How many seats will the Conservative Party win in Canada’s April 28, 2025 parliamentary election?
Here’s what Jeremy Lichtman‘s Multi-AI Oracle forecasts today:
* less than 172: 85%
* between 172 and 205: 12%
* between 206 and 240: 2%
* more than 240: 1%
Carolyn’s note: The Conservative Party must win at least 172 seats to gain control of the Parliament without needing to build a coalition with one of the smaller parties.
Here’s how his Multi-AI Oracle says it arrived at these probabilities: Obtaining outside data.
Checking internet sources, using keywords: Canada Conservative Party election seats
Results from around the web:
Okay, let’s tackle this query about the Canada Conservative Party’s election seats based on current web results. The user wants a summary of news sites, relevant Wikipedia pages, official party websites, and prediction markets, along with tables of any relevant data. They also mentioned a time series table with statistical measures.
First, I need to go through the provided search results. The search results include articles from Ipsos, YouGov, Angus Reid, and Politico. Let’s see what each says about the Conservative Party’s seat projections.
Looking at result [2] and [4] from YouGov, both mention a YouGov MRP projection showing the Liberals on track for a modest majority with 20 seats. That would imply the Conservatives aren’t leading. The Ipsos result [1] talks about the race tightening, with the Conservatives at 37%, Liberals at 33%, NDP 12%, Bloc 5%. But seat projections aren’t directly mentioned here. The Angus Reid report [3] discusses Poilievre’s favourability improving but notes the Liberals gaining in key urban areas like Toronto and Vancouver, which are crucial for seats. The Politico article [5] explains how the Conservative lead has collapsed from 25 points to nothing, predicting a Liberal victory.
Now, the user wants tables. The YouGov MRP projection in [2] and [4] gives specific numbers: Liberals 170 seats, Conservatives 115, NDP 28, Bloc 24, Greens 1. That’s a key data point. The Ipsos data in [1] is about vote share, not seats. Angus Reid’s [3] mentions regional gains but not seat counts. The Politico article [5] discusses the shift in support but doesn’t provide seat numbers.
For the time series table, the Politico article [5] mentions the Conservative lead dropping from 25 points in January to now being tied or behind. That could be converted into a time series with periods, but exact dates aren’t provided beyond January 2025 and April 2025. The Angus Reid report [3] from April 21 shows movement in Toronto and Vancouver, which might relate to seat changes. However, without specific monthly data, creating a detailed time series with statistical measures might be challenging. Maybe focus on the key shifts mentioned: January lead vs. current status.
I should summarize the seat projections from YouGov, mention the voter share trends from Ipsos and Angus Reid, and note the significant shift in Conservative support as per Politico. The tables would include the YouGov seat projections and maybe the vote share changes over time. Since the user asked for prediction markets, but none are cited in the search results, I have to note that no data from those sites is available here.
Putting it all together: start with the overall summary of the current state, then break down into sections for seat projections, regional trends, and the historical context of the Conservative lead collapse. Include the YouGov seat table and perhaps a vote share table with the changes. The time series might be limited but can show the drop from January to April. Also, mention the lack of prediction market data in the available sources.
As the 2025 Canadian federal election approaches on April 28, polling and seat projections indicate a highly competitive race with significant shifts in voter sentiment. Below is a synthesis of current data and analyses from leading pollsters:
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## **Key Seat Projections**
Recent YouGov MRP modeling projects the **Liberals** securing a modest majority with **170 seats**, while the **Conservative Party (CPC)** is forecasted to win **115 seats** [2][4]. Other parties trail significantly:
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## **Regional Battlegrounds**
– **Urban Centers**: Liberals dominate in Toronto (34-point lead), suburban Toronto (905 area), and Metro Vancouver, critical for securing pluralities [3].
– **Quebec**: The Bloc Québécois holds 24% support provincially but trails the Liberals (47% to 24% in Montreal) [3].
– **Prairie Provinces**: Conservatives maintain strongholds, though no specific seat counts are provided in recent reports.
—
## **Voter Sentiment and Trends**
– **Leadership Approval**: PM Mark Carney holds a 54% favourability rating, outpacing CPC leader Pierre Poilievre (38%) [3].
– **Top Issues**: Carney and Poilievre are tied on addressing cost of living (37% each), but Carney leads on trade relations (+24 pts) and healthcare (+13 pts) [3].
– **Strategic Voting**: 45% of recent Liberal supporters cite defeating the CPC as their primary motivator, up from 30% in March [3].
—
## **Historical Context**
The CPC’s 25-point lead in January 2025 collapsed amid Donald Trump’s escalating rhetoric on trade and annexation, which polarized Canadian voters [5]. Pollster Frank Graves noted this unprecedented reversal, attributing it to shifting perceptions of U.S.-Canada relations under a potential Trump administration.
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## **Limitations**
– No data from prediction markets (e.g., Kalshi, Polymarket) was available in the provided sources.
– Seat projections remain fluid, particularly in competitive ridings.
This analysis reflects polling up to April 23, 2025, with Liberals positioned for a potential majority but facing persistent challenges in key regions.
OpenAI gives a base rate of Proportion of elections where the Conservative Party wins between 172 and 205 seats (Carolyn’s note: This is, IMHO, an accurate base rate, but is NOT a forecast.)
The question difficulty is rated 7 (0 to 10)
Historical weighted factors include:
0.4, Current polls show the Conservatives trailing the Liberals
0.3, Liberals leading in key urban battlegrounds
0.2, Higher favorability rating for the Liberal leader compared to the Conservative leader
0.1, Increase in strategic voting against the Conservatives
A Bayesian calculation could be performed as follows:
Applying Bayesian updating involves adjusting the base rate of 0.1 by the likelihood ratios derived from current polling trends (favoring the Liberals), regional dominance (favoring the Liberals), and leader favorability (favoring liberals), resulting in an updated probability that is significantly lower than the base rate.
Bayesian base rate: 0.05
Sufficient news to provide a good forecast? 1 (0 or 1)
News is relevant, topical and unbiased? 1 (0 or 1)
Question classification: scenario_based_forecast
Expected time (days) until question close: 5
The following were considered in order to produce this base rate:
The base rate is based on past election results, where similar shifts and trends have been observed with the Liberals gaining last-minute momentum in key regions.
Ideally, the news feed would contain the following sorts of information for a better forecast:
More detailed regional seat projections and insights from prediction markets would help refine the forecast.
Some potential divergent considerations that might affect the base rate:
Factors such as sudden shifts due to a major political event, changes in voter turnout, or new policy announcements could increase Conservative chances.
The following chain of events are necessary for the question to resolve positively: (Carolyn’s note: meaning in favor of the Conservative Party)
– Canada Conservative Party wins between 172 and 205 seats Unlikely
– Canada Conservative Party wins between 206 and 240 seats Very unlikely
– Canada Conservative Party wins more than 240 seats Extremely unlikely
Querying Claude (AI predicts: [“less than 172”: 0.85, “between 172 and 205”: 0.12, “between 206 and 240”: 0.02, “more than 240”: 0.01] – confidence: 7)
Querying Mistral (AI predicts: [“less than 172”: 0.9, “between 172 and 205”: 0.07, “between 206 and 240”: 0.02, “more than 240”: 0.01] – confidence: 7)
Querying OpenAI (AI predicts: [“less than 172”: 0.7, “between 172 and 205”: 0.2, “between 206 and 240”: 0.08, “more than 240”: 0.02] – confidence: 6)
Question Type: Multiple Choice
Confidence: 7
MAPD: Avg: 0.066666666666667, Max: 0.13333333333333
# LLM responses: 3
Model value:
* less than 172: 85%
* between 172 and 205: 12%
* between 206 and 240: 2%
* more than 240: 1%
The AIs analyze the current political landscape with a focus on polling data indicating a significant decline in Conservative support and increased momentum for the Liberal Party, particularly in major urban centers such as Toronto and Vancouver. They note that strategic voting against the Conservatives has intensified, contributing to the Liberals’ projected lead in seat counts. However, they acknowledge the potential for shifts due to late campaign developments, unforeseen events, or changes in voter turnout. The fluidity in seat projections, especially in competitive ridings, introduces uncertainty to the predictions, and the absence of prediction market data adds to this unpredictability. Despite the current favorable position for the Liberals, unexpected changes in voter sentiment driven by late-breaking events could still alter the election outcome significantly.
Guide to Jeremy’s Bots Reports (These don’t all apply to the current version of his bot.)
MAPD: Mean Absolute Percentage Deviation calculates the average percentage difference between actual values and predicted values, providing a relative measure of error.
Median: We collect the output values from the LLMs in an array. We then take the median value. This is between 0 and 1 (i.e. 0.15 = 15%).
Base rate: Currently we take OpenAI’s value as the base rate. We are working on a better value using a different prompt that takes into account process. The base rate helps to determine if the median makes sense.
SD: The standard deviation between the median and the base rate.
Confidence: We query each of the LLMs on how confident they are of their predictions (between 0 and 10) and take a median. Because the LLMs tend to be overconfident, we take anything lower than 6 as being low confidence. This factors into the overall model.
Conf Mode: Based on the confidence value. >=9 is high confidence. Below 6 is low confidence (this is also triggered by an exceptionally high SD).
Mellers: This refers to Barbara Mellers, specifically a paper she wrote that includes a formula for moving values towards an extreme (i.e. 0 or 1).
Reverse Mellers: This uses the formula from above, but with a sub-1 coefficient to move the values closer to 50%.
Theory of Mind: We ask the LLMs what they think other LLMs would predict. We hope that this makes them consider the questions more deeply.
Beta Distribution: Currently unused, but possibly of interest. This is based on the median, the base rate and the SD.
Close Type: We noticed that the appropriate base case for some questions is closer to the extremes, while others are closer to 50%. When we have a low confidence value, this helps us to determine whether to extremize or de-extremize the value. ‘A’ implies closer to zero. ‘B’ implies closer to 50%. ‘C’ implies closer to 100%.
# LLM Responses: The count of LLMs that responded. We query 5 currently, but they have a relatively high API failure rate.
Model value: For normal confidence mode, this is the median. For high confidence, this is the Mellers value. For low confidence, this is either the Mellers or Reverse Mellers (depending on the Close Type).
