Every player on PadelOS has a rating that reflects their current skill level. This guide explains how that rating is calculated and why it changes the way it does.
The Basic Idea
PadelOS ratings are based on the Elo rating system — the same method originally created for ranking chess players. The core idea is simple:
Your rating changes based on whether you performed better or worse than expected — not just on whether you won or lost.
This means:
Beating a stronger opponent gains you a good number of points, because you outperformed expectations.
Beating a weaker opponent only gains you a few points, because you were expected to win anyway.
Losing to a stronger opponent costs you very little, since it was the likely outcome.
Losing to a weaker opponent costs you more, since it wasn't expected.
This keeps ratings fair and meaningful — a high rating actually reflects consistent performance against real competition, not just a high win count.
When Does My Rating Update?
Your rating only updates after competitive open matches — matches where both teams are genuinely playing to win. Friendly matches don't affect your rating.
How It Works, Step by Step
Step 1: Each Team Gets a Combined Rating
For doubles matches, a team's rating is simply the average of its two players' ratings. For singles, it's just the player's own rating.
Step 2: PadelOS Estimates Each Team's Chances of Winning
Based on the two teams' ratings, the system works out how likely each team is to win before the match happens. The team with the higher rating is expected to win — so a stronger team's "expected chance of winning" will be higher than 50%.
Step 3: Ratings Are Adjusted After the Match
Once the result comes in, each player's rating moves up or down depending on:
Whether they won or lost, and
How likely that result was in the first place
If you performed better than expected, your rating goes up by more. If you performed worse than expected, it goes down by more. If the result was exactly what was expected, the change is small either way.
Example: Seeing It in Action
Let's walk through a real match.
Before the match:
Team A | Rating | Team B | Rating |
Player 1 | 3.5 | Player 3 | 4.0 |
Player 2 | 4.0 | Player 4 | 3.0 |
Team ratings (the average of each pair):
Team A: 3.75
Team B: 3.5
Team A is slightly stronger on paper, so they're expected to win more often than not — roughly a 64% expected chance for Team A, and a 36% expected chance for Team B.
The result: Team B wins the match — the underdog pulls off the upset.
What happens to the ratings:
Player | Before | After | Change |
Player 1 (Team A) | 3.5 | 3.32 | −0.18 |
Player 2 (Team A) | 4.0 | 3.82 | −0.18 |
Player 3 (Team B) | 4.0 | 4.18 | +0.18 |
Player 4 (Team B) | 3.0 | 3.18 | +0.18 |
Because Team B was the underdog and still won, both of its players gain a solid chunk of rating. Because Team A was expected to win and didn't, both of its players lose a similar amount. If Team A had won as expected, the changes would have been much smaller for everyone.
Understanding the K Factor (For Club Admins)
The K factor is the setting that controls how much a player's rating can move after a single match. It's the one part of this system that's actually configurable per club — so it's worth understanding what it does.
In plain terms: K is like a volatility dial. A higher K means ratings swing more dramatically after each match. A lower K means ratings move more gradually and take longer to shift.
Example — same match, different K values:
Using the Team A vs. Team B example above (Team B, the underdog, wins with a 36% expected chance):
K Value | Rating Change for the Winning Underdogs (Team B) | Rating Change for the Losing Favourites (Team A) |
K = 0.10 | +0.06 | −0.06 |
K = 0.275 (PadelOS default) | +0.18 | −0.18 |
K = 0.50 | +0.32 | −0.32 |
The match result is identical in all three cases — only how strongly it affects ratings changes.
Why this matters for your club:
A higher K makes ratings feel more responsive — a few strong or weak performances can move a player noticeably, which some clubs like because it reflects recent form quickly. The trade-off is that ratings can feel less stable, with more visible swings from match to match.
A lower K makes ratings more stable over time and less reactive to a single surprising result — better for clubs that want ratings to represent a long-term track record rather than recent form. The trade-off is that it takes longer for genuinely improving players to see their rating catch up.
PadelOS uses a default K of 0.275, which balances these two effects for most clubs. If your club would prefer a different balance — for example, a more competitive league that wants ratings to move faster, or a casual/social club that wants steadier ratings — you can contact PadelOS support to request a custom K value for your club.
Why It's Designed This Way
This system rewards genuinely strong performances — beating players better than you — rather than just racking up wins against weaker opponents. Over time, it gives every player a rating that reflects a fair, consistent picture of their real level, and it encourages players to keep testing themselves against stronger competition.
The Technical Details (For the Curious)
If you'd like to see the exact math behind the example above, here's the formula PadelOS uses:
Team Rating
Team Rating = (Player 1 Rating + Player 2 Rating) / 2
Expected Chance of Winning
Team A Expectation = 1 / (1 + 10^(Team B Rating − Team A Rating))
Updated Rating After a Match
New Rating = Old Rating + K × (Result − Expectation)
Where:
Result = 1 if the player's team won, 0 if they lost
K = a constant that controls how much ratings move after each match (PadelOS uses 0.275). A higher K makes ratings more sensitive to each match; a lower K makes changes more gradual over time.