Skip to content

Latest commit

 

History

History
40 lines (29 loc) · 1.74 KB

README.md

File metadata and controls

40 lines (29 loc) · 1.74 KB

IPL data set analytics

Aim

To convert raw open data (run by run records in this case) into charts that tell some kind of story.

Preparation

raw data
The data for this exercise is sourced from kaggle.
NOTE: you might have to find data sources on your own. For example the country of origin for the Umpires

Instructions

  1. Download all the data needed. Consult your mentor if you have any problems accessing the raw data.
  2. Initialize rbenv for this project. Hint: create .ruby-version file.
  3. Enable ruby linting via. RuboCop for this project.
  4. All projects should have README.md with instructions on how to run this project.

What your program should do

From the CSV and other source files specified above, write ruby code to ...

  1. Read in the data.
  2. Write logic to slice / dice / accumulate / transform the data.
  3. Using gruff plot the plots specified in the problems section.

Problems

1. Total runs scored by team
Plot a chart of the total runs scored by each teams over the history of IPL. Hint: use the total_runs field.

2. Top batsman for Royal Challengers Bangalore
Consider only games played by Royal Challengers Bangalore. Now plot the total runs scored by every batsman playing for Royal Challengers Bangalore over the history of IPL.

3. Foreign umpire analysis
Obtain a source for country of origin of umpires. Plot a chart of number of umpires by in IPL by country. Indian umpires should be ignored as this would dominate the graph.

4. Stacked/Grouped chart of matches played by team by season
Plot a stacked/grouped bar chart of ...

  • number of games played
    • by team
    • by season