Level Change Simulator — why your number moved

Set the four levels, say how settled your rating is and how the match went, and this estimates how far a level system would move you — and explains the size of the move in plain words. It is a model, not any platform’s algorithm: it reproduces the behaviour of questionnaire-seeded, reliability-damped rating systems, using constants printed on this page. No booking app has published its own, so nobody can hand you the real one.

The match

Your side

Your level

3.0

How settled your rating is

Systems of this kind hold a confidence figure alongside your level and use it to damp the change. A new account moves several times as far as a settled one on the same result.

Your partner’s level

3.0

The other pair

First opponent

2.7

Second opponent

2.6

The result

How it went

The margin

The model runs entirely in your browser, so it needs JavaScript switched on. Every constant it uses, and a fully worked example, are written out in the questions further down the page.

What the model expected

Your pair

3.0

Their pair

2.7

Your win chance

72%

Estimated level change

+0.02 … +0.03

a range, because a point estimate would be a lie

Why that size

What this cuts both ways on

A win can barely move you. The model only pays for the part of the result it did not already expect. Beat a pair it had you beating and there is almost no surprise left to price, so the number hardly twitches. That is the mechanism working, not a fault.
A clear loss as the favourite takes the most. Confidence is what makes a correction expensive. The system was sure, the court disagreed, and it cuts hardest exactly where it was most certain — which is why the worst nights are the ones you were supposed to win comfortably.
Low reliability swings both ways. A new rating is allowed to move several times as far, because the system is still guessing at you. Once it is established the same match barely registers — the number gets stable and stubborn at the same time.

This is a model of how questionnaire-seeded, reliability-damped level systems behave — not any platform’s private algorithm. No booking app publishes its formula, so this page uses its own constants (a 0.075 base step, a reliability multiplier of 1.6, 1.0 or 0.45, and a margin multiplier of 0.75 or 1.2), prints them, and shows a range rather than pretending to a decimal it cannot know. If your app moved you by a different amount, your app is right about itself.

The band name in the explanation — club regular, strong club — comes from the same approximate level map the level test and the converter on this site use. No official conversions exist between level systems, so treat it as a description of how a pair plays, never as a translation of anyone’s number.

Why this matters

Two mechanisms explain almost every “why did it move like that” complaint, and neither of them is a conspiracy. The first is the team average: these systems rate the pair, not the person, so a night where a strong partner carried you against a weak pair can leave your number flat or lower even though you played well. The second is expectation — you are paid for the difference between what happened and what the system already believed would happen, which is why a comfortable win over a weaker pair is worth almost nothing.

The third mechanism is damping, and it is the one people notice last. Platforms like Playtomic describe a reliability percentage that damps changes as you play more — the lower the percentage, the more your level moves on a single result (their help centre sets this out). A new account is allowed to swing because the estimate is a guess; a settled one hardly moves, because it is defending an estimate with evidence behind it.

The arithmetic on this page is Elo’s, rescaled for levels: 0.1 of a level behaves like roughly 40 rating points, so a whole level is roughly the 400-point reference gap Elo uses, and the expectation curve here divides the level gap by 0.86 instead of 1.0 to sit slightly steeper. That is a modelling choice, printed openly so you can disagree with it — which is more than any platform offers about its own.

Questions

Why did my padel level drop after a win?

Two mechanisms do it, and both are in the maths rather than in anyone’s bad intentions. First, these systems rate the pair: your team level is the average of you and your partner, so winning alongside a much stronger partner against a weaker pair can be read as a result your side was always going to get. Second, you are only paid for the gap between the result and the expectation — if the model had you at 90% and you won, there was almost nothing left to award, and any nudge from the other side of the calculation can take you below where you started.

Why does a new account swing so much?

Because the system knows nothing about you yet and says so with a reliability figure. While that figure is low, each result is allowed to move your level several times as far as it would move a settled one — the estimate is being dragged toward the truth as fast as the evidence allows. Once it has enough matches, the same win or loss barely registers. Wild early swings are the system working, not a sign that it is broken.

Can I raise my level by beating weaker players?

Barely, and it gets worse the wider the gap. Expectation-based systems pay for surprise, so a win the model already predicted is worth close to nothing, and once your expected win chance is up near 99% the award rounds to zero. Beating players well below you is a way to keep a level, not a way to climb one. Climbing needs matches against pairs the model does not expect you to beat.

Is this the real algorithm?

No, and nobody outside those companies has it — none of them publish the formula. This page is a model of the shape those systems describe in their own help pages: a level seeded by a questionnaire, an expectation built from the two team averages, and a reliability figure that damps the change as you play more. Here it is in full, so you can check it by hand: E = 1 / (1 + 10^((opponents − team) / 0.86)), and the change is 0.075 × reliability (1.6 new, 1.0 settling, 0.45 established) × margin (0.75 close, 1.2 clear) × (result − E), where result is 1 for a win and 0 for a loss; the page then shows 70% to 130% of that figure. Worked example: a settling 3.0 pair beats a 2.7 and a 2.6 clearly, so E = 0.72 and the estimated change is +0.02 to +0.03. The constants are ours and the output is a range for exactly that reason — if your app moved you by a different amount, your app is right about itself.

How is a crew-local rating different?

It has nothing to protect and nothing to hide. A public matchmaking level has to place you against strangers, which is why it needs a questionnaire, a reliability figure and a private formula. A rating scoped to one crew is built only from matches those people actually played against each other, so every move can be shown in full: who you beat, what was expected, how far the number went and why. That is the version people stop arguing with.

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Rivals shows the whole calculation for every match: the expectation, the margin, and the exact points each of the four players moved. No hidden reliability figure, no algorithm to argue with.

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