Change from poll on November 16, 2025
| Poll on | Respondents | TS-LKD | LSDP | NA | DSVL | LVŽS | LRLS | Other | View poll |
|---|---|---|---|---|---|---|---|---|---|
| November 16, 2025 | 1,000 | 22.9 | 20.1 | 16.7 | 15.6 | 12.4 | 8.9 | 3.4 | |
| September 13, 2025 | 1,000 | 20.7 | 24.3 | 17.6 | 13.7 | 11.6 | 8.7 | 3.4 | |
| June 22, 2025 | 1,000 | 17.0 | 23.8 | 15.1 | 17.1 | 12.1 | 11.7 | 3.2 | |
| May 12, 2025 | 1,000 | 16.4 | 25.3 | 12.9 | 20.0 | 10.5 | 11.0 | 3.9 |
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Last on December 30, 2025
In the PolitPro Score for polling institutes, we assess the reliability of polling institutes based on their accuracy in elections and their deviation from the Election Trend. Significant deviations in party values compared to the Election Trend lead to deductions, as they can indicate favoritism or disadvantage for parties. The score has a maximum value of 100.
The PolitPro Election Trend, which calculates a time-weighted average of current election polls, serves as a reference. A poll value is considered higher/lower if it is more than one percentage point higher/lower than the Election Trend.
Accuracy shows how close pre-election polls were to the actual election result. We compare the pre-election polls of the institutes with the actual result of an election. We compare the average deviation of the parties' values and form an average from them. In the calculation, we consider all parties with an election result of 3% and higher. Election polls have a margin of error of 2-3 percentage points. We rate average deviations of up to one percentage point as very good (green), and up to 2 percentage points as good (yellow). Values up to 3 percentage points deviation are rated as ok (orange), but already show significant outliers for some parties. Average deviations of more than three percentage points indicate deficiencies in accuracy and significance and are rated as bad (red).
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PolitPro combines scientific data and current poll values to make politics tangible for everyone. We use datasets from leading research projects and supplement them with our own research, analyses, and algorithms. This way, we make complex political contexts understandable and accessible to all. Supported by AI.
