Common Lodging Houses

"As often as not the beds are verminous, and the kitchens invariably swarm with cockroaches or black beetles."

Common lodging houses, of which there are several hundred in London, are night-shelters specially licensed by the LCC. They are intended for people who cannot afford regular lodgings, and in effect they are extremely cheap hotels. It is hard to estimate the lodging house population, which varies continually, but it always runs into tens of thousands, and in the winter months probably approaches fifty thousand. Considering that they house so many people and that most of them are in an extraordinarily bad state common lodging houses do not get the attention they deserve.

To judge the value of the LCC legislation on this subject, one must realise what life in a common lodging house is like. The average lodging house (“doss-house,” it used to be called) consists of a number of dormitories, and a kitchen, always subterranean, which also serves as a sitting-room. The conditions in these places, especially in southern quarters such as Southwark or Bermondsey, are disgusting. The dormitories are horrible fetid dens, packing with anything up to a hundred men, and furnished with beds a good deal inferior to those in a London casual ward. Normally these beds are about 5ft 6in long by 2ft 6in wide, with a hard convex mattress and a cylindrical pillow like a block of wood; sometimes, in the cheaper houses, not even a pillow. The bed-clothes consist of two raw umber-coloured sheets, supposed to be changed once a week, but actually, in many cases, left on for a month, and a cotton counterpane; in winter there may be blankets, but never enough. As often as not the beds are verminous, and the kitchens invariably swarm with cockroaches or black beetles. There are no baths, of course, and no room where any privacy is attainable. These are the normal and accepted conditions in all ordinary lodging houses. The charges paid for this kind of accommodation vary between 7d and 1s 1d a night. It should be added that, low as these charges sound, the average common lodging houses brings in something like £40 net profit a week to its owner.

Besides the ordinary dirty lodging houses, there are a few score, such as the Rowton Houses and the Salvation Army hostels, that are clean and decent. Unfortunately, all of these places set off their advantages by a discipline so rigid and tiresome that to stay in them is rather like being in jail. In London (curiously enough it is better in some other towns) the common lodging house where one gets both liberty and a decent bed does not exist.

The curious thing about the squalor and discomfort of the ordinary lodging house is that these exist in places subject to constant inspection by the LCC. When one first sees the murky, troglodytic cave of a common lodging house kitchen, one takes it for a corner of the early nineteenth century which has somehow been missed by the reformers; it is a surprise to find that common lodging houses are governed by a set of minute and (in intention) exceedingly tyrannical rules. According to the LCC regulations, practically everything is against the law in a common lodging house. Gambling, drunkenness, or even the introduction of liquor, swearing, spitting on the floor, keeping tame animals, fighting – in short, the whole social life of these places – are all forbidden. Of course, the law is habitually broken, but some of the rules are enforceable, and they illustrate the dismal uselessness of this kind of legislation. To take an instance: some time ago the LCC became concerned about the closeness together of beds in common lodging houses, and enacted that these must be at least 3ft apart. This is the kind of law that is enforceable, and the beds were duly moved. Now, to a lodger in an already overcrowded dormitory it hardly matters whether the beds are 3ft apart or 1ft; but it does matter to the proprietor, whose income depends upon his floor space. The sole real result of this law, therefore, was a general rise in the price of beds. Please notice that though the space between the beds is strictly regulated, nothing is about the beds themselves – nothing, for instance, about their being fit to sleep in. The lodging house keepers can, and do, charge 1s for a bed less restful than a heap of straw, and there is no law to prevent them.

Another example of LCC regulations. From nearly all common lodging houses women are strictly excluded; there are a few houses specially for women, and a very small number – too small to affect the general question – to which both men and women are admitted. It follows that any homeless man who lives regularly in a lodging house is entirely cut off from female society – indeed, cases even happen of man and wife being separated owing to the impossibility of getting accommodation in the same house. Again, some of the cheaper lodging houses are habitually raided by slumming parties, who march into the kitchen uninvited and hold lengthy religious services. The lodgers dislike these slimming parties intensely, but they have no power to eject them. Can anyone imagine such things being tolerated in a hotel? And yet a common lodging house is only a hotel at which one pays 8d a night instead of 10s 6d. This kind of petty tyranny can, in fact, only be defended on the theory that a man poor enough to live in a common lodging house thereby forfeits some of his rights as a citizen.

One cannot help feeling that this theory lies behind the LCC rules for common lodging houses. All these rules are in the nature of interference-legislation – that is, they interfere, but not for the benefit of the lodgers. Their emphasis is on hygiene and morals, and the question of comfort is left to the lodging house proprietor, who, of course, either shirks it or solves it in the spirit of organised charity. It is worth pointing out the improvements that could actually be made in common lodging houses by legislation. As to cleanliness, no law will ever enforce that, and in any case it is a minor point. But the sleeping accommodation, which is the important thing, could easily be brought up to a decent standard. Common lodging houses are places in which one pays to sleep, and most of them fail in their essential purpose, for no one can sleep well in a packet dormitory on a bed as hard as bricks. The LCC would be doing an immense service if they compelled lodging house keepers to divide their dormitories into cubicles and, above all, to provide comfortable beds; for instance, beds as good as those in the London casual wards. And there seems no sense in the principle of licensing all houses for “men only” or “women only,” as though men and women were sodium and water and must be kept apart for fear of an explosion; the houses should be licensed for both sexes alike, as they are in some provincial towns. And the lodgers should be protected by law against various swindles which the proprietors and managers are now able to practice on them. Given these conditions, common lodging houses would serve their purpose, which is an important one, far better than they do now. After all, tens of thousands of unemployed and partially employed men have literally no other place in which they can live. It is absurd that they should be compelled to choose, as they are at present, between an easy-going pigsty and a hygienic prison.

3 September 1932

A child in Whitechapel. Photo: Getty Images.

Eric Blair, more commonly known by his pseudonym George Orwell, was a contributor of the New Statesman in the Thirties and Forties.

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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