# Hockey-Reference NHL Stats Scraper (`muhammadafzal/hockey-reference-nhl-stats-scraper`) Actor

Scrape public Hockey-Reference NHL season statistics for skaters, goalies, or team standings. Returns normalized, analysis-ready rows with player identity, scoring, goaltending, standings, and source metadata.

- **URL**: https://apify.com/muhammadafzal/hockey-reference-nhl-stats-scraper.md
- **Developed by:** [Muhammad Afzal](https://apify.com/muhammadafzal) (community)
- **Categories:** Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 nhl stat records

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Hockey-Reference NHL Stats Scraper

Scrape public Hockey-Reference season tables for NHL skaters, goalies, or team standings. The Actor returns one normalized dataset item per source row and writes run diagnostics to the `OUTPUT` key-value record.

### Input

```json
{ "year": 2025, "statType": "skaters", "maxResults": 100 }
```

`year` is the season ending year, so `2025` is the 2024-25 NHL season. `statType` is `skaters`, `goalies`, or `standings`. Use `maxResults: 1` for a cheap canary.

### Output

Rows include `statType`, `season`, source identity, common scoring fields, goalie fields, standings record, `sourceUrl`, and `scrapedAt`. Fields not applicable to the selected table are `null`. `OUTPUT` contains `status`, `records`, `sourceUrl`, `warnings`, and `blocked`.

The primary PPE event is Apify's synthetic `apify-default-dataset-item`, one event per validated dataset row. Synthetic start pricing is reported separately. The Actor never fabricates rows when Hockey-Reference returns an error or access challenge.

### Compliance

This Actor reads public Hockey-Reference pages using bounded direct HTTP requests. Respect Hockey-Reference terms, robots guidance, rate limits, and applicable law. It does not bypass authentication, CAPTCHAs, paywalls, or access controls.

### What data does Hockey-Reference NHL Stats Scraper return?

Results are written to the default Apify dataset or the output links declared by the Actor. Inspect the dataset schema and a small test run before building a production mapping.

### Use cases

- Schedule repeatable collection and export results to downstream workflows.
- Run a one-off research job and export the structured result as JSON, CSV, Excel, XML, or RSS from Apify.
- Schedule the same input to monitor changes over time and send completed datasets to a webhook or integration.
- Feed schema-shaped records into a database, spreadsheet, BI tool, or AI workflow with the source URL retained for verification.

### Run Hockey-Reference NHL Stats Scraper with the Apify API

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('muhammadafzal/hockey-reference-nhl-stats-scraper').call({
  "year": 2025,
  "statType": "skaters",
  "maxResults": 100
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```

You can also run the Actor from Apify Console, schedules, webhooks, the REST API, Make, Zapier, n8n, or the hosted Apify MCP server.

### Pricing and cost control

This Actor uses **pay-per-event** pricing. Charges follow the live event definitions shown below.

| Event | Price (USD) | When it is charged |
|---|---:|---|
| `apify-default-dataset-item` | $0.001 | One unique, schema-valid Hockey-Reference NHL skater, goalie, or standings row stored in the default dataset. |
| `apify-actor-start` | $0.00005 | Charged when the Actor starts running. Number of events charged depends on Actor memory (one event per GB, minimum one event). |

For example, 100 `apify-default-dataset-item` events cost **$0.1**, plus any enabled Actor-start event. Empty or failed work should be checked in the run log and dataset before reuse.

### Support

When reporting a problem, include the **Actor run ID**, a redacted input, the expected result, and a small public example URL when applicable. Do not post API tokens, cookies, credentials, or personal data in an issue.

### Frequently asked questions

#### Can I schedule Hockey-Reference NHL Stats Scraper?

Yes. Use an Apify schedule to run the same saved input at a chosen interval, then connect a webhook or integration to process the dataset when the run finishes.

#### How should I test a new input?

Begin with the prefilled example or a small limit. Confirm that the output fields, source coverage, runtime, and live charges match your workflow before increasing the scope.

#### How do I export the results?

Open the run's default dataset in Apify Console and export JSON, CSV, Excel, XML, or RSS. Applications can retrieve the same records through the Apify API client or REST dataset endpoint.

#### Can an AI agent call this Actor?

Yes. Add `muhammadafzal/hockey-reference-nhl-stats-scraper` through the hosted Apify MCP server or call it through the API. The Actor's input and dataset schemas help agents construct valid requests and interpret returned records.

### Recommended workflow

1. **Define the smallest useful scope.** Choose a representative public URL, query, identifier, or filter and keep the first result limit low.
2. **Run and inspect.** Check the run log, dataset item count, field coverage, source URLs, and live event or usage charges.
3. **Validate downstream assumptions.** Confirm nullable fields, deduplication keys, timestamps, and any locale-specific formats before importing records into another system.
4. **Scale gradually.** Increase limits or scheduling frequency only after the small run behaves as expected. Use Apify's maximum-cost and timeout controls to bound large jobs.
5. **Monitor changes.** Keep a small known-good input as a canary. If the source layout or API changes, compare the new dataset with a previously validated run and report the run ID when requesting support.

For recurring workflows, store the exact Actor input with your pipeline configuration. This makes runs reproducible and helps distinguish a source-data change from an input change.

# Actor input Schema

## `year` (type: `integer`):

NHL season ending year. For example, 2025 represents the 2024-25 season.

## `statType` (type: `string`):

Choose skater standard statistics, goalie statistics, or team standings.

## `maxResults` (type: `integer`):

Maximum rows to return in source order. Use 1 for a cheap canary.

## Actor input object example

```json
{
  "year": 2025,
  "statType": "skaters",
  "maxResults": 100
}
```

# Actor output Schema

## `results` (type: `string`):

Dataset URL containing normalized skater, goalie, or standings rows.

## `summary` (type: `string`):

OUTPUT record containing status, row count, source URL, warnings, and blocker state.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "year": 2025,
    "statType": "skaters",
    "maxResults": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("muhammadafzal/hockey-reference-nhl-stats-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "year": 2025,
    "statType": "skaters",
    "maxResults": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("muhammadafzal/hockey-reference-nhl-stats-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "year": 2025,
  "statType": "skaters",
  "maxResults": 100
}' |
apify call muhammadafzal/hockey-reference-nhl-stats-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,muhammadafzal/hockey-reference-nhl-stats-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/OqYd6MUwqES3fRN1n/builds/Z3H1CDLIBDrTj1uVL/openapi.json