At 2200 on Thursday 12th December 2019, the BBC/ITV/Sky Exit Poll was revealed to the nation and pointed to a large majority for the Conservatives. Unlike 2017, I was able to turn to my wife and say “it looks like I will be right this time!” By the end of the night, Gavin Freeguard from the Institute of Government was tweeting that not only was I the most accurate election forecaster of 2019, I was more accurate than the Exit Poll.
My forecast for the 2019 UK General Election this Thursday is that the Conservatives will win a majority of 72 seats. The margin of error in this forecast is very wide though due to the fact that 5 of of the last 7 general elections have seen a major polling error. If there is a repeat of the GE2017 underestimate of Labour, then there will be another hung Parliament.
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.
[Read more…] about EU Referendum #6 – Find your way out of the Brexit maze in 9 Days!
*** 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.
[Read more…] about EU Referendum #5 – Find your way out of the Brexit maze in 16 Days!
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!”.
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?
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.
My wife is American and so it should be easy to guess what we were talking about on the morning of 9th 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”
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.
Since the UK voted to leave the EU on 23rd 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 21st to 23rd 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 20th October 2016 where the top line numbers are: