Data Sources

{
  "player_id": "12345",
  "name": "Roger Federer",
  "country": "SUI",
  "ranking": {
    "current": 1,
    "best": 1
  },
  "stats": {
    "wins": 1242,
    "losses": 271,
    "win_percentage": 82.1,
    "titles": 103
  },
  "surface_stats": {
    "hard": {"wins": 783, "losses": 143},
    "clay": {"wins": 224, "losses": 67},
    "grass": {"wins": 192, "losses": 40}
  }
}

{
  "match_id": "67890",
  "tournament": "Wimbledon",
  "round": "Final",
  "surface": "grass",
  "date": "2023-07-16",
  "player1": {
    "id": "12345",
    "name": "Roger Federer",
    "seed": 1
  },
  "player2": {
    "id": "54321",
    "name": "Rafael Nadal",
    "seed": 2
  },
  "score": "6-4, 7-6, 6-3",
  "winner": "12345",
  "stats": {
    "duration": "2h 45m",
    "aces": [15, 8],
    "double_faults": [2, 4],
    "first_serve_percentage": [68, 71]
  }
}

# Example: Implementing request caching
import httpx
from functools import lru_cache
from datetime import datetime, timedelta

class CachedTennisClient:
    def __init__(self):
        self.cache_duration = timedelta(hours=24)
    
    @lru_cache(maxsize=128)
    async def get_player_cached(self, player_id: str, cache_key: str):
        """Cache player data for 24 hours."""
        # cache_key includes date to invalidate daily
        async with httpx.AsyncClient() as client:
            response = await client.get(f"/players/{player_id}")
            return response.json()
    
    async def get_player(self, player_id: str):
        cache_key = datetime.now().strftime("%Y-%m-%d")
        return await self.get_player_cached(player_id, cache_key)

from fastapi import FastAPI
import httpx

app = FastAPI()

@app.get("/api/player/{player_id}")
async def get_player_stats(player_id: str):
    """Fetch player data and process for OddsEngine."""
    async with httpx.AsyncClient() as client:
        response = await client.get(
            f"https://api.api-tennis.com/v1/players/{player_id}"
        )
        data = response.json()
        
        # Process for OddsEngine probability calculations
        processed = {
            "player_id": data["player_id"],
            "name": data["name"],
            "win_rate": data["stats"]["win_percentage"],
            "surface_performance": data["surface_stats"]
        }
        
        return processed

import pandas as pd
import httpx

async def analyze_player_performance(player_id: str) -> pd.DataFrame:
    """Fetch and analyze player match history."""
    async with httpx.AsyncClient() as client:
        response = await client.get(
            f"https://api.api-tennis.com/v1/players/{player_id}/matches"
        )
        matches = response.json()
    
    # Convert to DataFrame for analysis
    df = pd.DataFrame(matches)
    
    # Calculate surface-specific win rates
    surface_analysis = df.groupby('surface').agg({
        'result': lambda x: (x == 'win').sum() / len(x) * 100
    })
    
    return surface_analysis