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.

1. IPL ANALYSIS
PGDDS PROJECT ON

2. TEAM
HAARIS
ANMOL
ANIMESH
ADITYA
ANUNAY
‣ We Believe every team has dream of
winning the final 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 first 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

6. DATA SNAPSHOT

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

16. Result and Testing

17. Result and Testing

18. PLAYER RANKINGS

19. Objective and Statistical Model

20. Data Snapshot

21. Results
The first 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 first
component explains
62.61% of the
variation in the
data.
B Kumar was
the top bowler
in the 2016
season

25. PLAYER SEGMENTATION

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 significantly
higher than the first five
Season(41.79) .
▸ The average of middle order batsmen
in last four season (21.64 )is
significantly higher than the first five
Seasons (19.95).
▸ The average of lower middle order
batsmen in last four season (12.76)is
slightly higher than first five Seasons
(12.74).

35. WORD CLOUD

36. LAST MIN PREP..GRACIAS!