Clipping Probabilities: When It Helps and When It Lies
What probability clipping is
Probability clipping means forcing reported probabilities into a range:
• p_min to p_max
Example:
• clip 0.00 up to 0.01
• clip 1.00 down to 0.99
Clipping is common in systems that compute log loss, because log loss is unbounded at 0 and 1.
Why clipping exists
Reason 1: log loss explodes
If a user enters p = 1.00 and the event resolves NO, log loss is:
-log(1 - 1.00)
Which is infinite. Clipping avoids infinite scores.
Reason 2: people misuse 0 and 1
Most humans use 0 and 1 to mean “very likely”, not literally impossible or certain.
Clipping prevents one emotional extreme from dominating a season.
Reason 3: numerical stability
Even if you do not show log loss publicly, backends that use logs can break when p hits 0 or 1.
When clipping helps
Use clipping when:
• you publish log loss or train models using log likelihood
• you want to prevent a single extreme mistake from destroying a scoreboard
• your UI allows 0 and 1 but you know users treat them loosely
When clipping lies
Clipping becomes misleading when it hides behavior you should see.
Problem 1: it masks reckless certainty
If someone constantly enters 1.00, clipping makes them look less extreme than they are. That can reduce accountability and weaken incentives to be calibrated.
Problem 2: it creates fake comparability
Two platforms can show different log loss numbers simply because they clip differently (0.001 vs 0.01). Without disclosure, comparisons are fake.
Problem 3: it changes incentives
With heavy clipping, a user can get away with pushing probabilities toward extremes because the downside is capped. That can increase overconfidence.
How to do clipping responsibly
Rule 1: disclose your bounds
If you clip, publish p_min and p_max in your methodology.
Rule 2: clip only for log loss, not for Brier
Brier score is bounded and does not need clipping. If you clip Brier inputs, you are changing the metric unnecessarily.
Rule 3: keep raw probabilities for diagnostics
You can compute log loss with clipped values but still display and analyze raw forecast behavior. For example, you can show a “percent of forecasts at 0 or 1” warning badge.
Rule 4: consider UI constraints instead
A clean alternative is to prevent 0 and 1 in the UI and cap at 0.01 and 0.99 directly. Then the user learns that absolute certainty is not allowed.
What clipping means on a leaderboard
If you use log loss with clipping, state:
• clipping bounds
• whether you score per forecast equally or weight by something
• checkpoint rule, so timing does not dominate
Otherwise users will misread scores and think the system is arbitrary.
Takeaway
Clipping is a practical fix for log loss and numerical stability, but it can hide reckless certainty and break comparability across platforms. Use it only when needed, disclose the bounds, and keep raw probabilities visible for diagnostics and behavior feedback.
Related
• Log Loss