API Reference¶

`core` ¶

Core data structures and utilities for rfpy.

`GoalGrid` ¶

A grid representing all possible goal combinations between home and away teams.

Creates two DataFrames: - home: entry [away_goals, home_goals] = home_goals - away: entry [away_goals, home_goals] = away_goals

Both DataFrames use consistent naming: - index.name = "away" (represents away team goals) - columns.name = "home" (represents home team goals)

Attributes:

Name	Type	Description
`n`	`int`	Maximum number of goals for either team (default: 10).

`default() -> GoalGrid` `classmethod` ¶

Create a default GoalGrid with n=10.

`__attrs_post_init__() -> None` ¶

Initialize the home and away goal matrices after object creation.

`models` ¶

Statistical models for betting analysis.

`Distribution` ¶

Bases: ABC

Base class for all probability distributions.

`pmf(data: GoalGrid | pd.Series) -> pd.Series | pd.DataFrame` `abstractmethod` ¶

Calculate probability mass function.

`plot(data: GoalGrid | pd.Series, *args: Any, **kwds: Any) -> Axes` `abstractmethod` ¶

Plot the distribution.

`UnivariateDistribution` ¶

Bases: Distribution

Base class for univariate distributions.

`cdf(data: pd.Series) -> pd.Series` `abstractmethod` ¶

Calculate cumulative distribution function.

`BivariateDistribution` ¶

Bases: Distribution

Base class for bivariate distributions.

`pmf(data: GoalGrid) -> pd.DataFrame` `abstractmethod` ¶

Calculate probability mass function.

`plot(data: GoalGrid, *args: Any, **kwds: Any) -> Axes` ¶

Plot the distribution as a heatmap.

`Poisson` ¶

Bases: UnivariateDistribution

Poisson distribution for modeling goal counts.

`pmf(data: pd.Series) -> pd.Series` ¶

Calculate probability mass function for given values.

`cdf(data: pd.Series) -> pd.Series` ¶

Calculate cumulative distribution function for given values.

`plot(data: pd.Series, *args: Any, **kwds: Any) -> Axes` ¶

Plot the distribution.

`Skellam` ¶

Bases: UnivariateDistribution

Skellam distribution for modeling goal differences.

`pmf(data: GoalGrid | pd.Series) -> pd.Series` ¶

Calculate probability mass function for goal differences.

`cdf(data: GoalGrid | pd.Series) -> pd.Series` ¶

Calculate cumulative distribution function for goal differences.

`plot(data: GoalGrid | pd.Series, *args: Any, **kwds: Any) -> Axes` ¶

Plot the distribution.

`BivariatePoisson` ¶

Bases: BivariateDistribution

Bivariate Poisson distribution for modeling home and away goals.

`pmf(data: GoalGrid) -> pd.DataFrame` ¶

Calculate probability mass function for home and away goals.

`plot(data: GoalGrid, *args: Any, **kwds: Any) -> Axes` ¶

Plot the distribution as a heatmap.

`DixonColes` ¶

Bases: BivariateDistribution

Dixon-Coles adjusted bivariate Poisson distribution.

This class implements the Dixon-Coles (1997) adjustment to the independent bivariate Poisson distribution. The adjustment modifies the probabilities for low-scoring outcomes (0-0, 1-0, 0-1, 1-1) to account for correlation between home and away goals.

The adjustment factors are

P(0,0) *= 1 - λ_home * λ_away * ρ
P(1,0) *= 1 + λ_away * ρ
P(0,1) *= 1 + λ_home * ρ
P(1,1) *= 1 - ρ

Where ρ (rho) is the dependence parameter. Typically ρ < 0 is used to increase the probability of low-scoring draws relative to the independent Poisson model.

Attributes:

Name	Type	Description
`dist`	`BivariatePoisson`	The base bivariate distribution to be adjusted.
`rho`	`float`	Dependence parameter. Must satisfy constraints to ensure valid probabilities.

References

Dixon, M. J., & Coles, S. G. (1997). Modelling Association Football Scores and Inefficiencies in the Football Betting Market. Journal of the Royal Statistical Society: Series C (Applied Statistics), 46(2), 265-280.

`pmf(data: GoalGrid) -> pd.DataFrame` ¶

Calculate probability mass function with Dixon-Coles adjustment.

This method adjusts the basic bivariate Poisson PMF by modifying the probabilities for the four low-scoring outcomes (0-0, 1-0, 0-1, 1-1).

Parameters:

Name	Type	Description	Default
`data`	`GoalGrid`	GoalGrid containing home and away goal combinations to calculate probabilities for.	required

Returns:

Type	Description
`DataFrame`	DataFrame with adjusted probabilities for each home-away goal combination.

`plot(data: GoalGrid, *args: Any, **kwds: Any) -> Axes` ¶

Plot the distribution as a heatmap.

`calibrate(tg_quote: Quote, ah_quote: Quote, rho: float = -0.1, xtol: float = 0.001) -> DixonColes` `classmethod` ¶

Calibrate a Dixon-Coles model from Total Goals and Asian Handicap quotes.

Uses a two-step root-finding procedure:

Total lambda: finds the total scoring rate that matches the implied over-probability from the Total Goals quote.
Lambda split: finds the home/away split that matches the implied home-probability from the Asian Handicap quote.

Parameters:

Name	Type	Description	Default
`tg_quote`	`Quote`	A Total Goals market quote (with margin; will be stripped).	required
`ah_quote`	`Quote`	An Asian Handicap market quote (with margin; will be stripped).	required
`rho`	`float`	Dixon-Coles dependence parameter (typically negative).	`-0.1`
`xtol`	`float`	Solver tolerance for root-finding.	`0.001`

Returns:

Type	Description
`DixonColes`	A calibrated DixonColes instance.

Raises:

Type	Description
`ValueError`	If quotes are for the wrong market type.

`RingfinityModelZero` ¶

Bases: DixonColes

Dixon-Coles model with a fixed dependence parameter (ρ = -0.10).

This is a convenience preset that freezes the Dixon-Coles rho parameter, so only the base BivariatePoisson distribution needs to be supplied.

Attributes:

Name	Type	Description
`dist`	`BivariatePoisson`	The base bivariate Poisson distribution.

`market` ¶

Market classes for betting quotes and odds.

`Market` ¶

Bases: Enum

Represents the different markets for betting quotes.

`BetState` ¶

Bases: Enum

Represents the side of a betting quote.

`QuotedLine` ¶

Represents a betting line value with quarter-point precision.

Betting lines are typically quoted in increments of 0.25 points and can be: - Unit lines: whole numbers (0, 1, 2, etc.) - push possible - Half lines: x.5 values (0.5, 1.5, etc.) - no push possible - Quarter lines: x.25, x.75 values - split between adjacent half-lines

Attributes:

Name	Type	Description
`value`	`float`	The line value, must be a multiple of 0.25.

`__attrs_post_init__() -> None` ¶

Validate that the line value is a multiple of 0.25.

`is_unit_line() -> bool` ¶

Check if this is a unit line (whole number).

`is_half_line() -> bool` ¶

Check if this is a half line (x.5).

`is_quarter_line() -> bool` ¶

Check if this is a quarter line (x.25 or x.75).

`neighbours() -> tuple[float, float]` ¶

Get the neighbours for quarter line splitting.

Quarter lines are split between two adjacent half-lines. For other lines, both neighbours are the same value.

Returns:

Type	Description
`tuple[float, float]`	Tuple of (lower_bound, upper_bound) for line calculation

`QuotedOdds` ¶

Bases: ABC

Abstract base class for betting odds.

This class serves as a base for different odds representations (e.g., DecimalOdds). Subclasses must implement the implied_probability method.

Attributes:

Name	Type	Description
`value`	`float`	The odds value, typically a float representing the odds ratio.

`implied_probability() -> float` `abstractmethod` ¶

Calculate the implied probability from the odds.

Returns:

Type	Description
`float`	The implied probability as a float.

`DecimalOdds` ¶

Bases: QuotedOdds

Decimal odds representation.

Attributes:

Name	Type	Description
`value`	`float`	The odds value, typically a float representing the odds ratio.

`implied_probability() -> float` ¶

Calculate the implied probability from the decimal odds.

Returns:

Type	Description
`float`	The implied probability as a float.

`from_probability(prob: float) -> DecimalOdds` `classmethod` ¶

Create DecimalOdds from an implied probability.

Parameters:

Name	Type	Description	Default
`prob`	`float`	The implied probability as a float.	required

`QuoteLeg` ¶

Represents a price quote for a betting outcome.

Attributes:

Name	Type	Description
`market`	`Market`	The type of betting market.
`line`	`QuotedLine`	The betting line for the quote.
`side`	`BetState`	Which side of the line (Goal Total, Home/Away).
`odds`	`QuotedOdds`	The odds for the bet.

`__attrs_post_init__() -> None` ¶

Validate side based on market type.

`Quote` ¶

Represents a pair of complementary price quotes.

This class is used to group together two price quotes that are mutually exclusive (e.g., Home Win and Away Win).

Attributes:

Name	Type	Description
`side_a`	`QuoteLeg`	The first price quote.
`side_b`	`QuoteLeg`	The second price quote.

`market: Market` `property` ¶

Get the market type of the quote pair.

`line: QuotedLine` `property` ¶

Get the line value of the quote pair.

`__attrs_post_init__() -> None` ¶

Validate that the two sides are complementary.

`build(market: Market, line: QuotedLine, side_pair: tuple[BetState, BetState], odds_pair: tuple[QuotedOdds, QuotedOdds]) -> Quote` `classmethod` ¶

Build a Quote from market, line, sides, and odds.

Parameters:

Name	Type	Description	Default
`market`	`Market`	The type of betting market	required
`line`	`QuotedLine`	The betting line for the quote	required
`side_pair`	`tuple[BetState, BetState]`	A tuple of two BetState values representing the sides	required
`odds_pair`	`tuple[QuotedOdds, QuotedOdds]`	A tuple of two QuotedOdds values representing the odds	required

Returns: A Quote instance with the specified parameters.

`implied_margin() -> float` ¶

Calculate the bookmaker's margin for the quote pair.

Returns:

Type	Description
`float`	The juice as a float.

`margin_strip() -> Quote` ¶

Return a new Quote with de-margined odds.

Uses the basic proportional method to remove the bookmaker's margin.

Returns:

Type	Description
`Quote`	A new Quote with de-margined odds.

`margin_set(target_margin: float) -> Quote` ¶

Return a new Quote with specified margin.

Uses the basic proportional method to set the bookmaker's margin.

Parameters:

Name	Type	Description	Default
`target_margin`	`float`	The desired margin as a float (e.g., 0.05 for 5%)	required

Returns:

Type	Description
`Quote`	A new Quote with the specified margin.

`repr() -> str` ¶

Return string representation of Quote.

`payoffs` ¶

Payoff calculation classes for different betting markets.

`PayoffSurface` ¶

Bases: ABC

Abstract base class for betting payoff surfaces.

A payoff surface calculates betting outcomes across all possible goal combinations for a given line. Subclasses implement specific bet types (Asian handicap, over/under).

Attributes:

Name	Type	Description
`line`	`QuotedLine`	The betting line (automatically converted to QuotedLine).

`market: Market` `abstractmethod` `property` ¶

The market type for this payoff surface.

Returns:

Type	Description
`Market`	The market type as a Market enum (e.g., Market.TOTAL_GOAL, Market.ASIAN_HCAP)

`grid_payoff(grid: GoalGrid | None = None) -> pd.DataFrame` `abstractmethod` ¶

Calculate the payoff surface for this bet type.

Returns:

Type	Description
`DataFrame`	DataFrame with payoff values for all goal combinations

`grid_payoff_prob(model: BivariateDistribution, grid: GoalGrid | None = None) -> pd.DataFrame` ¶

Convenience method to calculate probability-weighted payoff.

Parameters:

Name	Type	Description	Default
`model`	`BivariateDistribution`	BivariateDistribution representing the probability distribution	required
`grid`	`GoalGrid \| None`	Optional GoalGrid for calculations (defaults to GoalGrid.default())	`None`

Returns:

Type	Description
`DataFrame`	DataFrame with expected (model probability weighted) payoff values for
`DataFrame`	all goal combinations

`grid_payoff_ev(model: BivariateDistribution, grid: GoalGrid | None = None) -> pd.DataFrame` ¶

Calculate the expected payoff given a probability distribution.

Parameters:

Name	Type	Description	Default
`model`	`BivariateDistribution`	BivariateDistribution representing the probability distribution	required
`grid`	`GoalGrid \| None`	Optional GoalGrid for calculations (defaults to GoalGrid.default())	`None`

Returns:

Type	Description
`DataFrame`	DataFrame with expected (model probability weighted) payoff values for
`DataFrame`	all goal combinations

`implied_prob(model: BivariateDistribution, grid: GoalGrid | None = None) -> dict[BetState, Any]` ¶

Calculate model-implied probabilities of winning on each side of the bet.

Parameters:

Name	Type	Description	Default
`model`	`BivariateDistribution`	BivariateDistribution representing the probability distribution	required
`grid`	`GoalGrid \| None`	Optional GoalGrid for calculations (defaults to GoalGrid.default())	`None`

Returns:

Type	Description
`dict[BetState, Any]`	Tuple with probabilities of (under, over, push)

`implied_odds(model: BivariateDistribution, grid: GoalGrid | None = None) -> Quote` ¶

Calculate model-implied probabilities of winning on each side of the bet.

Parameters:

Name	Type	Description	Default
`model`	`BivariateDistribution`	BivariateDistribution representing the probability distribution	required
`grid`	`GoalGrid \| None`	Optional GoalGrid for calculations (defaults to GoalGrid.default())	`None`

Returns:

Type	Description
`Quote`	Tuple with probabilities of (under, over, push)

`AsianHandicap` ¶

Bases: PayoffSurface

Asian handicap betting surface.

Asian handicap gives one team a goal advantage/disadvantage. The line represents the handicap applied to the home team: - Positive line: home team gets goals added - Negative line: home team gets goals subtracted

Payoff calculation: (home_goals - away_goals) + line - Win: final result > 0 - Push: final result = 0 (unit lines only) - Loss: final result < 0

`market: Market` `property` ¶

The market type for this payoff surface.

Returns:

Type	Description
`Market`	The market type as a Market enum (Market.ASIAN_HCAP)

`grid_payoff(grid: GoalGrid | None = None) -> pd.DataFrame` ¶

Calculate Asian handicap payoffs.

Returns:

Type	Description
`DataFrame`	DataFrame with payoff values based on goal difference minus handicap line

`TotalGoals` ¶

Bases: PayoffSurface

Goal Total (totals) betting surface.

Goal Total betting is on the total number of goals scored by both teams. The line represents the threshold for total goals: - Over: bet wins if total goals > line - Under: bet wins if total goals < line - Push: total goals = line (unit lines only)

Payoff calculation: (home_goals + away_goals) - line - Win: final result > 0 (Over wins) - Push: final result = 0 (unit lines only) - Loss: final result < 0 (Under wins)

`market: Market` `property` ¶

The market type for this payoff surface.

Returns:

Type	Description
`Market`	The market type as a Market enum (Market.TOTAL_GOAL)

`grid_payoff(grid: GoalGrid | None = None) -> pd.DataFrame` ¶

Calculate Goal Total payoffs.

Returns:

Type	Description
`DataFrame`	DataFrame with payoff values based on total goals minus line

`utilities` ¶

Core data structures and utilities for pyrf.

`calc_total_mass(df: pd.DataFrame) -> float` ¶

Calculate total probability mass.

`sum_diagonals(npy: np.ndarray) -> pd.Series` ¶

Sum the diagonals of a 2D numpy array representing a PMF, yielding goal differences.

`pyrf_data` ¶

Client for accessing Ringfinity match data (FootyIQ watcher API + bet3 ratings).

`PyRfDataClient` `dataclass` ¶

Client for the FootyIQ watcher data API.

Returns DataFrames with flattened data by default. Use *_raw methods for the original nested JSON response.

`__post_init__() -> None` ¶

Initialize tokens and paths from environment if not provided.

`fetch_scores_raw(limit: int = 1000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None, scores_agree: bool | None = None) -> list[dict[str, Any]]` ¶

Fetch game scores from the API as raw nested JSON.

Parameters:

Name	Type	Description	Default
`limit`	`int`	Maximum number of records to fetch.	`1000`
`from_date`	`Timestamp \| None`	Start date as pd.Timestamp (defaults to 2000-07-07).	`None`
`to_date`	`Timestamp \| None`	End date as pd.Timestamp (defaults to 2029-08-07).	`None`
`scores_agree`	`bool \| None`	If True, only return games where watcher and NowGoal agree. If False, only return games where they disagree. If None, return all games regardless.	`None`

Returns:

Type	Description
`list[dict[str, Any]]`	Raw nested JSON response (country -> comps -> seasons -> rounds -> fixtures).

`fetch_full_export_raw(limit: int = 500000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> dict[str, Any]` ¶

Fetch full export data as raw JSON.

Returns:

Type	Description
`dict[str, Any]`	Raw JSON with 'fixtures' list and metadata.

`fetch_scores(limit: int = 1000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None, scores_agree: bool | None = None) -> pd.DataFrame` ¶

Fetch game scores as a flattened DataFrame.

Parameters:

Name	Type	Description	Default
`limit`	`int`	Maximum number of records to fetch.	`1000`
`from_date`	`Timestamp \| None`	Start date as pd.Timestamp (defaults to 2000-07-07).	`None`
`to_date`	`Timestamp \| None`	End date as pd.Timestamp (defaults to 2029-08-07).	`None`
`scores_agree`	`bool \| None`	If True, only return games where watcher and NowGoal agree. If False, only return games where they disagree. If None, return all games regardless.	`None`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: fixture_id, start_time, country, comp_id, comp_name,
`DataFrame`	comp_short, season, round, team1_id, team1_name, team2_id, team2_name,
`DataFrame`	score_ft, score_ht, team1_goals, team2_goals, scores_agree, pitch,
`DataFrame`	weather, temp.

`fetch_scores_agreed(limit: int = 500000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> pd.DataFrame` ¶

Fetch all trusted data where watcher and NowGoal scores agree.

This is the recommended method for fetching reliable historical data.

Returns:

Type	Description
`DataFrame`	DataFrame with one row per fixture.

`fetch_scores_disagreed(limit: int = 1000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> pd.DataFrame` ¶

Fetch games where watcher score differs from NowGoal score.

Useful for investigating data quality or discrepancies.

Returns:

Type	Description
`DataFrame`	DataFrame with one row per fixture.

`fetch_full_export(limit: int = 500000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> pd.DataFrame` ¶

Fetch full export with detailed match statistics as DataFrame.

Includes per-team stats: goals, chances, half-chances, deliveries, ooohs, choos - broken down by half.

Returns:

Type	Description
`DataFrame`	DataFrame with detailed match statistics.

`fetch_ratings_club_raw() -> list[dict[str, Any]]` ¶

Fetch club team ratings as raw JSON.

Returns:

Type	Description
`list[dict[str, Any]]`	List of rating objects, one per home/away club matchup, each
`list[dict[str, Any]]`	containing alpha/beta/lambda parameters, supremacy (sup), total
`list[dict[str, Any]]`	goals (tg), and team/league metadata.

`fetch_ratings_club() -> pd.DataFrame` ¶

Fetch club team ratings as a DataFrame.

Returns:

Type	Description
`DataFrame`	DataFrame with one row per club matchup, including alpha/beta/lambda
`DataFrame`	parameters, supremacy, total goals, and team/league metadata.
`DataFrame`	The `updated_at` column is parsed to datetime.

`fetch_ratings_international_raw() -> list[dict[str, Any]]` ¶

Fetch international (national-team) ratings as raw JSON.

Returns:

Type	Description
`list[dict[str, Any]]`	List of rating objects, one per nation, each containing supremacy
`list[dict[str, Any]]`	(sup), total goals (tg, goals), per-side goal expectations,
`list[dict[str, Any]]`	normalised alpha/beta, and team/NowGoal metadata.

`fetch_ratings_international() -> pd.DataFrame` ¶

Fetch international (national-team) ratings as a DataFrame.

Returns:

Type	Description
`DataFrame`	DataFrame with one row per nation, including supremacy, total goals,
`DataFrame`	per-side goal expectations, normalised alpha/beta, and team/NowGoal
`DataFrame`	metadata. The `updated_at` column is parsed to datetime.

`load_wcplayer_export_raw() -> dict[str, Any]` ¶

Load the local wcplayerexport.json file as raw JSON.

Returns:

Type	Description
`dict[str, Any]`	Dict with keys `wcteamplayers` (list of player records),
`dict[str, Any]`	`exportedAt`, and `count`.

`load_wcplayer_export() -> pd.DataFrame` ¶

Load the local wcplayerexport.json as a flat DataFrame.

Nested fs / ng / tm sub-objects are flattened with dot-separated column names (e.g. tm.cpm, ng._id). Date columns (createdAt, updatedAt, v1StarRatingChangedAt, v2StarRatingChangedAt) are parsed to datetime.

Returns:

Type	Description
`DataFrame`	DataFrame with one row per player.

`fetch_wcplayer_export_raw(limit: int = 5000) -> dict[str, Any]` ¶

Fetch the wcteamplayers export from the API as raw JSON.

Returns:

Type	Description
`dict[str, Any]`	Dict with keys `wcteamplayers` (list of player records),
`dict[str, Any]`	`exportedAt`, and `count`.

`fetch_wcplayer_export(limit: int = 5000, *, cache: bool = True) -> pd.DataFrame` ¶

Fetch the wcteamplayers export from the API as a flat DataFrame.

When cache is True (default) and rf_local_path is set, the raw JSON response is written to <rf_local_path>/wcplayerexport.json, overwriting any existing file.

Returns:

Type	Description
`DataFrame`	DataFrame with one row per player, matching `load_wcplayer_export`.

`fetch_wc2026_squads(*, update: bool = False, cache_path: Path | str | None = None, url: str = WC2026_SQUADS_URL) -> pd.DataFrame` ¶

Return the 2026 World Cup squads as one row per (nation, player).

Reads data/wc2026_players_flashscore.parquet (or rf_local_path equivalent) by default. Pass update=True to re-scrape the FlashScore article at url and overwrite the cache.

See docs/specs/wc2026_v2.md for the column contract.

API Reference¶

core ¶

GoalGrid ¶

default() -> GoalGrid classmethod ¶

__attrs_post_init__() -> None ¶

models ¶

Distribution ¶

pmf(data: GoalGrid | pd.Series) -> pd.Series | pd.DataFrame abstractmethod ¶

plot(data: GoalGrid | pd.Series, *args: Any, **kwds: Any) -> Axes abstractmethod ¶

UnivariateDistribution ¶

cdf(data: pd.Series) -> pd.Series abstractmethod ¶

BivariateDistribution ¶

pmf(data: GoalGrid) -> pd.DataFrame abstractmethod ¶

plot(data: GoalGrid, *args: Any, **kwds: Any) -> Axes ¶

Poisson ¶

pmf(data: pd.Series) -> pd.Series ¶

cdf(data: pd.Series) -> pd.Series ¶

plot(data: pd.Series, *args: Any, **kwds: Any) -> Axes ¶

Skellam ¶

pmf(data: GoalGrid | pd.Series) -> pd.Series ¶

cdf(data: GoalGrid | pd.Series) -> pd.Series ¶

plot(data: GoalGrid | pd.Series, *args: Any, **kwds: Any) -> Axes ¶

BivariatePoisson ¶

pmf(data: GoalGrid) -> pd.DataFrame ¶

plot(data: GoalGrid, *args: Any, **kwds: Any) -> Axes ¶

DixonColes ¶

pmf(data: GoalGrid) -> pd.DataFrame ¶

plot(data: GoalGrid, *args: Any, **kwds: Any) -> Axes ¶

calibrate(tg_quote: Quote, ah_quote: Quote, rho: float = -0.1, xtol: float = 0.001) -> DixonColes classmethod ¶

RingfinityModelZero ¶

market ¶

Market ¶

BetState ¶

QuotedLine ¶

__attrs_post_init__() -> None ¶

is_unit_line() -> bool ¶

is_half_line() -> bool ¶

is_quarter_line() -> bool ¶

neighbours() -> tuple[float, float] ¶

QuotedOdds ¶

implied_probability() -> float abstractmethod ¶

DecimalOdds ¶

implied_probability() -> float ¶

from_probability(prob: float) -> DecimalOdds classmethod ¶

QuoteLeg ¶

__attrs_post_init__() -> None ¶

Quote ¶

market: Market property ¶

line: QuotedLine property ¶

__attrs_post_init__() -> None ¶

build(market: Market, line: QuotedLine, side_pair: tuple[BetState, BetState], odds_pair: tuple[QuotedOdds, QuotedOdds]) -> Quote classmethod ¶

implied_margin() -> float ¶

margin_strip() -> Quote ¶

margin_set(target_margin: float) -> Quote ¶

__repr__() -> str ¶

payoffs ¶

PayoffSurface ¶

market: Market abstractmethod property ¶

grid_payoff(grid: GoalGrid | None = None) -> pd.DataFrame abstractmethod ¶

grid_payoff_prob(model: BivariateDistribution, grid: GoalGrid | None = None) -> pd.DataFrame ¶

grid_payoff_ev(model: BivariateDistribution, grid: GoalGrid | None = None) -> pd.DataFrame ¶

implied_prob(model: BivariateDistribution, grid: GoalGrid | None = None) -> dict[BetState, Any] ¶

implied_odds(model: BivariateDistribution, grid: GoalGrid | None = None) -> Quote ¶

AsianHandicap ¶

market: Market property ¶

grid_payoff(grid: GoalGrid | None = None) -> pd.DataFrame ¶

TotalGoals ¶

market: Market property ¶

grid_payoff(grid: GoalGrid | None = None) -> pd.DataFrame ¶

utilities ¶

calc_total_mass(df: pd.DataFrame) -> float ¶

sum_diagonals(npy: np.ndarray) -> pd.Series ¶

pyrf_data ¶

PyRfDataClient dataclass ¶

__post_init__() -> None ¶

fetch_scores_raw(limit: int = 1000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None, scores_agree: bool | None = None) -> list[dict[str, Any]] ¶

fetch_full_export_raw(limit: int = 500000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> dict[str, Any] ¶

fetch_scores(limit: int = 1000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None, scores_agree: bool | None = None) -> pd.DataFrame ¶

fetch_scores_agreed(limit: int = 500000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> pd.DataFrame ¶

fetch_scores_disagreed(limit: int = 1000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> pd.DataFrame ¶

`core` ¶

`GoalGrid` ¶

`default() -> GoalGrid` `classmethod` ¶

`__attrs_post_init__() -> None` ¶

`models` ¶

`Distribution` ¶

`pmf(data: GoalGrid | pd.Series) -> pd.Series | pd.DataFrame` `abstractmethod` ¶

`plot(data: GoalGrid | pd.Series, *args: Any, **kwds: Any) -> Axes` `abstractmethod` ¶

`UnivariateDistribution` ¶

`cdf(data: pd.Series) -> pd.Series` `abstractmethod` ¶

`BivariateDistribution` ¶

`pmf(data: GoalGrid) -> pd.DataFrame` `abstractmethod` ¶

`plot(data: GoalGrid, *args: Any, **kwds: Any) -> Axes` ¶

`Poisson` ¶

`pmf(data: pd.Series) -> pd.Series` ¶

`cdf(data: pd.Series) -> pd.Series` ¶

`plot(data: pd.Series, *args: Any, **kwds: Any) -> Axes` ¶

`Skellam` ¶

`pmf(data: GoalGrid | pd.Series) -> pd.Series` ¶

`cdf(data: GoalGrid | pd.Series) -> pd.Series` ¶

`plot(data: GoalGrid | pd.Series, *args: Any, **kwds: Any) -> Axes` ¶

`BivariatePoisson` ¶

`pmf(data: GoalGrid) -> pd.DataFrame` ¶

`plot(data: GoalGrid, *args: Any, **kwds: Any) -> Axes` ¶

`DixonColes` ¶

`pmf(data: GoalGrid) -> pd.DataFrame` ¶

`plot(data: GoalGrid, *args: Any, **kwds: Any) -> Axes` ¶

`calibrate(tg_quote: Quote, ah_quote: Quote, rho: float = -0.1, xtol: float = 0.001) -> DixonColes` `classmethod` ¶

`RingfinityModelZero` ¶

`market` ¶

`Market` ¶

`BetState` ¶

`QuotedLine` ¶

`__attrs_post_init__() -> None` ¶

`is_unit_line() -> bool` ¶

`is_half_line() -> bool` ¶

`is_quarter_line() -> bool` ¶

`neighbours() -> tuple[float, float]` ¶

`QuotedOdds` ¶

`implied_probability() -> float` `abstractmethod` ¶

`DecimalOdds` ¶

`implied_probability() -> float` ¶

`from_probability(prob: float) -> DecimalOdds` `classmethod` ¶

`QuoteLeg` ¶

`__attrs_post_init__() -> None` ¶

`Quote` ¶

`market: Market` `property` ¶

`line: QuotedLine` `property` ¶

`__attrs_post_init__() -> None` ¶

`build(market: Market, line: QuotedLine, side_pair: tuple[BetState, BetState], odds_pair: tuple[QuotedOdds, QuotedOdds]) -> Quote` `classmethod` ¶

`implied_margin() -> float` ¶

`margin_strip() -> Quote` ¶

`margin_set(target_margin: float) -> Quote` ¶

`repr() -> str` ¶

`payoffs` ¶

`PayoffSurface` ¶

`market: Market` `abstractmethod` `property` ¶

`grid_payoff(grid: GoalGrid | None = None) -> pd.DataFrame` `abstractmethod` ¶

`grid_payoff_prob(model: BivariateDistribution, grid: GoalGrid | None = None) -> pd.DataFrame` ¶

`grid_payoff_ev(model: BivariateDistribution, grid: GoalGrid | None = None) -> pd.DataFrame` ¶

`implied_prob(model: BivariateDistribution, grid: GoalGrid | None = None) -> dict[BetState, Any]` ¶

`implied_odds(model: BivariateDistribution, grid: GoalGrid | None = None) -> Quote` ¶

`AsianHandicap` ¶

`market: Market` `property` ¶

`grid_payoff(grid: GoalGrid | None = None) -> pd.DataFrame` ¶

`TotalGoals` ¶

`market: Market` `property` ¶

`grid_payoff(grid: GoalGrid | None = None) -> pd.DataFrame` ¶

`utilities` ¶

`calc_total_mass(df: pd.DataFrame) -> float` ¶

`sum_diagonals(npy: np.ndarray) -> pd.Series` ¶

`pyrf_data` ¶

`PyRfDataClient` `dataclass` ¶

`__post_init__() -> None` ¶

`fetch_scores_raw(limit: int = 1000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None, scores_agree: bool | None = None) -> list[dict[str, Any]]` ¶

`fetch_full_export_raw(limit: int = 500000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> dict[str, Any]` ¶

`fetch_scores(limit: int = 1000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None, scores_agree: bool | None = None) -> pd.DataFrame` ¶

`fetch_scores_agreed(limit: int = 500000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> pd.DataFrame` ¶

`fetch_scores_disagreed(limit: int = 1000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> pd.DataFrame` ¶

`fetch_full_export(limit: int = 500000, from_date: pd.Timestamp | None = None, to_date: pd.Timestamp | None = None) -> pd.DataFrame` ¶