US Senate does something unbelievable: passes a bill

Taxmaggedon's not averted, but the competition is on fair ground

The United States got a little more likely to avoid Taxmaggedon yesterday, as the Senate voted narrowly to pass the Democrats' bill extending "middle-class" tax cuts 51-48.

On December 31st, 2012, the tax cuts passed by George Bush will all expire at once, along with a number of other tax cuts and spending provisions. If this isn't averted, the resulting economic shock – dubbed a "fiscal cliff" by Fed chairman Ben Bernanke, and Taxmaggedon by others – has been predicted to knock 4 per cent from US growth in 2013.

The strange thing about the situation, though, is that both parties want to avert it. Unfortunately, their chosen outcomes are different enough that each would rather let the nation burn and blame it on the other than pass something they don't agree with.

The desired outcome for Republicans is keeping all the tax cuts except for two – Obama's payroll tax cut, and the tax cuts implemented in the 2009 stimlus package. Not coincedentally, these are two of the cuts which affect low-income people most, and as a result, the party isn't hugely eager to mention that they are in favour of repealing them with the "Tax Hike Prevention Act of 2013" (which will directly implement tax hikes. America).

The Democrats, however, want to keep those low-income tax cuts, and also all of the Bush tax cuts up to $250,000 per year. Despite the fact that only 2 per cent of the country earns above that, they have come to be called the "middle-class" tax cuts. In return, they want to soak the rich a bit more, reverting marginal tax rates above that level to where they were in the Clinton era, and implementing the so-called "Buffet rule" to prevent brazen tax avoidance.

It is clear, however, that there are a large number of tax hikes which both parties want to avoid. So why the reticence? Because after the election – indeed, after Taxmaggedon actually takes effect – it will be a lot easier to get bipartisan support. Right now, the Democratic position involves tricking or cajoling Repbulicans into voting for tax hikes, even if only on the rich. But coming to that same position in 2013 will involve voting for tax cuts, since the hikes they want will happen automatically. That vote is a far more palatable prospect.

So while the Democrat-controlled Senate passed the their preferred bill, the Republican House of Representatives in gearing up to reject it out of hand. It will not make it to the President's table in this form, and nothing is likely to until at least November. 

But there is, buried in this, a small bit of good news. Because the Senate did something rather unusual: they had a vote which was won by the side with the most people on it. Normally, the arcane standing orders of the Senate require a supermajority, of at least 60, to win any vote - otherwise it can be filibustered indefinitely, preventing any other business from occurring. The fact that this was passed by a simple majority could mean a simmering of tensions on the matter, or an eagerness (however slight) to work together. Or it could be that they knew it wouldn't pass the House and weren't in a mood to fight.

Time, as ever, will tell.

Senate majority leader Harry Reid, the Democrats' man in the Senate. Photograph: Getty Images

Alex Hern is a technology reporter for the Guardian. He was formerly staff writer at the New Statesman. You should follow Alex on Twitter.

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