Case study · Sports Analytics

From scattered sports data to a real-time analytics engine.

How Tenplus replaced manual spreadsheets and inconsistent team names with a unified, near real-time sports analytics platform.

Multi-source ingestionAutomated scrapingMDM API integrationMedallion pipelineData quality

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Sports analytics · data platform

One canonical model for thousands of events a day.

99%+Match accuracy

15–20 hrsSaved weekly

<30 minProcessing time

Client background

A sports analytics and predictions company.

The client operates in the sports analytics and predictions space. Their analysts needed accurate, fast, unified data to drive predictions, track performance and power dashboards, but their pipelines were fragmented, manual and unreliable.

At a glance

Internal systems (Sporacles)
Web spiders
MDM external APIs

The problem

Scattered data, inconsistent names and manual processing.

Event data lived across spiders, internal systems and MDM with no unified model.

A single team could appear as 5 to 10 name variations.

Analysts spent 15 to 20 hours per week cleaning spreadsheets.

Missing historical records made backtesting nearly impossible.

It took 24 to 48 hours for data to be actionable, by which point odds were outdated.

No unified way to compare predictions across sources.

An estimated 15 to 20% error rate in manually consolidated data.

What they needed

A clean, real-time, analytics-ready platform
No manual effort
Consistent team and event naming
A foundation for deeper analysis

Solution overview

An end-to-end sports data platform.

Tenplus delivered multi-source ingestion, normalisation and analytics-ready canonical tables with observability built in.

Multi-source ingestion

from spiders, internal systems and MDM APIs.

Automated data scraping

replacing manual collection.

MDM API integration

for master reference data.

A medallion-style staging and normalisation pipeline

for clean, consistent data.

Analytics-ready canonical tables

one model for every consumer.

Data quality checks and observability

with error handling throughout.

Built with Multi-source ingestionAutomated scrapingMDM API integrationMedallion pipelineData quality

How we built it

From many sources to one analytics-ready model.

Step 1Multi-source data integration architecture

Step 2Data normalisation and standardisation

Step 3Staging layer architecture

Step 4Final analytics-ready data model

Step 5Automated data quality checks and monitoring

Results

Promises kept: automation, accuracy and scale.

Complete automation and a unified data model.
100% team-name consistency.
Near real-time data updates and a full historical archive.
Scalable architecture for thousands of events per day.
15 to 20 hours saved weekly, with 99%+ matching accuracy.
Processing time reduced to under 30 minutes; multi-year analysis unlocked.

★★★★★

Tenplus transformed our entire sports data operation. We moved from manual spreadsheets and inconsistent team names to a fully automated pipeline with near real-time accuracy. Their normalisation engine, staging design and data model gave us a real competitive edge.

Head of Sports Analytics

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