Betting Research

Multi-source sports betting research tool. Aggregates odds, team form, head-to-head history, weather conditions, and injury data to identify value betting opportunities. Use when user asks about specific matches, wants betting analysis, or needs data-driven predictions for sports betting.

Multi-source data aggregator for sports betting analysis. Pulls from free and paid APIs to give you an edge.

Data Sources

Source	Data	Cost	Rate Limit
TheSportsDB	Fixtures, results, H2H, team info	Free	No limit
API-Football	Lineups, injuries, player stats, xG	100 calls/day free	100/day
Open-Meteo	Weather (precipitation, wind)	Free	No limit
The Odds API	Current odds, bookmaker prices	500 calls/month free	500/mo

Quick Start

bash

# Research a specific match
python3 ~/.openclaw/workspace/skills/betting-research/scripts/betting_research.py "Liverpool vs Everton"

# Research a team
python3 ~/.openclaw/workspace/skills/betting-research/scripts/betting_research.py --team "Bolton Wanderers"

What You Get

Basic Analysis (Free APIs)

• ✅ Team form (last 5 matches)
• ✅ Win/draw/loss record
• ✅ Goals scored/conceded
• ✅ Upcoming fixtures
• ✅ Head-to-head history
• ✅ Weather conditions

Pro Analysis (with API-Football key)

• ⭐ Confirmed lineups (1 hour before kickoff)
• ⭐ Injury/suspension reports
• ⭐ Expected goals (xG) data
• ⭐ Player form and ratings
• ⭐ Manager tactics info

Setup

The Odds API (already have)

Key stored at: ~/.config/the-odds-api/key

API-Football (optional, for pro features)

1. Sign up: https://www.api-football.com/pricing
2. Get free tier: 100 calls/day
3. Save key:

bash

mkdir -p ~/.config/api-football
echo '{"api_key": "YOUR_KEY"}' > ~/.config/api-football/config.json

Usage Examples

Pre-match research:

bash

python3 ~/.openclaw/workspace/skills/betting-research/scripts/betting_research.py "Bolton vs Plymouth"

Full analysis (uses more API calls):

bash

python3 ~/.openclaw/workspace/skills/betting-research/scripts/betting_research.py "Liverpool vs Man City" --full

Check multiple games efficiently:

bash

# Uses only 3-4 API calls per match
# 100 calls = ~25 matches per day

Interpreting Results

Value Indicators

• Form streaks — teams on hot/cold runs
• H2H dominance — historical advantage
• Weather impact — rain = lower scoring
• Odds mispricing — bookie odds vs. true probability

What NOT to Expect

• ❌ Guaranteed winners
• ❌ "Bet on this" tips
• ❌ Inside information

What TO Expect

• ✅ Data patterns
• ✅ Value opportunities
• ✅ Informed decisions

Pro Tips

1. Check lineups 1 hour before kickoff — key players resting = different bet
2. Weather matters — rain, wind affect totals (under value)
3. H2H at this venue — home advantage varies by team
4. Motivation check — cup games, relegation battles

API Call Budgeting

Task	Calls
Basic match research	3-4
Full analysis + lineups	8-10
Team form check	2-3
Odds comparison	1

100 calls/day = 10-15 full match analyses

See references/data-sources.md for API details.