Sports Event Data Management & Analytics Platform

Client Background

The client operates in the sports analytics and predictions space.
They collect data from:

• Internal systems (Sporacles)

• Web spiders

• MDM external APIs

Their analysts needed accurate, fast, and unified data to drive predictions, track performance, and power dashboards. But their pipelines were fragmented, manual, and unreliable.

The Problem: Scattered Data, Inconsistent Names, and Manual Processing

Before working with Tenplus, the organisation struggled with major challenges:

• Fragmented Data Sources: Event data lived across spiders, internal systems, and MDM with no unified model.

• Inconsistent Team Naming: A single team could appear as 5–10 variations ("Man United", "Manchester United", "MUFC", etc.).

• Manual Data Processing: Analysts spent 15–20 hours per week cleaning spreadsheets.

• Missing Historical Records: Backtesting and long-term analysis were nearly impossible.

• Slow Insights: It took 24–48 hours for data to be actionable by then, odds and predictions were outdated.

• No Prediction Tracking: There was no unified way to compare predictions across sources.

• High Error Rates: Manually consolidated data had an estimated 15–20% error rate.

The client needed a clean, real-time, analytics-ready data platform that removes manual effort and unlocks deeper analysis.

Solution Overview

Tenplus delivered an end-to-end data platform with:

• Multi-source ingestion.

• Automated data scraping.

• MDM API integration.

• A full medallion-style staging + normalisation pipeline.

• Analytics-ready canonical tables.

• Data quality checks, error handling, and observability.

Below is the detailed breakdown.

Data
Analysts

Our in-house data analysts have a hands-on approach to data.

5

45

Companies analyzed

Last year, we were able to help nearly 50 companies.

Process

Step 1: Multi-Source Data Integration Architecture

Step 2: Data Normalisation and Standardisation

Step 3: Staging Layer Architecture

Step 4: Final Analytics-Ready Data Model

Step 5: Automated Alerts and Reporting

Data designers

Our designers have an extensive background in data analysis.

3

1340

Visualizations made

From presentations to marketing materials and beyond.

23

Ongoing projects

Data changes, but our team stands by your side when you need us.

Results: Promises Kept by Tenplus

Tenplus delivered a powerful, trusted, AI-driven platform that:

• Complete automation

• Unified data model

• 100% team name consistency

• Near real-time data updates

• Full historical archive

• Scalable architecture for thousands of events per day

• Analytics-ready tables for ML and reporting

• 15–20 hours saved weekly

• 99%+ accuracy in team and event matching

• Processing time reduced to under 30 minutes

• 1000+ events processed daily with scaling potential

• Multi-year historical analysis unlocked

• Reliable and repeatable workflows

⭐⭐⭐⭐⭐

“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

We’ve helped them

We've partnered with many household brand names to deliver insights and solutions to their problems with big data.

Happy clients

Don't just take our word for it – our clients frequently stay in touch with us and work with us on future projects that require big data insights.

John Bjerrand

I work in agricultural consulting, meaning that we often need to use big and complex datasets to justify expensive investments for our clients. Lilo's consultants helped us see the big picture with their visualization tools and expertise.

Anna Claudio

As the in-house supplier manager, it's sometimes had to understand which manufacturers work best for our needs. Lilo's data analysis helped us identify where we could save and which manufacturers worked the best for us in the long term.

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