# Polymarket World Cup 2026 Prediction Markets Scraper (`jungle_synthesizer/polymarket-world-cup-2026-prediction-markets-scraper`) Actor Scrapes FIFA World Cup 2026 prediction markets from Polymarket — live probabilities, volume, liquidity, and outcome prices for tournament markets including outright winner, group results, top scorer, and player props. - **URL**: https://apify.com/jungle\_synthesizer/polymarket-world-cup-2026-prediction-markets-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Sports, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Polymarket World Cup 2026 Prediction Markets Scraper Scrapes FIFA World Cup 2026 prediction markets from [Polymarket](https://polymarket.com) — the highest-volume sports prediction market. Returns live probabilities, trading volumes, liquidity, and outcome prices for every WC 2026 market: outright tournament winner, group-stage qualifiers, top scorer, Golden Ball, individual player props, and more. ### What it does The actor pulls markets from two sources and merges them into a single deduplicated dataset: 1. **Canonical event** — the "World Cup Winner" event group on Polymarket (event ID 30615), which contains ~60 embedded markets covering every competing nation's outright-winner probability. 2. **Keyword search** — a broad search for `2026 FIFA World Cup` on Polymarket's market API, catching additional markets (player props, group qualifiers, award markets) that aren't grouped under the main event. Each record gives you the **implied probability** (`last_trade_price`), total USD volume traded, current liquidity, and all outcome labels in one flat row — ready for arbitrage detection, calibration analysis, or tournament-bracket modelling. ### Use cases - **Arbitrage & sharp money tracking** — monitor price movements across WC 2026 markets in real time - **Prediction calibration** — compare market-implied probabilities against FIFA rankings and match data - **Tournament tracker bots** — Discord/Telegram bots that surface mispriced markets as the tournament progresses - **Research & journalism** — crowd-sourced win probabilities for every team at any point in the tournament ### Input | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `maxItems` | integer | 15 | Maximum records to return. Set to 0 for no limit (returns all available WC 2026 markets, typically 70–110). | ### Output Each dataset record is a flat JSON object: ```json { "market_id": "558934", "slug": "will-spain-win-the-2026-fifa-world-cup-963", "question": "Will Spain win the 2026 FIFA World Cup?", "description": "This market will resolve according to the national team that wins the 2026 FIFA World Cup...", "event_slug": "world-cup-winner", "event_title": "World Cup Winner", "category": "Soccer", "end_date_utc": "2026-07-20", "resolved": false, "outcomes": "Yes | No", "outcome_prices": "0.1385 | 0.8615", "last_trade_price": 0.138, "volume_usd": 61005650.27, "liquidity_usd": 6483128.85, "open_interest_usd": 0, "created_at": "2025-07-02T16:54:40.860413Z", "tags": "", "source": "polymarket.com" } ```` #### Output fields | Field | Type | Description | |-------|------|-------------| | `market_id` | string | Unique Polymarket market ID | | `slug` | string | URL-friendly market slug | | `question` | string | Market question text | | `description` | string | Full resolution criteria | | `event_slug` | string | Parent event slug (e.g. `world-cup-winner`) | | `event_title` | string | Parent event display title | | `category` | string | Market category (Soccer) | | `end_date_utc` | string | Market resolution date (ISO 8601) | | `resolved` | boolean | Whether the market is closed/resolved | | `outcomes` | string | Pipe-delimited outcome labels (e.g. `Yes | No`) | | `outcome_prices` | string | Pipe-delimited last-trade probabilities (0–1) for each outcome | | `last_trade_price` | number | Most recent trade price = implied probability for the primary outcome | | `volume_usd` | number | Total USD traded since market inception | | `liquidity_usd` | number | Current USD liquidity in the order book | | `open_interest_usd` | number | Open interest in USD | | `created_at` | string | Market creation timestamp | | `tags` | string | Pipe-delimited topic tags | | `source` | string | Data source (`polymarket.com`) | ### Notes - **Open public API** — no authentication, no proxy required. - **Market count** — the WC 2026 slate grows as the tournament progresses; expect 70–110 markets at full depth. - **Prices are probabilities** — `outcome_prices` and `last_trade_price` are expressed as decimals (0.14 = 14% implied probability). Multiply by 100 for percentages. - **Multi-outcome markets** — some markets (e.g. top scorer, Golden Ball nominees) have more than two outcomes; `outcomes` and `outcome_prices` are pipe-delimited with a value per outcome. - **Deduplication** — the two API sources are automatically deduplicated; no market appears twice. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of prediction market records to return. Set to 0 for no limit. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/polymarket-world-cup-2026-prediction-markets-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/polymarket-world-cup-2026-prediction-markets-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 }' | apify call jungle_synthesizer/polymarket-world-cup-2026-prediction-markets-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/polymarket-world-cup-2026-prediction-markets-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Polymarket World Cup 2026 Prediction Markets Scraper", "description": "Scrapes FIFA World Cup 2026 prediction markets from Polymarket — live probabilities, volume, liquidity, and outcome prices for tournament markets including outright winner, group results, top scorer, and player props.", "version": "0.1", "x-build-id": "wio2zRbZrVhMwm8GB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~polymarket-world-cup-2026-prediction-markets-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-polymarket-world-cup-2026-prediction-markets-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~polymarket-world-cup-2026-prediction-markets-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-polymarket-world-cup-2026-prediction-markets-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~polymarket-world-cup-2026-prediction-markets-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-polymarket-world-cup-2026-prediction-markets-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of prediction market records to return. Set to 0 for no limit.", "default": 15 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```