Americans normally aren’t that keen on “soccer” but things are a bit different what with the FIFA World Cup taking place down in Brazil.

Excitment is reaching fever pitch with the tournament in full swing and the 1/16 finals well underway. Social media is flooded with photos of the event and other interesting tidbits happening in Brazil; wagers are being placed on the teams people think will bring home the coveted cup, predictions are abundant; fans are at each other’s throats; sports analysts are scrutinizing every player, every game, every pass, every shot…

But who is right and who is wrong, and who will the champions be? Can the USA defeat Belgium this afternoon, or is the American dream about to be shattered in 90 minutes?

You can ask all the expert analysts you want, but here at SiliconANGLE we prefer asking Big Data for our answers…

Who will win the cup?

Defending champions Spain were many people’s favorites, while others had their bets on England and Italy as strong contenders. That’s because these teams all have pedigree – they’ve all won the cup before and so many people expect them to be strong again, but things didn’t work out that way.

Unfortunately for those who bet on the above three teams (who all crashed out in round one), there are so many factors that can affect a team’s performance and this is where Big Data plays a role. Big Data is able to take into consideration factors such as temperature, fan support, popularity of players, weather, the time of day the game kicks off, and so on, in order to predict to will win this year’s tournament.

Analysis shows England often struggle in hot and humid climes

Most fans were shocked that England was eliminated so early, but Stephen Hawkings, the renowned theoretical physicist, correctly predicted the team’s poor performance. In Hawking’s model, he predicted that England’s chance of winning a game drops by 60 percent every time the temperature increases by 5ºC. Moreover, he found that England plays better in stadiums situated 500 meters above sea level, and perform better if the games kick off later than 3PM local time. In short, England’s soccer team struggles to cope with the conditions in hot and humid countries.

Using its own Big Data model, Goldman Sachs predicts that Brazil and Argentina will face off in the final, with Brazil’s home court advantage – translation: lots of screaming fans –  playing a huge role in its victory.

Big Data on the field

Using Big Data to analyze the matches in real-time might be fun, but one country is going a step further – they’re actually using Big Data to improve their player’s performance.

SAP has provided Germany’s national team with its Match Insight software which makes training, preparation, and tournament analysis easier. With Match Insight, Germany’s coaches will be able to process huge amounts of data that helps them effectively assess key situations in each match – knowledge which can be used to improve player and team performance.

Not surprisingly, Germany is doing fairly well in this year’s tournament. According to its FIFA page, the team scores a goal on average every 39 minutes, taking only 5.3 attempts to do so. Compare this with Brazil’s team averages and it’s clear they’re doing far better. Brazil has only managed to score a goal every 49 minutes, and needs 8.8 attempts to do so. These numbers are based on the teams’ performance in round one.

SAP has previously collaborated with the German club team TSG 1899 Hoffenheim, and the experiment has been very succesful. According to Bernhard Peters, Hoffenheim’s Director for Sports and Youth Training, the club plans to equip every player with sensors embedded in their uniforms, shin guards and the ball – this will enable them to leverage SAP’s software to better analyze player performances and movement, identify where they are weak, then work on this to increase their chances of winning.

SAP plans to launch Match Insight on a wider scale after the World Cup tournament, which means it could be available to more football teams or even be used in other sports.

#FIFA #social #media #gone #crazy!

Oi, Brazil’s largest telecommunications company, aims to make this year’s World Cup the most connected in history. It provides connectivity in all 12 stadiums used for the event, and is confident that it will generate more data than the Sochi Winter Olympics.

Will it succeed? Most likely, especially when so many quirky things have happened – such as Italy striker Mario Balotelli’s request to get a kiss from Queen Elizabeth II in exchange for preventing England’s elimination from the cup (he didn’t and so she didn’t); Uruguay’s Luis Suarez’s biting problem, the opening ceremony, epic plays, and we can expect more as the month-long tournament continues.

Facebook and Twitter are both seeing huge amounts of FIFA-related traffic. Social media users are tweeting and posting about everything from their most liked and disliked players, which teams are going to win, which FIFA song is better, FIFA sponsors and so on. Thanks to all this social media buzz, Facebook and Twitter are learning more about their users, such as which nation’s fans are the most talkative, which players they love, which nationalities actually came to Brazil and so on.

SAP has leveraged Twitter and come up with an infographic detailing some interesting FIFA facts. According to the infographic, the US tops the list as the most talkative country on, closely followed by the UK. The most talked about players include England’s Wayne Rooney and Portugal’s Christiano Ronaldo. Meanwhile, another useful stat gleaned by SAP reveals that more people like Jennifer Lopez’s World Cup song than Shakira’s.

Facebook has gathered its own data to learn more about football fans and compiled this into a video so it can easily be understood. According to Facebook, there are more male fans that female fans, which isn’t surprising as most sport fans belong to the XY club. More people from the US traveled to Brazil than any from any other country, and in the US, there are more football fans in New York than any other state.

photo credits: Felipe Quintanilha via photopin cc; proforged via photopin cc; Ali Brohi via photopin cc