Win chance

Doubles and mixed

A pair's rating is the average of its two players' ratings in that discipline. The expected score uses the two pair averages, and after the match both players on a side move by the same amount. A player who changes partner keeps their own rating, so a new pair starts from what each partner has earned, not from 1500.

Men's doubles, women's doubles and mixed are rated apart: a mixed specialist and a men's doubles specialist are judged only on their own events. For a chance, /v1/predict takes player1 and partner1 against player2 and partner2, with the discipline.

From ratings to a chance

The ratings move on the classic Elo scale of 400: a 400-point gap means the stronger side is expected to win ten times as often as the weaker. In badminton a rating gap wins more often than that: best of three games to 21 gives the better player many chances to show it. So the chance we publish uses its own scale, fitted every night to every rated match since 2010: the scale that would have scored those matches best. A match where any player has fewer than 10 rated matches in the event uses a second scale (225), fitted the same way on those matches alone: a rating built on few results is read differently, and in practice a newcomer drawn against an established player loses more often than the gap alone says, so that scale comes out the sharper of the two.

The scale is in every answer as meta.model.chance_scale, the newcomer scale as meta.model.chance_scale_newcomer with newcomer_below_matches, and the matches they were fitted on and their Brier score, so you can reproduce any chance from two ratings yourself.

The fair price

fair_price is one divided by the chance: the decimal price at which backing that side would break even if the chance were exactly right. A 62.5% chance is a fair price of 1.60. A bookmaker's price carries a margin, so a market price is shorter than fair on both sides; when a market price is longer than our fair price, our rating thinks that side is under-rated by the market.

That is a starting point, not a bet. The market knows about injuries, form inside a tournament and conditions; the rating knows only results. Compare the two with /v1/odds/{id} before you act on either.

How accurate it is

Every match is scored with the ratings as they stood before it was played, so this is a fair test: nothing from the match or after it leaks in. Across 91,343 senior draw matches since 2010, the side our rating made favourite won 71.2%, with a Brier score of 0.1854 (the average squared gap between chance and result: 0 is perfect, 0.25 is a coin toss).

A chance is only useful if it means what it says: of all the matches where the favourite was given 70%, about 70% should be won. This is how that has held since 2010, band by band.

Against the betting market: on the 10,675 of those matches with a closing price, the market's favourite won 75.3% (Brier 0.1660) and ours won 74% (Brier 0.1757). The closing market is sharper, as it should be: it sees team news and money, the rating sees only results. The rating is a fair, independent second opinion, and most useful where there is no price at all.

Using it well

• Check the matches behind each rating. Every player comes with matches. A player with no rated match is taken at 1500, and the chance says so in meta.note.
• Use the date for the past. date gives the chance as it would have been on that day, from results before it only, so a backtest is honest.
• Singles needs no discipline. For two singles players it is worked out from the first player; doubles needs discipline and both partners.
• Read it beside the head to head. head_to_head counts their meetings in the discipline; a long one-sided record the rating does not show can mean a match-up it misses.

The answer, field by field

Today's top two in men's singles, GET /v1/predict?player1=pl_9k1rzjad&player2=pl_d1p4a658:

{
    "data": {
        "discipline": "MS",
        "date": "2026-10-12",
        "side1": {
            "players": [
                {
                    "id": "pl_9k1rzjad",
                    "name": "Viktor Axelsen",
                    "country": "DEN",
                    "rating": 2044,
                    "matches": 612,
                    "last_rated": "2025-10-26"
                }
            ],
            "rating": 2044,
            "chance": 0.5198,
            "fair_price": 1.92
        },
        "side2": {
            "players": [
                {
                    "id": "pl_d1p4a658",
                    "name": "Shi Yuqi",
                    "country": "CHN",
                    "rating": 2033,
                    "matches": 469,
                    "last_rated": "2026-09-29"
                }
            ],
            "rating": 2033,
            "chance": 0.4802,
            "fair_price": 2.08
        },
        "head_to_head": {
            "meetings": 13,
            "side1_wins": 9,
            "side2_wins": 4
        }
    },
    "meta": {
        "model": {
            "name": "Badminton API rating",
            "kind": "Elo-style, from every senior draw result in order; our own model, not an official BWF figure",
            "start": 1500,
            "chance_scale": 320,
            "fitted_on_matches": 91343,
            "brier": 0.1854,
            "built": "2026-10-12T04:40:45Z"
        },
        "note": "A player never rated starts at 1500; a chance built on few rated matches is rough. Compare it with the market at /v1/odds/{id}."
    }
}

discipline

The discipline the chance is for. For singles without one, it is worked out from the first player.

date

The day the ratings stand on; today when you left it out.

side1.players

Each player on the side: id, name, country, their own rating (null if never rated), matches behind it and last_rated.

side1.rating

The side's rating: the player's own in singles, the average of the two in doubles. A never-rated player counts as 1500.

side1.chance

The chance this side wins, 0 to 1, on the fitted scale. The two sides add up to 1.

side1.fair_price

One divided by the chance: the decimal price with no margin.

head_to_head

Their meetings in this discipline (from the first player on each side): meetings, side1_wins, side2_wins. Not used in the chance.

meta.model

The model: chance_scale, fitted_on_matches, brier and built, so any chance can be checked by hand.

Recipes

Words used here

Elo

A rating system made for chess, where points pass from loser to winner and the amount depends on how expected the result was.

Expected score

The share of the match a side would win if the ratings were exactly right; the rating moves by the gap between this and the result.

K

How far one result can move a rating: 32 for a player's first 20 matches in a discipline, 20 after.

Scale

The rating gap at which the stronger side is ten times as likely to win. Ratings move on 400; the published chance uses 320, fitted to results.

Active

Rated in the 365 days before the date asked for. Tables list only active players unless active=false.

Brier score

The average of (chance minus result) squared over many matches. 0 is perfect, 0.25 is what always saying 50% scores. Lower is better.

Calibration

Whether chances mean what they say: of matches given 70%, about 70% should be won.

Fair price

One divided by the chance, in decimal odds, with no bookmaker's margin.

Questions

Why does the chance differ from the bookmaker's?

The rating sees only results; the market also sees injuries, form within a tournament and money. Differences are worth a look, not proof that one side is wrong.

Can I get the chance for a match that was played years ago?

Yes. Pass date as the day before the match and you get the chance from the ratings that stood then.

Does the head to head change the chance?

No. The chance comes from the ratings alone; the head to head is given beside it for you to weigh.

How is the scale chosen?

Each night the scale that gives the lowest Brier score on every rated match since 2000 is chosen, and it is in meta.model.chance_scale.

More on the rating: how our rating works, and the rating in charts.