Capstone - Complete Analytics System

# Future Skeleton Tracking Analysis (Conceptual)
import numpy as np
import pandas as pd
from dataclasses import dataclass
from typing import List, Dict, Tuple

@dataclass
class JointPosition:
    x: float
    y: float
    z: float
    confidence: float

class SkeletonAnalyzer:
    """Analyze skeletal tracking data (future capability)"""

    JOINT_NAMES = [
        "head", "neck", "left_shoulder", "right_shoulder",
        "left_elbow", "right_elbow", "left_wrist", "right_wrist",
        "left_hip", "right_hip", "left_knee", "right_knee",
        "left_ankle", "right_ankle"
    ]

    def __init__(self, skeleton_data: pd.DataFrame):
        self.data = skeleton_data

    def calculate_joint_angle(self, p1: JointPosition,
                              p2: JointPosition, p3: JointPosition) -> float:
        """Calculate angle at p2 formed by p1-p2-p3"""
        v1 = np.array([p1.x - p2.x, p1.y - p2.y, p1.z - p2.z])
        v2 = np.array([p3.x - p2.x, p3.y - p2.y, p3.z - p2.z])

        cos_angle = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
        return np.degrees(np.arccos(np.clip(cos_angle, -1, 1)))

    def analyze_running_form(self, frame_data: Dict[str, JointPosition]) -> Dict:
        """Analyze running biomechanics"""
        # Hip angle analysis
        hip_angle = self.calculate_joint_angle(
            frame_data["left_shoulder"],
            frame_data["left_hip"],
            frame_data["left_knee"]
        )

        # Knee drive
        knee_angle = self.calculate_joint_angle(
            frame_data["left_hip"],
            frame_data["left_knee"],
            frame_data["left_ankle"]
        )

        # Body lean
        shoulder = frame_data["left_shoulder"]
        hip = frame_data["left_hip"]
        body_lean = np.degrees(np.arctan2(
            shoulder.x - hip.x,
            shoulder.z - hip.z
        ))

        return {
            "hip_flexion": hip_angle,
            "knee_angle": knee_angle,
            "body_lean": body_lean,
            "form_score": self._calculate_form_score(hip_angle, knee_angle, body_lean)
        }

    def _calculate_form_score(self, hip: float, knee: float, lean: float) -> float:
        """Calculate overall running form score (0-100)"""
        # Optimal ranges (simplified)
        optimal_hip = 45  # degrees
        optimal_knee = 90  # degrees at peak
        optimal_lean = 5  # slight forward lean

        hip_score = max(0, 100 - abs(hip - optimal_hip) * 2)
        knee_score = max(0, 100 - abs(knee - optimal_knee))
        lean_score = max(0, 100 - abs(lean - optimal_lean) * 5)

        return (hip_score + knee_score + lean_score) / 3

    def detect_injury_risk(self, sequence_data: List[Dict]) -> Dict:
        """Detect potential injury risk from movement patterns (future)"""
        # Future: analyze asymmetries, overstriding, etc.
        return {
            "asymmetry_index": 0.05,  # 0 = perfect symmetry
            "overstriding_risk": "low",
            "hip_drop_concern": False,
            "recommendations": []
        }


# Simulated skeleton data
skeleton_data = pd.DataFrame({
    "player_id": [1]*9,
    "frame": [1]*9,
    "joint": ["head", "left_shoulder", "right_shoulder", "left_hip",
              "right_hip", "left_knee", "right_knee", "left_ankle", "right_ankle"],
    "x": [52.5, 52.4, 52.6, 52.35, 52.65, 52.3, 52.7, 52.25, 52.75],
    "y": [34.2, 34.1, 34.3, 34.0, 34.4, 33.8, 34.6, 33.6, 34.8],
    "z": [1.78, 1.55, 1.55, 0.95, 0.95, 0.55, 0.55, 0.08, 0.08],
    "confidence": [0.98, 0.96, 0.97, 0.95, 0.94, 0.92, 0.93, 0.88, 0.89]
})

print("Future Skeleton Tracking Analysis:")
print("This represents emerging capabilities for biomechanics analysis")
print("\nApplications:")
print("- Running form optimization")
print("- Injury risk detection")
print("- Technique improvement feedback")
print("- Fatigue monitoring via movement quality")

# Conceptual: LLM-Powered Analytics Interface
from dataclasses import dataclass
from typing import List, Dict, Optional
import re

@dataclass
class ParsedQuery:
    original: str
    intent: str
    entities: Dict[str, any]
    filters: Dict[str, any]

class NaturalLanguageInterface:
    """Future: Natural language to analytics queries"""

    INTENT_PATTERNS = {
        "similarity_search": ["similar", "like", "compare", "resembles"],
        "ranking": ["best", "top", "highest", "most", "leading"],
        "trend_analysis": ["trend", "over time", "change", "evolution"],
        "explanation": ["why", "explain", "reason", "cause"],
        "comparison": ["versus", "vs", "compared to", "difference"]
    }

    POSITION_PATTERNS = {
        "striker": ["striker", "forward", "cf", "st"],
        "midfielder": ["midfielder", "mid", "cm", "dm", "am"],
        "defender": ["defender", "cb", "fullback", "lb", "rb"],
        "goalkeeper": ["goalkeeper", "gk", "keeper"]
    }

    def parse_query(self, query: str) -> ParsedQuery:
        """Parse natural language query into structured format"""
        query_lower = query.lower()

        # Detect intent
        intent = "general_query"
        for intent_type, patterns in self.INTENT_PATTERNS.items():
            if any(p in query_lower for p in patterns):
                intent = intent_type
                break

        # Extract position
        position = None
        for pos, patterns in self.POSITION_PATTERNS.items():
            if any(p in query_lower for p in patterns):
                position = pos
                break

        # Extract metrics
        metric_patterns = ["xg", "goals", "assists", "passes", "tackles",
                          "press", "dribbles", "shots"]
        metrics = [m for m in metric_patterns if m in query_lower]

        # Extract player names (simplified - future: NER)
        player_match = re.search(r"like (\w+)|similar to (\w+)", query_lower)
        reference_player = player_match.group(1) or player_match.group(2) if player_match else None

        return ParsedQuery(
            original=query,
            intent=intent,
            entities={"position": position, "reference_player": reference_player},
            filters={"metrics": metrics}
        )

    def to_analytics_query(self, parsed: ParsedQuery) -> str:
        """Convert parsed query to analytics system query"""
        # Future: Generate actual database/API queries
        if parsed.intent == "similarity_search":
            return f"SIMILARITY_SEARCH(player={parsed.entities['reference_player']}, metrics={parsed.filters['metrics']})"
        elif parsed.intent == "ranking":
            return f"RANK(position={parsed.entities['position']}, by={parsed.filters['metrics']})"
        return f"SEARCH({parsed.original})"


class TacticalReportGenerator:
    """Future: LLM-powered tactical report generation"""

    def generate_match_report(self, match_data: Dict) -> str:
        """Generate natural language match report from data"""
        # Future: Feed to LLM with structured prompt
        # For now, template-based

        template = f"""
MATCH ANALYSIS REPORT
=====================

{match_data.get('home_team', 'Home')} vs {match_data.get('away_team', 'Away')}

KEY STATISTICS:
- Possession: {match_data.get('possession', '50-50')}
- xG: {match_data.get('xG', 'N/A')}
- Shots: {match_data.get('shots', 'N/A')}

TACTICAL OBSERVATIONS:
[Future: LLM-generated narrative based on event data,
 tracking data, and tactical patterns]

RECOMMENDATIONS:
[Future: AI-suggested tactical adjustments]
"""
        return template


# Demo
interface = NaturalLanguageInterface()

queries = [
    "Find me strikers similar to Haaland",
    "Who are the top pressing midfielders?",
    "Compare Arsenal and Liverpool pressing"
]

print("Natural Language Query Processing:")
print("=" * 50)

for q in queries:
    parsed = interface.parse_query(q)
    print(f"\nQuery: {q}")
    print(f"  Intent: {parsed.intent}")
    print(f"  Entities: {parsed.entities}")
    print(f"  Analytics query: {interface.to_analytics_query(parsed)}")

# Fan-Facing Analytics Products
import numpy as np
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class MatchState:
    minute: int
    home_goals: int
    away_goals: int
    home_xG: float
    away_xG: float
    possession: Dict[str, float]
    shots_on_target: Dict[str, int]

class LiveAnalytics:
    """Generate fan-facing live analytics"""

    def __init__(self, match_state: MatchState):
        self.state = match_state

    def win_probability(self) -> Dict[str, float]:
        """Calculate win probability"""
        score_diff = self.state.home_goals - self.state.away_goals
        xg_diff = self.state.home_xG - self.state.away_xG
        mins_remaining = 90 - self.state.minute

        # Simple model
        base_prob = 1 / (1 + np.exp(-(score_diff * 0.5 + xg_diff * 0.2)))
        home_win = base_prob * (1 - mins_remaining/180)
        draw = (1 - abs(score_diff) * 0.2) * mins_remaining/90
        draw = max(0, min(draw, 1 - home_win))

        return {
            "home": round(home_win * 100, 1),
            "draw": round(draw * 100, 1),
            "away": round((1 - home_win - draw) * 100, 1)
        }

    def generate_insights(self) -> List[str]:
        """Generate fan-friendly insights"""
        insights = []

        # xG vs goals
        if self.state.home_xG > self.state.home_goals + 1:
            insights.append("Home team creating chances but struggling to convert")
        if self.state.away_xG > self.state.away_goals + 1:
            insights.append("Away team unlucky based on chances created")

        # Possession
        if self.state.possession["home"] > 60:
            insights.append("Home team dominating possession")
        elif self.state.possession["away"] > 60:
            insights.append("Away team controlling the ball")

        # Efficiency
        home_efficiency = self.state.home_goals / max(self.state.home_xG, 0.1)
        away_efficiency = self.state.away_goals / max(self.state.away_xG, 0.1)

        if home_efficiency > 1.3:
            insights.append("Home team clinical in front of goal")
        if away_efficiency > 1.3:
            insights.append("Away team taking their chances well")

        return insights

    def momentum_score(self) -> Dict[str, float]:
        """Calculate momentum (simplified)"""
        # Future: analyze sequence of events
        xg_momentum = (self.state.home_xG - self.state.away_xG) / max(self.state.minute, 1) * 90
        possession_momentum = (self.state.possession["home"] - 50) / 10

        return {
            "home_momentum": round(50 + xg_momentum * 10 + possession_momentum * 5, 1),
            "away_momentum": round(50 - xg_momentum * 10 - possession_momentum * 5, 1)
        }

    def full_dashboard(self) -> Dict:
        """Generate complete analytics dashboard"""
        return {
            "win_probability": self.win_probability(),
            "xG": {"home": self.state.home_xG, "away": self.state.away_xG},
            "momentum": self.momentum_score(),
            "insights": self.generate_insights(),
            "key_stats": {
                "possession": self.state.possession,
                "shots_on_target": self.state.shots_on_target
            }
        }


# Example
state = MatchState(
    minute=65,
    home_goals=1,
    away_goals=1,
    home_xG=1.8,
    away_xG=0.9,
    possession={"home": 62, "away": 38},
    shots_on_target={"home": 5, "away": 3}
)

analytics = LiveAnalytics(state)
dashboard = analytics.full_dashboard()

print("Live Match Analytics Dashboard")
print("=" * 40)
print(f"\nWin Probability:")
print(f"  Home: {dashboard['win_probability']['home']}%")
print(f"  Draw: {dashboard['win_probability']['draw']}%")
print(f"  Away: {dashboard['win_probability']['away']}%")
print(f"\nxG: Home {dashboard['xG']['home']} - Away {dashboard['xG']['away']}")
print(f"\nInsights:")
for insight in dashboard["insights"]:
    print(f"  - {insight}")

# Python: Future Multi-Modal Data Integration
from dataclasses import dataclass, field
from typing import Dict, List
from datetime import datetime

@dataclass
class EmergingDataSource:
    name: str
    current_state: str
    future_2027: str
    applications: List[str]

EMERGING_SOURCES = [
    EmergingDataSource(
        "Skeleton Tracking", "Research only", "Elite clubs",
        ["Biomechanics", "Injury prevention", "Technique analysis"]
    ),
    EmergingDataSource(
        "Ball Physics", "Position only", "Spin rate added",
        ["Pass quality", "Shot analysis", "Goalkeeper metrics"]
    ),
    EmergingDataSource(
        "Biometrics", "HR + GPS", "Lactate estimation",
        ["Workload management", "Recovery", "Substitution timing"]
    ),
    EmergingDataSource(
        "Audio Analysis", "Not captured", "Experimental",
        ["Communication patterns", "Fatigue detection", "Leadership"]
    )
]

@dataclass
class FuturePlayerProfile:
    """Multi-modal player profile with emerging data"""
    player_id: str
    timestamp: datetime = field(default_factory=datetime.now)

    # Traditional
    tracking: Dict = field(default_factory=lambda: {
        "avg_speed": 8.2, "high_speed_distance": 850, "sprint_count": 22
    })

    # Future: Biomechanics
    biomechanics: Dict = field(default_factory=lambda: {
        "running_efficiency": 0.88, "stride_symmetry": 0.95,
        "injury_risk": "low", "technique_scores": {"shooting": 82}
    })

    # Future: Physiological
    physiological: Dict = field(default_factory=lambda: {
        "fatigue_level": 0.35, "recovery_status": "optimal",
        "readiness_score": 88
    })

    # Future: Communication
    communication: Dict = field(default_factory=lambda: {
        "leadership_moments": 5, "team_coordination": 0.82
    })


print("Emerging Data Sources Timeline:")
for source in EMERGING_SOURCES:
    print(f"  {source.name}: {source.current_state} -> {source.future_2027}")

profile = FuturePlayerProfile(player_id="player_001")
print(f"\nFuture Profile Sample:")
print(f"  Biomechanics Efficiency: {profile.biomechanics['running_efficiency']}")
print(f"  Readiness Score: {profile.physiological['readiness_score']}")

# Python: Integrated Performance Science Platform
from dataclasses import dataclass
from typing import Dict, List

@dataclass
class PlayerHealthData:
    fitness_level: float  # 0-10
    injury_risk: str  # low/medium/high
    confidence: float  # 0-10
    nutrition_compliance: float  # 0-1
    form_rating: float  # 0-10

def calculate_readiness(data: PlayerHealthData) -> Dict:
    weights = {
        "physical": 0.25, "medical": 0.25, "psychological": 0.20,
        "nutritional": 0.15, "recent_form": 0.15
    }

    risk_map = {"low": 90, "medium": 60, "high": 30}

    scores = {
        "physical": min(100, data.fitness_level * 10),
        "medical": risk_map.get(data.injury_risk, 50),
        "psychological": data.confidence * 10,
        "nutritional": data.nutrition_compliance * 100,
        "recent_form": data.form_rating * 10
    }

    overall = sum(scores[k] * weights[k] for k in scores)

    recommendations = []
    if scores["physical"] < 70: recommendations.append("Reduce training load")
    if scores["psychological"] < 70: recommendations.append("Sport psychology session")
    if scores["medical"] < 70: recommendations.append("Medical clearance needed")

    return {
        "overall_readiness": round(overall, 1),
        "scores": scores,
        "recommendations": recommendations
    }

# Example
player = PlayerHealthData(
    fitness_level=8.5, injury_risk="low", confidence=7.5,
    nutrition_compliance=0.85, form_rating=7.8
)

result = calculate_readiness(player)
print(f"Integrated Readiness: {result['overall_readiness']} / 100")
for dim, score in result["scores"].items():
    print(f"  {dim}: {score}")

# Python: Analytics ROI Calculator
def calculate_analytics_roi(investment: dict, outcomes: dict) -> dict:
    total_investment = sum(investment.values())

    recruitment = outcomes["better_signings"] + outcomes["avoided_bad_signings"]
    injury_savings = outcomes["reduced_injury_matches"] * outcomes["match_revenue_impact"]
    performance = outcomes["position_improvement"] * outcomes["position_value"]
    fan_revenue = outcomes["content_revenue"] + outcomes["partnerships"]

    total_value = recruitment + injury_savings + performance + fan_revenue
    roi = (total_value - total_investment) / total_investment * 100

    return {
        "investment": total_investment,
        "value": total_value,
        "roi_percentage": round(roi, 1)
    }

investment = {"staff": 500000, "data": 300000, "tech": 200000, "training": 50000}
outcomes = {
    "better_signings": 5000000, "avoided_bad_signings": 2000000,
    "reduced_injury_matches": 15, "match_revenue_impact": 50000,
    "position_improvement": 2, "position_value": 1000000,
    "content_revenue": 200000, "partnerships": 100000
}

result = calculate_analytics_roi(investment, outcomes)
print(f"Analytics ROI:")
print(f"  Investment: £{result['investment']:,}")
print(f"  Value Created: £{result['value']:,}")
print(f"  ROI: {result['roi_percentage']}%")

# Python: Complete Future Analytics Framework
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Callable, Any
from datetime import datetime
import json
import os

@dataclass
class FrameworkConfig:
    """Configuration for the analytics framework"""
    llm_api_key: str = field(default_factory=lambda: os.getenv("LLM_API_KEY", ""))
    tracking_api_key: str = field(default_factory=lambda: os.getenv("TRACKING_API_KEY", ""))
    enable_ai_features: bool = True
    privacy_level: str = "high"

class FutureAnalyticsFramework:
    """Comprehensive framework for future-ready football analytics"""

    def __init__(self, config: Optional[FrameworkConfig] = None):
        self.config = config or FrameworkConfig()
        self.data_sources: Dict[str, Any] = {}
        self.models: Dict[str, Any] = {}
        print("Future Analytics Framework initialized")

    def integrate_sources(
        self,
        tracking: List[Dict],
        events: List[Dict],
        sports_science: Optional[List[Dict]] = None
    ) -> List[Dict]:
        """Multi-modal data integration"""
        # Create lookup dictionaries for joining
        event_lookup = {
            (e["match_id"], e["event_time"]): e
            for e in events
        }

        ss_lookup = {}
        if sports_science:
            ss_lookup = {
                (s["player_id"], s["match_id"]): s
                for s in sports_science
            }

        integrated = []
        for t in tracking:
            record = {**t}

            # Join events
            event_key = (t.get("match_id"), t.get("timestamp"))
            if event_key in event_lookup:
                record.update(event_lookup[event_key])

            # Join sports science
            ss_key = (t.get("player_id"), t.get("match_id"))
            if ss_key in ss_lookup:
                ss_data = ss_lookup[ss_key]
                record.update(ss_data)

                # Calculate integrated metrics
                physical_load = record.get("physical_load", 50)
                mental_stress = record.get("mental_stress", 30)
                fatigue = record.get("fatigue", 20)
                injury_risk = record.get("injury_risk", 10)
                accumulated_load = record.get("accumulated_load", 100)

                record["integrated_load"] = physical_load * (1 + mental_stress/100)
                record["readiness_score"] = min(100,
                    0.4 * (100 - fatigue) +
                    0.3 * (100 - injury_risk) +
                    0.3 * (100 - accumulated_load/10)
                )

            integrated.append(record)

        return integrated

    def generate_insight(
        self,
        data: Any,
        question: str,
        context: Optional[Dict] = None
    ) -> Dict:
        """LLM-powered analysis generation"""
        prompt = f"""You are a football analytics expert.
Context: {json.dumps(context or {})}
Data summary: {self._summarize_data(data)}
Question: {question}
Provide a concise, actionable insight."""

        # Call LLM (simulated here)
        response = self._call_llm(prompt)

        return {
            "question": question,
            "insight": response["content"],
            "confidence": response["confidence"],
            "data_points_used": len(data) if hasattr(data, "__len__") else 1,
            "generated_at": datetime.now().isoformat()
        }

    def build_prediction_pipeline(
        self,
        target: str,
        features: List[str],
        data: List[Dict]
    ) -> Dict:
        """Build and register a prediction model"""
        # Feature engineering would happen here
        model = {
            "target": target,
            "features": features,
            "trained_at": datetime.now().isoformat(),
            "performance": {"rmse": 0.15, "r2": 0.78}
        }

        self.models[target] = model
        return model

    def monitor_match_live(
        self,
        match_id: str,
        callback: Optional[Callable] = None
    ):
        """Real-time match monitoring"""
        import random

        print(f"Starting live monitoring for match: {match_id}")

        for minute in range(1, 91):
            live_data = self._fetch_live_data(match_id, minute)

            insights = {
                "minute": minute,
                "xg_home": live_data["xg_home"],
                "xg_away": live_data["xg_away"],
                "momentum": live_data["momentum"],
                "key_events": live_data["events"]
            }

            if callback:
                callback(insights)

    def generate_report(
        self,
        player_id: str,
        include_ai: bool = True
    ) -> Dict:
        """Generate comprehensive player report"""
        report = {
            "player_id": player_id,
            "generated_at": datetime.now().isoformat(),
            "sections": {}
        }

        # Traditional metrics
        report["sections"]["performance"] = self._calculate_performance_metrics(player_id)

        # Physical data
        report["sections"]["physical"] = self._calculate_physical_metrics(player_id)

        # AI-generated insights
        if include_ai and self.config.enable_ai_features:
            report["sections"]["ai_insights"] = self.generate_insight(
                data=report["sections"]["performance"],
                question="What are the key areas for improvement?"
            )

        return report

    def _summarize_data(self, data: Any) -> str:
        """Summarize data for LLM context"""
        if isinstance(data, list):
            return json.dumps({"rows": len(data), "sample": data[:3] if data else []})
        elif isinstance(data, dict):
            return json.dumps(data)
        return str(data)

    def _call_llm(self, prompt: str) -> Dict:
        """Call LLM API (simulated)"""
        return {
            "content": "Based on the data, focus on improving progressive passing in the final third.",
            "confidence": 0.85
        }

    def _fetch_live_data(self, match_id: str, minute: int) -> Dict:
        """Fetch live match data (simulated)"""
        import random
        return {
            "xg_home": random.uniform(0, 0.1) * minute/30,
            "xg_away": random.uniform(0, 0.1) * minute/30,
            "momentum": random.choice(["home", "away", "neutral"]),
            "events": []
        }

    def _calculate_performance_metrics(self, player_id: str) -> Dict:
        return {
            "xg_per_90": 0.45,
            "xa_per_90": 0.23,
            "progressive_carries": 12.5,
            "pressures_per_90": 18.2
        }

    def _calculate_physical_metrics(self, player_id: str) -> Dict:
        return {
            "total_distance": 10.5,
            "high_speed_runs": 45,
            "sprint_distance": 890,
            "acceleration_load": 78.5
        }

# Usage Example
framework = FutureAnalyticsFramework()
report = framework.generate_report("player_123")
print(f"Report generated at: {report['generated_at']}")
print(f"Performance xG/90: {report['sections']['performance']['xg_per_90']}")
print(f"AI Insight: {report['sections']['ai_insights']['insight']}")

# Python: Your Analytics Journey Tracker
from dataclasses import dataclass
from typing import List
import math

@dataclass
class LearningMilestone:
    quarter: int
    focus_area: str
    skill: str
    milestone: str

def create_learning_roadmap(
    current_skills: List[str],
    target_role: str,
    timeline_months: int = 24
) -> List[LearningMilestone]:
    skill_gaps = {
        "technical": ["Deep Learning", "LLM APIs", "Cloud Deployment"],
        "domain": ["Advanced Tactics", "Recruitment Analytics"],
        "soft": ["Executive Communication", "Project Management"]
    }

    all_skills = []
    focus_areas = []
    for area, skills in skill_gaps.items():
        all_skills.extend(skills)
        focus_areas.extend([area.title()] * len(skills))

    num_quarters = math.ceil(timeline_months / 3)
    roadmap = []

    for i in range(min(num_quarters, len(all_skills))):
        roadmap.append(LearningMilestone(
            quarter=i + 1,
            focus_area=focus_areas[i],
            skill=all_skills[i],
            milestone=f"Complete {all_skills[i]} certification/project"
        ))

    print("=" * 52)
    print("  YOUR FOOTBALL ANALYTICS LEARNING ROADMAP")
    print("=" * 52)
    print()

    for milestone in roadmap:
        print(f"Quarter {milestone.quarter}: {milestone.focus_area}")
        print(f"  Skill: {milestone.skill}")
        print(f"  Milestone: {milestone.milestone}")
        print()

    print("Remember: The best analysts combine technical excellence")
    print("with deep football understanding and continuous curiosity.")
    print()
    print("Good luck on your journey!")

    return roadmap

# Create your roadmap
roadmap = create_learning_roadmap(
    current_skills=["Python", "R", "SQL", "Basic ML"],
    target_role="Senior Football Analyst"
)