Sports & Media

Sports Apps Built for the Moment When Everyone Opens Them at Once

Sports apps have a load profile unlike any other category: minimal traffic for 22 hours, then every user opens simultaneously when the game starts. We build for that moment as the design condition.

The Problem

The reliability problem that only matters when everything is on the line

Sports is the only consumer app category where failure happens in real time in front of the entire user base simultaneously.

Where it breaks down:

  • Peak-concurrent architecture: designed for broadcast scale, not average daily traffic
  • Real-time inference at scale: ML updates within seconds of game events
  • Personalisation at volume: collaborative filtering without latency on the critical real-time path

Relevant Service Tracks

AI/MLiOSAndroid

Featured Work

Projects in Sports & Media

NFLLine

Real-time ML inference pipeline, win probability, injury impact, collaborative filtering, broadcast-scale load architecture

PythonPyTorchCollaborative FilteringMLOpsReal-time

Why App Stop

We architect for peak load, not average load

01

Architect for peak load, not average load

02

Real-time inference that's actually real-time (seconds, not minutes)

03

Personalisation that doesn't slow the critical path

Expected
Outcomes

3D Star Asset
01

Infrastructure holds during peak broadcast events

02

ML updates within seconds of triggering game event

03

Personalised content that improves with engagement

04

Live Activities and widgets for score updates without app open

Questions

Industry FAQ

How do you handle the traffic spike at game start?

We build horizontally scalable backend architectures using edge caching and distributed databases to absorb simultaneous loads.

Can you integrate live score/stats from data providers?

Yes, we have deep experience integrating low-latency websocket feeds from providers like Sportradar and Opta.

How do you build personalisation without launch data?

We start with cold-start heuristics based on user onboarding choices, then rapidly transition to collaborative filtering models as data accrues.

Second-screen experiences synced to live broadcast?

We use acoustic watermarking or synchronized server time offsets to align app events precisely with what's happening on TV.

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