acesapi-skills

Plugin acesapi-skills from marketplace acesapi in noahowsh/acesapi-mcp. Commands read from the repo's manifest — today.

Skills (16)

• acesapi-tool

  Default data source for the AcesAPI skills. Routes every MCP or REST call for ATP and WTA tennis: tournaments, draws, match results with set and tiebreak scores, schedules, players, head to head, surface splits, rankings, Elo ratings by surface, and sportsbook moneylines. Use when any other skill ne

• backtesting

  Walk-forward backtests of tennis betting rules against historical moneylines from Grand Slams (mid-2020 on) and ATP Masters and WTA 1000 events (August 2024 on): closing-line value, ROI with bootstrap intervals, drawdown, edge compression, and the sample sizes needed. Use when the user asks if a str

• dispatch

  Routes tennis analytics requests to the right 2 or 3 skills. Load this FIRST when an ATP or WTA data or analytics request comes in and you are unsure which skills to activate. Prevents loading all 16 skills when only a few are needed. Also sets expectations on what the data cannot answer (serve stat

• edge-detection

  Comparing a tennis model's probability with the market: no-vig close, expected value at the best available price, closing line value, fractional Kelly sizing, and the checks that separate an estimated edge from noise. Use when the user has a probability and wants to know whether a price is better th

• feature-engineering

  Building leak-free features for tennis match prediction from AcesAPI data: pre-match Elo (overall and surface), rating gaps, rolling form, days of rest and recent load, head-to-head with a time guard, surface and format flags, and a symmetric pair layout. Use when the user is building a model, asks

• head-to-head-analysis

  Head-to-head analysis for two tennis players: wins each, sets won, record by surface and in finals, every meeting with its score, and how much weight a small head-to-head deserves. Use when the user asks who leads a rivalry, how two players match up on a surface, or whether a past result predicts th

• match-lookup

  Finding ATP and WTA matches, draws, schedules and tournaments: results by day, a tournament's draw round by round, today's and this week's schedule, finals of a season, set and tiebreak scores. Use when the user asks who won, who plays today, what the score was, how a tournament draw looked, or whic

• match-preview

  One-page pre-match brief for an upcoming ATP or WTA match: tournament and surface context, both players' ratings overall and on the surface, recent form, head to head, route through the draw, and the market price if the event is covered. Use when the user asks for a preview, a matchup breakdown, or

• odds-analysis

  Odds math for two-way tennis markets: American to implied probability, margin, devigging (multiplicative, power, Shin), no-vig fair odds, consensus across books, favorite-longshot bias, and expected value of a price. Use when the user asks what a price implies, how to remove the vig, what the true p

• odds-explorer

  Multi-book tennis moneyline comparison, best price, margin (vig) per book, and open-to-close line movement for Grand Slam, ATP Masters and WTA 1000 matches. Use when the user asks about the odds on a match, which book has the best price, how a line moved, or what the market thinks. Do not use for de

• player-scouting

  Scouting and comparing ATP and WTA players: profile, ranking, Elo overall and by surface, career record, titles, form, record by surface, round and opponent ranking, and ranking trajectory. Use when the user asks about a player, who is better on a surface, recent form, how a player does against top-

• probability-calibration

  Checking and fixing tennis win probabilities: reliability tables, calibration slope and intercept, Brier decomposition, Platt scaling, isotonic regression, temperature scaling, and calibrating against the market close. Use when the user asks whether a model's 70 percent means 70 percent, why a model

• ranking-history

  ATP and WTA ranking lists and trajectories: the current top list, the list in force on a past date, a player's ranking history, points movement, ranking versus Elo, and seeding context. Use when the user asks who is number one, how a ranking changed, what the ranking was on a date, or whether the ra

• set-score-modeling

  Modeling tennis scores without serve data: match win probability from set and game win probabilities, best-of-three versus best-of-five, set-score distributions (2-0, 2-1, 3-0), tiebreak rates, total games and games handicap lines, and the games_total and games_spread1 fields in match odds. Use when

• surface-elo

  Tennis Elo by surface: how AcesAPI ratings are built, reading overall versus hard, clay and grass ratings, converting a rating gap to a win probability, tracking rating change over time, and building your own surface-weighted blend. Use when the user asks who is best on clay, how likely A is to beat

• walk-forward-validation

  Time-ordered validation of tennis models: expanding or rolling windows, season-by-season walk-forward, purging, log loss, Brier score and accuracy, baselines (Elo, ranking, market close), and significance tests. Use when the user asks if a model is any good, how to split data, whether cross-validati

MCP servers (1)

• acesapi

  connects to: https://mcp.acesapi.com/mcp

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