World Cup Fever with IBM Rational Focal Point!
Since World Cup fever currently has the world in a frenzy – particularly the Netherlands, where I am writing this from just after their 5-1 victory against Spain – I thought it would be good to share a footballing story from about 4 years ago!
Note that as a Brit I shall be steadfastly refusing to talk about Soccer!
Take a stroll down memory lane, to South Africa, with the dulcet tones of the Vuvuzela giving every team a great excuse for playing badly, and also giving the worlds media something to discuss between games.
A then colleague of mine had the wonderful idea of using IBM
He spent a good deal of time entering as much information as he could, which was readily available from the FIFA website, to build a Focal Point 2010 World Cup analysis environment:
He included detailed statistics about the teams and the players:
He ran the analysis in Focal Point – and out came the following result:
Which, for those of you who were following at the time, is pretty interesting if matched with the final FIFA results:
So the question is: If England were ranked first based on all of the statistical input that was available from FIFA, why did Focal Point not see that they would come out 13th at the end of the tournament? Or that Uruguay would have come from 29th up to 4th place?
The answer to this is pretty simple – my colleague only fed the tool with quantitative information. In fact he stopped feeding information before the start of the tournament to ensure that the results would accurately reflect this difference between the statistical analyses of the expected result versus what would actually happen. He never entered any of the qualitative information such as the real performance of individual players in each of the qualifiers, how well the teams were coalescing, the combinations of players and how they were performing under pressure.
If fact, none of those ‘softer’ factors that Focal Point is specifically designed to handle!
We could have added one qualitative question, and got a much closer result from Focal Point: Which team is playing better?
Using the pair-wise comparison capability we would have seen England drop down the list at a pace that might even have kept up with Ronaldo, Messi or Rooney on the pitch!
The reality is that IBM Rational Focal Point is not a tool for prediction (so please do not start filling it with the latest data for Brazil and heading straight to the bookies!), rather it is a tool for helping businesses understand where to focus – if we consider each of the teams to be an idea for a product, an application, a service, or a project then we start to think about this slightly differently.
Everything that falls in the top 30% includes projects that should be funded, or where resources should be focused. Everything in the bottom 30% should be avoided. The middle 30% needs to be thought through, considering what is available in terms of budget and resource versus expected results.
On the other hand, as we factor in the qualitative analysis and start to see England drop dramatically down the list we should be paying attention, trying to work out why this is happening, and either mitigating the risk, or stopping the project. Watching Uruguay speeding up the list might alert us that something has changed in our market or competitive situation and we might choose to divert budget and resource into this project since it is clear that we will get more from this than we originally anticipated.
By working this way we can be reactive to the market, smarter in how we allocate budget and resource, and ultimately more successful overall.
Thanks to Nasser Ahmed for performing the original work. Please connect with me on Twitter or LinkedIn to talk more about decision support and governance factoring in both quantitative and qualitative performance indicators. For more World Cup analysis I’m probably not your best source of information!