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.

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Install skill "openfootball" with this command: npx skills add outsharp/shipp-skills/outsharp-shipp-skills-openfootball

openfootball / football.json API

openfootball is a free, open, public domain collection of football (soccer) data. The 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
ComponentDescriptionExamples
{season}Season directory — cross-year or calendar year2024-25, 2023-24, 2025, 2019
{code}League code in {country}.{division} formaten.1, de.1, es.1, it.1, fr.1

Available Leagues

England

CodeLeagueTier
en.1English Premier League1st division
en.2English Championship2nd division
en.3English League One3rd division
en.4English League Two4th division

Germany

CodeLeagueTier
de.1Deutsche Bundesliga1st division
de.22. Bundesliga2nd division
de.33. Liga3rd division

Spain

CodeLeagueTier
es.1Primera División (La Liga)1st division
es.2Segunda División2nd division

Italy

CodeLeagueTier
it.1Serie A1st division
it.2Serie B2nd division

France

CodeLeagueTier
fr.1Ligue 11st division
fr.2Ligue 22nd division

Note: Not all leagues are available for all seasons. The football.json repo is continuously updated — check the repository 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).

FormatUsageExamples
YYYY-YYEuropean club seasons (Aug–May)2024-25, 2023-24, 2015-16
YYYYCalendar-year competitions2025, 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:

{
  "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

FieldTypeDescription
namestringHuman-readable league name and season
matchesarrayArray of match objects

Match Object Fields

FieldTypeRequiredDescription
roundstringYesRound/matchday name (e.g., "Matchday 1", "Round of 16")
datestringYesMatch date in YYYY-MM-DD format
timestringNoKick-off time in HH:MM format (24-hour, local time)
team1stringYesHome team name
team2stringYesAway team name
scoreobjectNoScore object (absent for unplayed future matches)
statusstringNoSpecial status (e.g., "awarded" for administratively decided results)

Score Object Fields

FieldTypeDescription
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)

curl -s "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json" | jq .

Fetch and Parse Match Data (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)

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)

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)

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)
    if resp.status_code == 200:
        data = resp.json()
        total = len(data["matches"])
        played = sum(1 for m in data["matches"] if m.get("score", {}).get("ft"))
        print(f"{name}: {played}/{total} matches played")
    else:
        print(f"{name}: not available for {season}")

Fetch and Parse (Node.js)

const url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json";

const res = await fetch(url);
const data = await res.json();

console.log(`League: ${data.name}`);
console.log(`Matches: ${data.matches.length}`);

data.matches.slice(0, 10).forEach((m) => {
  const ft = m.score?.ft;
  if (ft) {
    console.log(`  ${m.date}  ${m.team1} ${ft[0]}-${ft[1]} ${m.team2}`);
  }
});

Fetch and Parse (bash + jq)

# Get all results for a specific team
curl -s "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json" \
  | jq -r '.matches[]
    | select(.team1 == "Liverpool FC" or .team2 == "Liverpool FC")
    | select(.score.ft)
    | "\(.date) \(.team1) \(.score.ft[0])-\(.score.ft[1]) \(.team2)"'

Compare Head-to-Head Results (Python)

import requests

url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json"
data = requests.get(url).json()

team_a = "Arsenal FC"
team_b = "Liverpool FC"

h2h = [m for m in data["matches"]
       if {m["team1"], m["team2"]} == {team_a, team_b}
       and m.get("score", {}).get("ft")]

for m in h2h:
    ft = m["score"]["ft"]
    ht = m["score"].get("ht", ["?", "?"])
    print(f"{m['date']}: {m['team1']} {ft[0]}-{ft[1]} {m['team2']} (HT: {ht[0]}-{ht[1]})")

Aggregate Stats Across Seasons (Python)

import requests

base = "https://raw.githubusercontent.com/openfootball/football.json/master"
seasons = ["2022-23", "2023-24", "2024-25"]
team = "Manchester City FC"
all_results = {"W": 0, "D": 0, "L": 0, "GF": 0, "GA": 0}

for season in seasons:
    resp = requests.get(f"{base}/{season}/en.1.json")
    if resp.status_code != 200:
        continue
    data = resp.json()
    for m in data["matches"]:
        ft = m.get("score", {}).get("ft")
        if not ft:
            continue
        if m["team1"] == team:
            gf, ga = ft
        elif m["team2"] == team:
            ga, gf = ft
        else:
            continue
        all_results["GF"] += gf
        all_results["GA"] += ga
        if gf > ga:
            all_results["W"] += 1
        elif gf == ga:
            all_results["D"] += 1
        else:
            all_results["L"] += 1

print(f"{team} across {', '.join(seasons)}:")
print(f"  W{all_results['W']} D{all_results['D']} L{all_results['L']}")
print(f"  Goals: {all_results['GF']} scored, {all_results['GA']} conceded")

Find High-Scoring Matches (bash + jq)

# Find all matches with 5+ total goals in the Premier League 2024/25
curl -s "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json" \
  | jq -r '.matches[]
    | select(.score.ft)
    | select((.score.ft[0] + .score.ft[1]) >= 5)
    | "\(.date) \(.team1) \(.score.ft[0])-\(.score.ft[1]) \(.team2) (Total: \(.score.ft[0] + .score.ft[1]))"'

Get Results for a Specific Matchday (Python)

import requests

url = "https://raw.githubusercontent.com/openfootball/football.json/master/2024-25/en.1.json"
data = requests.get(url).json()

matchday = "Matchday 38"
matches = [m for m in data["matches"] if m["round"] == matchday]

print(f"--- {matchday} ---")
for m in matches:
    ft = m.get("score", {}).get("ft")
    if ft:
        print(f"  {m['date']} {m.get('time', '')}  {m['team1']} {ft[0]}-{ft[1]} {m['team2']}")
    else:
        print(f"  {m['date']} {m.get('time', '')}  {m['team1']} vs {m['team2']}")

Other Repositories (Source Data)

The football.json files are auto-generated from plain-text Football.TXT source files in country-specific repos:

RepoContentGitHub URL
englandEPL, Championship, League One, League Twoopenfootball/england
deutschlandBundesliga, 2. Bundesliga, 3. Liga, DFB Pokalopenfootball/deutschland
espanaLa Liga, Segunda Divisiónopenfootball/espana
italySerie A, Serie B, Coppa Italiaopenfootball/italy
franceLigue 1, Ligue 2openfootball/europe (in /europe)
worldcupFIFA World Cup (2022, 2018, 2014, etc.)openfootball/worldcup
euroEuro 2024, 2020, 2016, etc.openfootball/euro
champions-leagueUCL & Europa Leagueopenfootball/champions-league
clubsClub & stadium metadataopenfootball/clubs
worldLeagues from N. America, Asia, Africa, Australiaopenfootball/world

Country repos also have their own GitHub Pages JSON mirrors. For example:

https://openfootball.github.io/england/2024-25/1-premierleague.json

You can convert Football.TXT source files to JSON yourself using the fbtxt2json CLI tool:

# Convert a single league file
fbtxt2json england/2025-26/1-premierleague.txt -o en.1.json

# Convert an entire country repo at once
fbtxt2json . -o ./_site

Rate Limits

GitHub does not publish specific rate limits for raw content, but general guidelines:

GuidelineRecommendation
Polling interval≥ 60 seconds between requests for the same file
Concurrent requestsKeep reasonable (< 20 concurrent)
CachingCache responses locally — data changes infrequently
Unauthenticated GitHub API60 requests/hour per IP (only applies to API endpoints, not raw content)

Tip: Since match data doesn't change after a game is completed, you can aggressively cache historical seasons. Only poll the current season for updates.


Error Handling

HTTP StatusMeaningAction
200SuccessParse the JSON
404File not foundCheck the season, league code, or URL spelling
429Rate limitedBack off and retry after a delay
5xxServer errorRetry with exponential backoff

Tips

  • No auth needed — all data is fully public. Start fetching immediately.
  • Check for score before accessing — future/unplayed matches won't have a score field.
  • Check for ht separately — some matches have ft but no ht data.
  • Team names include suffixes — e.g., "Arsenal FC", "Manchester United FC", "Borussia Dortmund". Use exact string matching.
  • Team names vary by league — German teams use German names ("FC Bayern München"), English teams use English names ("Arsenal FC").
  • The status field is rare — it appears on administratively decided matches (e.g., "awarded").
  • Cache aggressively — completed seasons never change. Only the current season gets updates.
  • Build tables from the data — the dataset provides raw match results; you compute standings, form, H2H, etc.
  • Cross-reference leagues — fetch multiple league files to compare across countries.
  • Public domain (CC0) — use the data however you want with no restrictions whatsoever.
  • Don't edit the JSON directly — if contributing, edit the Football.TXT source files in the country repos; JSON is auto-generated.
  • Time field is local — the time value represents the local kick-off time for the match venue.
  • Scores are [home, away]score.ft[0] is always the team1 (home) goals, score.ft[1] is always the team2 (away) goals.

Changelog

  • 0.1.0 — Initial release with league data access, JSON schema documentation, and common usage patterns.

Source Transparency

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