Moneyball meets football

13272340 Moneyball meets football

Recent crucial football matches have been won by the application of the kind of number crunching that has already changed baseball – known as the moneyball approach, writes Simon Kuper.

In 2004, Arsenal’s French manager Arsène Wenger was looking for an heir to midfielder Patrick Vieira.

He wanted a player who could cover lots of ground, so he scanned statistics from European leagues and spotted an unknown teenager at Olympique de Marseille, Mathieu Flamini, who was running 14km a game.

But could he play football? Wenger went to look and signed him for peanuts.

Flamini prospered at Arsenal before moving to AC Milan.

Back then, Wenger was one of the few in football who used data to inform his decisions. In a traditionally anti-intellectual sport, he was a keen mathematician with an economics degree.

But now a data revolution is sweeping the sport. Most big European clubs employ sophisticated number crunchers.

Arsenal’s data department is led by a German with a background in investment banking.

Data-crunching computers began infiltrating most professions in the 1980s, but sport was immune.

The first to change was American baseball, which saw the rise of a subculture of nerdy statisticians who, in their free time, played with the numbers of their beloved sport.

Their dean was Bill James, a Kansas factory janitor, who, in his typewritten Baseball Abstract, tore apart the game’s traditional wisdoms.

He proved, statistically, that traditional ploys like base stealing made no sense.

Eventually, some inside professional baseball noticed the Jamesians.

Billy Beane, general manager of the Oakland As, had been an intellectual baseball player who, at 27, walked into the head office and said he wanted to quit playing and become a scout.

Beane read James’ theories with fascination.

He was fed up with the “gut wisdom” of gnarled scouts, and hired a 20-something Harvard-educated statistician to find new players.

Using new stats, the team identified undervalued talent.

It turned out that baseball’s respect for natural athletes was misplaced. Fat men with good ball sense did just as well.

“‘Big-boned’ is the term we prefer to use,” says Beane.

For years, the fat players punched well above their weight for the team, winning more games than a penniless club had a right to expect.

Richer clubs finally copied them. Recently, Beane marvels, the mighty New

York Yankees had 21 statisticians on the payroll.

Beane’s story was told in Michael Lewis’ book Moneyball.

It sold over 1 million copies, became a Hollywood movie with Brad Pitt as Billy Beane, and is surely the most influential sports book ever written.

Moneyball changed almost all ball games. Inspired by Beane (and latterly by Pitt), sport executives began using statistics to win matches.

A few people in English football read Moneyball. Stats had entered the game in the mid-1990s, when data providers began cataloguing metrics such as the number of kilometres, passes and tackles per player per match.

Football executives made the pilgrimage to California to meet Beane, who had fallen hard for soccer (he’d seen the game on a holiday with his wife), and quizzed the visitors about their game.

Mike Forde, now performance director at Chelsea, jokes that in the last half-hour of their conversation, he finally managed to ask Beane about baseball.

The Frenchman Damien Comolli, a former assistant of Wenger’s at Arsenal, visited too.

Comolli briefly lived in northern California in his youth, was an Oakland As fan, and clicked with Beane.

In 2005, Comolli became sporting director at Tottenham Hotspur, with a chance to use stats to unearth talent.

He did find some excellent players for Spurs – notably Dimitar Berbatov, Luka Modric and an unknown 17-year-old named Gareth Bale, now a superstar – but he also ran into opposition from Spurs’ traditionalist coaches.

The typical football coach left school young, isn’t an expert statistician and makes decisions based on a “gut instinct” acquired as a player.

Having seen many less educated men replaced by computers, Spurs’ coach wasn’t keen to have the same thing happen to him.

And he wasn’t about to listen to a Frenchman whose playing career had peaked in Monaco’s youth team.

Eventually, Tottenham ousted Comolli. He joined Saint-Etienne.

French clubs are dominated by emotional presidents, who seldom follow developments in American baseball.

In France, Comolli was a pioneer.

Saint-Etienne rarely bought players, because it lacked money, but it did have to decide whether or not to offer players new contracts.

If you had a starting player aged 30, and you gave him a new two-year contract, that might cost 2 million euros (R26 million).

At 30, the guy was still good.

But how could you know if he’d still be good enough at 32?

Comolli would look at his statistical trends: Had the player been making fewer sprints each year, were his passes in the opponents’ half declining?

If the trends were heading downward fast, you wouldn’t offer a new contract.

Comolli and others were learning from Beane how to apply data to sport.

But did he learn anything from European soccer clubs?

They, too, had built up over a century’s worth of know-how.

Beane eventually told me: “They were so much further ahead of us in terms of nutrition.”

Otherwise, when Beane looked at soccer, he saw an emotional sport, which meant irrational decisions.

In 2010, the American businessman John Henry, who had once tried to lure Beane to run his baseball team, the Boston Red Sox, bought Liverpool Football Club.

Henry knew nothing about football. He spoke to Beane, who advised him to hire Comolli, who became sporting director of one of the world’s biggest clubs.

Late at night, he’d call Beane.

Comolli’s tenure was a failure. He used numbers to establish that a young striker, Andy Carroll, was the best at heading in high crosses.

He signed Carroll for €40 million and bought players with good passing statistics to feed him.

But the experiment failed, because, as is becoming clear from the data, high crosses are a bad way of scoring goals, a truth Liverpool had already demonstrated in practice.

Short, low passes are much more productive.

Comolli had bet the company on the wrong strategy.

In April 2012 he left Liverpool.

The greatest advances have been made in planning set pieces: corners, free kicks, penalties and throw-ins.

A set piece is when a football match stops for a moment and becomes an easily analysable static tableau similar, in a way, to a baseball game.

It’s at set pieces that data now regularly prove decisive.

Manchester City’s data department analysed about 400 corners in several national leagues over seasons and concluded that the most dangerous corner is the inswinger: the ball that swings in towards goal.

The data team took this finding to City’s manager Roberto Mancini, who had played football for many years, and his gut told him the most dangerous corner was the outswinger.

But City’s outswingers kept on not producing goals.

Mancini’s assistant David Platt came to chat to the data analysts, and they noticed that City had begun taking inswinging corners.

In the 2011/12 season, City scored 15 goals from corners, the most in the English Premier League.

Vincent Kompany’s headed goal against Manchester United, which, in effect, clinched the championship for City, came from an inswinging corner.

Not only was that season’s Premier League arguably decided by data analysis, the European Champions League was too.

In the final against Bayern Munich, Chelsea’s goalkeeper Petr Cech dived the right way to all six Bayern penalties, saving two.

Afterwards, he said: “I either guessed pretty well, or I was ready to guess well.”

But he hadn’t just guessed.

Chelsea’s data department had supplied him with a DVD of every Bayern penalty since 2007.

A penalty analysis almost decided the last world cup final.

The world’s leading expert on penalties is Ignacio Palacios-Huerta, a game theorist and economics professor at the London School of Economics.

He has assembled a database of more than 9 000 penalties since 1995.

I grew up in the Netherlands and, when Holland reached the World Cup final against Spain in 2010, I emailed an official in the Dutch camp: Would they like a report on Spain’s penalty takers, prepared by Palacios-Huerta? The Dutch said yes.

Palacios-Huerta (a Basque, happy to see Spain lose) worked 48 hours straight to produce his report.

With five minutes left in extra time in the final, the score was still 0-0. A penalty shoot-out seemed imminent.

I was reading the file with Palacios-Huerta’s report on my laptop. Spain’s Fernando Torres usually shot low, he wrote; Xavi and Andrés Iniesta would probably shoot to the keeper’s right.

What if Ignacio’s advice was all wrong? But then Iniesta scored, and penalties were averted.

Today, the most statistically minded team in international football, Germany, is using data as the game is in motion.

A group of professors and students from the Cologne Sporthochschule (the Higher School for Sports) has worked for Germany’s national team for years.

During last year’s European championship, “Team Cologne” produced a dossier several hundred pages thick on each team Germany faced.

Football is slowly learning to play moneyball, but it’s about 20 years behind. – Agence Global


The post Moneyball meets football appeared first on City Press.

Powered by WPeMatico

This entry was posted in Uncategorized. Bookmark the permalink.

Comments are closed.