Could an independent Yorkshire Win the World Cup?

Oooooh. Football. Image: Getty.

With less than a week until the start of the 2018 World Cup in Russia, it’s worth remembering, that another World Cup – the 2018 ConIFA World Cup for stateless people, minorities, and regions unaffiliated with FIFA - is also taking place in London.

Though happening in the UK, neither of the local ConIFA members will be competing. The Ellan Vannin team from the Isle of Man withdrew midway; and the latest ConIFA member, Yorkshire, only gained membership earlier this year.

One of Yorkshire’s most obvious characteristics, is that it’s absolutely huge compared to most other UK counties. It also – probably – has the highest contemporary population of any of the historic British counties. Indeed, as recently as this February the region resisted attempts to split control of the region up, demanding a “One Yorkshire” devolution deal instead of the proposed control to regions surrounding four of it’s major cities – and in May, a vocal proponent of such a “One Yorkshire” devolution, Dan Jarvis, the Labour MP for Barnsley, was elected as mayor of one of the Sheffield City region.

Given its size, ConIFA membership, and pushes for further devolution, I was wondering how Yorkshire would do as an independent full FIFA member. If it seceded as a whole from the rest of the UK could it field a team that could challenge internationally? Could any of the historic British counties?

Overall, there are 88 historic counties in Great Britain, plus the 6 counties of Northern Ireland (I couldn’t find shapefiles for the older subdivisions) which could be potential independent FIFA members.

Once I had these, I needed some way to rate potential players, and therefore teams. Luckily, the popular video game FIFA18 maintains up to date ratings of thousands of players across 36 different stats (e.g. dribbling, heading, pace etc.). After scraping an online database of players, I’m left with 18,058 players of various nationalities and abilities.

Using a simple regression model, I can use these abilities and the player’s listed preferred positions to predict what each players rating for each position, and use these position ratings to train a computer to pick optimal teams across a variety of formations. If we do this do for every nation that has at least 11 players in the database (10 outfield + 1 goalkeeper), the best 4 national teams that can be fielded are from Brazil, Germany, Spain, and Belgium.

To pick the teams for each county, though, I first had to find the birthplace of player. To simplify things a bit I only check players listed as English, Scottish, Welsh, Northern Irish, or Irish (due to the weirdness of the Irish FA) in my database of FIFA players. For each of these I ran a script to look the player up on wikipedia and scrape their birthplace. Once this was geocoded, I had a map of each British player and their birthplace, and therefore, the county of their birth.

Unsurprisingly, it basically shows a population density map of the UK, with more players born in the urban centres of London, Birmingham, the Lancashire cities and the West Yorkshire urban centres. After binning the players by county of birth, twenty historic counties have enough players to field a team.

On this chart, ‘FIFA_ability’ is the perceived ability of the optimal 11 players in a starting line up for that county, as judged by FIFA stats.

Perhaps a little surprisingly, the Lancashire team is rated slightly higher than the Yorkshire team – though looking at the sheer number of players they can select from it makes sense. Elsewhere, the home counties do well, as do Glasgow and Warwickshire (which contains much of contemporary Birmingham).

Looking at the selections the alogirthm chooses, it’s pretty clear some of these teams tend to be a bit flawed but overall make sense. The Yorkshire/Lancashire teams in particular are full of England international players and are lacking only an experienced top level goalkeeper.

In order to predict how these teams would do at a World Cup, I needed some form of quantifiable rating of a team;s ability. Luckily, ELO ratings in chess can do exactly that: the likelihood of any team A beating a team B is a direct function in the difference in their ELO rating.

Plotting the ELO ratings of each actual national team (an up to date calculation is maintained at ELOrating.net) against the ability of each national team as judged by FIFA18 shows a pretty clear linear trend. Using a regression model of this relationship, we can predict the ability of each hypothetical county national team.

When plotted, these ELO ratings show that some of the counties are definitely in the ball park of established world cup qualifiers – and so we might expected a post-super-devolution Britain to be sending multiple teams to the World Cup.

In fact, Yorkshire and Lancashire are predicted to be about as good as the national teams of Serbia and Sweden. Lagging a bit behind, Essex and Surrey – both of which take in large chunks of what is now London – could expect to be competititve with teams like Turkey and Morocco.

However, just finding out how good these teams would be wasn’t what I wanted to know. I wanted to see if an independent British county could win the World Cup.

To do this, I swapped each of these counties in for the national English team and ran 10000 simulations of the post-devolution 2018 World Cup, uusing the same draws and fixtures as the real tournament uses.

The bad news is, the real-life favourites tend to dominate the simulations. Brazil or Germany were predicted to win the tournament in almost half of all the simulations. On the graph, it;s just possible to make out the red bars of Yorkshire and Lancashire, both of which won 41 out of 10000 simulations (a 0.41 per cent chance of winning any random World Cup).

This seems pretty low – but is comparable to pretty respectable teams like Denmark (0.775 per cent), Senegal (0.217 per cent), and even higher than the Iceland team which knocked england out of Euro2016 (0.339 per cent). It’s way higher than the chances the simulation gives the Russian hosts (0.07 per cent).

Scaling down to just these pretty hopeless nations/counties really shows how little hope the independent British counties would have at an international tournament. However, the best four counties (Lancashire, Yorkshire, Essex, and Surrey) all have about a 0.2 per cent or higher chance, or 500-1 odds, at winning the 2018 World Cup were they to replace England at the last minute. This is an order of magnitude greater than the 5000-1 odds given to Leicester City at the start of 2015-2016 Premier League season, so there’s always a chance.

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Tackling toxic air in our cities is also a matter of social justice

Oh, lovely. Image: Getty.

Clean Air Zones are often dismissed by critics as socially unfair. The thinking goes that charging older and more polluting private cars will disproportionately impact lower income households who cannot afford expensive cleaner alternatives such as electric vehicles.

But this argument doesn’t consider who is most affected by polluted air. When comparing the latest deprivation data to nitrogen dioxide background concentration data, the relationship is clear: the most polluted areas are also disproportionately poorer.

In UK cities, 16 per cent of people living in the most polluted areas also live in one of the top 10 per cent most deprived neighbourhoods, against 2 per cent who live in the least deprived areas.

The graph below shows the average background concentration of NO2 compared against neighbourhoods ranked by deprivation. For all English cities in aggregate, pollution levels rise as neighbourhoods become more deprived (although interestingly this pattern doesn’t hold for more rural areas).

Average NO2 concentration and deprivation levels. Source: IMD, MHCLG (2019); background mapping for local authorities, Defra (2019).

The graph also shows the cities in which the gap in pollution concentration between the most and the least deprived areas is the highest, which includes some of the UK’s largest urban areas.  In Sheffield, Leeds and Birmingham, there is a respective 46, 42 and 33 per cent difference in NO2 concentration between the poorest and the wealthiest areas – almost double the national urban average gap, at around 26 per cent.

One possible explanation for these inequalities in exposure to toxic air is that low-income people are more likely to live near busy roads. Our data on roadside pollution suggests that, in London, 50 per cent of roads located in the most deprived areas are above legal limits, against 4 per cent in the least deprived. In a number of large cities (Birmingham, Manchester, Sheffield), none of the roads located in the least deprived areas are estimated to be breaching legal limits.

This has a knock-on impact on health. Poor quality air is known to cause health issues such as cardiovascular disease, lung cancer and asthma. Given the particularly poor quality of air in deprived areas, this is likely to contribute to the gap in health and life expectancy inequalities as well as economic ones between neighbourhoods.


The financial impact of policies such as clean air zones on poorer people is a valid concern. But it is not a justifiable reason for inaction. Mitigating policies such as scrappage schemes, which have been put in place in London, can deal with the former concern while still targeting an issue that disproportionately affects the poor.

As the Centre for Cities’ Cities Outlook report showed, people are dying across the country as a result of the air that they breathe. Clean air zones are one of a number of policies that cities can use to help reduce this, with benefits for their poorer residents in particular.

Valentine Quinio is a researcher at the Centre for Cities, on whose blog this post first appeared.