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AI SPORTS INTELLIGENCE
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One match. Every source in view.

EX Insight is a proposed pre-match research tool for sports editors and analysts. Review a complete sample brief with source records, conflicting updates and revision history.

Visit EX Insight Read a sample brief
PRE-MATCH BRIEF · FICTIONALv0.2

Northbridge vs Riverside

DEMO-001 · Cutoff: 30 minutes before kickoff

UNCONFIRMEDWill Morgan start?

A reporter expects a start. The later club update leaves it pending. The brief preserves both reports.

T−45 minReporter’s preview
T−35 minClub availability update
Read the brief and sources
SourcesTrace each claim
ContextKeep uncertainty visible
RevisionsReview what changed
A COMPLETE WORKED EXAMPLE

Read the summary. Check the evidence.

Authored example. Teams, reports and quotations are fictional; this is not live analysis.

DEMO-001 / v0.2

Northbridge vs Riverside

Cutoff: 30 minutes before kickoff

Summary for editorial review

Northbridge midfielder Alex Morgan’s starting place remains unconfirmed. A reporter expects a start, but the later club update leaves the decision pending. Keep the lineup open until a team sheet is available.

SourcesTraining bulletin [01]Reporter’s preview [02]Club availability update [03]

How to read this finding

An unconfirmed starting place does not mean the player is ruled out. This example checks availability; it does not infer a match result or change a probability.

Source records · all text is fictional

Expand each record for its text and interpretation. References point to this page, not external news.

[01]Training bulletin
Fictional club report
Morgan completed part of the session. Match selection will be reviewed.

Training participation is reported; selection is not confirmed.

[02]Reporter’s preview
Fictional editorial report
I expect Morgan to start for Northbridge.

An expectation from the reporter, not an official lineup.

[03]Club availability update
Fictional club report
A decision on Morgan’s starting place is still pending. The team sheet will confirm selection.

The later update keeps the starting place unresolved.

Revision history
  1. v0.1 · T−45 min

    Recorded the training report and reporter’s expectation. Marked selection as unconfirmed.

  2. v0.2 · T−30 min

    Added the T−35 club update. Preserved the earlier expectation and flagged the difference for review.

What would make this brief useful in your workflow?

Discuss an Insight pilot
EX INSIGHT / PROPOSED FIRST USE CASE

One fixture. A briefing you can check.

The proposed pilot focuses on sports editors and researchers preparing pre-match coverage. The task is to reconcile scattered updates into a source-linked briefing—not to provide a betting recommendation. The first competition and data sources would be agreed before a pilot.

WHO

Editors and research teams

People who repeatedly check fixture context, player availability and changing reports before producing a briefing. Their frequency of use and willingness to pay still need validation.

TASK

Reconcile the information

Fix the event and cut-off time, preserve the source for each claim, flag conflicting reports and separate confirmed facts from interpretation.

OUTPUT

A reviewable research brief

A concise summary, source list, unresolved questions and revision notes. The pilot would test whether this reduces checking effort without sacrificing accuracy.

This is a proposed pilot scope. The current website shows illustrative interfaces, not a connected research service or verified customer results.

WORKED EXAMPLE / FICTIONAL

From a conflicting update to a clear review task.

Imagine an editor preparing a Northbridge–Riverside preview. One report says a player will start; another says the decision is pending. This example explains the intended workflow, not an analysis generated by a live EX model.

01 / INPUT

Keep both reports

Attach source, publication time and the exact availability claim to the same fixture. Do not silently discard the conflicting report.

02 / REVIEW

Mark the uncertainty

The brief should say that availability is unconfirmed and identify which official update would resolve the conflict. It should not invent a confirmed lineup.

03 / OUTPUT

Give the editor a next step

Present the two sources, a cautious summary and a recheck task. If the information changes, preserve a revision note for the reader to inspect.

Pilot measures: time to a reviewable brief, citation accuracy, unresolved-claim handling and repeat use. Record a manual-workflow baseline and agree thresholds before testing; no improvement rate is claimed yet.

PRODUCT IN PRACTICE

Put the capabilities in context.

Explore the intended information structure and workflow below. These examples are not live data or an available service.

DEMO-001 / FICTIONAL FIXTURE

Northbridge vs Riverside

A useful briefing shows what is known, what changed and what still needs confirmation.

Research window
Illustration: 30 minutes before kickoff
Outcome scope
Home win / draw / away win
Data context
Synthetic inputs, no live source

Questions to investigate

Read the full briefing and sources
  • Are lineup assumptions confirmed?
  • Do all inputs refer to the same update time?
  • Which change has affected the estimate?

The fictional teams and inputs explain the intended interface. They do not represent a real match or forecast.

PRODUCT CAPABILITIES

Inside EX Insight.

Bring the match, the model and the market into one research workflow. Understand what changed, why it matters and how different probability estimates compare.

Intended product experience. Contact the team for current availability and access.
01

AI match briefings

Review the fixture, recent performance and relevant match context alongside the factors behind a model estimate. Separate observed information from the model’s interpretation.

For a pre-match review, start with the competition, kick-off time, expected lineup and relevant historical window. If a player’s availability is unconfirmed, the briefing should preserve that uncertainty instead of silently treating an assumption as a fact.

EXPECTED OUTPUT

A structured briefing with key factors and update context.

02

A consistent sports data view

Bring schedules, results and event state together with sources and timestamps, so comparisons refer to the same event and moment. Missing or delayed inputs should remain visible.

The intended view should make event identifiers, time zones and corrections easy to reconcile. Two records with similar team names are not necessarily the same fixture, and a historical result should not be confused with a current event state.

EXPECTED OUTPUT

Aligned event records, source context and freshness information.

03

Probability comparison

Compare model estimates with market-implied probabilities. A 52% model estimate versus a 47% market view is a 5-percentage-point difference, not a guaranteed opportunity. Timing, assumptions and model error matter.

A meaningful comparison uses the same outcome definition and a comparable snapshot. A regulation-time result and an eventual tournament winner are different questions; comparing their percentages would create a misleading gap even if both figures were correctly calculated.

EXPECTED OUTPUT

Side-by-side estimates with an explicitly labeled difference.

04

Expert context and change alerts

Combine quantitative outputs with human analysis. Follow material updates and inspect what changed before revisiting a view, rather than reacting to an unexplained score.

A useful alert explains the changed input, its source and how the interpretation differs from the previous version. Readers should be able to revisit the earlier context and decide whether the change is material to their original research question.

EXPECTED OUTPUT

Contextual analysis and a traceable history of meaningful changes.

05

Enterprise API services

A planned integration route for structured event information and model outputs. Partners can discuss required fields, update frequency and permitted use for research tools, media products or internal workflows.

An integration discussion should cover event identifiers, timestamp format, versioning, update limits and correction handling. A small agreed sample can help a partner check fit before committing to a broader workflow; no live endpoint or service level is announced here.

EXPECTED OUTPUT

An integration scope agreed with the team; API access is not confirmed here.

USE CASES

From a real question to a useful output.

01

Pre-match research

A researcher needs to understand which inputs changed before kickoff, without rebuilding a briefing from separate sources.

INTENDED RESULT

A match brief with source timing, model estimates, material changes and unresolved questions.

02

Media and audience context

An editorial team wants to explain why a probability moved and which assumptions still matter.

INTENDED RESULT

Explainable context for editorial use, with observed facts separated from model interpretation.

03

Enterprise integration

A data team wants to place structured intelligence inside an existing research or reporting workflow.

INTENDED RESULT

An agreed field mapping, delivery scope and evaluation plan before an integration is enabled.

INTENDED WORKFLOW

A clear path, step by step.

  1. 01

    Choose an event

    Start with the match and outcome you want to understand.

  2. 02

    Read the briefing

    Review the relevant data, sources and latest update.

  3. 03

    Compare perspectives

    Examine model estimates, expert context and market pricing together.

  4. 04

    Revisit after changes

    Follow new information and reassess the underlying assumptions.

PREPARE TO CONNECT

Start with a clear brief.

This site demonstrates the information structure, not a connected data feed. Coverage, API documentation, pricing and access arrangements must be confirmed with the team.

Coverage and rights

Specify sports, competitions, historical depth and intended use. Confirm source permissions and permitted redistribution.

Delivery and freshness

Agree on event identifiers, fields, update frequency, freshness indicators and handling of delayed or missing inputs.

Evaluation before expansion

Review data quality and model calibration against agreed criteria. No accuracy, latency or service-level guarantee is specified here.

Data Network

Understand how sources, timestamps and feedback support intelligence quality.

EX Sport

Explore the separate market layer that can sit beside an analytical view.

COMMON QUESTIONS

Know more before you act.

Does a higher model probability guarantee an opportunity?

No. The difference shows that the model and market disagree. Data timing, assumptions, estimation error and trading costs all affect its interpretation. A model is decision support, not a guarantee.

Which sports and competitions are covered?

A confirmed coverage list is not published here. Tell the team which competitions and data you need to discuss the current scope.

Can my team access an API?

API services are part of the product direction. Share your use case, required fields and expected update frequency to discuss access and integration requirements.

EX INSIGHT / NEXT STEP

Discuss EX Insight access

Tell us which sports, analysis workflow or integration you are exploring.

Visit EX InsightProduct inquiry
Explore the technical architecture and product principles
FROM EVENT STREAM TO EXPLAINABLE PROBABILITY

Every output should show the probability, the change, the reason and the time.

EX Insight is being developed to turn fragmented sports information into a structured analytical workflow. Rather than offering a black-box score, it connects every probability to its context, update time and model version.

01

Intelligence pipeline

Relevant event and reference inputs are collected, normalized and transformed into model-ready features before an estimate reaches the interface.

  • Input normalization
  • Feature generation
  • Versioned inference
02

Calibration, not certainty

A probability is an estimate. Historical evaluation, error analysis and uncertainty ranges help users understand where the model is strong and where conditions remain unclear.

  • Backtesting
  • Calibration analysis
  • Uncertainty context
03

Multiple delivery modes

The same intelligence can support a concise match briefing, movement alert, scenario comparison or structured response inside a partner workflow.

  • Briefings and alerts
  • Scenario comparison
  • Enterprise APIs
HOW IT WORKS

How an intelligence output is formed

A transparent pipeline makes it possible to inspect the path from raw input to user-facing probability.

01
COLLECT

Gather relevant inputs

Bring schedules, results, event state and context into a consistent stream.

02
TRANSFORM

Create model features

Resolve entities, align timestamps and prepare features for inference.

03
ESTIMATE

Generate probability

Run a versioned model and attach confidence and calibration context.

04
EXPLAIN

Publish the reasoning

Surface important changes, contributing signals and update time.

EX PRINCIPLE / FROM EVENT STREAM TO EXPLAINABLE PROBABILITY

Designed to make a model inspectable.

The product direction prioritizes clear assumptions, visible movement and decision support—not guaranteed outcomes.

CONTINUE EXPLORINGEX Sport