Swish Analytics
Swish Analytics is a machine-learning sports analytics company that prices and originates sportsbook markets. Its B2B API delivers hyper-accurate player-prop pricing, pre-match and in-play match/team markets, single and parlay (accumulator) bet-request pricing, and market results across NFL, NBA, MLB, NHL, NCAA basketball and football, ATP and WTA tennis, and soccer. The read-only JSON API is authenticated with an ApiKey header, supports multi-value filtering and incremental delta sync via a modifiedAtGreater timestamp, and is documented at docs.swishanalytics.com. Swish positions itself as a global leader in player-props pricing and odds origination, risk-management and trading software for U.S. sportsbooks.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Swish Analytics the way a machine reads it — 13 machine-readable artifacts across 10 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Swish Analytics scores 42.0/100 (thin), with a separate agent-readiness read of 55/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Swish Analytics
Each block below is one kind of artifact we hold for Swish Analytics. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 10
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Swish Analytics ATP Tennis API
The ATP Tennis API from Swish Analytics — 3 operation(s) for atp tennis.
Swish Analytics Bet Request API
The Bet Request API from Swish Analytics — 3 operation(s) for bet request.
Swish Analytics MLB API
The MLB API from Swish Analytics — 12 operation(s) for mlb.
Swish Analytics NBA API
The NBA API from Swish Analytics — 10 operation(s) for nba.
Swish Analytics NCAA Basketball API
The NCAA Basketball API from Swish Analytics — 4 operation(s) for ncaa basketball.
Swish Analytics NCAA Football API
The NCAA Football API from Swish Analytics — 6 operation(s) for ncaa football.
Swish Analytics NFL API
The NFL API from Swish Analytics — 12 operation(s) for nfl.
Swish Analytics NHL API
The NHL API from Swish Analytics — 5 operation(s) for nhl.
Swish Analytics Soccer API
The Soccer API from Swish Analytics — 8 operation(s) for soccer.
Swish Analytics WTA Tennis API
The WTA Tennis API from Swish Analytics — 3 operation(s) for wta tennis.
Scroll within the panel for all 10 ·
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
swish-analytics-mcp.yml
MCP SERVERSecurity Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for Swish Analytics — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 2
Reference material describing how the API behaves
Agent Surfaces 4
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 6
Pagination, idempotency, versioning, errors, and events
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 1
Status, limits, changes, and where to get help
Company 1
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/swish-analytics · machine-readable index on apis.io