Luck and chance have roles to play in predicting the winner of a World Cup, according to Luis Amaral.
Amaral is a professor of engineering whose expertise is in the field of complex social and structural networks, which he has applied in subjects such as gender discrimination and gun violence. But the coming 2018 World Soccer Cup has piqued the interest of Amaral who is very passionate about soccer, more than enough to make him try to predict who will emerge as the champion in soccer’s version of the Olympics.
He used his expertise in network complexity to create an algorithm that objectively ranks professional soccer players. With the help of his students, Amaral has created a network for each team that will participate in the World Cup
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Taken as a whole, the AFR values of all the players is an indication of their team’s strength.
Speaking to Clare Milliken of Northwestern Now, Amaral said a player with an AFR above 70 is a superhuman while an AFR above 70 over many seasons is god-like. The world’s top three soccer players -- Lionel Messi, Neymar, and Cristiano Ronaldo -- all have similar AFR ratings of 73, which may not be a surprise to soccer fans at all.
Besides generating the player values, the algorithm also constructs a network for each team, made up of nodes or circles (one for each player), and lines that connect the nodes or players. The nodes and lines have various sizes, with larger nodes representing better players and wider lines indicating a stronger connection between players.
When Amaral used the AFR to gauge the teams in the 2008 Euro Cup, it showed that Spain was the best team and Xavier Hernandez Creus, a member of the Spanish team, was to be the best player. True enough, Spain won the 2008 Euro Cup and Creus wound up as the best player of the tournament.
Amaral also created his own “World Cup Dream Team,” made up of the best soccer players who will play in the 2018 World Cup in their respective
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When asked who will win the 2018 World Cup, Amaral is more circumspect, pointing out that his algorithm can only predict what the likelihood of outcomes would be. He stresses that this is similar to guessing who will make it to the playoffs, as against winning the Super Bowl.
But since Neymar, Ronaldo and Messi have similar AFR scores, it is easier to predict which country -- maybe Portugal, Brazil or Argentina -- will make it to the finals and win the World Cup.
Electronic Technology to Go with Big Data
The rise of Big Data in sports analytics is matched by developments in video technology.
Julian Nagelsmann revolutionized soccer training by using a 6x3 meter video screen during training sessions. The screen enabled Nagelsmann to give immediate video feedback to his players on how to improve their game.
All the 20 Premier League soccer stadiums in the UK are equipped with a set of 8 to 10 digital cameras that monitor every player on the field. Ten data points are collected every second for each of the 22 players, producing a total of 1.4 million data points per game. The collected data will be coded to identify every tackle, shot or pass to help coaches and sports analysts find out what exactly happens during a