A Tale Of Two Soccer Teams: Lessons From The Champions League

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Soccer

For the second time in three years, two clubs from Madrid are set to contest the Champions League final, club soccer’s biggest occasion. In 2014, star power trumped near-perfect organisation, and Real Madrid triumphed over their neighbours. But the match was closer than the financial profiles – and, indeed, the player profiles – of the teams might lead you to expect.

Despite their shared location, the two teams could hardly be more different in their approach. Both, though, once again find themselves just a game away from the biggest prize in club soccer. Why?

Never Give Up On An Asset

Though far from poor, Atletico Madrid’s financial power is dwarfed by that of their neighbours: Real are valued at an astounding $3.645bn compared to Atletico’s $633m. Consequently, Real can afford to buy the world’s top players and, just as importantly, pay to keep them at the club.

Atletico have certainly spent hard-earned cash every summer, as is necessary to stay competitive in the Spanish football league, La Liga, but the list of players whom they’ve failed to keep is an impressive one. Fernando Torres, Sergio Aguero, Radamel Falcao, Diego Costa, David Villa – all have gone on to take their place among Europe’s best (if they hadn’t already while at Atletico), and all were sold by Atletico to bigger clubs at a profit.

This constant process, in which top players are consistently found and then sold, would demoralise many clubs. Despite the significant profit margin, the rarity of true talent can’t be denied: at some point, surely even Atletico won’t able to find another star striker. Smaller clubs must at times feel like casting agencies for the big clubs’ show. But Atletico have chosen not to see matters in this way.

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Atletico’s Antoine Griezmann celebrates the second of two goals that would send holders Barcelona crashing out in April.

For Atletico, there is always value to be had. You find a top player and you recruit him. While at the club, he contributes; if he’s sold, you make sure to get maximum value. They never see a player as a lost cause and always seek to maximise the profit he might generate on the transfer market.

Sometimes, such transactions work out especially well, as was the case with Radamel Falcao. In his final year at the club, the ageing and injury-prone striker inflated his goal totals with a signifiant number of penalties. Atletico knew it was time to sell, and a quick glance across the league’s of Europe will find Falcao consuming huge wages for little return.

The lesson is simple but could hardly be more important: know what you have in an asset and never, ever give up on it. Sell at maximum value, buy at minimum value, and, no matter what, always seek to increase the value of even those assets that you will certainly lose.

Analytics – Never Neglect A Competitive Edge

The world of soccer analytics is growing. Several of Europe’s biggest clubs have data analysts on their payroll, and no doubt many we don’t know of make use of numbers as well.

To take just one example, consider soccer’s ‘expected goals’ model (the version outlined here is a creation of Michael Caley’s). The model is an intuitive one: it predicts the likelihood of each shot’s resulting in a goal based on obvious criteria. Taking into account a large number of factors – such as shot location, angle of shot, pass leading to shot, type of shot (heading, shot with foot), etc. – Caley has created six equations with a well-tested set of inputs in order to assign an expected value to every shot.

A bad chance, such as a header from 18 yards, will have a very low value (say, 0.09); a good chance, like a central strike from 15 yards after a dribble, will have a very high value (say, 0.6). (‘1’ would be a certain goal and, naturally, never occurs.)

The model isn’t perfect, of course. For instance, it has trouble predicting goal outputs for Barcelona, Real Madrid, and Bayern Munich, seemingly due to the very high quality of finishing which those teams consistently produce. Further, it has yet to account for defender location, clearly a significant factor in the quality of any chance. Most of all, the model does a much better job at predicting over the long term. A map will occasionally fail to tell the story of a single match. (Stats are also an excellent way of complementing your scouting project. Here’s a story about Rakitic from long before Barcelona acquired him.)

But the point is never to neglect a competitive advantage. Caley’s method has proven to be effective, and to fail to use it is simply to give another club an opportunity. In the wider world of business, the same thing applies: just because something may not be perfect, doesn’t mean it can responsibly be neglected. Take every advantage that you can – it’s the only way to stay ahead.

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Cristiano Ronaldo’s golden boot confounds statistical analysis

Luck

Data analysis can tell us something else: luck plays a huge role in football. Sometimes, a player finishes a chance worth 0.03, sending a screamer into the top corner from 40 yards. An organisation can’t plan for such events. Frequently, one hears a manager emphasise performance over results, and that is why.

Any business would do well to remember the influence of fate. Control what you can and accept that you can’t control everything. Always do what is most to likely to lead to the best long-term results, and don’t let the vagaries of the business world sway your choices excessively.

Saturday’s final could go either way – that’s soccer. But both sides have already achieved something special in making it to the highest of stages. Any organisations that have succeeded in such a competitive market have something to teach us all.

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