ICC Cricket World Cup (1975 - 2015): a visual history

Being the cricket obsessed fan that I am (my obsession has gotten worse since I moved away from India and Australia to the US where I am starved of watching live cricket - distance does make the heart grow fonder!), I worked on a fun project this weekend.  I first started with a story map tour of all the cities that have hosted the finals of the various ICC Cricket World Cup tournaments from inception (1975) until the last edition (2015).

You can access the story map tour web app here: http://arcg.is/2l6QaOM

Next, I created a map of all the countries that have ever participated in the World Cup.  Here is what the map looks like:




The size of the flag indicates the number of times that a country has been the world champion (min value = 0; max value = 5).

Then I focused on the two World Cups (held in 1987 and 2011) that were hosted by Asian countries.  For each of these editions, I created a map that marked the various venues where the cricket matches were played.  Here is what the map looks like:



The size of the bubbles represents the number of matches played at the venue.  To easily identify the venues that were added / dropped in 2011 and the difference in the number of matches played at each venue, I created a Story Map Swipe web app where if you scroll right, you can only see the 1987 venues where as if you scroll left, you can see the 2011 venues appear.  I think this is a really cool web app (even if I say so myself!).

You can view this Story Map Swipe web app in all it's glory here: http://arcg.is/2la9Hh5

Finally, to put all this together in a story that (hopefully!) flows nicely, I created the following Story Map Journal app:


You can directly view the Story Map Journal web app in all its glory here: http://arcg.is/2la792B

I hope you enjoyed my visual history of the ICC Cricket World Cup (1975 - 2011).  I'd love to hear your thoughts in the comments section.


Comments

Post a Comment

Popular posts from this blog

AI, ML, NN and DL: a visual explanation

Image
There appears to be a lot of confusion between the terms Artificial Intelligence (AI), Machine Learning (ML), Neural Networks (NN) and Deep Learning (DL).  Based on research from various popular blogs and articles, here is my attempt at a simple visual explanation:
Read more

Understanding And Interpreting Gain And Lift Charts

Image
Lift and Gain Charts are a useful way of visualizing how good a predictive model is. In SPSS, a typical gain chart appears as follows: In today's post, we will attempt to understand the logic behind generating a gain chart and then discuss how gain and lift charts are interpreted. To do this, we will use the example of a direct mailing company. Let us assume that based on experience, the company knows that the average response rate on its direct mail campaigns is 10%. Let us further make the following assumptions: * Cost per ad mailed = $1 * Return per response = $50 Additionally, let us assume that the company mails out ads in lots of 10,000. Based on these assumptions, if the company mails out 100,000 ads, a table summarizing the results it would obtain from this campaign is provided below: Now let us assume that the company uses SPSS Modeler to develop a predictive model using data from previous campaigns. "Response / No Response" is identified as the ...
Read more

Creating a time series forecast using IBM SPSS Modeler

Image
In today's post, we discuss how to create a time series forecast using IBM SPSS Modeler.  For the purposes of our exercise, we will use historical sales data at a SKU (stock keeping unit) level.  This data is provided in a MicroSoft Excel .xlsx file and must be in the following format: In the image above, AAAAA through EEEEE are SKU numbers with the relevant monthly sales data provided in the respective columns.  There is also a column that indicates the grand total of all SKUs sold in a month (AAAAA +...+ EEEEE + other SKUs not shown in the image above).  The last column in the image above reflects the months for which historical sales data are provided. As with any modeling exercise, we first insert a source node into the modeling canvas.  Since our data is in the MicroSoft Excel .xlsx file format, we insert an Excel source node as follows: On exporting to a Table node, we see the output display as follows: We then add a Filter node to select t...
Read more