Scraping Transfermarkt Player Values and Transfer History with Python (2026)
Scraping Transfermarkt Player Values and Transfer History with Python (2026)
Transfermarkt is the definitive source for football transfer data. Every serious analysis of player valuations, transfer market trends, or club spending patterns starts here. The site tracks estimated market values for over 800,000 players across more than 1,000 competitions worldwide, along with complete transfer histories, contract details, injury records, and active transfer rumors.
There is no public API. Transfermarkt briefly offered a paid data product through a partnership with a sports data company, but for independent researchers and developers, scraping the HTML is the only path. The site is server-rendered (no JavaScript framework needed for most pages), which actually makes extraction easier than many modern platforms — but their anti-bot defenses have caught up in 2026.
What Data Is Available
Transfermarkt pages expose an enormous amount of structured football data:
- Player profiles — name, age, nationality, position, height, preferred foot, current club, contract expiration date, shirt number, agent
- Market valuations — current estimated value, value history over time (with dates and values at each update), peak value and date
- Transfer history — every transfer and loan, with source club, destination club, transfer fee (reported or estimated), date, and whether it was a free transfer or loan
- Contract details — contract start and end dates, contract extensions, release clause amounts (where known)
- Injury history — injury type, duration, games missed, dates
- Transfer rumors — rumored destination clubs, rumor sources, probability ratings, first reported date
- Competition data — league tables, top scorers, squad market values by club, all-time transfer records by league
- Club data — squad market value, average age, nationalities, historical data
The valuation history is particularly valuable — it gives you time-series data on how a player's perceived worth has changed across their career, correlated with performance, injuries, age, and contract status.
Why Football Data Is Commercially Useful
Football analytics has grown from a niche hobby to a major industry. Beyond hobbyist use, Transfermarkt data powers:
- Gambling and betting models — Player values and recent transfer activity are strong predictors of upcoming match outcomes
- Fantasy football platforms — Valuation trends help build dynamic pricing models for fantasy leagues
- Club scouting systems — Independent analytics startups sell scouting intelligence to lower-league clubs that can't afford full data subscriptions
- Media and journalism — Data journalism features on transfer window spending require structured historical records
- Academic sports science — Research into career trajectories, positional value trends, and market inefficiencies
Anti-Bot Measures
Transfermarkt has progressively hardened their defenses in 2026:
Cloudflare with aggressive bot scoring. Transfermarkt uses Cloudflare's Bot Management product (not just the basic WAF). This evaluates TLS fingerprint, IP reputation, request patterns, and JavaScript challenge results. Datacenter IPs are blocked outright in most cases.
Mandatory User-Agent and headers. Requests without a complete set of browser-like headers (Accept, Accept-Language, Accept-Encoding, plus a valid User-Agent) get 403 responses even before the Cloudflare layer.
Rate limiting. Sustained request rates above roughly 20 requests per minute from a single IP trigger temporary blocks. The blocks escalate — first a Cloudflare challenge page, then a hard 403 for the IP that can last hours.
Cookie validation. Transfermarkt sets multiple tracking cookies on first visit. Subsequent requests without these cookies are treated as new sessions and face repeated challenges. Maintaining a persistent cookie jar across requests is critical.
Page structure obfuscation. While the site is server-rendered HTML, class names and element IDs change periodically. Transfermarkt appears to rotate some CSS class names on a roughly monthly basis, likely to break hardcoded selectors in scrapers.
Setting Up the Scraper
Because Transfermarkt is server-rendered, you can often get by with httpx plus proper headers — you don't always need a full browser. But Cloudflare challenges on datacenter IPs push most setups toward Playwright anyway.
pip install httpx playwright playwright-stealth parsel
playwright install chromium
The httpx approach (works with residential IPs that pass Cloudflare):
import httpx
import time
import random
from parsel import Selector
def create_session(proxy: str = None) -> httpx.Client:
"""Create a persistent session with browser-like headers."""
headers = {
"User-Agent": (
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
"AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/125.0.0.0 Safari/537.36"
),
"Accept": (
"text/html,application/xhtml+xml,"
"application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8"
),
"Accept-Language": "en-US,en;q=0.9,de;q=0.8",
"Accept-Encoding": "gzip, deflate, br",
"Referer": "https://www.transfermarkt.com/",
"DNT": "1",
"Sec-Fetch-Dest": "document",
"Sec-Fetch-Mode": "navigate",
"Sec-Fetch-Site": "same-origin",
}
client_kwargs = {
"headers": headers,
"follow_redirects": True,
"timeout": 30.0,
}
if proxy:
client_kwargs["proxy"] = proxy
client = httpx.Client(**client_kwargs)
# Warm up session with homepage
resp = client.get("https://www.transfermarkt.com/")
if resp.status_code == 200:
print("Session established successfully")
else:
print(f"Warning: homepage returned {resp.status_code}")
time.sleep(random.uniform(1.5, 3.0))
return client
Scraping Player Profiles
Transfermarkt player URLs follow a predictable pattern: /player-name/profil/spieler/{player_id}. The player page contains the core profile data and current market value:
def scrape_player(player_url: str, session: httpx.Client) -> dict | None:
"""Scrape a player profile page for current metadata."""
resp = session.get(player_url)
if resp.status_code != 200:
print(f"Failed to fetch {player_url}: {resp.status_code}")
return None
sel = Selector(text=resp.text)
result = {"url": player_url}
# Player name — primary h1
result["name"] = sel.css(
"h1.data-header__headline-wrapper::text"
).get("").strip()
# Market value (the main headline figure)
value_parts = sel.css(
"a.data-header__market-value-wrapper::text"
).getall()
result["market_value"] = " ".join(
v.strip() for v in value_parts if v.strip()
)
# Profile detail items from the info table
info_items = sel.css("ul.data-header__items li")
for item in info_items:
label = item.css("span.data-header__label::text").get("").strip().lower()
value = item.css(
"span.data-header__content::text, a::text"
).get("").strip()
if "date of birth" in label:
result["birth_date"] = value
elif "citizenship" in label:
result["nationality"] = value
elif "height" in label:
result["height"] = value
elif "position" in label:
result["position"] = value
elif "foot" in label:
result["preferred_foot"] = value
elif "agent" in label:
result["agent"] = value
elif "shirt" in label or "number" in label:
result["shirt_number"] = value
# Current club
club = sel.css("span.data-header__club a::text").get("").strip()
result["current_club"] = club if club else None
# Contract expiration
contract = sel.css(
"span.data-header__content[itemprop='endDate']::text"
).get("").strip()
result["contract_until"] = contract if contract else None
# Player ID from URL
parts = player_url.rstrip("/").split("/")
result["player_id"] = parts[-1] if parts else None
return result
Extracting Market Value History
The value history is loaded on the player's market value page and embedded as JavaScript data for the chart rendering:
import json
import re
def scrape_value_history(player_id: str,
session: httpx.Client) -> list[dict]:
"""Extract historical market value data from the chart JavaScript."""
url = (f"https://www.transfermarkt.com/player/"
f"marktwertverlauf/spieler/{player_id}")
resp = session.get(url)
if resp.status_code != 200:
return []
# The chart data is embedded in a JavaScript variable
# Try multiple patterns Transfermarkt has used over the years
patterns = [
r"var\s+chartData\s*=\s*(\[.*?\]);",
r"'data':\s*(\[.*?\])\s*[,}]",
r"marktwertverlauf\s*=\s*(\[.*?\]);",
]
match = None
for pattern in patterns:
match = re.search(pattern, resp.text, re.DOTALL)
if match:
break
if not match:
return []
try:
raw = match.group(1)
# Normalize JavaScript object notation to valid JSON
# JS objects use unquoted keys; JSON requires quoted keys
raw = re.sub(r"(?<=[{,\[])\s*(\w+)\s*:", r'"\1":', raw)
raw = raw.replace("'", '"')
data = json.loads(raw)
values = []
for point in data:
values.append({
"date": point.get("datum_mw") or point.get("x") or point.get("date"),
"value": point.get("mw") or point.get("y") or point.get("value"),
"club": point.get("verein") or point.get("club"),
"age": point.get("age"),
"nationality": point.get("nat"),
})
return values
except (json.JSONDecodeError, TypeError) as e:
print(f"Failed to parse value history for player {player_id}: {e}")
return []
def get_peak_value(value_history: list[dict]) -> dict | None:
"""Find the peak market value entry in a player's history."""
if not value_history:
return None
def parse_value(v: str) -> float:
"""Parse '€45.00m' or '€900k' to float."""
if not v:
return 0.0
v = v.replace("€", "").replace(",", "").strip()
if "m" in v.lower():
return float(v.lower().replace("m", "")) * 1_000_000
elif "k" in v.lower():
return float(v.lower().replace("k", "")) * 1_000
try:
return float(v)
except ValueError:
return 0.0
return max(value_history,
key=lambda x: parse_value(str(x.get("value", "0"))))
Transfer History Extraction
The transfer history table is standard HTML and parseable without JavaScript:
def scrape_transfers(player_id: str,
session: httpx.Client) -> list[dict]:
"""Extract complete transfer history for a player."""
url = (f"https://www.transfermarkt.com/player/"
f"transfers/spieler/{player_id}")
resp = session.get(url)
if resp.status_code != 200:
return []
sel = Selector(text=resp.text)
transfers = []
# Main transfer table rows
rows = sel.css("div.grid-view table.items tbody tr")
for row in rows:
cells = row.css("td")
if len(cells) < 5:
continue
raw_fee = cells[5].css("a::text, ::text").get("").strip() if len(cells) > 5 else ""
mv_at_time = cells[6].css("::text").get("").strip() if len(cells) > 6 else None
transfer = {
"season": cells[0].css("::text").get("").strip(),
"date": cells[1].css("::text").get("").strip(),
"from_club": cells[2].css("a::text").get("").strip(),
"from_club_country": cells[2].css("img::attr(title)").get(""),
"to_club": cells[4].css("a::text").get("").strip(),
"to_club_country": cells[4].css("img::attr(title)").get(""),
"fee": raw_fee,
"market_value_at_time": mv_at_time,
}
# Classify transfer type from fee text
fee_lower = raw_fee.lower()
if "loan" in fee_lower or "leihe" in fee_lower:
transfer["type"] = "loan"
elif "free" in fee_lower or "ablösefrei" in fee_lower:
transfer["type"] = "free_transfer"
elif "end of loan" in fee_lower:
transfer["type"] = "loan_end"
elif any(c in raw_fee for c in ["€", "$", "£", "¥"]):
transfer["type"] = "paid"
elif "?" in raw_fee:
transfer["type"] = "undisclosed"
else:
transfer["type"] = "unknown"
if transfer["from_club"] or transfer["to_club"]:
transfers.append(transfer)
time.sleep(random.uniform(1.5, 3.0))
return transfers
Transfer Rumors
The rumors page aggregates active transfer speculation:
def scrape_rumors(player_id: str,
session: httpx.Client) -> list[dict]:
"""Scrape active transfer rumors for a player."""
url = (f"https://www.transfermarkt.com/player/"
f"geruechte/spieler/{player_id}")
resp = session.get(url)
if resp.status_code != 200:
return []
sel = Selector(text=resp.text)
rumors = []
for row in sel.css("div.large-8 table.items tbody tr"):
destination = row.css("td.hauptlink a::text").get("").strip()
if not destination:
continue
rumors.append({
"destination_club": destination,
"destination_league": row.css(
"td.hauptlink img::attr(title)"
).get(""),
"source": row.css("td.zentriert a::text").get("").strip(),
"probability": row.css(
"td.zentriert img::attr(title)"
).get(""),
"date_reported": row.css("td.zentriert::text").get("").strip(),
"fee_expectation": row.css(
"td.rechts::text"
).get("").strip(),
})
return rumors
def scrape_injury_history(player_id: str,
session: httpx.Client) -> list[dict]:
"""Scrape injury history for a player."""
url = (f"https://www.transfermarkt.com/player/"
f"verletzungen/spieler/{player_id}")
resp = session.get(url)
if resp.status_code != 200:
return []
sel = Selector(text=resp.text)
injuries = []
for row in sel.css("table.items tbody tr"):
cells = row.css("td")
if len(cells) < 5:
continue
injuries.append({
"season": cells[0].css("::text").get("").strip(),
"injury": cells[1].css("::text").get("").strip(),
"from_date": cells[2].css("::text").get("").strip(),
"until_date": cells[3].css("::text").get("").strip(),
"days_missed": cells[4].css("::text").get("").strip(),
"games_missed": cells[5].css("::text").get("").strip() if len(cells) > 5 else "",
})
return injuries
Proxy Configuration
Transfermarkt's Cloudflare Bot Management is the hardest obstacle in this entire scraping pipeline. With a datacenter IP, you won't get past the challenge page consistently — the IP reputation score alone is enough to trigger a block, regardless of how well your headers and TLS fingerprint match a real browser.
ThorData's residential proxies are the practical solution. Residential IPs carry the trust score needed to pass Cloudflare's checks on the first request. For Transfermarkt specifically, European residential IPs (Germany, UK, Spain) tend to work best since the site is based in Hamburg and the majority of its traffic is European.
PROXY_USER = "your_user"
PROXY_PASS = "your_pass"
PROXY_HOST = "proxy.thordata.com"
PROXY_PORT = 9000
def get_eu_proxy() -> str:
"""Get a European residential proxy URL for Transfermarkt."""
# Rotate between Germany, UK, and Spain for best results
country = random.choice(["de", "gb", "es"])
auth = f"{PROXY_USER}:{PROXY_PASS}"
return f"http://{auth}@{PROXY_HOST}:{PROXY_PORT}?country={country}"
# Create session with European proxy
session = create_session(proxy=get_eu_proxy())
Keep your request rate under 15 per minute. Transfermarkt's rate detection is IP-based, and even residential IPs get flagged if the request cadence looks automated. Add time.sleep(random.uniform(4.0, 8.0)) between page requests.
Storing Results in SQLite
import sqlite3
def init_db(path: str = "transfermarkt.db") -> sqlite3.Connection:
"""Initialize database schema for Transfermarkt data."""
conn = sqlite3.connect(path)
conn.executescript("""
CREATE TABLE IF NOT EXISTS players (
id TEXT PRIMARY KEY,
name TEXT,
nationality TEXT,
position TEXT,
height TEXT,
preferred_foot TEXT,
current_club TEXT,
market_value TEXT,
birth_date TEXT,
contract_until TEXT,
agent TEXT,
shirt_number TEXT,
scraped_at TEXT DEFAULT (datetime('now'))
);
CREATE TABLE IF NOT EXISTS transfers (
id INTEGER PRIMARY KEY AUTOINCREMENT,
player_id TEXT,
season TEXT,
date TEXT,
from_club TEXT,
from_club_country TEXT,
to_club TEXT,
to_club_country TEXT,
fee TEXT,
type TEXT,
market_value_at_time TEXT,
FOREIGN KEY (player_id) REFERENCES players(id)
);
CREATE TABLE IF NOT EXISTS valuations (
id INTEGER PRIMARY KEY AUTOINCREMENT,
player_id TEXT,
date TEXT,
value TEXT,
club TEXT,
age TEXT,
FOREIGN KEY (player_id) REFERENCES players(id)
);
CREATE TABLE IF NOT EXISTS injuries (
id INTEGER PRIMARY KEY AUTOINCREMENT,
player_id TEXT,
season TEXT,
injury TEXT,
from_date TEXT,
until_date TEXT,
days_missed TEXT,
games_missed TEXT,
FOREIGN KEY (player_id) REFERENCES players(id)
);
CREATE INDEX IF NOT EXISTS idx_transfers_player
ON transfers(player_id);
CREATE INDEX IF NOT EXISTS idx_valuations_player
ON valuations(player_id);
""")
conn.commit()
return conn
def save_player_complete(conn: sqlite3.Connection,
player: dict,
transfers: list[dict],
valuations: list[dict],
injuries: list[dict] = None) -> None:
"""Save complete player data including history tables."""
pid = player.get("player_id")
if not pid:
return
conn.execute("""
INSERT OR REPLACE INTO players
(id, name, nationality, position, height, preferred_foot,
current_club, market_value, birth_date, contract_until,
agent, shirt_number)
VALUES (?,?,?,?,?,?,?,?,?,?,?,?)
""", (
pid, player.get("name"), player.get("nationality"),
player.get("position"), player.get("height"),
player.get("preferred_foot"), player.get("current_club"),
player.get("market_value"), player.get("birth_date"),
player.get("contract_until"), player.get("agent"),
player.get("shirt_number"),
))
for t in transfers:
conn.execute("""
INSERT INTO transfers
(player_id, season, date, from_club, from_club_country,
to_club, to_club_country, fee, type, market_value_at_time)
VALUES (?,?,?,?,?,?,?,?,?,?)
""", (
pid, t.get("season"), t.get("date"),
t.get("from_club"), t.get("from_club_country"),
t.get("to_club"), t.get("to_club_country"),
t.get("fee"), t.get("type"),
t.get("market_value_at_time"),
))
for v in valuations:
conn.execute("""
INSERT INTO valuations (player_id, date, value, club, age)
VALUES (?,?,?,?,?)
""", (pid, v.get("date"), v.get("value"),
v.get("club"), v.get("age")))
if injuries:
for inj in injuries:
conn.execute("""
INSERT INTO injuries
(player_id, season, injury, from_date,
until_date, days_missed, games_missed)
VALUES (?,?,?,?,?,?,?)
""", (
pid, inj.get("season"), inj.get("injury"),
inj.get("from_date"), inj.get("until_date"),
inj.get("days_missed"), inj.get("games_missed"),
))
conn.commit()
Full Scrape Pipeline
def scrape_player_complete(player_id: str,
player_name_slug: str,
session: httpx.Client,
conn: sqlite3.Connection) -> None:
"""Scrape all data for a single player and save to DB."""
base_url = "https://www.transfermarkt.com"
# Profile
profile_url = f"{base_url}/{player_name_slug}/profil/spieler/{player_id}"
profile = scrape_player(profile_url, session)
if not profile:
print(f"Failed to scrape profile for {player_id}")
return
print(f" {profile.get('name', player_id)}: "
f"{profile.get('market_value', 'N/A')}")
time.sleep(random.uniform(3, 6))
# Transfer history
transfers = scrape_transfers(player_id, session)
print(f" {len(transfers)} transfers")
time.sleep(random.uniform(3, 6))
# Value history
valuations = scrape_value_history(player_id, session)
print(f" {len(valuations)} valuation data points")
time.sleep(random.uniform(3, 6))
# Injury history
injuries = scrape_injury_history(player_id, session)
print(f" {len(injuries)} injury records")
time.sleep(random.uniform(3, 6))
# Save everything
save_player_complete(conn, profile, transfers, valuations, injuries)
Legal Note
Transfermarkt's Terms of Service prohibit automated scraping. The site has sent cease-and-desist letters to projects that published large-scale scraped datasets. While market value estimates are Transfermarkt's editorial product (not raw facts), transfer records are factual data that may have different legal treatment depending on your jurisdiction. EU database directive protections may apply to the compiled dataset. Keep your usage limited, don't redistribute raw data, and consult local regulations before building anything public.
Key Takeaways
- Transfermarkt is server-rendered HTML, which makes parsing simpler than JavaScript-heavy SPAs —
parselorBeautifulSoupwork directly on the response body without needing a headless browser, provided your IP passes Cloudflare. - Market value history is embedded as JavaScript chart data, not HTML tables — extract it with regex from the page source, then parse as JSON.
- Cloudflare Bot Management is the primary obstacle. Datacenter IPs fail consistently. ThorData residential proxies with European country targeting provide the IP reputation needed to pass challenges reliably.
- Keep request rates under 15 per minute with randomized delays — Transfermarkt's rate detection is aggressive and blocks escalate from challenges to hard 403s that last hours.
- CSS selectors rotate roughly monthly. Build your scraper to log extraction failures so you can update selectors promptly rather than collecting empty data silently.
- Transfer fee data includes a mix of confirmed figures, estimates, and undisclosed amounts — normalize this in your storage layer to avoid treating all values as equally reliable.
Advanced Market Value Analysis
With a player database populated, you can run sophisticated analysis on the football transfer market:
def market_value_analysis(conn: sqlite3.Connection) -> None:
"""Analyze market value patterns across collected players."""
print("=== Transfermarkt Market Value Analysis ===\n")
# Distribution of current values
print("Player value tiers:")
for row in conn.execute("""
SELECT
CASE
WHEN market_value LIKE '%€50m%'
OR market_value LIKE '%€100m%'
OR market_value LIKE '%€200m%' THEN 'Elite (€50m+)'
WHEN market_value LIKE '%€20m%'
OR market_value LIKE '%€30m%'
OR market_value LIKE '%€40m%' THEN 'High (€20-50m)'
WHEN market_value LIKE '%€10m%'
OR market_value LIKE '%€15m%' THEN 'Mid (€10-20m)'
WHEN market_value LIKE '%€%m%' THEN 'Low (< €10m)'
ELSE 'Unknown'
END as tier,
COUNT(*) as count
FROM players
GROUP BY tier
ORDER BY count DESC
"""):
print(f" {row[0]:20}: {row[1]} players")
# Most expensive positions
print("\nMost transferred positions:")
for row in conn.execute("""
SELECT position, COUNT(*) as transfer_count,
COUNT(DISTINCT player_id) as players
FROM transfers t
JOIN players p ON t.player_id = p.id
WHERE t.type = 'paid'
AND p.position IS NOT NULL
GROUP BY position
ORDER BY transfer_count DESC LIMIT 10
"""):
print(f" {row[0]:20}: {row[1]:4} paid transfers "
f"({row[2]} players)")
# Transfer fee escalation over seasons
print("\nAverage transfer activity by season:")
for row in conn.execute("""
SELECT season, COUNT(*) as transfers,
COUNT(CASE WHEN type = 'paid' THEN 1 END) as paid
FROM transfers
WHERE season IS NOT NULL AND season != ''
GROUP BY season
ORDER BY season DESC LIMIT 8
"""):
print(f" {row[0]}: {row[1]:4} total transfers, "
f"{row[2]:3} paid")
# Players with most clubs
print("\nMost-traveled players (most clubs):")
for row in conn.execute("""
SELECT p.name, p.nationality, p.position,
COUNT(DISTINCT t.to_club) as clubs
FROM players p
JOIN transfers t ON p.id = t.player_id
WHERE t.to_club != '' AND t.to_club IS NOT NULL
GROUP BY p.id
ORDER BY clubs DESC LIMIT 10
"""):
print(f" {row[0]:25} ({row[1]}, {row[2]}): {row[3]} clubs")
def find_value_trajectory(conn: sqlite3.Connection,
player_id: str) -> None:
"""Analyze how a player's value changed over their career."""
player = conn.execute(
"SELECT name, position, current_club FROM players WHERE id = ?",
(player_id,)
).fetchone()
if not player:
print(f"Player {player_id} not found")
return
print(f"\n=== Value History: {player[0]} ({player[1]}) ===\n")
history = conn.execute("""
SELECT date, value, club
FROM valuations
WHERE player_id = ?
ORDER BY date
""", (player_id,)).fetchall()
if not history:
print("No valuation history available")
return
for entry in history:
print(f" {entry[0]:12}: {entry[1]:12} — {entry[2]}")
# Find peak
print(f"\nCurrent club: {player[2]}")
print(f"Career entries: {len(history)}")
def transfer_network_analysis(conn: sqlite3.Connection,
top_n_clubs: int = 15) -> dict:
"""Analyze transfer flows between clubs."""
print(f"\n=== Transfer Flow Analysis (Top {top_n_clubs} Clubs) ===\n")
# Most active clubs as buyers
print("Top buying clubs (by paid transfer count):")
for row in conn.execute("""
SELECT to_club, COUNT(*) as purchases
FROM transfers
WHERE type = 'paid' AND to_club IS NOT NULL AND to_club != ''
GROUP BY to_club
ORDER BY purchases DESC LIMIT 10
"""):
print(f" {row[0]:30}: {row[1]:3} purchases")
# Most active clubs as sellers
print("\nTop selling clubs:")
for row in conn.execute("""
SELECT from_club, COUNT(*) as sales
FROM transfers
WHERE type = 'paid'
AND from_club IS NOT NULL AND from_club != ''
GROUP BY from_club
ORDER BY sales DESC LIMIT 10
"""):
print(f" {row[0]:30}: {row[1]:3} sales")
# Most common transfer corridors
print("\nMost common transfer routes:")
for row in conn.execute("""
SELECT from_club, to_club, COUNT(*) as moves
FROM transfers
WHERE type IN ('paid', 'free_transfer')
AND from_club != '' AND to_club != ''
AND from_club IS NOT NULL AND to_club IS NOT NULL
GROUP BY from_club, to_club
ORDER BY moves DESC LIMIT 10
"""):
print(f" {row[0]:25} -> {row[1]:25}: {row[2]} players")
return {}
These analytical capabilities — combined with the injury and rumor data — give you the building blocks for a proper football analytics product. The transfer market data is particularly valuable for betting models, where historical transfer activity between clubs and player career trajectory analysis can inform match outcome predictions.