Poker machine expenditure by venue, Victoria, 2008-09 to 2021-22
liveHistorical gaming expenditure by venue from the Victorian Gambling and Casino Control Commission. Each row is one gaming venue and one financial year, with the venue name, metro or country region, local government area, venue type and electronic gaming machine expenditure in dollars. The latest years are in the companion dataset of current expenditure by venue.
Also called: Historical gaming expenditure by venue, Victorian pokies losses by venue history, EGM expenditure by venue 2008 to 2022.
Part of Electronic gaming machine expenditure by venue, Victoria, 2 tables the publisher releases together: Poker machine expenditure by venue, Victoria, last three years.
- What does each row hold?
- One hotel or club venue with gaming machines and one financial year from 2008-09 to 2021-22, with the venue's region, council area and type and the amount players lost on its machines in that year, in dollars. Each sheet of the publisher's workbook is one year, and its name is kept in the financial year column.
- What is in the publisher's file that is not here?
- The workbook also gives each venue's number of machines, under a column whose name changes every year, so it is left out. The row with no venue name is the publisher's total for the state. Each sheet ends with notes on venues that changed name or closed, which are not rows of data. Venues closed from March 2020 under the COVID-19 restrictions, so 2019-20 to 2021-22 are lower.
Made for agents
Connect your AI agent
Any agent that connects to publicdata.au/mcp can find this dataset, read its 6 fields, count and filter across all 7,102 rows and diff its versions. Every answer names the version and carries VGCCC's attribution. No key.
Then ask it: What does Poker machine expenditure by venue, Victoria, 2008-09 to 2021-22 hold, and what changed in the newest version?
claude mcp add --transport http publicdata https://publicdata.au/mcpOne command. No key, no account, no sign-up.
Settings, then Connectors, then Add custom connector, and paste https://publicdata.au/mcp.
On the web and in the desktop app.
https://publicdata.au/mcpStreamable HTTP. Cursor, VS Code or any client that connects to a remote MCP server.
Using https://publicdata.au/llms.txt, how many people died on Queensland roads in 2025, by month?For an assistant that reads the web and has no connector.
A dashboard over every row. Click to filter, add panels, share a link or embed a frame.
Query APIFilter and count from a URLaggregate?group=lga_name&metric=sum.expenditure&financial_year=eq.Detail%20Data%202021%20-%202022®ion=neq.Total
Poker machine losses ($) by council area where financial year (sheet) is Detail Data 2021 - 2022 and region is not Total: 128,396,442 City of Brimbank, 114,664,060 City of Casey, 104,550,447 City of Hume, 104,148,198 City of Greater Geelong.
Download it as Excel, CSV, JSON and 6 more formats
Pick a format and a version. Excel is picked first because it opens in the tools most offices have. The URL is yours to keep. A dated version never changes.
In your own tools
Use it in Excel, R, Python and more
Excel and Power BI read the CSV from its address and refresh from it. The R and Python packages take any dataset on this site by its slug, so a new dataset needs no new release. The DuckDB file attaches read-only over HTTPS, and a query reads only the blocks it touches.
The dated URL in the code never changes. https://publicdata.au/d/vic-gaming-expenditure-by-venue-history/latest/ redirects to the newest version.
https://publicdata.au/d/vic-gaming-expenditure-by-venue-history/latest/data.csvIn Excel choose Data, then From Web, and paste this address. Excel keeps it, so Refresh All reads the newest version. A sheet holds about a million rows; past that, load the query to the Data Model.
https://publicdata.au/d/vic-gaming-expenditure-by-venue-history/latest/data.csvIn Power BI Desktop choose Get data, then Web, paste this address and choose Anonymous when asked how to sign in. A scheduled refresh reads the same address, so the report follows each new version.
library(publicdataau)
df <- pd_read("vic-gaming-expenditure-by-venue-history")
pd_attribution(df)install.packages("publicdataau"). pd_read() fetches the version's Parquet file; pd_rows() and pd_aggregate() ask the query API instead, and pd_connect() attaches the DuckDB file.
import publicdata_au as pd_au
df = pd_au.read("vic-gaming-expenditure-by-venue-history")
df.attrs["publicdata"]["attribution"]pip install "publicdata-au[pandas]". read() fetches the version's Parquet file; rows() and aggregate() ask the query API, and connect() attaches the DuckDB file.
INSTALL httpfs; LOAD httpfs;
ATTACH 'https://publicdata.au/d/vic-gaming-expenditure-by-venue-history/v/2025-08-26/data.duckdb' AS vic_gaming_expenditure_by_venue_history (READ_ONLY);
SELECT financial_year, count(*) FROM vic_gaming_expenditure_by_venue_history.records GROUP BY 1 ORDER BY 2 DESC;The DuckDB file attaches read-only over HTTPS and only the blocks a query touches are read. Parquet works the same way: FROM read_parquet(url).
const res = await fetch("https://publicdata.au/api/v1/datasets/vic-gaming-expenditure-by-venue-history/aggregate?group=lga_name&metric=sum.expenditure&financial_year=eq.Detail%20Data%202021%20-%202022®ion=neq.Total");
const { rows, publicdata } = await res.json();
console.log(rows, publicdata.attribution);The query API answers a page on any site as well as Node, with no key. It returns the rows and the attribution the licence asks for.
What is in it
Column names are made snake_case and the publisher's original header is kept beside each one. Blank cells are null. Nothing is added, removed, ranked or summarised.
| Field | Type | Publisher's header | Note |
|---|---|---|---|
| financial_year | string | (sheet) | The publisher's sheet name, for example Detail Data 2021 - 2022. Two sheet names end in a space. |
| venue | string | (row header 1) | The venue's name in capitals. Blank on the publisher's state total row. |
| region | string | (row header 2) | Metro or Country. |
| lga_name | string | (row header 3) | The council area as the publisher writes it. A few names carry an asterisk the publisher's notes explain. |
| venue_type | string | (row header 4) | Hotel or Club. |
| expenditure | number | (cell) | The net amount lost by players on the venue's gaming machines in the financial year, in dollars. |
Download this table as a data dictionary (Excel), or read it as a Frictionless Table Schema at schema.json.
A sample of 10 rows
The ten venues where players lost the most in the newest financial year, largest first. A blank cell is shown as null.
| financial_year | venue | region | lga_name | venue_type | expenditure |
|---|---|---|---|---|---|
| Detail Data 2021 - 2022 | KEALBA HOTEL | Metro | City of Brimbank | Hotel | 20448031.31 |
| Detail Data 2021 - 2022 | SKYWAYS TAVERNER | Metro | City of Moonee Valley | Hotel | 18535598.56 |
| Detail Data 2021 - 2022 | HIGHWAYS SANDOWN | Metro | City of Greater Dandenong | Club | 17662273.74 |
| Detail Data 2021 - 2022 | EPPING PLAZA HOTEL | Metro | City of Whittlesea | Hotel | 17154480.46 |
| Detail Data 2021 - 2022 | DEER PARK HOTEL | Metro | City of Brimbank | Hotel | 16897892.63 |
| Detail Data 2021 - 2022 | ROXBURGH PARK HOTEL | Metro | City of Hume | Hotel | 16828815.71 |
| Detail Data 2021 - 2022 | GLADSTONE PARK HOTEL | Metro | City of Hume | Hotel | 16777670.01 |
| Detail Data 2021 - 2022 | EXCELSIOR HOTEL | Metro | City of Whittlesea | Hotel | 16331845.13 |
| Detail Data 2021 - 2022 | BUNDOORA TAVERNER | Metro | City of Whittlesea | Hotel | 16047871.75 |
| Detail Data 2021 - 2022 | PLOUGH HOTEL | Metro | City of Whittlesea | Hotel | 15767325.25 |
The first row as JSON
As it appears in data.json.
GET https://publicdata.au/d/vic-gaming-expenditure-by-venue-history/latest/data.json
{
"financial_year": "Detail Data 2008 - 2009",
"venue": "GEELONG FOOTBALL CLUB",
"region": "Country",
"lga_name": "City of Greater Geelong",
"venue_type": "Club",
"expenditure": 4416532.9
}
Query it without downloading
The query API returns only the rows you ask for, as JSON, NDJSON or CSV, from the same typed rows as the files. It needs no key. Build a query here and run it, then copy the URL or the code into your own work.
How filters work
Each filter is field=operator.value in the query string, and every filter must match. A number or date field compares as a number or date, and a text field compares as text. Values are case-sensitive except with ilike. Prefix not. to negate a filter, as not.eq.value.
| Operator | What it matches |
|---|---|
| eq.value | Equal to the value. |
| neq.value | Not equal to the value. A blank cell does not match. |
| gt.value | Greater than the value. |
| gte.value | Greater than or equal to the value. |
| lt.value | Less than the value. |
| lte.value | Less than or equal to the value. |
| like.*text* | Matches a pattern where * stands for any run of characters. Case-sensitive. |
| ilike.*text* | The same as like, ignoring case. |
| in.(a,b,c) | Equal to any value in the list. A value cannot contain a comma. |
| is.null | Blank in the source, or suppressed by the publisher. |
| Parameter | What it does |
|---|---|
| select | Fields to return, comma-separated. Every field when absent. |
| order | field.asc or field.desc, comma-separated. The publisher's row order when absent. |
| limit | Rows per page, 1 to 10,000. 100 when absent. |
| offset | Rows to skip. The next URL in each answer sets it for you. |
| group | On aggregate, fields to group by, comma-separated. |
| metric | On aggregate, count, sum.field, avg.field, min.field or max.field, comma-separated. count when absent. |
| format | json, ndjson or csv. JSON carries the provenance header, and the others carry it in response headers. |
Without a version the API answers from the newest loaded version, and that answer changes when the publisher releases again. Put versions/<date>/ before rows or aggregate for an answer that never changes. /api/v1/datasets/<slug>/versions lists the loaded versions. The 1 newest version is loaded; versions lists them.
The API allows 60 requests in 10 seconds from one address. Above that it answers 429 for 10 seconds with a Retry-After header, a RateLimit-Policy header and a JSON body that gives the limit. Every API answer carries the same RateLimit-Policy. A client should wait the Retry-After seconds, or read the files, which have no limit. openapi.json describes this dataset's query paths for client generators and agents.
https://publicdata.au/mcp is a remote MCP server over Streamable HTTP with the same tools, defined in the same place, so a tool added to the pages is added here too. It needs no key and no account. The row tools are held to the query API's limit of 60 queries in 10 seconds from one address, and each call is two queries because it also counts the matching rows. Add it to Claude Code with claude mcp add --transport http publicdata https://publicdata.au/mcp, add the URL in Claude as a custom connector, or give it to any client that connects to remote MCP servers. Each queryable dataset is also a resource at https://publicdata.au/d/<slug>/fields.json, which lists its fields with their types, the publisher's descriptions, their ranges and the values they hold, so an agent can read a dataset's shape before it writes a query.
What changed
One version for every release the publisher has made since this site started following the dataset. The date is the day the file changed on the portal.
- 2025-08-26 7,102 rows6 fieldsxlsx50317563d34e
versions.json · changes.json · history.tar.zst (83 KB, every version's Parquet and manifest)
Questions
How do I download Poker machine expenditure by venue, Victoria as a CSV file?
Open https://publicdata.au/d/vic-gaming-expenditure-by-venue-history/latest/data.csv. It redirects to the newest dated version, which is https://publicdata.au/d/vic-gaming-expenditure-by-venue-history/v/2025-08-26/data.csv today. The same path serves Excel, JSON, Parquet, SQLite, DuckDB, NDJSON, Arrow. A dated URL never changes, so use it when the file must stay the same.
Can I open Poker machine expenditure by venue, Victoria in Excel?
Yes. https://publicdata.au/d/vic-gaming-expenditure-by-venue-history/v/2025-08-26/data.xlsx is a workbook with the 7,102 rows on a records sheet, the field list on a second sheet and the provenance on a third. The CSV also opens in Excel. https://publicdata.au/d/vic-gaming-expenditure-by-venue-history/v/2025-08-26/data.csv.gz is the CSV at about a tenth of the size.
What years does Poker machine expenditure by venue, Victoria cover?
The current version covers the years since 2008. Each release from VGCCC becomes a new dated version here, and earlier versions stay online.
How often is Poker machine expenditure by venue, Victoria updated?
VGCCC no longer updates it. This site checks the portal every week and adds a dated version when the file changes.
Can I use Poker machine expenditure by venue, Victoria commercially?
Yes. CC BY 4.0 allows commercial use, redistribution and derived works as long as the attribution is kept. The attribution string is in this page's side column and inside every file.
Is this the official source for Poker machine expenditure by venue, Victoria?
No. The publisher is Victorian Gambling and Casino Control Commission, and its page is https://discover.data.vic.gov.au/dataset/historical-gaming-expenditure-by-venue. This site republishes the publisher's file without changing its content. The original sits beside every version as source.xlsx with its SHA-256, so the two can be compared.
What this site did to the data. Cells were typed, headers were renamed and the encoding was made UTF-8. Rows were left alone. The publisher's file sits beside every version as source.xlsx so the change can be checked.
The publisher releases Electronic gaming machine expenditure by venue, Victoria as 2 files at once, so all 2 tables here share a version date.