Hi all, this is the last day of the Dashboard week. Today’s challenge is about a very interesting topic: The Big Bash League (BBL). Quickly introduce, The Big Bash League (often abbreviated to BBL or Big Bash) is an Australian professional franchise Twenty20 Cricket league, which was established by Cricket Australia. Personally, I have never seen anyone playing cricket in real life before, therefore today’s challenge is not just about building a dashboard, but also about getting used to this sport as well. The interesting thing about this challenge is we received the dataset at 9 o’clock in the morning and we will have to finish building the dashboard and write a blog by 3 o’clock in the afternoon. So besides adaption, Time-boxing is also my enemy today.

The Dataset

We have been given access to this dataset, which contains 4 files in total. There are 2 separate files deliveries.csv and matches.csv. These files actually contain information from those 2 other files, but having further data until 2021 so using those 2 files will be enough for me in this case. About the detail, those 2 files contain the information of each match summary, ball-by-ball details, and player’s statistics. The deliveries.csv file seems to have complex statistics about a cricket game, therefore I won’t use this table since I believe having a deeper understanding of this sport is crucial in order to analyze these stats. And yes my friend, respecting the time-boxing rule is essential to nail this challenge.

My Approach

Having a look through the matches.csv, I saw a variable that is tossed decision and toss winner. Therefore, I have popped out a question right away: “Is toss decision and winning the toss minigame will impact the team’s performance in cricket?” Well, my friend, there is one way to find out. Whether the answer is yes or no, personally I think this will be an interesting point of view to consider.

Data Preparation

Luckily in this challenge the data is quite clean therefore I don’t have to clean or prepare any of the data. However, this dataset has a problem. The dataset has some missing games, which means that there are some games are happened during 2010-2021 however was not included in the dataset. For example, each team should have played at least 8 games (regular season) in total in season 2012-2013 as the information I got from the official website:

However in reality there are some teams actually played less than 8 games in this season, based on the dataset we have:

As we can observe from above, there are only 7 teams in the dataset that played this season, so the Hobart Hurricanes is missing in the dataset. That’s the only thing that I found, however We will move on and analyze our idea based on what we have. Therefore the insights I got from this challenge will not gonna be a 100% confidence conclusion.


First, as usual, let’s have a look through the dashboard

Key things about this dashboard:

  • We can filter out the team by selecting the team from the bar chart and it will apply to those 2 charts in the bottom.
  • The bar chart shows us the number of time each team won the Toss minigame.
  • The scatterplot shows the relationship between the number of winning games and the number of time they win as toss winner.
  • The Line chart shows the variable of number of winning game and number of toss winner overtime.
  • The Pie chart shows how they pick their toss decision in their winning game.


  • There are clear relationship between the number of time you win toss minigame and the number of winning game. Which means that If you can make the toss decision, there are higher chance you can get the W.
  • Toss decision (bat or field) not really impact on your winning rate, as in this case the number of game you win when picking bat is almost 50/50

As I have mentioned from the beginning, since there are some exclusion in this dataset, therefore it’s hard for us to safely conclude the answer. However, based on the data we have, due to aforementioned analysis, we may consider the answer as Yes. Well, in sport, having a bit of luck is always better isn’t it ?


The Data School
Author: The Data School