Football Value Bets

Generate professional, data-driven football bet slips. No gut feelings, purely statistics and value analysis.

When to activate

Activate this skill when the user:

• Asks for a football bet, bet slip, accumulator, or picks
• Asks for match analysis for betting purposes
• Asks for bet statistics, results, or ROI
• Asks for a pick or tip for football

Prerequisites

Install dependencies (one-time):

pip install requests

Set API key (free via football-data.org/client/register):

export FOOTBALL_DATA_API_KEY="your-key-here"

Or fill in the key at {baseDir}/config/settings.json under api_key.

---

Mode 1: Generate Bet Slip

Step 1 -- Fetch data

Fetch current match data:

python3 {baseDir}/scripts/match_fetcher.py --mode full

This returns a JSON package with:

• All scheduled matches for today and tomorrow
• League standings and form data per team
• Head-to-head history (last 5 encounters)

Coverage: Premier League, Bundesliga, Serie A, La Liga, Ligue 1, Eredivisie, Champions League.

If the user wants a specific date:

python3 {baseDir}/scripts/match_fetcher.py --mode full --date-from 2026-02-15 --date-to 2026-02-15

Step 2 -- Analyse each match

Analyse EVERY match from the data on these factors (in order of importance):

A. Form (last 5-10 matches)

• Use the form field from the standings data (e.g. "W,W,D,L,W")
• Calculate win percentage, goals per match
• Pay attention to home form vs away form trends

B. Home/away statistics

• Home team: goals for/against at home
• Away team: goals for/against away
• Compare goal difference at home vs on the road

C. Head-to-head (H2H)

• Review the h2h data: who wins historically?
• Look for patterns: does one team always score? Are these low- or high-scoring games?

D. Table position and motivation

• Top of the table (title/CL spots): high motivation, consistent
• Mid-table: unpredictable, be cautious
• Bottom (relegation): can be surprisingly strong or weak
• Difference in table position as an indicator

E. Additional factors (via web search) After the data analysis, search the web for EVERY shortlisted match:

• Injuries and suspensions of key players (FotMob, Transfermarkt)
• Recent news (manager change, internal issues, transfer saga)
• xG (expected goals) data from recent matches
• Weather conditions for outdoor matches in winter months

Use search terms like: "[Team A] vs [Team B] team news injuries" and "[Team A] xG stats 2025-26"

F. Odds estimation

• Estimate realistic odds per outcome (based on your analysis)
• Compare with common bookmaker odds (search the web if needed)
• VALUE = when your estimated probability is higher than the implied probability of the odds
• Implied probability = 1 / odds (e.g. odds 2.00 = 50% implied)

Step 3 -- Pick selection

Select ONLY picks that meet all criteria:

1. Estimated win probability > 45%
2. Odds per pick: 1.70 - 2.50 (value range)
3. Clear motivation based on data
4. No more than 1 pick per match

Allowed pick types:

• 1 / X / 2: Home win, draw, away win
• 1X / X2: Double chance
• BTTS Yes/No: Both Teams To Score
• Over/Under 2.5 goals
• Handicap -1: Team wins by 2+ goals

Step 4 -- Honesty check

If you find FEWER than 3 picks with real value:

Do NOT generate a bet slip. Say honestly:

"No strong bet slip possible today. The matches offer insufficient value based on the data. Reasons: [brief explanation]. Wait for better odds or the next matchday."

This is MANDATORY. Never force it. Honesty > revenue.

Step 5 -- Generate output

Generate TWO variants:

Variant A: Main slip (3-4 picks, total odds 8x-15x)

Match	Pick	Odds	Motivation	Prob %	Risk
Ajax - PSV	BTTS Yes	1.85	Both teams scored in 8/10 recent matches, H2H always goals	52%	Low
Arsenal - Chelsea	Over 2.5	1.90	Avg 3.2 goals per match this season, both attack-minded	48%	Medium
...	...	...	...	...	...

Total odds: XX.XX Recommended stake: 5-10 EUR Potential payout: XX.XX EUR

Variant B: Backup (2-3 picks, total odds 4x-8x)

Same table format but with safer picks (higher probabilities, lower odds).

Always include at the bottom:

• Estimated hitrate per variant (e.g. "Main slip: ~15% chance of all picks hitting, individual picks ~50% average")
• Alternative advice: "Consider singles if you want to play it safer"
• Disclaimer: "Gamble responsibly. Max stake 5-10 EUR per slip, monthly limit 75 EUR."

Step 6 -- Save bet

Automatically save the bet slip for tracking:

python3 {baseDir}/scripts/bet_tracker.py --mode save --data '{
  "type": "main",
  "stake": 5.0,
  "picks": [
    {
      "match": "Ajax - PSV",
      "competition": "Eredivisie",
      "pick": "BTTS Yes",
      "odds": 1.85,
      "estimated_probability": 0.52,
      "motivation": "Both teams scored in 8/10 recent matches",
      "risk": "low"
    }
  ],
  "total_odds": 12.50,
  "notes": ""
}'

Repeat for the backup slip with "type": "backup".

---

Mode 2: Record results

When the user says a bet has won or lost:

# Mark entire slip
python3 {baseDir}/scripts/bet_tracker.py --mode result --date 2026-02-15 --slip-idx 1 --result win

# Mark specific pick
python3 {baseDir}/scripts/bet_tracker.py --mode result --date 2026-02-15 --slip-idx 1 --pick-idx 2 --result loss

Result options: win, loss, void

Always ask the user for the date and which slip (main or backup) it concerns.

---

Mode 3: View statistics

When the user asks about results, stats, ROI, or hitrate:

python3 {baseDir}/scripts/bet_tracker.py --mode stats

Present the JSON output as a readable summary:

• All-time hitrate and ROI
• Current month: staked, returned, profit/loss, budget remaining
• Best and worst month

Warn if the monthly budget is almost spent (>80% used).

---

Mode 4: View history

python3 {baseDir}/scripts/bet_tracker.py --mode history --days 30

Show an overview of all bets from the last N days.

---

Analysis quality rules

These rules are HARD. Always follow them:

1. Never more than 5 picks per slip. More picks = exponentially lower chance.
2. Never odds below 1.50 or above 3.00 per pick. Too low = no value, too high = too risky.
3. Never 2 picks from the same match. Correlation destroys value.
4. Never a pick without data backing. "Gut feeling" is not an argument.
5. Always run the honesty check. No slip is better than a bad slip.
6. Avoid derbies and cup matches unless the data is overwhelming. Too unpredictable.
7. Always check injuries. A team without key players is a different team.
8. Factor in the season phase. Early season = unreliable data. End of season = varying motivation.

Budget and responsible gambling

Configuration is in {baseDir}/config/settings.json:

• Per bet: 5-10 EUR
• Monthly limit: 75 EUR
• Always check the stats for monthly spending before generating a new slip

If the monthly budget is at or nearly at its limit (>90%):

• Inform the user
• Advise waiting until next month
• Do NOT generate a new slip unless the user explicitly states they want to proceed

Tone and style

• Output in English
• Professional but accessible
• No hype, no promises
• Data and percentages front and center
• Honest about risks