Deucepoint

Deep per-player statistics, head-to-head comparison, and first-principles match and draw simulation for both the ATP and WTA tours — built from raw match data, with the working shown.

Note: Claude Code used for frontend design.

Live: deucepoint.net — API at api.deucepoint.net

Why this exists

Three things no free tennis site does well together:

• Deep per-player statistics for ATP and WTA on the same footing. Most sites treat the women's tour as an afterthought or omit it entirely.
• Every player, not just the famous ones. Around 1.6 million matches across tour, Challenger, Futures and ITF, and over 115,000 players. A player ranked 400 gets a real page, not an empty one — see ADR-0003.
• Head-to-head comparison with surface, era, and form context — not just a win-loss tally.
• Match and draw simulation from first principles, showing every intermediate step from point-win probability up to match probability.

The closest prior art, Ultimate Tennis Statistics, is ATP-only. This project's differentiation is WTA parity, the simulators, and an interface that is designed rather than assembled.

Architecture

flowchart LR
    subgraph sources["Data sources (CC BY-NC-SA 4.0)"]
        A["Sackmann-lineage mirrors<br/>ATP + WTA, all tiers"]
        B["Tennismylife<br/>current seasons + this week"]
        C["Match Charting Project<br/>shot-by-shot"]
    end

    A --> I["cmd/ingest"]
    B --> I
    C --> I

    I --> PG[("PostgreSQL 16")]
    PG --> R["cmd/rate<br/>Elo engine"]
    R --> PG
    PG --> API["cmd/api<br/>Go + chi"]
    RD[("Redis 7")] <--> API
    API --> SIM["internal/simulate"]
    SIM --> API
    API --> WEB["web/<br/>React + TS + Vite"]

Loading

Go handles ingestion, rating, simulation and the read-only API; PostgreSQL does the statistical work. The API, Postgres and Redis run on a single k3s node, with a weekly CronJob catching the data up and an hourly one for this week's results. The frontend is a static SPA on Cloudflare Pages. Design decisions are recorded in docs/decisions/, and the system and runbook in docs/architecture.md and docs/deployment.md.

Rating methodology

Ratings are computed from scratch over every match, replayed in draw order, with no ratings imported from anywhere. It is standard Elo with a K-factor that decays with experience, scaled by how much a match matters (a Grand Slam final at 1.20, Futures at 0.60):

$$K(n) = \frac{250}{(n + 5)^{0.4}}$$

Each player has five series: overall, hard, clay, grass and carpet. For display and simulation, a surface is blended with overall by how much of it the player has played:

$$\text{blended} = w \cdot \text{surface} + (1 - w) \cdot \text{overall}, \qquad w = \min\left(0.75, \frac{\text{surface matches}}{40}\right)$$

At tour level the ratings pick the winner 69.7% of the time, two points better than the official ATP and WTA rankings on the same matches. The full derivation, validation and simulation chain are in docs/methodology.md, rendered at deucepoint.net/methodology.

Data and license

The match data comes from the work of Jeff Sackmann / Tennis Abstract, licensed CC BY-NC-SA 4.0, through license-compliant redistributions, since the original repositories are no longer public. The site is non-commercial, attributes the source on every page, and redistributes derived data under the same license. Provenance is in DATA_LICENSE.md.

Code is MIT; data is CC BY-NC-SA 4.0 (ADR-0001).