A closer look at some rugby statistics in anticipation of the upcoming Rugby World Cup.
The statistics are running through an OLAP model and the beauty of OLAP technology, often referred to as “cube technology,” lies in its ability to allow data to be viewed from multiple angles.
Unique perspectives can unveil insights that other technologies might not provide. In this case, I’ve employed OLAP to investigate a distinct facet of Rugby World Cup data—specifically, the concept of “dream killers.“
A “dream killer” is essentially a team that emerges victorious in a knockout stage game, shattering the dreams of the team they defeat. To illustrate, let’s delve into some historical examples. In the inaugural Rugby World Cup of 1987, New Zealand emerged as a dream killer by defeating France, Scotland, and Wales—these were the semifinal, quarterfinal, and final matches respectively. Australia, France, and Wales also acted as dream killers by knocking out teams in the quarterfinals.
This exploration leads us to consider patterns. The question arises: Do specific teams tend to be the “dream killers” for others?
Here, a curious insight emerges. New Zealand, often regarded as having a historic rivalry with France, has actually knocked France out three times, whereas France has eliminated New Zealand twice. This dynamic is worth noting as New Zealand and France find themselves in the same pool in the upcoming World Cup.
This data’s true power lies in its aggregation, and this is where OLAP technology excels. When we tally up the instances, New Zealand leads the pack in terms of knockout wins, trailed by Australia, England, and South Africa. It’s noteworthy to mention that South Africa missed the first two World Cups, which slightly skews the average in favour of Australia and New Zealand.
In conclusion, OLAP technology not only showcases its utility in business analytics but also demonstrates its potential for engaging analyses in the world of sports, such as rugby. By pivoting data perspectives, making the selections we wish, looking across and throughout the dimensions of a model—here, years, teams, results, standings, etc.—we can uncover intriguing trends and insights that would not be immediately apparent.
There is a key difference between sports and business analysis that should be noted: in a sports model we can review performances and anticipate wins from the perspective of a devoted fan interested in such outcomes. In business, planning for “wins” on the basis of the type of sophisticated model we can build with OLAP cubes will potentially lead to greater profitability, more market share, staff bonuses—all to the greater good for the “team” that is the business!