Media, Gaming & Sports
Sports Event Intelligence API
Sports data is abundant and fragmented, and the fast, reliable version of it is almost always licensed. The build is as much a rights question as an engineering one.
- Sports apps
- Broadcasters
- Sports media publishers
- Fantasy platforms
- Hospitality and venue operators
What your customers would see
example interfaceMockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.
The problem this solves
A sports product lives or dies on the seconds after something happens. A goal, a wicket, a lead change — if your app is thirty seconds behind the broadcast, users keep a rival's app open instead. That latency requirement is what makes sports data genuinely hard, and it is also what makes the fast feeds expensive.
The fragmentation compounds it. Fixtures come from one place, live events from another, historical statistics from a third, and team and player reference data has to be reconciled across all of them because identifiers never agree. Every product team that builds this discovers the same thing: the integration and entity-resolution work is larger than the feature work sitting on top.
Then there is context, which is where most products are thin. A score is a fact available everywhere. What that result means for the table, the run of form behind it, the head-to-head history, the statistical oddity worth a push notification — that is the layer that differentiates, and it is computed rather than licensed.
Who buys this
Sports apps, Broadcasters, Sports media publishers, Fantasy platforms, Hospitality and venue operators
What the API returns
What comes back
- Normalised fixtures and schedules across competitions with a single identifier scheme
- Live event streams: scores, period changes, substitutions and significant incidents
- Match statistics at team and player level, as granular as the licensed feed permits
- Standings, form and head-to-head records computed from the event history
- Contextual summaries and notable-fact detection suitable for notifications
- Resolved entity identifiers for teams, players, venues and competitions
- Historical archive with consistent identifiers across seasons
- Provenance and licence tags on every field, so you know what you may display and redistribute
Endpoints
| Method | Path | Returns |
|---|---|---|
| GET | /v1/fixtures | Scheduled fixtures across competitions |
| GET | /v1/matches/{id} | Current state and statistics for one match |
| GET | /v1/competitions/{id}/standings | Standings with form and computed context |
| GET | /v1/matches/{id}/summary | Generated contextual summary for a match |
| GET | /v1/entities/resolve | Resolve a team or player name to a stable identifier |
| WS | /v1/stream | Push live events as they are received from the feed |
Request and response
illustrativeRequest
{
"competition_id": "comp_epl",
"match_id": "mt_8841022",
"include": ["state", "statistics", "context", "summary"],
"stat_depth": "licensed_max"
}Response
{
"match_id": "mt_8841022",
"competition": "comp_epl",
"state": {
"status": "in_progress",
"clock": "67'",
"score": { "home": 2, "away": 1 },
"last_event": { "type": "goal", "minute": 64, "team": "home",
"player_id": "ply_20714" },
"feed_latency_ms": 820
},
"statistics": {
"possession": { "home": 0.57, "away": 0.43 },
"shots_on_target": { "home": 6, "away": 3 },
"source": "licensed_official_feed",
"depth_note": "Player-level tracking data not included in current licence"
},
"context": {
"home_form_last_5": "WWDLW",
"head_to_head_last_10": { "home_wins": 4, "draws": 3, "away_wins": 3 },
"table_implication": "A home win moves them to 3rd, level on points with 2nd",
"notable": [
"First time the home side has led at this ground since February"
]
},
"summary": "The home side lead 2-1 after 67 minutes, having scored twice in eleven minutes against a visiting team unbeaten in four.",
"licence": { "display": "permitted", "redistribution": "restricted",
"attribution_required": true }
}Where the data comes from
| Licensed official data feeds | League and federation feeds, usually via an authorised distributor — fast, accurate, and the main cost line in this build |
| Secondary and free sources | Useful for less commercial competitions and as a sanity check, with slower latency and weaker guarantees |
| Your own editorial data | Reporter inputs, local coverage and corrections, which often beat the feed for lower leagues |
| Reference and identifier data | Team, player, venue and competition registries, needed to reconcile identifiers that never match across sources |
| Historical archives | Past seasons for form, head-to-head and record detection — frequently licensed separately from the live feed |
| Venue and broadcast schedules | Kick-off times, channels and blackout rules, which differ by territory |
The intelligence layer
The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.
- A single canonical entity model with deterministic and fuzzy resolution, because reconciling identifiers across feeds is the largest hidden cost in any sports build
- Event normalisation into one schema across sports, so a consuming app does not branch per competition
- Multi-source reconciliation with precedence rules and conflict logging, since feeds disagree surprisingly often on minute and attribution
- Latency instrumentation end to end, measured against broadcast where you can observe it — latency is the product here
- Context computed from your stored event history rather than licensed, which is both cheaper and under your control
- Summary generation constrained to facts present in the match record, with every claim traceable; unverifiable summaries are withheld
- Licence and provenance tagging at field level, so display and redistribution permissions travel with the data
- Correction handling: feeds restate events, and a product that does not propagate corrections publishes wrong statistics indefinitely
What it changes for the business
Latency parity
Being within a second or two of broadcast is the difference between your app being open and a rival's being open.
One integration
Product teams build against a single schema instead of maintaining several feed integrations.
Differentiating context
Form, records and notable facts are computed from your own archive, so they are yours rather than every competitor's.
Notification material
Automatically detected notable facts give a defensible, non-spammy reason to push.
How it fits into your product
How it embeds
- WebSocket for live events, REST for state and statistics, which is the split most sports front ends already assume
- One identifier scheme across competitions so your app does not maintain per-league mapping tables
- Webhooks into your notification system for goals, lead changes and detected notable facts
- Field-level licence tags surfaced in the response so your front end can enforce display rules automatically
- Historical endpoints for statistics pages and season archives
- Deployment and caching shaped around your licence terms, including territory restrictions where the feed requires them
Included in every build
- Authentication, scoped API keys and per-customer rate limiting
- Input validation with predictable, documented error responses
- Versioned endpoints so your integration never breaks without notice
- Request and response logging for audit and dispute resolution
- Latency, error-rate and usage monitoring with alerting
- OpenAPI specification, sandbox environment and client examples
- Backups, failover design and a documented recovery procedure
- An agreed support arrangement once the service is live
How we would build it
about 12 weeksDiscovery and data review
Weeks 1–2
We agree the exact question the API answers, review the sources you already hold, check what has to be licensed, and write down the response contract your developers will code against.
Architecture and prototype
Weeks 3–4
You get a written architecture plus a working endpoint on real data — narrow, but genuinely callable — so integration can be tested against something concrete rather than a document.
Production build
Weeks 5–10
Pipelines, models, storage, authentication, rate limiting, logging, tests and documentation. Security and observability are part of this phase, not a later one.
Integration and handover
Weeks 11–12
We support your engineers through their integration, run load tests at your expected volume, and agree how the service is monitored and supported once it is live.
Commercial models
There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.
Fixed-scope prototype
A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.
Fixed-price build
An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.
Build then retainer
We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.
White-label product
The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.
Questions buyers ask
How much does the data cost?
More than most teams expect, and it is usually the dominant cost of this build rather than the engineering. Official league data is licensed, priced by competition, depth and use case, and often restricts redistribution or requires attribution. Fast in-play data for a major league is a serious commercial commitment. We would establish exactly what your intended use requires during discovery, because the answer changes the architecture.
Can we use free sources instead?
For some purposes. Lower-profile competitions and non-live use are often workable from freely available or self-collected data. For top-tier live scoring, free sources are slower, less reliable and frequently prohibited from commercial redistribution by their own terms. We would be specific per competition rather than give you a blanket answer.
How fast is live data really?
Bounded by your feed, not by us. A premium official feed delivers in roughly a second; cheaper tiers run several seconds to a minute behind. Our part is to add as little as possible and to measure what we add. We would not promise a latency the licensed feed cannot support.
What about betting-related use?
That changes everything — licensing, regulation and in several markets a separate data rights regime. It is a different conversation with different constraints, and we would scope it explicitly rather than let a media product quietly become a betting feed.
Who owns the context and summaries?
The computed layer — form, head-to-head, records, notable facts, generated summaries — is derived from your archive and is yours, subject to the derived-data terms of whichever feed the underlying events came from. Those terms vary, and we check them per source rather than assume.
Related blueprints
Ticket Price Intelligence API
Event price comparison, resale trend tracking and availability signals for ticketing marketplaces and event discovery products.
Audience Intelligence API
Interest profiles, consumption trends and segment analysis computed from your own audience data and returned as an API your product teams can query.
Content Recommendation API
Personalised rankings, related-item lists and discovery rows for streaming services and publishers, delivered as an API your product calls.
Want this built for your platform?
Tell us what your product does today and what you want another system to be able to ask it. We will come back with an approach, a scope and a price.