Report cache backend, per-adapter healthcheck, and quota status.

Returns: HealthReport-shaped dict with cache_backend, cache_ok, adapters (per-source ok/detail), and quotas.

Return the FIFA World Cup 2026 group draw and advancement format.

Returns: data.groups: {group_letter: [4 team codes]} for all 12 groups. data.format: 48-team / 12-group / top-2 + 8-best-thirds rule. data.teams: team-code -> {name, fifa_code} metadata. meta.source: adapter that served the data.

Return World Cup 2026 fixtures (live providers, else the group schedule).

Args: limit: Max fixtures to return, 1..200 (default 50). offset: Number of fixtures to skip for paging (default 0).

Returns: data.fixtures: page of {home, away, date/group, status, home_goals, away_goals}. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data (static_seed = group schedule only).

Return current World Cup 2026 group standings.

Args: limit: Max standing rows to return, 1..200 (default 50). offset: Number of rows to skip for paging (default 0).

Returns: data.standings: page of {rank, team, group, points, played, goals_diff}. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.

Return a national team's World Cup squad.

Args: team: Team code or name (e.g. "ARG"). Without an API-Football key, the static seed serves an empty-but-valid squad (rosters are a follow-up).

Returns: data.squad: list of {name, number, position, age}. meta.source: adapter that served the data.

Return a team's aggregate World Cup tournament statistics.

Network-only enrichment: requires a configured API-Football (or football-data.org) key. There is no offline static fallback, so without a key the call returns a clean ALL_SOURCES_FAILED envelope.

Args: team: API-Football numeric team id (not a country code).

Returns: data.team_stats: {team, played, wins, goals_for, goals_against}. meta.source: adapter that served the data.

Return the World Cup 2026 top scorers.

Returns: data.scorers: list of {name, team, goals, assists}. meta.source: adapter that served the data.

Return live market head-to-head odds for upcoming World Cup 2026 matches.

Sourced from The Odds API (requires THEODDS_KEY). Without a key the call returns a clean ALL_SOURCES_FAILED envelope rather than crashing.

Args: team: Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to return every WC event.

Returns: data.events: list of {event_id, home, away, commence_time, bookmakers: [{name, home, draw, away}]} with decimal 1X2 prices per bookmaker. meta.source: adapter that served the data (theodds / cache:stale).

Estimate a match's expected goals and win/draw/loss probabilities.

Args: home_team: First team code (e.g. "ARG"). away_team: Second team code (e.g. "BRA"). neutral: True for a neutral venue (no home advantage). World Cup default.

Returns: data: {expected_home_goals, expected_away_goals, home_win, draw, away_win}. meta.estimated: true.

Predict a single match: most likely scoreline + outcome probabilities.

Args: home_team: First team code. away_team: Second team code. neutral: True for a neutral venue (World Cup default).

Returns: data: {most_likely_score, home_win, draw, away_win, predicted_winner}. meta.estimated: true.

Monte Carlo one group's round-robin -> per-team qualification probabilities.

Args: group: Group letter A-L. iterations: Number of simulations (clamped to 100..20000).

Returns: data.teams: {code: {p_first, p_second, p_third, p_fourth, p_advance, avg_points}}. data.iterations: iterations actually run. meta.estimated: true. meta.conditioned_matches: completed matches locked in.

Monte Carlo the full World Cup 2026 — per-team round + title probabilities.

Simulates all 12 groups, advances the top 2 + 8 best third-placed teams to a 32-team knockout, and plays it to a champion, iterations times.

Args: iterations: Number of tournament simulations (clamped to 100..20000; ~10000 gives stable ±2% probabilities). seed: Optional RNG seed for reproducible output.

Returns: data.teams: {code: {reach_r32, reach_r16, reach_qf, reach_sf, reach_final, win}} sorted by win probability descending. data.champion: most likely winner. data.iterations: iterations run. meta.estimated: true. meta.conditioned_matches: completed matches locked in (played group results fixed, decided knockout ties locked).

Example: football_simulate_bracket() football_simulate_bracket(iterations=20000, seed=42)

Round-by-round survival probabilities for one team in the full sim.

Args: team: Team code (e.g. "FRA"). iterations: Number of tournament simulations (clamped to 100..20000). seed: Optional RNG seed.

Returns: data: {team, reach_r32, reach_r16, reach_qf, reach_sf, reach_final, win}. meta.estimated: true.

Surface the largest gaps between the model's win probability and the market.

De-vigs each market's 1X2 decimal odds (removes the margin so implied probabilities sum to 1) and compares them to this server's own match-outcome probabilities — the same Elo/Poisson path football_match_predictor uses. Where the model probability exceeds the de-vigged market probability by at least min_edge, the outcome is flagged with its edge and the model's fair odds.

Args: team: Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to scan every WC 2026 odds event. min_edge: Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05 (5 percentage points).

Returns: data.value_bets: list of {event_id, home, away, outcome, model_prob, fair_odds, market_odds, edge, bookmaker}, sorted by edge descending. data.events_analysed: events with both teams rated (model-comparable). meta.estimated: true. meta.is_stale reflects the odds freshness.

Return rolling form, goal record, and xG trend for a football team.

Args: team: Team name (e.g. "Brazil", "Argentina").

Returns: data: {form_string, wins, draws, losses, goals_scored, goals_conceded, xg_for, xg_against, recent_trend, matches_analysed}. meta.estimated: true — derived from available fixture data.

Model the joint probability of several match outcomes from the top model-vs-market gaps.

Calls football_find_value_bets internally to fetch live odds, then selects the strongest legs and combines them under the joint-probability model.

Args: legs: Number of legs (2-8). Default 3. min_edge: Minimum edge threshold per leg. Default 0.05.

Returns: data: {legs, legs_used, combined_odds, combined_model_prob, combined_edge, risk_flag, independence_warning}. meta.estimated: true.

Return F1 sessions for a given year, optionally filtered by country.

Args: year: Championship year (e.g. 2025). country: Optional country name to filter (e.g. "Monaco").

Returns: data.sessions: list of session objects with session_key, session_type, date. meta.source: adapter that served the data.

Return driver list for a specific F1 session.

Args: session_key: OpenF1 session identifier.

Returns: data.drivers: list of driver objects with driver_number, full_name, team. meta.source: adapter that served the data.

Return lap times for a driver in a specific F1 session.

Args: session_key: OpenF1 session identifier. driver_number: Driver's race number (e.g. 1 for Verstappen). limit: Max laps to return, 1..200 (default 100 — covers most full races). offset: Number of laps to skip for paging (default 0).

Returns: data.laps: page of lap objects with lap_number and lap_duration. OpenF1 does not put compound/tyre_life here — those live on the stints endpoint. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.

Return F1 driver and constructor championship standings for a year.

Args: year: Championship year (e.g. 2025).

Returns: data.driver_standings: driver championship positions and points. data.constructor_standings: constructor championship positions and points. meta.source: adapter that served the data.

Return the final classification for one F1 race, keyed by year and round.

Args: year: Championship year (e.g. 2025). round: Round number within the season (1-based; e.g. 1 for the opener).

Returns: data.results: Ergast/Jolpica RaceTable payload — finishing order, times, grid positions, points, and fastest laps for the race. meta.source: adapter that served the data.

Return weather data for a specific F1 session.

Args: session_key: OpenF1 session identifier.

Returns: data.weather: list of weather snapshots with temperature, rainfall, wind. meta.source: adapter that served the data.

Fit a tyre degradation model for a driver + compound in a session.

Args: session_key: OpenF1 session identifier. driver_number: Driver's race number. compound: Tyre compound (SOFT, MEDIUM, HARD, INTER, WET).

Returns: data: {intercept, slope, residual_std, sample_count}. meta.estimated: true — model output, not telemetry oracle.

Estimate whether an undercut is viable for the attacker against the target.

Args: session_key: OpenF1 session identifier. attacker_number: Attacking driver's race number. target_number: Target driver's race number. current_lap: Current lap number in the race.

Returns: data: {laps_to_clear, viable, marginal}. meta.estimated: true.

Compare lap-time pace distribution between two drivers in a session.

Args: session_key: OpenF1 session identifier. driver_a: First driver's race number. driver_b: Second driver's race number.

Returns: data: {driver_a_avg_s, driver_b_avg_s, delta_s, faster_driver}. meta.estimated: true.

Analyse weather data and recommend compound or pit-window adjustments.

Args: session_key: OpenF1 session identifier.

Returns: data: {has_rain, avg_track_temp_c, compound_recommendation, recommendation}. meta.estimated: true.

Predict the optimal pit-stop strategy for a driver in an F1 race session.

Args: session_key: OpenF1 session identifier for a recorded race. driver_number: Driver's race number (e.g. 1 for Verstappen). current_lap: Current lap to project from (default 1 = full race ahead). total_laps: Total race laps. If omitted, inferred from the highest observed lap_number in the fetched laps (correct for Monaco 78 / Spa 44), falling back to 57 when no laps are available. An explicit value always wins.

Returns: data.stop_laps: recommended pit laps. data.compound_sequence: tyre compounds for each stint. data.expected_finish_position: currently always None (not modelled). data.confidence: 0.0-1.0 model confidence. meta.total_laps: race length used (explicit arg, else inferred from laps). meta.estimated: true.

Example: f1_predict_pit_strategy(session_key=9158, driver_number=1) f1_predict_pit_strategy(session_key=9158, driver_number=16, current_lap=20, total_laps=78)

Analyse a qualifying session: best lap per driver, gap to pole, projected grid.

Args: session_key: OpenF1 session identifier for a Qualifying session.

Returns: data.grid: [{position, driver_number, full_name, team_name, best_lap_gap_s}]. data.pole_time_s: pole lap duration in seconds. data.drivers_analysed: count of drivers with valid laps. meta.estimated: true — grid derived from session laps, not official timing.

Compare race-pace and tyre degradation between two F1 drivers in a session.

Args: session_key: OpenF1 session identifier. driver_a: First driver's race number. driver_b: Second driver's race number.

Returns: data: {by_compound, overall_faster, compounds_compared}. meta.estimated: true — degradation model fit, not official timing.

Return all currently live cricket matches across all series.

Returns: data.matches: list of live match objects (team names, score, status). meta.source: which adapter served the response. meta.is_stale: true if data is from stale cache.

Return the full scorecard for a specific match.

Args: match_id: The match identifier (e.g. from cricket_get_live_matches).

Returns: data: full scorecard with innings, partnerships, bowling figures. meta.source: adapter that served the data.

Return the points table / standings for a cricket series.

Args: series_id: The series identifier (e.g. IPL 2026 series ID from CricAPI).

Returns: data: points table rows with team, P, W, L, NRR, Points. meta.source: adapter that served the data.

Return the upcoming match schedule, optionally filtered by series.

Args: series_id: Optional. Filter to a specific series. If omitted, returns all upcoming fixtures across all active series. limit: Max matches to return, 1..200 (default 50). offset: Number of matches to skip for paging (default 0).

Returns: data.matches: page of upcoming matches with teams, date, venue. data.pagination: {total, count, offset, limit, has_more, next_offset}. meta.source: adapter that served the data.

Return the squad roster for a cricket team, optionally for a specific series.

Args: team: Team code or name (e.g. "MI", "CSK", "IND", "AUS"). series_id: Optional. Series ID to pull the tournament-specific squad. If omitted, falls back to static seed data.

Returns: data.players: list of players with name, role, and credits. meta.source: adapter that served the data (cricapi / static_seed).

Return live market head-to-head odds for upcoming/live IPL matches.

Sourced from The Odds API (requires THEODDS_KEY). Without a key the call returns a clean ALL_SOURCES_FAILED envelope rather than crashing.

Args: team: Optional team name to filter events (case-insensitive substring, matched against both sides). Omit to return every IPL event. The Odds API uses its own opaque event ids, so a CricAPI match_id cannot be resolved to an event yet — filtering is by team name.

Returns: data.events: list of {event_id, home, away, commence_time, bookmakers: [{name, home, away}]} with decimal h2h prices per bookmaker. meta.source: adapter that served the data (theodds / cache:stale).

Recommend an optimal fantasy XI + captain + vice-captain for one fixture.

Args: match_id: CricAPI match identifier; resolves team_a/team_b/venue automatically. team_a: First team code/name (e.g. MI). Required if match_id is absent. team_b: Second team code/name (e.g. CSK). Required if match_id is absent. venue: Venue key/name (e.g. wankhede). Required if match_id is absent. strategy: "balanced" only in Phase 2; future variants reserved.

Returns: data.players: 11 picked players with name/role/credits/team/projected_points. data.captain: name of the chosen captain. data.vice_captain: name of the chosen VC. data.total_credits: sum of credits used (<= 100). data.total_projected_points: fantasy points including C x2 and VC x1.5 boosts. meta.estimated: true — projections are model output, not a fantasy oracle.

Example: cricket_build_dream11_team(team_a="MI", team_b="CSK", venue="wankhede") cricket_build_dream11_team(match_id="abc123")

Return the top-3 captain candidates ranked by projected points.

Args: match_id: CricAPI match identifier; resolves team_a/team_b/venue automatically. team_a: First team code/name. Required if match_id is absent. team_b: Second team code/name. Required if match_id is absent. venue: Venue key/name. Required if match_id is absent.

Returns: data.candidates: list of 3 dicts with name/role/team/projected_points. meta.source: model:captain_score. meta.estimated: true.

Suggest low-ownership picks with positive projected upside.

Ownership is estimated — proxied by credit weight (lower-credit players tend to have lower ownership), not real ownership data. Flagged estimated: true in the response.

Args: match_id: CricAPI match identifier; resolves team_a/team_b/venue automatically. team_a: First team code/name. Required if match_id is absent. team_b: Second team code/name. Required if match_id is absent. venue: Venue key/name. Required if match_id is absent. ownership_threshold: percent ownership cap; affects estimated label.

Returns: data.picks: list of {name, role, team, credits, projected_points, estimated_ownership_pct}. meta.source: model:captain_score (filtered). meta.estimated: true.

Report a 0-100 form score for a player using the player_stats chain.

Args: player_id: Upstream player identifier (CricAPI/Cricbuzz id).

Returns: data.form_score: 0..100 indicator. data.trend: "rising" / "stable" / "falling". data.samples: how many recent innings were available. meta.source: which adapter served the underlying stats. meta.estimated: true.

Summarise pitch characteristics for a venue.

Args: venue: Venue key (e.g. wankhede), official name, or city.

Returns: data: {batting_friendly 0..1, expected_first_inn, recommendation, venue, pitch_type}. meta.source: which adapter served the venue record.

Compare model probabilities against market-implied IPL odds. Requires THEODDS_KEY.

NOTE: cricket has no calibrated team-strength model wired yet (unlike the football Elo/Poisson path), so this tool currently returns an EMPTY value_bets list — scoring an edge against a neutral 50/50 prior would flag every market underdog, which would be misleading. It still reports how many events were screened so callers know odds were available. For raw de-vigged prices use cricket_get_live_odds. Real edge detection lands when a cricket win model is wired (see cricket_head_to_head).

Args: team: Optional team name to filter events (case-insensitive substring). Omit to scan every IPL odds event. min_edge: Minimum edge (model_prob - devigged_market_prob), 0..1. Default 0.05. Currently informational only (no bets emitted).

Returns: data.value_bets: always [] until a cricket model is wired. data.events_analysed: count of events screened (both teams present). data.model: "neutral_baseline". data.note: why no bets are emitted. meta.estimated: true.

Compare two cricket teams head-to-head using squad form and player stats.

Args: team_a: First team code or name (e.g. "MI", "India"). team_b: Second team code or name (e.g. "CSK", "Australia").

Returns: data: {team_a, team_b, team_a_edge_count, team_b_edge_count, key_players_a, key_players_b, h2h_win_rate_a, h2h_win_rate_b, win_prob_a, win_prob_b}. meta.estimated: true.

Analyse the head-to-head matchup between two cricket players based on role and career stats.

Args: player_a: Player ID or name for the first player. player_b: Player ID or name for the second player.

Returns: data: {matchup_type, edge_holder, edge_reason, signals, role_a, role_b}. meta.estimated: true — heuristic model, not ball-by-ball H2H data.

Model the joint probability of multiple outcomes across football and cricket.

Args: legs: Total legs across both sports (2-8). Default 3. min_edge: Minimum edge per leg. Default 0.05.

Returns: data: same shape as football_build_accumulator, with sport field per leg. meta.estimated: true.