IPL Anaytics
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An analytics project on Ball by Ball data of 9 IPL seasons to predict patterns and insights team and player wise. Apart from that a MLR model to predict the score at the end of innings.
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- 2. TEAM HAARIS ANMOL ANIMESH ADITYA ANUNAY We Believe every team has dream of winning the 鍖nal cup or trophy the dream becomes their goal and that goal is achieved only by planned action for unfavourable scenarios.
- 3. INDEX Introduction Season wise Analysis Player wise Analysis Team wise Analysis Model Player Rankings Player Segmentation Key Takeaway Conclusion
- 4. IPL is a professional Twenty20 cricket league in India contested during April and May of every year by teams representing Indian Cities. Founded by BCCI and it is now the most attended cricket league in the world and ranks sixth among all sports league. In 2010 ,the IPL became the 鍖rst sporting event in the world to be broadcasted live on YouTube. QUICK FACTS
- 5. Data Consist Of Data of 9 IPL Seasons from 2008 to 2016 Ball by Ball details of 577 matches Match details Player details Season wise best performers
- 8. Team Data Player Match
- 9. SEASON Top 10 Batsmen in last 9 Seasons Out of top 10 batsmen 7 are Indians Rains and Kohli are on the top when it comes to scoring M S Dhoni is more consistent in scoring runs
- 10. Top 10 Batsmen on the basis of Average Runs
- 11. Stadium wise win percentage
- 12. PLAYER Player performance Vs Teams
- 13. TEAM Team performance across different Stadium Dark colour indicates most numbers of wins on that ground
- 14. MODEL Objective and Statistical Model
- 15. Data Snapshot
- 18. PLAYER RANKINGS
- 19. Objective and Statistical Model
- 20. Data Snapshot
- 21. Results The 鍖rst component explains 72.36%of the variation in the data V Kohli was the top batsman in 2016 season followed by David Warner and AB de Villiers As V Kohli and A B De Villiers were among the top 3 batsmen of 2016 season ,they were retained by RCB for the 2017 season R G Sharma and DA Warner were retained by their respective teams.
- 22. Objective and Statistical Model
- 23. DATA SNAPSHOT
- 24. Results The 鍖rst component explains 62.61% of the variation in the data. B Kumar was the top bowler in the 2016 season
- 26. Objective and Statistical Model
- 27. DATA SNAPSHOT
- 28. Results The cluster 3 is the one which has batsmen of the highest caliber followed by cluster 1. The cluster 2 has batsmen with lowest caliber . The R-squared is computed as ratio of Between Cluster Variability to total Variability was found to be 64.7% AB De Villiers ,Virat Kohli and David Warner belong to the 3rd cluster which can be though as the Premium players.
- 29. Objective and Statistical Model
- 30. DATA SNAPSHOT
- 31. Results The cluster 1 is the one which has bowlers of the highest caliber followed by cluster 2. The cluster 3 has bowlers with lowest caliber The R-squared is computed as ratio of Between Clusters Variability Of total Variability was found to be 64.2% As seen B Kumar, A Zampa and Y Chahal are included in cluster 1 as they were among the top 10 bowlers in 2016
- 32. KEY TAKEAWAY
- 33. DATA SNAPSHOT Data for opening batsmen for last 5 years and current 4 years . Similarly we have sliced batsman_scored data for 6th and 7th order batsman.
- 34. CONCLUSIONS The average of opening batsmen in last four season (47.79)is signi鍖cantly higher than the 鍖rst 鍖ve Season(41.79) . The average of middle order batsmen in last four season (21.64 )is signi鍖cantly higher than the 鍖rst 鍖ve Seasons (19.95). The average of lower middle order batsmen in last four season (12.76)is slightly higher than 鍖rst 鍖ve Seasons (12.74).
- 35. WORD CLOUD