnish Sharma, Pao-Ann Hsiung and Sagar Juneja.

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Section: State Of Artmentioning

confidence: 99%

Analyzing and Exploring the Impact of Big Data Analytics in Sports Sector

Kaur¹,

Kaur²,

Jagdev

2021

SN COMPUT. SCI.

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“…By support and confidence of the players, selectors get the idea to filter out players for the next season. The work in [13] discussed the prediction tool and machine learning algorithms which are used to analyze the past performance of players, and it will be beneficial for team authorities to select the right player. HBase an open source, distributed prediction tool is presented to keep the data related to matches and players of IPL seasons.…”

Section: Related Workmentioning

confidence: 99%

Data visualization and toss related analysis of IPL teams and batsmen performances

Kanungo

Tulasi

2019

IJECE

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Sports play a very significant role in the development of the human persona. Getting involved in games like Cricket and other various sports help us to build character, discipline, confidence and physical fitness. Indian Premier League, IPL provides the most successful form of cricket as it gives opportunities to young and talented players to show case their talents on various pitch. Decision-makers are the utmost customers for all fundamentals in the sports analytics framework. Sports analytics has been a smash hit in shaping success for many players and teams in various sports. Sports analytics and data visualization can play a crucial role in selecting the best players for a team. This paper is about the Toss Related analysis and the breadth of data visualization in supporting the decision makers for identifying inherent players for their teams.

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“…In addition to building a model for predicting match winners, Singh and Kaur 9 and Raviteja et al 10 both, in addition, perform data visualization to help show player performance in addition to predicting the winner and loser of the match. Tekade et al 11 use different supervised machine learning outcomes to predict the result of a match.…”

Section: Introductionmentioning

confidence: 99%

Calculating expected win percentage of an Indian Premier League team

Hoskins

2023

International Journal of Sports Science & Coaching

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The Indian Premier League is the most prestigious cricket league globally. There are significant finances in terms of both team ownership and player salaries. It is, therefore, essential to understanding if a team’s record is due to luck (good or bad) or if a team’s record is due to the team’s overall performance. The research presented here is motivated by how to accurately predict a team’s winning percentage in the Indian Premier League based on underlying statistics. A similar analysis has been done in other sports, mainly based on the concept of the Pythagorean expectation. This research derives a similar model for the IPL based on historical data. However, the structure of a match in the Indian Premier League is fundamentally different than the structure of games in other sports. As a result of this structural difference, this study creates additional models using both least absolute shrinkage and selection operator and stepwise regression to identify variables that are good predictors for calculating the expected winning percentage. These models compare favorably to the Pythagorean expectation model. This article presents a model combining both the determined variables and Pythagorean expectation.

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IPL Visualization and Prediction Using HBase

Cited by 20 publications

References 2 publications

Analyzing and Exploring the Impact of Big Data Analytics in Sports Sector

Analyzing and Exploring the Impact of Big Data Analytics in Sports Sector

Data visualization and toss related analysis of IPL teams and batsmen performances

Calculating expected win percentage of an Indian Premier League team

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