The lessons of communism

A reply to John Gray

This is a guest post by David Priestland, author of The Red Flag: Communism and the Making of the Modern World (Allen Lane, £35)

In the many discussions of the anniversary of the fall of communism in Europe, much more attention has been given to the revolutions of 1989 than to the lessons of the communist experience itself. John Gray, however, in his very generous review of my history of communism The Red Flag in the New Statesman, did address this central question of 20th-century history. Communism, he argued, was a "radical humanist" project, and its failures marked the death of progressive politics. It is therefore no surprise that "left progressives are beached", even though the recent crisis of financial capitalism should have given it the best opportunity in decades.

Gray, I believe, is right to argue that the communist experience is intimately connected with the leftist tradition. But I think he is wrong to insist that the failures of communism discredit the leftist project as a whole.

Certainly, we should not be surprised that the collapse of Marxist regimes has caused a crisis of the broader, non-communist left. Communists departed from Marx's thought in many ways, but they did pursue two projects which are very much part of the progressive tradition: they promoted a radical form of equality by means of revolution, and they favoured technocratic, state-led modernisation. As I showed in my book, communist reigmes tended to zigzag between the two, but both had serious drawbacks: the first caused chaos and a great deal of violence, while the second created an ossified bureaucratic caste and economic failure.

But state involvement in the economy, and popular participation in politics, were not utopian ideas in themselves. Rather, the communists' failures were caused by their love of absolute solutions: their belief that the bourgeoisie was evil and had to be destroyed completely; and their view that an alliance of mobilised masses and state officials could rule without the market. By demonising the market, they ignored its uses -- as a spur for innovation, and as a balance to an overmighty state. It was this extreme, class-warrior view of the world that led to both the violence and economic stagnation of the communist world.

Nevertheless, these undoubted Marxist failures do not invalidate two central progressivist insights: first, that left to themselves, markets reinforce inequalities, which lead to blighted lives, economic stagnation and sometimes popular anger and violence. And second, that the only effective counter to the uncontrolled market is an alliance of popular action from below and the state.

The history of the 20th century, and of communism, bears out both of these lessons. Communism and revolutionary violence arose when capitalists supported aristocratic or imperialist regimes -- whether in pre-1789 France, pre-1917 Russia, or the Nazi, Japanese, European or postwar American empires. Stark social or racial inequalities caused anger and violence.

But even when capitalists disentangled themselves from aristocrats and generals and pursued more liberal, laissez-faire politics -- as in the 1920s in the United States -- they created dangerously unequal societies, and destabilised capitalism itself. Finance was let off the leash and money poured into speculation. The result was super-profits, high levels of inequality and massive indebtedness. The bubble inevitably burst, and the state had to step in to stave off starvation and revolution.

Prosperity and justice have flourished most when the state has accepted the market, but imposed strict controls on it, as in the two decades after the Second World War. Capitalists had discredited themselves -- both by collaborating with the Nazis, and by their irresponsibility during the 1920s. And governments believed that western Europe was on the verge of becoming communist. They were therefore willing to regulate the market, and share power and profits with trade unions and other popular groups. In doing so, they presided over one of the most prosperous eras in the history of the west.

The post-1945 system of course had its failings. It was too technocratic. It also excluded large groups -- including women, the young and the global "south". And it was seriously challenged when it failed to cope with the economic crises of the 1970s. But rather than seek to adapt the post-1945 system to new conditions, an American-led global elite has sought to dismantle it and return to the world of the 1920s. The defeat of the USSR and the end of communism were seen merely as proof that the progressive project was dead and untrammelled markets would be the world's saviour.

The result has been predictable. As in the 1920s, businessmen preferred to invest in financial speculation and assets rather than job-producing industries. The result has been rocketing inequality, with a small group of global rich bidding up house, art and oil prices, and a stagnating middle class encouraged to take on debt to keep up. Now the bubble has burst, and the west seems set for on a long period of relative decline.

Thus, the progressive left, far from having no answers, has the only solution: markets must be tamed. So why is the left so weak? Why is the right winning throughout Europe, while the Conservative Party astonishingly claims that the state has to be cut back?

In large part, the problem is that the present generation of centre-left leaders entered politics during the defeats of socialism and the fall of communism in the late 1970s and early 1980s. Under Bill Clinton, Tony Blair and Gerhard Schröder, the traumatised left became true believers in the free-market faith. Meanwhile, former Trotskyists such as Alistair Darling, or socialists such as Gordon Brown, swapped one rigid ideology for another. It is, ironically, easier for the paternalistic right of Nicolas Sarkozy or Angela Merkel to use the language of state intervention -- even if they are unlikely to do much in practice.

But the progressive left is also to blame. It, too, needs to learn the lessons of the communist experience, and show how the state can co-ordinate the economy without the rigid technocracy of the past. It also needs to make the decentralised democracy championed by the young Marx and abandoned by his successors seem practicable.

Even so, new ideas are emerging that challenge the dominance of market thinking. Theorists of "deliberative democracy" are considering effective ways of involving citizens in decision-making. Meanwhile people such as Elinor Ostrom, winner of the 2009 Nobel Prize in Economics -- are showing how local collectives can manage scarce resources more effectively than either markets or state bureaucracies.

There is a real danger that we fall back into the ways of 1930: a year after the shock of the financial crisis, governments have recovered their balance and are returning to politics as usual. Keynesian ideas are more influential now than they were then, but even so we are far from a new, truly international Bretton Woods-style agreement, in which states guarantee a new, just and fair economic order. The result will be stagnation, high unemployment and, possibly, political extremism. Ultimately, it took six years of war and 55 million deaths before Keynes's ideas were fully accepted. We can only hope that history does not repeat itself.

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How science and statistics are taking over sport

An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others.

In the mid-1990s, statistics undergraduates at Lancaster University were asked to analyse goal-scoring in a hypothetical football match. When Mark Dixon, a researcher in the department, heard about the task, he grew curious. The analysis employed was a bit simplistic, but with a few tweaks it could become a powerful tool. Along with his fellow statistician Stuart Coles, he expanded the methods, and in doing so transformed how researchers – and gamblers – think about football.

The UK has always lagged behind the US when it comes to the mathematical analysis of sport. This is partly because of a lack of publicly available match data, and partly because of the structure of popular sports. A game such as baseball, with its one-on-one contests between pitcher and batter, can be separated into distinct events. Football is far messier, with a jumble of clashes affecting the outcome. It is also relatively low-scoring, in contrast to baseball or basketball – further reducing the number of notable events. Before Dixon and Coles came along, analysts such as Charles Reep had even concluded that “chance dominates the game”, making predictions all but impossible.

Successful prediction is about locating the right degree of abstraction. Strip away too much detail and the analysis becomes unrealistic. Include too many processes and it becomes hard to pin them down without vast amounts of data. The trick is to distil reality into key components: “As simple as possible, but no simpler,” as Einstein put it.

Dixon and Coles did this by focusing on three factors – attacking and defensive ability for each team, plus the fabled “home advantage”. With ever more datasets now available, betting syndicates and sports analytics firms are developing these ideas further, even including individual players in the analysis. This requires access to a great deal of computing power. Betting teams are hiring increasing numbers of science graduates, with statisticians putting together predictive models and computer scientists developing high-speed software.

But it’s not just betters who are turning to statistics. Many of the techniques are also making their way into sports management. Baseball led the way, with quantitative Moneyball tactics taking the Oakland Athletics to the play-offs in 2002 and 2003, but other sports are adopting scientific methods, too. Premier League football teams have gradually built up analytics departments in recent years, and all now employ statisticians. After winning the 2016 Masters, the golfer Danny Willett thanked the new analytics firm 15th Club, an offshoot of the football consultancy 21st Club.

Bringing statistics into sport has many advantages. First, we can test out common folklore. How big, say, is the “home advantage”? According to Ray Stefani, a sports researcher, it depends: rugby union teams, on average, are 25 per cent more likely to win than to lose at home. In NHL ice hockey, this advantage is only 10 per cent. Then there is the notion of “momentum”, often cited by pundits. Can a few good performances give a weaker team the boost it needs to keep winning? From baseball to football, numerous studies suggest it’s unlikely.

Statistical models can also help measure player quality. Teams typically examine past results before buying players, though it is future performances that count. What if a prospective signing had just enjoyed a few lucky games, or been propped up by talented team-mates? An ongoing challenge for analysts is to disentangle genuine skill from chance events. Some measurements are more useful than others. In many sports, scoring goals is subject to a greater degree of randomness than creating shots. When the ice hockey analyst Brian King used this information to identify the players in his local NHL squad who had profited most from sheer luck, he found that these were also the players being awarded new contracts.

Sometimes it’s not clear how a specific skill should be measured. Successful defenders – whether in British or American football – don’t always make a lot of tackles. Instead, they divert attacks by being in the right position. It is difficult to quantify this. When evaluating individual performances, it can be useful to estimate how well a team would have done without a particular player, which can produce surprising results.

The season before Gareth Bale moved from Tottenham Hotspur to Real Madrid for a record £85m in 2013, the sports consultancy Onside Analysis looked at which players were more important to the team: whose absence would cause most disruption? Although Bale was the clear star, it was actually the midfielder Moussa Dembélé who had the greatest impact on results.

As more data is made available, our ability to measure players and their overall performance will improve. Statistical models cannot capture everything. Not only would complete understanding of sport be dull – it would be impossible. Analytics groups know this and often employ experts to keep their models grounded in reality.

There will never be a magic formula that covers all aspects of human behaviour and psychology. However, for the analysts helping teams punch above their weight and the scientific betting syndicates taking on the bookmakers, this is not the aim. Rather, analytics is one more way to get an edge. In sport, as in betting, the best teams don’t get it right every time. But they know how to win more often than their opponents. 

Adam Kucharski is author of The Perfect Bet: How Science and Maths are Taking the Luck Out of Gambling (Profile Books)

This article first appeared in the 28 April 2016 issue of the New Statesman, The new fascism