EU Referendum

EU Election 2019 #1 – How many MEPs will each party win?

May 21, 2019 By Nigel

Here is my forecast for the election that was not supposed to be happening in the UK. The Brexit Party is well on course to be the largest party and could even set the record for the best ever vote share by a party in the d’Hondt era of EU elections in the UK. At the same time, the Liberal Democrats have the possibility of beating both the Conservatives and Labour parties in a UK-wide election for the first time since 1906.

EU Referendum #6 – Find your way out of the Brexit maze in 9 Days!

March 20, 2019 By Nigel

Theresa May has just asked the EU Council for a 3 month extension to Article 50, the Speaker won’t allow another meaningful vote without meaningful changes, Jeremy Corbyn is pondering backing another referendum and in 9 days time, the UK could be leaving the EU without a deal. Just another week in the Parliamentary Brexit Maze but I have updated my Brexit Voting Factions after last week’s votes and identified an 8th faction for you to play with in your voting permutations.
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EU Referendum #5 – Find your way out of the Brexit maze in 16 Days!

March 12, 2019 By Nigel

*** This post is not yet complete. However you will find a link to the data near the bottom and a link to a twitter thread for some of the images ***

Within the next 10 days, the House of Commons will get a second Meaningful Vote on the Withdrawal Agreement which could be followed by 2 more significant votes on No-Deal and Article 50 Extension. I have been tracking how MPs have voted on the first Meaningful Vote and subsequent Amendments which I summarised in two posts “Find your way out of the Brexit Maze in 57 days and 43 days.” Following further amendments at the end of February and with no more amendments planned before the next meaningful vote, I have redone my cluster analysis to predict what the outcome of these votes might be. As far as possible, I am trying to base my predictions on what MPs have done rather than what they say but I will compare my analysis with that of Election Maps who have been tracking MP’s statements.
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EU Referendum #4 – Find your way out of the Brexit maze in 43 Days!

February 15, 2019 By Nigel

Rather than celebrating love on Valentine’s day, Parliament chose to use the occasion to emphasise their discord over the EU withdrawal process, 43 days before the UK is due to leave the EU. Three amendments were voted on and this allows me to update my Brexit voting blocks which I first described in “Find your way out of the Brexit maze in 57 days!”.

This post was updated on 22nd February 2019 to take into account the resignations of 9 MPs from the Labour party. and 3 MPs from the Conservative party.

EU Referendum #3 – Find your way out of the Brexit maze in 57 days!

January 31, 2019 By Nigel

January 2019 has been a month of considerable parliamentary drama in the UK as MPs wrestle over whether to approve the Withdrawal Agreement between the UK and the EU. There is no shortage of political punditry and quotes from politicians and the whole episode is proving to be a classic example of uncertainty. For statisticians like myself, uncertainty occurs when you cannot properly price the odds of an event happening unlike risk which occurs when you can price the odds. Since the current state of affairs will ultimately be determined by parliamentary votes one way or the other, is it possible to use parliamentary vote data so far to estimate the odds of certain scenarios?

UK General Elections #4 – Jeremy Corbyn’s road to Downing Street

January 14, 2019 By Nigel

The fevered political climate in the UK at the moment is all about Brexit and possible second referendums and general elections. Jeremy Corbyn made it clear recently that he wanted a General Election now so that he could take over the Brexit negotiations. With that in mind, I decided to take a look at what Labour’s target seat strategy could look like based on the results of the 2017 general election. What I see at the moment is that Labour has many ways of becoming the largest party in Parliament but the road to a working majority is much harder than people realise due to the Brexit realignment in 2016 and the Nationalist realignment in Scotland in 2015.

Forecasting#1 – How do you identify a good forecaster?

April 3, 2017 By Nigel

“I think the people in this country have had enough of experts”

Michael Gove, Sky News, 3^rd June 2016

This was one of the most memorable quotes during the EU referendum in 2016 and came in response to a question as to why the forecasts of a whole list of organisations such as the IMF should be ignored. It prompted a flurry of rebuttals and articles supporting or damning him and the debate has not gone away.

Like so many quotes, it has already become distorted. I strongly recommend you listen to the full question and answer because here is his quote in its entirety.

“I think the people in this country have had enough of experts… from organisations with acronyms saying that they know what is best and getting it consistently wrong.”

When I read this full quote I realised I am in complete agreement with Michael Gove.

Segmentation #1 – Who has more in common? Leave & Trump voters or Remain & Clinton voters? Analysis of Sentiments

February 22, 2017 By Nigel

My wife is American and so it should be easy to guess what we were talking about on the morning of 9^th November 2016. Donald Trump’s victory in the US Presidential election was a surprise to many people and prompted much discussion on the similarities between Trump voters and the Leave voters in June. However, my wife remarked that people may be looking at this the wrong way round and perhaps the correct question to ask is whether there is greater similarity between Clinton & Remain voters.

Identifying similarities and differences between groups of people is a cornerstone of the field of market research known as customer segmentation. It is one of my favourite areas of statistics and can be used regardless of whether the data comes from a survey or from customer records. When my wife posed her question I immediately thought of 2 ways I could answer this using segmentation methods.

Look at how people feel (their sentiments) which is what this post is about.
Look at how people voted (their behaviour) which I will cover in another post “Who has more in common? Leave & Trump voters or Remain & Clinton voters? Analysis of voting behaviour”

EU Referendum 2016 #2 – Did your constituency vote Remain or Leave?

February 19, 2017 By Nigel

NOTE 19/2/16: This post is not yet complete. I will do my best to complete it soon as possible. Since I started writing this post, additional data has been made available and I now think that 400 out of 650 seats voted Leave. You can see an outline of my older model in my YouTube clip published in November 2016.

For those used to staying up for election night, the BBC coverage of the EU Referendum on 23rd June 2016 must have been disconcerting. Where were the figures showing how many seats Leave & Remain had won? Unlike a general election where the winner is the party with the largest number of seats, the referendum was decided by a popular vote with Leave winning with 17,410,742 votes to Remain’s 16,141,241 votes.

Also different was that the results were declared for the 399 counting areas (CA) used in EU elections rather than the more familiar 650 parliamentary constituencies. Of the 399 CAs, Leave won a majority in 270 CAs as shown in figure 1. However, the counting areas vary enormously in size from 1,799 eligible voters for the Isles of Scilly to 707,293 eligible voters for the city of Birmingham which makes it difficult to compare CAs. The apparently overwhelming victory for Leave in terms of CAs could be a statistical mirage with Leave winning small CAs and Remain winning large CAs.

By-Election Model #1 – How to predict outcomes in the Brexit era

February 19, 2017 By Nigel

Since the UK voted to leave the EU on 23^rd June 2016, there have been 3 contested parliamentary by-elections (Witney, Richmond Park, Sleaford & North Hykeham) and one uncontested by-election (Batley & Spen which was the late Jo Cox’s seat). Many commentators have analysed these results to see how the referendum result has impacted on parliamentary voting intentions. Whatever voter dynamics are revealed, it is reasonable to assume that they are likely to influence future by-elections. In late October 2016 just after the Witney result, I realised it could be possible to build a by-election model by combining two sources of data.

My own estimates of the Leave & Remain votes in each of the 650 parliamentary constituencies where I calculated that 400 out of 650 seats voted Leave.
My interpretation of the Lord Ashcroft “exit poll” carried out on 21^st to 23^rd June 2016 and published immediately after the results were announced.

At the time, I described my by-election modelling approach in a youtube clip and that is worth listening to. I have made some changes to my model since then so this post is the most up to date version of my model. I will illustrate the basic principle using the Witney by-election (David Cameron’s former seat) of 20^th October 2016 where the top line numbers are: