Sports Technology Careers

The ecosystem — what data exists, who collects it, who buys it. Spreadsheets, Power BI and storytelling before any code.

Python (Pandas, NumPy) and SQL applied to open cricket and football datasets. No prior coding assumed.

Building and evaluating KPIs. Normalisation, small-sample traps, contextual adjustment. How to present uncertainty honestly.

End-to-end: problem framing, feature engineering, model selection, validation. Applied to match outcome and player performance data.

GPS and wearable data types. Workload monitoring frameworks. Ethics of predictive models applied to people.

Power BI dashboards for coaches, commercial teams and board-level stakeholders — different data, different framing, different decisions.

Using LLMs to draft match reports and commentary. Editorial standards. Attribution and disclosure.

End-to-end analysis of a real open dataset with a final presentation.

Research methods adapted for each user group. Observational research at sport contexts. Synthesising competing needs.

Ergonomics, grip, wearability, thermal comfort, movement constraint. Designing for physiological reality rather than assumption.

Wearable interfaces, training apps, coach dashboards. Information hierarchy at a glance. Designing for distraction and fatigue.

Ticketing, matchday companion apps, membership journeys, loyalty programmes. Designing for the full fan lifecycle.

Accessibility standards applied to sport contexts. Para-sport as a design discipline. India 2030 Commonwealth Games context.

Broadcast graphics, performance dashboards, real-time scoreboard UX. Legibility under time pressure.

Building a design system that scales across app, web, venue and broadcast.

Full product design case — research through specification.

WebSockets, server-sent events, Redis pub/sub. The matchday concurrency problem and how to design against it.

Sportradar, Cricbuzz, StatsBomb and equivalent APIs. Data quality, latency, cost, and rate-limit handling.

Ball-by-ball ingestion, transformation and delivery to multiple surfaces: web, mobile, broadcast overlay.

Queue architectures, fairness, fraud surface, refund flows. Designing systems that hold under spike load.

Fan app architecture, notification systems, recommendation basics, preference management and consent.

HLS, DASH, CDN architecture, latency trade-offs for live vs VOD. Relevant for anyone building for sports media.

Scheduling systems, bracket logic, result propagation, athlete registration and accreditation flows.

A working fan platform handling live data under simulated match-day load.

How sports properties make money. Rights valuations, sponsorship deal structures, ticketing economics, membership revenue modelling.

First-party data strategy, identity resolution, behavioural segmentation. The technical and organisational challenges.

Valuation frameworks, activation planning, ROI measurement, reporting to brand partners.

Matchday campaign planning. Audience targeting, demand forecasting, ticket yield management, post-match retargeting.

Membership and season-ticket lifecycle. Churn signals and retention interventions. Email, push, and loyalty programme mechanics.

Matchday content workflows, social media operations, short-form video, AI-assisted production, Indian-language localisation.

ASCI disclosure rules, deal structuring, performance measurement for creator campaigns in sport.

Technical SEO for sports media, structured data for events and scores, visibility in AI-generated answers.

A complete fan engagement campaign: segmentation, channel plan, content calendar, KPI framework and sponsor reporting template.