Cleanse

The Cleanse tab lets you define find-and-replace rules that normalise event data before it feeds into measures, metrics, and visualizations. Inconsistent naming — different spellings of a team, variations of a player's name, or legacy event labels — can fragment your analysis. Cleanse rules eliminate these inconsistencies so every downstream calculation works from a single, standardised source of truth.

Overview

The Data Cleanse page operates in two modes: a list view showing all defined rules, and an editor view for creating or modifying individual rules.

List View

When rules exist, the list view displays a table with the following columns:

Rules are applied in ascending priority order (lower numbers first), then by last update time where priorities tie. This means you can chain rules — for example, a priority-100 rule that normalises "FC Bayern" to "Bayern Munich", followed by a priority-200 rule that standardises "Bayern Munich" to "Bayern".

Empty State

When no cleanse rules have been defined, the page displays a prompt to create your first rule. Click Create Your First Rule to open the editor.

Creating a Rule

Click New Rule (or Create Your First Rule when the list is empty) to open the rule editor.

Rule Configuration

The editor provides the following fields:

Field

The type of data the rule operates on. Choose from:

Match Type

Determines how the pattern is compared against the target value:

For Event Name rules, the match type is always Exact because event names are matched as discrete identifiers rather than free text.

Find Pattern

The text to locate. For Exact matches, this is the full value to replace. For Contains matches, this is a substring that may appear anywhere in the target value.

When the Field is set to Event Name, this field is labelled Event Name to Match — select or type the exact event name you want to target.

Replacement

The text to substitute in place of the matched pattern. Leave this field blank to remove the matched text entirely — useful for stripping prefixes, suffixes, or unwanted qualifiers.

Team Scope (Player Name only)

When the Field is Player Name, an optional Team dropdown appears. Use this to limit the rule to players on a specific team. Leave it blank to apply the rule across all teams. This prevents accidentally renaming a "J. Smith" on Team A when you only meant to target the one on Team B.

Label & Option Scope (Event Name only)

When the Field is Event Name, optional Label Name and Option Name filters appear. These let you target a specific variant of an event. For example:

• Match event Shot with Label Outcome and Option Goal to rename only goal-scoring shots.
• Match event Pass with Label Type and Option Cross to isolate cross-field passes.

Leave these blank to match the event regardless of its labels or options.

Priority

A numeric value that determines execution order. Lower numbers run first. The default is 100. Use priority to sequence dependent rules — for example, run a broad normalisation at priority 100, then a more specific refinement at priority 200.

Rule Enabled

Toggle to activate or deactivate the rule. Disabled rules are preserved but skipped during rebuilds. Use this to temporarily suspend a rule without deleting it.

Tips Panel

The right-hand panel displays contextual guidance that changes based on the selected field type. It always includes the critical reminder:

After saving or editing a rule, use Repair/Rebuild in the page header to apply changes to analytics data.

Additional tips appear for specific field types:

• Team Name: "Rename a team across all events. Useful for normalizing variations like 'FC Bayern' and 'Bayern Munich'."
• Player Name: "Add a Team filter to limit this rename to players on a specific team."
• Event Name: "Use Label and Option filters to precisely target event types."

Saving and Applying Rules

Click Save Rule (or Update Rule when editing) to persist the rule to your cleanse configuration. The rule appears immediately in the list view, but it is not yet applied to your analytics data.

To activate the rule:

1. Return to the list view.
2. Click Rebuild in the Analytics 360 toolbar (the orange button visible on every tab).
3. The rebuild re-processes all loaded games, applying every enabled cleanse rule in priority order.

Managing Rules

Editing a Rule

From the list view, click any row or use the kebab menu (⋮) and select Edit. The editor opens pre-populated with the rule's current configuration. Make your changes and click Update Rule, then rebuild to apply.

Deleting a Rule

Use the kebab menu (⋮) on any row and select Delete. Confirm in the dialog that appears. To delete multiple rules at once, check the desired rows and click Delete Selected (n).

Searching Rules

The search bar at the top of the list filters rules by pattern text, replacement text, and field type. Use it to quickly locate a specific rule in large configurations.

Example: Normalising a Team Name

A common use case is standardising team names that appear inconsistently across games. For example, a team might appear as "Gaels Seniors" in early-season matches and simply "Gaels" in later ones.

To normalise this:

1. Click New Rule.
2. Set Field to Team Name.
3. Set Match Type to Exact.
4. Enter Gaels Seniors in Find Pattern.
5. Enter Gaels in Replacement.
6. Leave Priority at 100.
7. Ensure Rule enabled is toggled on.
8. Click Save Rule.
9. Click Rebuild in the toolbar to apply the change.

After the rebuild, all events tagged with "Gaels Seniors" will report under "Gaels" in the Explorer, measures, metrics, and every visualization.

Rule Execution Order

When multiple rules could match the same value, Analytics 360 resolves conflicts using a deterministic order:

1. Priority — Lower numbers execute first.
2. Update time — When priorities are equal, the most recently updated rule runs first.

This means a value is transformed sequentially through matching rules. Plan your priorities accordingly:

• Use low numbers (10–50) for broad, foundational normalisations.
• Use the default range (100) for typical renames.
• Use higher numbers (150–200) for refinements that depend on earlier rules.

Next Steps

With cleanse rules in place and a rebuild complete, your event data is ready for exploration:

1. Explorer — Filter and browse your normalised event data
2. Playlists — Build clip collections from tagged events
3. Measures — Define event-count measures from standardised data