ICTLSM KELANTAN

Win probabilities are often presented as clean percentages, but those numbers sit on top of mathematical assumptions. This review examines the Core Distributions Behind Win Probabilities using clear criteria: explanatory power, practical usefulness, transparency, and misuse risk. The goal is to compare common distributional approaches, explain when each is appropriate, and make a recommendation about how they shouldand shouldntbe used.

Im not endorsing a single model. Im evaluating fit.

Criterion One: Explanatory Power

A distribution earns its place if it explains why outcomes vary, not just that they vary. In practice, win probabilities rely on a few recurring distribution families.

Discrete distributions are often used when outcomes are binary or count-based. Continuous distributions appear when performance varies along a spectrum. The explanatory test is whether the chosen distribution matches the mechanism generating outcomes.

If a model assumes smooth variation where outcomes are lumpy, explanatory power drops. That mismatch is common, and its the first red flag.

Explanation precedes prediction.

Criterion Two: Alignment With Real-World Outcomes

Alignment asks a simple question: does the distribution resemble observed behavior over time? Analysts often back-test by comparing predicted frequencies with realized ones.

Some distributions fit short horizons well but drift over longer samples. Others capture long-run tendencies but struggle with game-to-game volatility. Neither is universally better. Fit depends on purpose.

Guides that introduce Probability Distribution Basics usually stress this point for good reason: assumptions determine accuracy boundaries. Ignoring those boundaries invites overconfidence.

Fit is contextual, not absolute.

Criterion Three: Sensitivity to Assumptions

Good models are sensitive in the right places. Bad models are fragile everywhere. Sensitivity analysis reveals how small assumption changes alter outputs.

Distributions that swing probabilities wildly with minor input tweaks score poorly here. They may look precise, but theyre unstable. More conservative distributions often trade sharpness for robustness, which can be a reasonable exchange.

As a reviewer, I prefer models that degrade gracefully rather than collapse suddenly.

Stability matters more than elegance.

Criterion Four: Practical Interpretability

A distribution isnt useful if decision-makers cant interpret it. Some approaches produce outputs that require translation before they inform action.

Interpretability doesnt mean simplicity. It means clarity. Can you explain what a probability shift represents in plain language? Can you connect it to observable changes?

When interpretation fails, misuse rises. Numbers start to persuade rather than inform.

Opaque math invites misreading.

Criterion Five: Risk of Misuse and Overclaiming

The biggest risk with win probabilities isnt errorits overclaiming. Distributions get treated as truth rather than as lenses.

This risk increases when probabilities are presented without ranges, caveats, or explanation. It also increases in environments where incentives reward certainty over accuracy.

Consumer-awareness resources like scamwatch exist partly because numeric authority is easy to exploit. A clean percentage can mislead if its uncertainty is hidden.

Precision without context is dangerous.

Comparative Assessment Across Criteria

Across these criteria, no single distribution dominates. Simpler distributions often score higher on interpretability and stability, while more complex ones may offer better short-term fit at the cost of fragility.

The pattern is consistent: models perform best when used for the question they were designed to answerand worst when stretched beyond it.

Misalignment, not math, causes most failures.

Recommendation: Conditional Use With Clear Guardrails

My recommendation is conditional use. Core distributions behind win probabilities are valuable tools when their assumptions are explicit, their limits are respected, and their outputs are framed as estimates, not guarantees.

I do not recommend using any distribution as a standalone decision-maker. Probabilities should inform judgment, not replace it. When models are treated as advisors rather than oracles, their value increases.