Analysis and Comparative Study of 10 Years Cricket Sports Data of Indian Premiere League (Ipl) Using R Programming
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International Journal of Computer Engineering & Technology (IJCET) Volume 9, Issue 5, September-October 2018, pp. 10–15, Article ID: IJCET_09_05_002 Available online at http://iaeme.com/Home/issue/IJCET?Volume=9&Issue=5 Journal Impact Factor (2016): 9.3590(Calculated by GISI) www.jifactor.com ISSN Print: 0976-6367 and ISSN Online: 0976–6375 © IAEME Publication ANALYSIS AND COMPARATIVE STUDY OF 10 YEARS CRICKET SPORTS DATA OF INDIAN PREMIERE LEAGUE (IPL) USING R PROGRAMMING Dr. Parag C. Shukla Assistant Professor & Head, Department of MCA, Atmiya University, Rajkot, India Dr. Hetal R. Thaker Assistant Professor, Department of MCA, Atmiya University, Rajkot, India ABSTRACT Nowadays, analytics is playing a key role in any field. Analytics is used in our day to day life. People don’t want to purchase even without analytics. Before few months IPL (Indian Premier League for Cricket) auction was taking placed. Even franchises are interested to pick a cricket player based on their past performances. For all this, they need accurate data and analytics with the comparative study to pick a particular player for their team. Here, we are comparing the last 10 years cricket sports data of IPL with interesting facts. We can perform much analytics but here we are focusing on the comparison between popular players, team, and fielders. Performance of top-10 players in each category like batting, bowling and fielding. Valuable player for the specific team. Key words: Data Analytics, IPL Analytics, Sports Analytics. Cite this Article: Dr. Parag C. Shukla and Dr. Hetal R. Thaker, Analysis and Comparative Study of 10 Years Cricket Sports Data of Indian Premiere League (IPL) using R Programming. International Journal of Computer Engineering and Technology, 9(5), 2018, pp. 10-15. http://iaeme.com/Home/issue/IJCET?Volume=9&Issue=5 1. INTRODUCTION Cricket is popular sports in India, and many people want to know comparative study between different players, team etc. Before few months IPL (Indian Premier League for Cricket) auction was taking placed. Most of the franchises were interested to pick a cricket player based on their past performances. Many franchises have hired data analysts to do the analysis of the player. For all this, they need an accurate data and analytics with the comparative study to pick the particular player for their team. Here, we are comparing the last 10 years cricket sports data of IPL with interesting facts. We can perform much analytics but here we are http://iaeme.com/Home/journal/IJCET 10 [email protected] Analysis and Comparative Study of 10 Years Cricket Sports Data of Indian Premiere League (IPL) using R Programming focusing on Comparison between popular players, team. Performance of top-10 players in each category like batting, bowling, and fielding. Valuable player for a specific team. Here, we analyze the past 10 years data of IPL and summarize total runs by applying sum function for batsman_runs and we arranged the same in descending order. So, we can get the first cricketer who made the highest runs. Suresh Raina is the person who made the highest runs in the history of IPL. Surprisingly if you find the top-10 catcher of IPL history and if you are not counting wicket keepers catch then Suresh Raina is again come in the top of the table. So, we can conclude that Suresh Raina is the best batsman and best fielder in IPL. Top-10 Batsman in IPL History from 2008 to 2017 Figure 1 Top-10 Batsman in IPL History from 2008 to 2017 Top-10 Catcher in IPL History from 2008 to 2017 Figure 2 Top-10 Catcher in IPL History from 2008 to 2017 http://iaeme.com/Home/journal/IJCET 11 [email protected] Dr. Parag C. Shukla and Dr. Hetal R. Thaker 2. PERFORMANCE OF SURESH RAINA Figure 3 Performance of Suresh Raina Comparison between Virat Kohli and Suresh Raina – Runs by each Season Figure 4 Comparison between Kohli and Raina – Runs by each Season http://iaeme.com/Home/journal/IJCET 12 [email protected] Analysis and Comparative Study of 10 Years Cricket Sports Data of Indian Premiere League (IPL) using R Programming Comparison between Virat Kohli and Suresh Raina – Type of Dismissals Figure 5 Virat Kohli Vs Suresh Raina – Type of Dismissals Comparison between Virat Kohli and Suresh Raina – Strike Rate By Over Figure 6 Virat Kohli Vs Suresh Raina – Strike Rate By Over http://iaeme.com/Home/journal/IJCET 13 [email protected] Dr. Parag C. Shukla and Dr. Hetal R. Thaker Comparison between Virat Kohli & Suresh Raina – By Number of Over Faced Figure 7 Virat Kohli Vs Suresh Raina – By Number of Over Faced Comparison of Tendulkar, Ganguly, Dhoni, Kohli and Raina – By Strike Rate Figure 8 Comparison of Tendulkar, Ganguly, Dhoni, Kohli, Raina Strike Rate http://iaeme.com/Home/journal/IJCET 14 [email protected] Analysis and Comparative Study of 10 Years Cricket Sports Data of Indian Premiere League (IPL) using R Programming 3. CONCLUSIONS Suresh Raina is the person who made the highest runs in the history of IPL from 2008 to 2017. Surprisingly if you find the top-10 catcher of IPL history and if you are not considering wicket keepers catch then Suresh Raina again comes on top of the table. So, we can conclude that Suresh Raina is the best batsman and best fielder in IPL. See a figure-4 comparison between Kohli and Raina runs through each season. In 2008,2009,2010,2012,2014,2017 Raina was the top-scorer than Kohli. We also did the comparison of both the stars in the type of dismissals, strike rate by over and Number of overs faced by both. We also did the comparison of stars like Sachin Tendulkar, Saurav Ganguly, MS Dhoni, Suresh Raina & Virat Kohli innings strike rate over wise and no doubt god of the cricket Sachin Tendulkar's strike rate is highest in 20th over in IPL history with compare to Ganguly, Dhoni, Kohli, and Raina. ACKNOWLEDGEMENT We are very much thankful to Kaggle for providing us ball by ball data. This is never ever possible without Deliveries and Matches information that we get from the Kaggle. Also thankful to open source community to teach us how to write code in R. We are also thankful to the coder of kernels, who gave us hint that how to write a script in R. 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