The World Cup Is So Predictable
Apparently some savante has come up with a formula for soothsaying the winner of this year’s World Cup. Wired UK ran an image visualising the algorithm. Designed by the always awesome SectionDesign.
The Appliance Of Science
How much energy does a microphone use? Or a blender? Or a sateillite dish? And how much does that add up to over a year, money-wise and planet-wise? A lovely playful app from Pentagram team of Lisa Strausfield and Mike Deal (of Charting The Beatles fame). (It’d be nice to change the currency though).
The Four Seasons Visualized!
Beautiful synaesthesic bubble trip through Vivaldi’s classics. An experiment by MotStudio [Via DataVisualisation.ch]
>Die Hard Index
I don’t understand this beautiful map because it’s about sports. Apparently it depicts baseball’s most ardent fanboys. But it looks so great. It’s like information ice-cream. You know, for your eyes. Ice cream man Russ Maschmeyer.
Still hungry?
- How much gas? This is a neat approach at visualising stats for states (Thanks to Felix Sherrington-Kendall for sending)
- What Alleviates Depression? Not the most upbeat design – perhaps intentionally? – but an interesting subject and interesting way to research the data.
- All 91 Cover Designs For Information Is Beautiful Hey – we got there in the end. Just be glad you *never* have to work with me.
As ever, if you come across any tasty information designs you’d love to share with us, please send them over.
18 Comments
Thanks for the mention :) Got my IIB book last week, love it!
(ps. you’re missing a g off the url to the Die Hard Index pic)
The link from the image is broken. It’s missing the final “g” in the .png suffix: http://interactiondesign.sva.edu/images/projects/Die-Hard-Index_big.png
Die Hard Index is a broken link. Missing the last letter.
How can Toronto have a per cap income that is so much higher than everyone else?
And does stadium capacity matter in the way prescribed in the formula. If you sell 75% of your tickets and suddenly double capacity the index falls by 50 although overall support is still the same. Furthermore, the scarcity of tickets is also embedded in ticket prices, so there is some double counting here.
Also, many fans would argue that the overall record is that matters, not the home record. And to some extend you should be using multi-year averages. It takes time to build a fan base if you sucked for a long time (see Tampa).
I’m no statistician, but the World Cup formula sounds like voodoo to me.
The blurb says that the algorithm is “based on mainly economic data”, but when you look closer the weighting given to economic factors is tiny compared to that given to experience, which in the absence of further definition one can only assume is derived from the historical performance of that country in the World Cup.
And then there’s the mysterious “weighted to allow for teams doing better or worse than predicted in the past” clause.
I think a nice addition to the World Cup prediction would be the inclusion of altitude data. The reason for this is that teams who come from a country which has a very high altitude would benefit by playing games at altitude (This world cup will have many venues at a very high altitude – interestingly: because the air pressure is 5% less than at sea level the ball will travel 5% faster, travel 5% further and curl 5% less and your energy will drain quicker if you are not used to breathing in a reduced amount of oxygen).
Rugby teams who have been touring South Africa for years (not in one go) always mention the added difficulty playing at altitude if you’re not used to it.
If I got it right, the formula for the World Cup prediction is based on population, GDP per capita and competition experience. How the hell does Serbia get to the final then? Must be a mistake.
OK – there are a number of problems with this World Cup prediction.
• The stated accuracy is 72%, which sounds suspiciously like an R squared value. Any statistician will tell you that this is not how to interpret the R squared.
• Even if it were true, each time another game is played the accuracy is compounded. For example, 72% accuracy in the first game becomes 52% accuracy after the second game and 37% after the third etc… By the time you reach the final the accuracy will be squat.
• As others have pointed out, experience is just a black box – how on earth did they calculate it?
• The text makes it clear that the data used was from 1980 to 2001. What happened to the last decade? This would be the most relevant data so why have they used such obsolete data?
I wouldn’t ascribe a great deal of value to the predictions presented.
The depression index thing is a bit off – the sample size is only 944 people, all of which are from one community, with no apparent checking for multiple accounts from single users and all that jazz that ain’t double-blind badass.
I LOVE your blog! The infographics are so extremely beautiful and aesthetic and clever.
I don’t know if you’ve seen this, but it’s an interactive graphic displaying the erosion of privacy on Facebook over the years. Thought you/readers might be interested considering the current situation: http://mattmckeon.com/facebook-privacy/
If you get it right, based on the formula for predicting the World Cup on population and per capita gross domestic product and experience of competition. How the hell does Serbia’s access to the final after that? There must be a mistake.
Yeah, Serbia even beats Germany, despite Germany having 11 times the population and 3.5 times the GDP. To compensate for this, Serbia would need 1.65 times the experience of Germany (3x WC, 3x EC). But Serbia as a football team exists just since 2006, so between 1980 and 2001 they were technically unbeaten ;-)
I was showing a friend the INFOGRAPHICAL MORSELS NO 7 MAY 10, 2010 The World Cup Is So Predictable while he wisely observed: “GDP per capita of a COUNTRY is completely different from GDP per capita of a football TEAM”. A high percentage of PLAYERS do not play in their birth country, they play in foreign countries normally with a HIGHER GDP per capita. So you should calculate the average GDP per capita of EACH TEAM and the continue with the formula. As an example: Argentinian Martin de Michelis plays in Germany, a country that doubles Argentina’s population and doubles Argentina’s GDP per capita. This “detail” is not included in your formula.
Another detail forgotten in the formula: The vuvuzelas!
Nice try, but Serbia in the final game? Better luck next time.
Seems to me that the accuracy is 56% in stead of 72%; they’ve got 7 countries ‘misspredicted’ in the knock-out phase…!
Fortunately, I didn’t base my pool bets on this thing! ;)
But it looks great, it sure is beautiful information (and not so much valuable)
9 correct 7 wrong: 56% of effectiveness
science or roulette?
On the other hand, who would ever had predicted that 2 teams that never won the world cup would end up in the final?
I guess luck and doubtful referees are of more influence than was counted for :-)