Player Scouting

Default data tool: AcesAPI (acesapi-tool). Start with search_players (1 credit) for the id. get_player (5) is the one-call profile. Add get_surface_splits (3), get_player_stats (3) and get_rankings (2, with player_id) for depth. get_player_matches (5 per page) is the match log.

You build scouting reports from results data. Be honest about what the data is: scores, surfaces, rankings and Elo. It has no serve statistics, no injuries and no fitness data, so a report on "serve strength" is a proxy report and must say so.

When to Use

• "Tell me about Jannik Sinner" or "profile Iga Swiatek"
• "Who is better on clay, A or B?" (see also head-to-head-analysis)
• "How does she do against top-10 players?"
• "Is he in form?" "Has the ranking moved?"
• "Compare these two players across everything"

When NOT to Use

• One match's score or schedule: see match-lookup
• Two players' direct meetings only: see head-to-head-analysis
• A win probability for a specific match: see surface-elo
• Betting prices: see odds-explorer

Commands Available

Commands That Do NOT Exist

How It Works

Step 1: Resolve the id. search_players {"name": "alcaraz"}. Real output includes player_id, country, date_of_birth, matches, last_match, current_rank (null outside the top 100 to 150), plays. If two players match, ask or use country. For the WTA add "league": "wta".

Step 2: Profile. get_player {"player_id": "3782"} for bio, rating and last 10 matches. This is the single best 5-credit call.

Step 3: Surface fit. get_surface_splits returns, per surface, win_pct, games_won_pct, tiebreaks_won, tiebreaks_lost, deciding_sets_won, sets_won_6_0, and a ratings block with a rating and match count per surface. Real example, trimmed (Alcaraz, career at the time of the call):

{"overall": {"wins": 307, "losses": 69, "win_pct": 0.816, "games_won_pct": 0.58},
 "splits": [
  {"surface": "hard",  "wins": 168, "losses": 46, "win_pct": 0.785, "games_won_pct": 0.579},
  {"surface": "clay",  "wins": 104, "losses": 19, "win_pct": 0.846, "games_won_pct": 0.588},
  {"surface": "grass", "wins": 35,  "losses": 4,  "win_pct": 0.897, "games_won_pct": 0.569}],
 "ratings": {"clay": {"rating": 2247, "matches": 121}, "grass": {"rating": 2135, "matches": 39}, "hard": {"rating": 2216, "matches": 204}}}

Read it with sample size in view: 39 grass matches is a small sample, so a 0.897 win rate carries far more uncertainty than 0.846 over 123 clay matches. A Wilson interval is the right way to say so (see Method).

Step 4: Quality of opposition. get_player_stats returns record by opponent ranking band. A player with a high win rate built against opponents outside the top 50 is a different player from one who is 60 percent against the top 10. Report both.

Step 5: Trajectory. get_rankings {"player_id": "3623"} returns best_rank and history[] (season, week, ranking_date, rank, previous_rank, points). Points can fall while the rank holds (the real history for the world number one fell from 13,450 to 11,000 points over a few months with the rank unchanged at 1, because old results dropped off). Use get_rankings for the shape, not for form.

Step 6: Form. Use get_player_matches {"player_id": "...", "limit": 20} or the last 10 in get_player. Form is the last N completed matches, weighted by opponent rating. Net rating change over the last 10 (rating_after of the newest minus rating_before of the oldest) is the cleanest one-number form signal here: it already adjusts for who the opponents were.

Method: Honest Intervals on Small Samples

Wilson 95 percent interval for a win rate with w wins in n matches:

p = w / n;  z = 1.96
center = (p + z^2/(2n)) / (1 + z^2/n)
half   = z * sqrt(p(1-p)/n + z^2/(4n^2)) / (1 + z^2/n)
interval = center +/- half

For 35 wins in 39 matches this gives roughly 0.76 to 0.96. Do not rank two players' surface win rates when their intervals overlap heavily. Say "similar".

Serve and Return Without Serve Data

The data has no serve statistics, so do not invent them. What the scores do support:

Label every one of these "proxy".

Comparing Two Players

Build one table and name the winner of each row only when the gap is meaningful:

                    Player A      Player B
Rank / points       1 / 11000     3 / 5560
Elo overall         2407          ...
Elo clay            2209          2247
Win pct clay        .xxx (n)      .xxx (n)
vs top 10           W-L           W-L
Tiebreaks W-L       ...           ...
Last 10 rating chg  +xx           -xx

Then one paragraph of judgment. For the direct record use head-to-head-analysis.

Credit Usage

Anti-patterns

What to Do Next