""" World Cup Quant - Quantitative Analysis Prompt

A structured prompt for AI-powered World Cup statistical analysis, predictions, and data-driven football insights. """

WORLD_CUP_QUANT_SYSTEM_PROMPT = """ You are World Cup Quant — an elite quantitative analyst and football data scientist specializing in FIFA World Cup statistics, predictive modeling, and performance analytics.

Your core capabilities:

• Deep statistical analysis of World Cup match data (1930–present)
• Player and team performance metrics (xG, pass completion, pressing intensity, PPDA, etc.)
• Tournament bracket probability modeling and Monte Carlo simulations
• Historical trend analysis: host nation advantage, group stage exit patterns, upset probability
• Elo rating systems applied to international football
• Comparative era analysis (accounting for tournament expansion and format changes)

Your analytical style:

• Lead with numbers, then narrative — always anchor insights in data
• Quantify uncertainty explicitly (e.g., "Team A has a 63% win probability ± 8%")
• Distinguish between correlation and causation
• Flag small sample sizes (World Cup data is inherently limited)
• Use football-specific terminology accurately (pressing, high block, transitions, set pieces)
• When asked for predictions, provide probability distributions, not just point estimates

Output formats you can produce:

• Statistical summaries with key metrics
• Head-to-head comparison tables
• Probability breakdowns for match outcomes (Win / Draw / Loss)
• Tournament simulation results (e.g., "X ran 10,000 simulations — here's the distribution")
• Narrative scouting reports backed by underlying numbers
• Historical context ("This would be only the 3rd time since 1966 that...")

Constraints & intellectual honesty:

• Acknowledge data gaps for older tournaments (pre-1966 limited tracking data)
• Do not invent statistics — if data is unavailable, say so clearly
• Separate model output from expert opinion
• Always cite which metric or dataset a claim is based on when possible """

WORLD_CUP_QUANT_USER_PROMPT_TEMPLATE = """ {query}

Please structure your response as follows:

1. Quick Take — 2–3 sentence quantitative summary
2. Key Metrics — the most relevant stats, presented clearly
3. Deep Dive — detailed analysis with historical context
4. Model Output (if applicable) — probabilities, simulations, or projections
5. Caveats — data limitations or uncertainty flags """

── Example queries ────────────────────────────────────────────────────────────

EXAMPLE_QUERIES = [ "Compare Brazil and Argentina's World Cup win rates when playing as the favorite vs. underdog.", "What is the historical probability of a team advancing after losing their opening group stage match?", "Model the 2026 World Cup knockout stage probabilities based on current FIFA Elo ratings.", "Which defensive metrics best predict World Cup knockout stage success since 2010?", "How does host nation advantage quantifiably affect group stage performance?", ]

def build_prompt(user_query: str) -> dict: """ Build a ready-to-use messages payload for the Anthropic API.

Args:
    user_query: The specific World Cup question or analysis request.

Returns:
    A dict with 'system' and 'messages' keys for the Anthropic client.
"""
return {
    "system": WORLD_CUP_QUANT_SYSTEM_PROMPT.strip(),
    "messages": [
        {
            "role": "user",
            "content": WORLD_CUP_QUANT_USER_PROMPT_TEMPLATE.format(
                query=user_query
            ).strip(),
        }
    ],
}

── Optional: run with the Anthropic SDK ──────────────────────────────────────

def run_world_cup_quant(user_query: str, model: str = "claude-sonnet-4-20250514") -> str: """ Send a query to the World Cup Quant analyst via the Anthropic API.

Requires: pip install anthropic
Set your ANTHROPIC_API_KEY environment variable before running.

Args:
    user_query: Your World Cup analysis question.
    model:      Anthropic model to use.

Returns:
    The assistant's response as a string.
"""
try:
    import anthropic
except ImportError:
    raise ImportError("Run `pip install anthropic` to use this function.")

client = anthropic.Anthropic()  # reads ANTHROPIC_API_KEY from env
payload = build_prompt(user_query)

message = client.messages.create(
    model=model,
    max_tokens=2048,
    system=payload["system"],
    messages=payload["messages"],
)

return message.content[0].text

── CLI demo ───────────────────────────────────────────────────────────────────

if name == "main": import textwrap

demo_query = EXAMPLE_QUERIES[0]
print("=" * 70)
print("  WORLD CUP QUANT — Demo")
print("=" * 70)
print(f"\nQuery:\n  {demo_query}\n")
print("-" * 70)

payload = build_prompt(demo_query)
print("System prompt (truncated):")
print(textwrap.shorten(payload["system"], width=200, placeholder=" ..."))
print("\nUser message:")
print(payload["messages"][0]["content"])
print("\n" + "=" * 70)
print("Call run_world_cup_quant(query) to get a live response from Claude.")
print("=" * 70)