Sabah PRN 2025 Election Guesstimator Simulator

The Definitive Sabah PRN 2025 Election Guesstimator: Analyzing the Three-Bloc Political Contest

Created By : Ir. MD Nursyazwi

Welcome to the most advanced Sabah political simulation tool. This interactive model allows you to explore hundreds of potential outcomes by manipulating key variables like public mood, economic sentiment, and the opposition split.

This sophisticated probabilistic model runs 10,000 Monte Carlo scenarios with a 2.0% Standard Deviation to provide a realistic forecast of the next State Election.

1. Navigating Your Sabah Election Forecast Tool

This three-bloc Guesstimator is designed for intuitive political analysis. The Composite Overall Swing—the total momentum for or against the incumbent GRS—is automatically calculated from your inputs below.

1. Calibrate Key Sliders:
• Input values for the Base Political Momentum (GRS vs. Opposition).
• Adjust the Incumbent General Approval of the current government.
• Set the State Economic Sentiment Index (Booming vs. Poor).
• Set the internal Opposition Unity Volatility (WARISAN vs. BN).
2. Review the Composite Swing:

Observe how your inputs combine to form the final momentum value, which determines the overall political tide.

3. Analyze the Forecast:

Examine both the static, Deterministic Seat Estimate and the advanced Probabilistic Victory Likelihood based on 10,000 randomized simulations.

2. Data Input: Political Momentum Controls

1. Incumbent General Approval (%) ⓘ Gauges public sentiment towards the current Sabah state government. Higher approval creates a negative swing (favorable to GRS). Maximum influence: ± 4.0%. Neutral baseline: 50%. 50%

Low (0%) High (100%)

2. State Economic Sentiment Index ⓘ Measures the perceived health of Sabah's economy. A Booming index (high positive value) aids the incumbent GRS (negative swing). A Poor index (high negative value) significantly benefits the opposition. Maximum influence: ± 2.0%. 0

Poor (-5) Booming (+5)

3. Base Political Momentum (%) ⓘ Represents baseline electoral discontent or enthusiasm independent of economic and approval factors. Positive values signify a movement away from GRS (Pro-Opposition). Maximum influence: ± 5.0%. 0.0%

Pro-GRS (-5%) Anti-GRS (+5%)

Composite Overall Swing: 0.0% | Baseline

Breakdown: Mood (0.0%) + Approval (0.0%) + Economy (0.0%)

4. Opposition Unity Volatility (%) ⓘ Models vote leakage or solidification between the Opposition parties (WARISAN vs. BN). A positive swing indicates a Pro-WARISAN shift; a negative swing indicates a Pro-BN shift. 0.0%

Pro-BN (-5%) Pro-WARISAN (+5%)

3. The Deterministic Seat Estimate: Static Projection

This output provides a straightforward, static prediction of the election results based purely on the Composite Overall Swing and Opposition Split you have defined, excluding the random elements of local electoral variation.

Simple Majority Threshold (37 Seats)

--

Seat Distribution

0

0

73

4. Sabah PRN Forecast: Probabilistic & Seat Breakdown

Deterministic Seat Breakdown

WARISAN

--

BN

--

GRS

--

Victory Probability (10,000 Simulations)

WARISAN Majority

--

GRS Majority

--

BN Majority

--

Hung Assembly

--

Marginal Seats That Flip (Deterministic)

• No seats flipped in this deterministic run.

5. Visualizing Electoral Volatility: Advanced Charts

GRS Probabilistic Seat Distribution (10,000 Runs)

The height of each bar represents the frequency with which GRS achieved that specific number of seats across the 10,000 simulated scenarios, illustrating the breadth of potential outcomes.

0 Seats (Opposition Sweep) 37 (Majority) 73 Seats (GRS Sweep)

GRS Min/Max Seat Range

This range bar visually captures the minimum and maximum number of seats GRS secured in all Monte Carlo runs, providing a clear snapshot of their floor and ceiling performance.

37

Min: -- Max: --

6. Political Science Methodology: The Monte Carlo Core

The Three-Bloc Baseline Assumption

This predictive model is founded on a hypothetical 2025 baseline that assumes widespread, intense three-cornered contests throughout Sabah's 73 State Legislative Assembly (DUN) seats, with GRS, BN, and WARISAN running separate campaigns. Our starting allocation:

• WARISAN Baseline: 30 seats (Projected largest baseline bloc)
• GRS Baseline: 28 seats
• BN Baseline: 15 seats

The Simple Majority remains 37 seats. Seat margins are randomized based on historical results, focusing on tight races with margins between 0.5% and 4.5%.

Understanding Probabilistic SD (2.0%)

We utilize a Monte Carlo Simulation to move beyond simple linear projections. In each of the 10,000 simulated elections, a local, random swing is applied to every single seat. This swing is derived from a Normal Distribution using a Standard Deviation (SD) of 2.0%.

This 2.0% SD effectively models local candidate impact, regional issues, and unexpected turnout variations, meaning that while the average result follows your input, the reality in any one constituency can diverge significantly.

The Composite Swing Logic (Weighted Factors)

The final Composite Overall Swing applied to GRS's margin is the critical variable, resulting from the combination of your inputs with specific weightings to reflect their real-world impact on voters:

• Base Political Momentum (± 5.0%): Direct political sentiment input.
• Incumbent General Approval (± 4.0% Max): Approval rating is weighted to contribute up to ± 4.0% of the total swing.
• State Economic Sentiment (± 2.0% Max): Economic factors are weighted to contribute up to ± 2.0% of the total swing.

Crucially, the change in a seat's margin of victory is twice the calculated swing value.

7. Data Sources and Model Integrity

The hypothetical 2025 baseline seat allocation (GRS: 28, BN: 15, WARISAN: 30) is extrapolated from an in-depth analysis of the 2020 Sabah State Election results, recent by-election trends, and established coalition stability dynamics as of early 2025.

(Note: This model is purely theoretical and intended for analytical discussion. Actual political outcomes are influenced by countless factors beyond quantifiable modeling.)

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