Pricing
Football intelligence for every way you work
One platform. Clear entry points.
Buy files, query the API or BigQuery, create club and creator intelligence, or carry grounded football research in Telegram. Every product below is backed by the same inspectable data layer, with clear prices and a direct path to checkout.
Choose the outcome. See the price.
Every commercial Dribble product now starts here. Compare the entry point, open the full product detail, and continue to its secure checkout.
Data Downloads
Own six completed seasons, then receive rolling current-season snapshots.
REST API
Build apps and automated data pipelines on stable football datasets.
BigQuery
Run SQL, modelling and backtests directly against plan-isolated datasets.
Telegram Pro
Ask grounded questions, generate visuals, save research and receive alerts.
Touchline
Create opposition intelligence and recruitment dossiers from the same data layer.
Studio
Turn match data into differentiated scripts, angles, titles and production assets.
Scout Pen
Generate structured player reports with grades, evidence and a recruitment verdict.
StatsBomb and Opta charge $100,000+/year and only sell to clubs and broadcasters. Free CSV scrapes exist but are dirty, manually assembled, and months behind. Nobody is at the $50–$500/mo tier: clean, ML-ready, SQL-queryable football data with a commercial licence.
XY Event Data · 57M+ rows · 32 event-covered competitions · 2020-present
match_events provides 57M+ event rows with Opta pitch coordinates across 32 event-covered competitions from 2020 onward. Max adds a Premier League table with periods, pass end coordinates, and complete qualifier payloads.
Starter
Full dataset access for personal projects, academic research, and model building.
Pro
Commercial licence and weekly refresh for production data products and apps.
Max
Daily validated snapshots with research-grade event detail, ordering, and revision history.
-- Mbappé xG per season — runs in <1s, costs <$0.01
SELECT s.name AS season,
COUNT(*) AS appearances,
ROUND(SUM(pm.expected_goals), 2) AS total_xg,
ROUND(AVG(pm.expected_goals), 3) AS xg_per_90
FROM `dribble360.football_pro.player_matches` pm
JOIN `dribble360.football_pro.players` p ON p.id = pm.player_id
JOIN `dribble360.football_pro.matches` m ON m.id = pm.match_id
JOIN `dribble360.football_pro.seasons` s ON s.id = m.season_id
WHERE p.known_name = 'Mbappé'
GROUP BY s.name
ORDER BY s.name DESC;-- Salah events, 2025/26 Premier League - Max includes enriched event fields
SELECT me.x, me.y, me.end_x, me.end_y,
me.period_id, me.type, me.outcome, me.match_id
FROM `dribble360.football_max.premier_league_events` me
WHERE me.player_id = (SELECT id FROM `dribble360.football_max.players` WHERE known_name = 'Mohamed Salah')
AND me.type IN ('GOAL', 'MISS', 'ATTEMPT_SAVED', 'POST')
AND me.season_name = '2025/2026';
-- Max: period, pass end coordinates, qualifier count, and complete qualifier JSONUse Cases
Built For
Data Scientists & ML Engineers
Query 1M+ player-match records in seconds with standard SQL. Skip the ETL — train xG models, compute feature vectors, run clustering directly in BigQuery.
Analytics Product Teams
Build on 8 joined tables with a commercial licence. No API keys, no rate limits, no pagination — just SQL. Your GCP project, your compute, your pricing.
Quant Researchers
Backtest across 6 seasons of match data in milliseconds. Compute per-90 windows, player-pair correlations, and form-adjusted expected points — at a fraction of enterprise data costs.
Process
How BigQuery Access Works
Choose Your Plan
Subscribe through Stripe. Your BigQuery workspace opens immediately with the exact tables, refresh cadence, and licence included in your tier.
Connect Your Google Identity
Choose a personal Google account or service account and submit its email. Dribble applies dataset access and project discovery permissions automatically.
Test, Sample, Then Build
Run the one-query connection check, download representative CSV samples, then copy a ready-made SQL, Python, or Node recipe from the dashboard.
Schema
BigQuery-Native Structure
Entity, match, appearance, transfer, and event tables with consistent IDs, ready for SQL.
-- Top scorers, Premier League 2025/26
SELECT p.known_name, SUM(pm.goals) AS goals
FROM `dribble360.football_pro.player_matches` pm
JOIN `dribble360.football_pro.matches` m ON m.id = pm.match_id
JOIN `dribble360.football_pro.players` p ON p.id = pm.player_id
JOIN `dribble360.football_pro.seasons` s ON s.id = m.season_id
JOIN `dribble360.football_pro.leagues` l ON l.id = s.league_id
WHERE s.name = '2025/2026'
AND l.slug = 'premier-league'
GROUP BY p.known_name
ORDER BY goals DESC
LIMIT 10;Schema reference PDF with every table, column family, and query example.
Download BigQuery Schema PDFQuestions