Media, Gaming & Sports
Game Matchmaking API
Matchmaking decides whether a session is worth playing. Get it wrong and the player does not complain — they stop launching the game.
- Multiplayer game studios
- Esports platforms
- Game backend providers
- Mobile game publishers
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
The matchmaking problem is three constraints fighting each other: match the player against someone of similar skill, do it quickly, and keep the population large enough that both are possible. Tighten skill tolerance and queue times climb. Loosen it and players get flattened. Split the population across modes, regions and platforms and both get worse simultaneously.
Most studios start with a visible numeric rating and a simple band search, which works while the population is large and breaks at the edges — off-peak hours, small regions, the top of the ladder, and newly released modes. Those edges are where the players most likely to churn actually live.
A proper system models skill as a distribution rather than a number, predicts match quality before the lobby forms, and relaxes constraints in a defined order when the queue is thin. It also has to handle smurfing, deliberate rank manipulation and the fact that a five-player team's effective strength is not the sum of its ratings. None of that is exotic, but it needs real data and honest evaluation.
Who buys this
Multiplayer game studios, Esports platforms, Game backend providers, Mobile game publishers
What the API returns
What comes back
- Skill estimates as distributions, with uncertainty that narrows as games accumulate
- Predicted match quality and win probability before a lobby is formed
- Team balancing proposals for a given set of queued players
- Role and position-aware balancing where the mode calls for it
- Queue-time forecasts under different skill tolerance settings
- Smurf and rank-manipulation indicators with the evidence behind them
- Per-mode and per-region population health metrics
- Explicit low-confidence outcomes for new players and thin queues
Endpoints
| Method | Path | Returns |
|---|---|---|
| POST | /v1/matchmake | Form balanced matches from a queued player pool |
| GET | /v1/players/{id}/skill | Current skill distribution and confidence |
| POST | /v1/matches/{id}/result | Report a completed match for rating updates |
| POST | /v1/quality | Predicted quality for a proposed lobby composition |
| GET | /v1/population/{mode} | Queue depth, wait times and population health |
| POST | /v1/simulate | Replay historical queues against a proposed configuration |
Request and response
illustrativeRequest
{
"mode": "ranked_5v5",
"region": "eu-west",
"queue": [
{ "player_id": "p_8821", "waiting_s": 42, "party_id": "pty_11" },
{ "player_id": "p_1047", "waiting_s": 38, "party_id": "pty_11" },
{ "player_id": "p_5520", "waiting_s": 91 }
],
"max_wait_s": 180,
"skill_tolerance": "adaptive"
}Response
{
"mode": "ranked_5v5",
"formed_at": "2026-10-22T19:41:08Z",
"matches": [
{
"match_id": "m_771204",
"predicted_quality": 0.84,
"predicted_win_probability": { "team_a": 0.51, "team_b": 0.49 },
"skill_spread": 1.9,
"teams": {
"team_a": ["p_8821", "p_1047", "p_5520", "p_9913", "p_2204"],
"team_b": ["p_6612", "p_3390", "p_7745", "p_1180", "p_4408"]
},
"constraints_relaxed": ["skill_band_widened_to_2.0"],
"notes": ["party pty_11 kept together"]
}
],
"unmatched": [
{ "player_id": "p_9002", "reason": "top_1pct_skill_thin_queue",
"forecast_wait_s": 420 }
],
"population": { "queued": 1284, "median_wait_s": 46, "health": "good" }
}Where the data comes from
| Your match history | Completed matches with rosters, outcomes and durations — the only source that can actually calibrate skill |
| In-match telemetry | Per-player contribution data, which separates genuine skill from carried wins far better than win/loss alone |
| Queue logs | Who queued when, how long they waited and who abandoned — needed to model the wait-time side of the trade-off |
| Account metadata | Account age, platform, region and mode history, used for new-player handling and smurf detection |
| Mode and map configuration | Team sizes, roles and asymmetries, since a balanced team is mode-specific |
| Player reports and sanctions | Optional, for behaviour-aware matching, and handled with the same care as any moderation data |
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.
- Skill modelled as a distribution with explicit uncertainty, updated per match, rather than a single visible number
- Team strength estimated from composition rather than summed ratings, because role coverage and party coordination both matter
- Match quality prediction trained on observed outcomes — margin, duration and early-quit rate, not just who won
- Defined constraint relaxation order so a thin queue degrades predictably instead of producing arbitrary lobbies
- Party handling that accounts for the real advantage a coordinated group has over five solo players
- Smurf detection from early-match performance against uncertainty, flagged for review rather than acted on automatically
- Offline replay of historical queues before any configuration change reaches live players
- Monitoring of quality, wait time and early-quit rate per mode and region, since population shifts break matchmaking quietly
What it changes for the business
Session retention
Closer matches and predictable wait times both reduce the quiet abandonment that follows a run of one-sided games.
Healthier off-peak play
Explicit relaxation rules keep thin queues playable instead of producing lobbies nobody enjoys.
New-player survival
Uncertainty-aware placement keeps beginners away from experienced players during the period they are most likely to quit.
Configuration confidence
Changes are replayed against historical queues before they touch live players.
How it fits into your product
How it embeds
- Called synchronously by your game server at lobby formation, with a latency budget that fits inside the queue loop
- Result reporting endpoint so ratings update as matches complete, in batch or per match
- Skill values retrievable for display, or kept hidden if your design does not expose a rating
- Deployment inside your own backend, which is usual since player data and match telemetry rarely leave it
- Simulation endpoint for testing tolerance and relaxation settings against real historical queues
- Per-mode configuration so a casual playlist and a ranked ladder can run different policies
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 20 weeksDiscovery and feasibility
Weeks 1–3
This one depends heavily on data access, so we start by proving the sources exist, can be licensed for your use, and actually carry the signal the model needs. If they do not, you find out here.
Architecture and baseline model
Weeks 4–6
A written architecture and a baseline model measured against a holdout period, so you know what accuracy you are buying before the full build starts.
Production build
Weeks 7–16
Data pipelines, model training and serving, historical archive, validation harness, API, security, monitoring and documentation.
Validation and integration
Weeks 17–20
Backtesting against held-out history, accuracy reporting your team can audit, integration support and an agreed retraining cadence.
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
Can it keep queue times short and matches close?
Not simultaneously, and any vendor claiming otherwise is selling you something. Those two goals trade off directly against population size. What this does is make the trade-off explicit and controllable: you set the tolerance, the API forecasts the resulting wait time, and relaxation happens in an order you chose rather than arbitrarily.
How many matches before skill estimates are reliable?
Roughly ten to twenty for a usable estimate in a standard team mode, more for modes with high variance or strong role dependence. Before that the uncertainty is wide and the API says so, which is what lets you avoid putting a new player into a competitive lobby too early.
Does it detect smurfs?
It flags accounts whose early performance is implausible for their uncertainty, which catches a good share of them. It is an indicator, not proof — a genuinely talented new player looks similar. We would route flags to review and fast-track skill convergence rather than issuing automatic sanctions.
Will it work for our mode if it is unusual?
Asymmetric modes, large battle royales and co-operative formats all need different quality definitions, and some of them need a different model entirely. We would establish during discovery whether your outcome data actually supports a quality prediction, and tell you if it does not.
Can we hide the rating from players?
Yes, and many studios should. The internal skill distribution and the displayed rank are separate concerns — the first needs to be accurate, the second needs to feel fair and progressive. We would build them as distinct layers.
Related blueprints
Game Fraud and Bot Detection API
Bot, cheat and account-abuse indicators derived from gameplay telemetry, returned with the evidence a moderator needs to make the call.
Content Recommendation API
Personalised rankings, related-item lists and discovery rows for streaming services and publishers, delivered as an API your product calls.
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.
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.