openfootball
openfootball (football.json) is a free, open, public domain collection of football (soccer) match data in JSON format. It covers major leagues worldwide including the English Premier League, Bundesliga, La Liga, Serie A, Ligue 1, World Cup, Euro, and Champions League. Use this skill to fetch historical and current season fixtures, results, and scores. No API key or authentication is required.
What this skill does
# openfootball / football.json API
[openfootball](https://github.com/openfootball) is a free, open, public domain collection of football (soccer) data. The [football.json](https://github.com/openfootball/football.json) repository provides pre-built JSON files for major leagues and tournaments worldwide. **No API key or authentication is required.**
---
## Data Sources
There are two ways to access the data:
### 1. Raw GitHub URLs (Primary)
```
https://raw.githubusercontent.com/openfootball/football.json/master/{season}/{league}.json
```
### 2. GitHub Pages Mirror
```
https://openfootball.github.io/{country}/{season}/{league-name}.json
```
> **Recommendation:** Use the raw GitHub URLs for the `football.json` repo — they use a simple, consistent naming convention and are the most reliable.
---
## URL Structure
```
https://raw.githubusercontent.com/openfootball/football.json/master/{season}/{code}.json
```
| Component | Description | Examples |
|-----------|-------------|---------|
| `{season}` | Season directory — cross-year or calendar year | `2024-25`, `2023-24`, `2025`, `2019` |
| `{code}` | League code in `{country}.{division}` format | `en.1`, `de.1`, `es.1`, `it.1`, `fr.1` |
---
## Available Leagues
### England
| Code | League | Tier |
|------|--------|------|
| `en.1` | English Premier League | 1st division |
| `en.2` | English Championship | 2nd division |
| `en.3` | English League One | 3rd division |
| `en.4` | English League Two | 4th division |
### Germany
| Code | League | Tier |
|------|--------|------|
| `de.1` | Deutsche Bundesliga | 1st division |
| `de.2` | 2. Bundesliga | 2nd division |
| `de.3` | 3. Liga | 3rd division |
### Spain
| Code | League | Tier |
|------|--------|------|
| `es.1` | Primera División (La Liga) | 1st division |
| `es.2` | Segunda División | 2nd division |
### Italy
| Code | League | Tier |
|------|--------|------|
| `it.1` | Serie A | 1st division |
| `it.2` | Serie B | 2nd division |
### France
| Code | League | Tier |
|------|--------|------|
| `fr.1` | Ligue 1 | 1st division |
| `fr.2` | Ligue 2 | 2nd division |
> **Note:** Not all leagues are available for all seasons. The `football.json` repo is continuously updated — check the [repository](https://github.com/openfootball/football.json) for the full list of available files.
---
## Available Seasons
Season directories in the `football.json` repo go back to `2010-11`. European leagues use cross-year format (`2024-25`), while some calendar-year leagues use single-year format (`2025`).
| Format | Usage | Examples |
|--------|-------|---------|
| `YYYY-YY` | European club seasons (Aug–May) | `2024-25`, `2023-24`, `2015-16` |
| `YYYY` | Calendar-year competitions | `2025`, `2020`, `2019` |
Known season directories: `2010-11`, `2011-12`, `2012-13`, `2013-14`, `2014-15`, `2015-16`, `2016-17`, `2017-18`, `2018-19`, `2019-20`, `2020-21`, `2021-22`, `2022-23`, `2023-24`, `2024-25`, `2025-26`, `2019`, `2020`, `2025`.
---
## JSON Response Format
All files follow the same JSON schema:
```json
{
"name": "English Premier League 2024/25",
"matches": [
{
"round": "Matchday 1",
"date": "2024-08-16",
"time": "20:00",
"team1": "Manchester United FC",
"team2": "Fulham FC",
"score": {
"ht": [0, 0],
"ft": [1, 0]
}
}
]
}
```
### Top-Level Fields
| Field | Type | Description |
|-------|------|-------------|
| `name` | string | Human-readable league name and season |
| `matches` | array | Array of match objects |
### Match Object Fields
| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `round` | string | Yes | Round/matchday name (e.g., `"Matchday 1"`, `"Round of 16"`) |
| `date` | string | Yes | Match date in `YYYY-MM-DD` format |
| `time` | string | No | Kick-off time in `HH:MM` format (24-hour, local time) |
| `team1` | string | Yes | Home team name |
| `team2` | string | Yes | Away team name |
| `score` | object | No | Score object (absent for unplayed future matches) |
| `status` | string | No | Special status (e.g., `"awarded"` for administratively decided results) |
### Score Object Fields
| Field | Type | Description |
|-------|------|-------------|
| `ft` | `[int, int]` | Full-time score `[home, away]` |
| `ht` | `[int, int]` | Half-time score `[home, away]` (may be absent for some matches) |
> **Note:** Some matches only have `ft` (full-time) without `ht` (half-time). Always check for the presence of `ht` before accessing it.
---
## Common Patterns
### Fetch a League Season (curl)
```bash
curl -s "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json" | jq .
```
### Fetch and Parse Match Data (Python)
```python
import requests
url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json"
data = requests.get(url).json()
print(f"League: {data['name']}")
print(f"Total matches: {len(data['matches'])}")
for match in data["matches"][:10]:
ft = match.get("score", {}).get("ft")
if ft:
print(f" {match['date']} {match['team1']} {ft[0]}-{ft[1]} {match['team2']}")
else:
print(f" {match['date']} {match['team1']} vs {match['team2']} (no score)")
```
### Build a League Table from Results (Python)
```python
import requests
from collections import defaultdict
url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json"
data = requests.get(url).json()
table = defaultdict(lambda: {"played": 0, "won": 0, "drawn": 0, "lost": 0,
"gf": 0, "ga": 0, "points": 0})
for match in data["matches"]:
score = match.get("score", {}).get("ft")
if not score:
continue
t1, t2 = match["team1"], match["team2"]
g1, g2 = score
for team, gf, ga in [(t1, g1, g2), (t2, g2, g1)]:
table[team]["played"] += 1
table[team]["gf"] += gf
table[team]["ga"] += ga
if gf > ga:
table[team]["won"] += 1
table[team]["points"] += 3
elif gf == ga:
table[team]["drawn"] += 1
table[team]["points"] += 1
else:
table[team]["lost"] += 1
# Sort by points, then goal difference
sorted_table = sorted(table.items(),
key=lambda x: (x[1]["points"], x[1]["gf"] - x[1]["ga"]),
reverse=True)
print(f"{'Team':<35} {'P':>3} {'W':>3} {'D':>3} {'L':>3} {'GF':>4} {'GA':>4} {'GD':>4} {'Pts':>4}")
print("-" * 70)
for i, (team, stats) in enumerate(sorted_table, 1):
gd = stats["gf"] - stats["ga"]
print(f"{i:>2}. {team:<32} {stats['played']:>3} {stats['won']:>3} "
f"{stats['drawn']:>3} {stats['lost']:>3} {stats['gf']:>4} "
f"{stats['ga']:>4} {gd:>+4} {stats['points']:>4}")
```
### Filter Matches by Team (Python)
```python
import requests
url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json"
data = requests.get(url).json()
team = "Arsenal FC"
matches = [m for m in data["matches"]
if team in (m["team1"], m["team2"]) and m.get("score", {}).get("ft")]
for m in matches:
ft = m["score"]["ft"]
opponent = m["team2"] if m["team1"] == team else m["team1"]
venue = "H" if m["team1"] == team else "A"
my_goals = ft[0] if m["team1"] == team else ft[1]
opp_goals = ft[1] if m["team1"] == team else ft[0]
result = "W" if my_goals > opp_goals else ("D" if my_goals == opp_goals else "L")
print(f" {m['date']} ({venue}) {result} {my_goals}-{opp_goals} vs {opponent}")
```
### Fetch Multiple Leagues (Python)
```python
import requests
leagues = {
"Premier League": "en.1",
"Bundesliga": "de.1",
"La Liga": "es.1",
"Serie A": "it.1",
"Ligue 1": "fr.1",
}
season = "2024-25"
base = "https://raw.githubusercontent.com/openfootball/football.json/master"
for name, code in leagues.items():
url = f"{base}/{season}/{code}.json"
resp = requests.get(url)
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.