Match Preview

Default data tool: AcesAPI (acesapi-tool). A full preview costs about 20 credits: get_schedule (2) or get_matches (2) to find the match, get_player (5) for each player, get_head_to_head (3), get_match (2) for the odds. Add get_surface_splits (3 each) when the surface matters.

You assemble a decision-ready brief. Lead with who the evidence favors and by how much, then the reasons. Keep every number sourced from a call you made. Say what is missing: there are no serve statistics, injury reports or court-speed data.

When to Use

• "Preview tomorrow's Alcaraz match"
• "Who is favored in the Shanghai third round, and why?"
• "Break down the final for me"

When NOT to Use

• A finished match: see match-lookup
• A single player's whole profile: see player-scouting
• Whether to bet: see edge-detection

Commands Available

Commands That Do NOT Exist

How It Works

Step 1: Find the match. get_schedule {"days": 2}. Real fields: match_id, tournament, surface, indoor, round, court, starts_at, player1, player2, seed1, seed2, status. indoor can be null when unknown; do not assume.

Step 2: Pull the match record and price. get_match {"match_id": "..."}. Before play, players[].rating is null (real response for a scheduled match), so take ratings from get_player or get_ratings. odds has one row per book if the event is a Grand Slam, a Masters or a WTA 1000; otherwise it is empty.

Step 3: Ratings and form. get_player for each player: overall rating, surface rating and match count on today's surface, career record, last 10. Compute the rating change over the last 10 completed matches as the form signal (see player-scouting).

Step 4: Head to head. get_head_to_head. Weigh by sample size (see head-to-head-analysis).

Step 5: Route. get_tournament for the event: list each player's wins so far, and total games or sets dropped. A player who has played three three-setters carries more load than one who has won in straight sets. This is a signal from scores, not from physical data, so say "load proxy".

Step 6: Model probability. Elo probability from surface-elo. If the event is covered, add the no-vig close from odds-analysis. Do not force them to agree; show both.

Step 7: Write the brief.

Worked Example (structure with real fields)

Match: Alexander Zverev vs Quentin Halys, Shanghai Masters 2026, Round 3, hard, scheduled 2026-10-11. From get_match:

• rating was null for both (not played yet), so ratings come from get_player
• odds had six books. Pinnacle close: Zverev -673, Halys +537, margin 2.8%, no-vig Zverev about 84.7%
• games_spread1 -3.5 and games_total 22.5 at Pinnacle (a two-set match line)

Brief:

Zverev (-673) vs Halys (+537), Shanghai R3, hard
Market: Zverev ~85% no-vig (Pinnacle), range across books [x]% to [y]%
Rating: Zverev [overall / hard], Halys [overall / hard]  -> model [z]%
H2H: [a]-[b] ([n] matches), last meeting [date]
Load: games and sets dropped so far this event
Games line: Zverev -3.5 games, total 22.5
Missing: serve data, injuries, court speed

Cost: 2 (schedule) + 2 (match) + 10 (two players) + 3 (head to head) = 17 credits.

Reading Tennis Matchups from Scores

Credit Usage

Anti-patterns

Output Format

[P1] vs [P2]  |  [Tournament], [Round], [Surface], [start time UTC]
Lean: [P] at [x]% (model), market [y]% (no-vig close, Pinnacle) 
Why: 1) ... 2) ... 3) ...
Risks and gaps: ...

Always include "Estimates, not guarantees" when probabilities are shown, and never present a lean as a bet recommendation.

What to Do Next