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St Paul, Caravaggio and the agonised Catholicism of Pasolini

San Paolo, published posthumously in 1977 and presented here for the first time in English as St Paul, is Pasolini’s screenplay for the life of the apostle. 

Poet and provocateur: Pasolini on location in Italy, 1970s. Photo: Mondadori via Getty

St Paul: a Screenplay
Pier Paolo Pasolini; translated by Elizabeth A Castelli
Verso, 143pp, £16.99

Roger Fry, the Bloomsbury art critic, thought that Caravaggio would have made a superb “cinema impresario”. With his dramatic use of light and dark, the Italian painter pretty well invented cinematic lighting. His great altarpiece of 1601, The Conversion of St Paul, glowed with such a photographic sharpness that contemporaries suspected some trick. In a revolutionary retelling of the scriptures, Paul lies prone beneath his horse on a dirt road to Damascus, his arms outstretched in proto-filmic shafts of light. There are no heavenly visions in Caravaggio, only human beings on the long, grubby pilgrimage of life.

Much has been made of Caravaggio’s influence on the fierce pauperist Catholicism of Pier Paolo Pasolini. At the end of his film Mamma Roma (1962), the working-class hero lies dying on a prison bed like a sanctified Baroque Jesus. The implied blasphemy of Caravaggio’s lowlife Christs and Virgin Marys thrilled the iconoclast in the Italian film-maker, whose miserable death was somehow foretold in his own work.

On the morning of 2 November 1975, in slumlands outside Rome, Pasolini was found beaten beyond recognition and run over by his Alfa Romeo Giulia. A 17-year-old rent boy was charged with the killing – a homosexual tryst gone murderously wrong. Or was Pasolini the victim of a political hit? His presumed killer turned out to be affiliated to Italy’s neo-fascist party; the verdict is still open. Pasolini was 53.

San Paolo, published posthumously in 1977 and presented here for the first time in English as St Paul, is Pasolini’s screenplay for the life of the apostle. Drafted in 1966 and subsequently rewritten, it was intended to be a sequel to The Gospel According to Matthew (1964), shot in the lunar landscape of Italy’s Basilicata region. The screenplay, with its New Testament voice-over, typically mingles an intellectual leftism with a Franciscan Catholicism: blessed are the poor, for they are exempt from the unholy trinity of materialism, money and property. The film was never made, for lack of funds.

Pasolini’s solidarity with the poor was at heart romantic. La ricotta, his 35-minute episode in the collaborative film RoGoPaG (1963), features Orson Welles as an American  director shooting a film in Rome about Christ’s Passion. Stracci (the name means “rags”), the sub-proletarian actor who plays the part of the good thief, dies on set from a case of real-life starvation. For all its manifest compassion, the film led to a suspended prison sentence for Pasolini on blasphemy charges. Over a tableau vivant inspired by a Caravaggio-like painting of the Deposition, Welles cries out sacrilegiously: “Get those crucified bastards out of here!”

Like La ricotta, St Paul champions those who have been disinherited by capitalism and the “scourge of money”. Pasolini believed that the consumerist “miracle” of 1960s Italy had undermined the semi-rural peasant values of l’Italietta (Italy’s little homelands). In the director’s retelling of the Bible, Paul stands as a bulwark against the “corruption” brought to Italy by Coca-Cola, chewing gum, jeans and other trappings of American-style consumerism.

Nevertheless, as the former Saul, a Pharisee and persecutor of Christians, Paul was an ambivalent figure for Pasolini. After his conversion on the road to Damascus in 33AD, he took his mission round the world and became the founding father of the Christian Church in Rome, with its hierarchy of prelates and pontiffs. So, in some measure, he lay behind the Catholic Church that Pasolini had come to know in 1960s Rome, with its Mafia-infiltrated Christian Democracy party and its pursuit of power and political favour. In the screenplay, Paul is by turns arrogant and slyly watchful of his mission.

The saint’s story is updated, cleverly, to the 20th century. Cohorts of SS and French military collaborationists in Vichy France stand in for the Pharisees. With a fanatic’s heart, Paul oversees the killing and mass deportation of Christians. The action then fast-forwards to 1960s New York, where the post-Damascus Paul is preaching to Greenwich Village “beats”, “hippies”, “blacks” and other outcasts from conformist America (“I appeal to you, brothers . . .”). His attempts to overturn capitalist values in Lyndon Johnson-era America are met with hostility by FBI operatives and White House flunkies. In the end he is murdered on the same hotel balcony where Martin Luther King was assassinated in 1968. Pasolini’s approximation of the apostle of black liberation to the apostle of orthodox Christianity just about works.

Though fascinating, St Paul is not the “literary work of the first magnitude” that the French philosopher Alain Badiou would have in his foreword. (Rather, it reads like a preliminary sketch for something to be coloured in later.) Inevitably one scans the screenplay for clues to the film-maker’s murder. Italo Calvino believed that Pasolini was killed from a “D’Annunzian” hankering after redemption through violence. The scene of the murder, a shanty town near the Idroscalo di Ostia, not far from Fiumicino Airport, presents a Pasolinian pasticcio of the poetic and the squalid: shacks lie scattered across a filthy, blackened beach and in the distance rise the tenement slums of Nuova Ostia. At best, Pasolini’s was a sleazy kind of martyrdom; at worst, it was a bludgeoning out of a tabloid crime sheet.

Ian Thomson is the author of “Primo Levi” (Vintage) and “The Dead Yard: a Story of Modern Jamaica” (Faber & Faber)

This article first appeared in the 18 June 2014 issue of the New Statesman, Islam tears itself apart

OLIVER BURSTON
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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