271 lines
10 KiB
Python
271 lines
10 KiB
Python
 
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#!/usr/bin/env python3
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"""
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Eurojackpot Treffer-Analyse (umschlüsselte Werte)
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Analysiert, wo die größten Treffer-Wahrscheinlichkeiten bei den umschlüsselten Werten liegen.
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"""
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import pandas as pd
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from collections import Counter, defaultdict
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import numpy as np
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def analyze_hit_probabilities():
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"""Analysiert die Treffer-Wahrscheinlichkeiten der umschlüsselten Werte."""
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# Umschlüsselte Daten laden
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df = pd.read_csv("/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/Eurojackpot/AlleEurojackpotzahlen_umschluesselt.csv", sep=';')
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print("🎯 TREFFER-ANALYSE UMSCHLÜSSELTE WERTE (1-10)")
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print("="*60)
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print(f"Analysierte Ziehungen: {len(df)}")
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# 1. Einzelne Gruppen-Wahrscheinlichkeiten pro Position
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print(f"\n🏆 HÖCHSTE TREFFER-WAHRSCHEINLICHKEITEN PRO POSITION:")
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print("="*55)
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positions = ['z1', 'z2', 'z3', 'z4', 'z5']
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position_stats = {}
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for pos in positions:
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group_counts = Counter(df[pos])
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total = len(df)
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# Beste Gruppe für diese Position
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best_group = max(group_counts, key=group_counts.get)
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best_count = group_counts[best_group]
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best_probability = (best_count / total) * 100
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position_stats[pos] = {
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'best_group': best_group,
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'probability': best_probability,
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'count': best_count,
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'all_groups': group_counts
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}
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print(f"\n{pos.upper()}: Gruppe {best_group} führt mit {best_probability:.1f}% ({best_count}/{total})")
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# Top 3 für diese Position
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top_3 = group_counts.most_common(3)
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print(f" Top 3: ", end="")
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for i, (group, count) in enumerate(top_3):
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prob = (count / total) * 100
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print(f"{i+1}.Gruppe {group}({prob:.1f}%)", end="")
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if i < 2:
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print(" > ", end="")
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print()
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# 2. Beste Gesamtkombination
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print(f"\n🔥 OPTIMAL-KOMBINATION (höchste Einzelwahrscheinlichkeiten):")
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print("="*60)
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optimal_combination = []
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total_probability = 1.0
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for pos in positions:
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best_group = position_stats[pos]['best_group']
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probability = position_stats[pos]['probability'] / 100
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optimal_combination.append(best_group)
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total_probability *= probability
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print(f"{pos}: Gruppe {best_group} ({position_stats[pos]['probability']:.1f}%)")
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optimal_string = '-'.join(map(str, optimal_combination))
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print(f"\nOptimal-Kombination: {optimal_string}")
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print(f"Theoretische Wahrscheinlichkeit: {total_probability*100:.6f}%")
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print(f"Das entspricht etwa 1 in {1/total_probability:,.0f} Ziehungen")
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# 3. Tatsächlich aufgetretene häufigste Kombinationen
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print(f"\n📊 REAL AUFGETRETENE HÄUFIGSTE KOMBINATIONEN:")
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print("="*50)
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combination_counts = Counter(df['kombination_umschluesselt'])
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print(f"Top 20 real aufgetretene Kombinationen:")
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for i, (combination, count) in enumerate(combination_counts.most_common(20), 1):
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probability = (count / len(df)) * 100
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print(f"{i:2}. {combination:15} {count}x ({probability:.2f}%)")
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# 4. Bereichs-Kombinationen mit höchster Wahrscheinlichkeit
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print(f"\n🎲 BEREICHS-KOMBINATIONEN MIT HÖCHSTER WAHRSCHEINLICHKEIT:")
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print("="*60)
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# Niedrig (1-3), Mittel (4-7), Hoch (8-10) Kombinationen
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range_combinations = defaultdict(int)
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for _, row in df.iterrows():
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ranges = []
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for pos in positions:
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val = row[pos]
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if 1 <= val <= 3:
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ranges.append('N') # Niedrig
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elif 4 <= val <= 7:
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ranges.append('M') # Mittel
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else:
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ranges.append('H') # Hoch
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range_pattern = ''.join(ranges)
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range_combinations[range_pattern] += 1
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print(f"Häufigste Bereichsmuster (N=Niedrig1-3, M=Mittel4-7, H=Hoch8-10):")
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sorted_patterns = sorted(range_combinations.items(), key=lambda x: x[1], reverse=True)
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for i, (pattern, count) in enumerate(sorted_patterns[:15], 1):
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probability = (count / len(df)) * 100
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pattern_readable = pattern.replace('N', 'Niedrig').replace('M', 'Mittel').replace('H', 'Hoch')
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print(f"{i:2}. {pattern:5} ({pattern_readable:25}) {count:3}x ({probability:5.1f}%)")
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# 5. Positions-spezifische Empfehlungen
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print(f"\n💡 POSITIONS-SPEZIFISCHE EMPFEHLUNGEN:")
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print("="*45)
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recommendations = {}
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for pos in positions:
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group_counts = position_stats[pos]['all_groups']
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total = len(df)
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# Top 3 Gruppen für maximale Abdeckung
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top_groups = [group for group, count in group_counts.most_common(3)]
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top_coverage = sum(group_counts[group] for group in top_groups)
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coverage_percentage = (top_coverage / total) * 100
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recommendations[pos] = {
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'top_groups': top_groups,
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'coverage': coverage_percentage
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}
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print(f"\n{pos.upper()}: Empfohlene Gruppen {top_groups}")
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print(f" Abdeckung: {coverage_percentage:.1f}% aller Ziehungen")
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# Wahrscheinlichkeitsverteilung
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print(f" Verteilung: ", end="")
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for group in top_groups:
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prob = (group_counts[group] / total) * 100
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print(f"Gruppe {group}({prob:.1f}%)", end="")
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if group != top_groups[-1]:
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print(", ", end="")
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print()
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# 6. Strategische Kombinationen
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print(f"\n🎯 STRATEGISCHE KOMBINATIONEN FÜR MAXIMALE TREFFER:")
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print("="*55)
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# Berechne verschiedene Strategien
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strategies = {
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'Konservativ': {
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'z1': [1, 2], # Top 2 der Position z1
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'z2': [3, 4], # Top 2 der Position z2
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'z3': [5, 6], # Top 2 der Position z3
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'z4': [7, 8], # Top 2 der Position z4
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'z5': [9, 10] # Top 2 der Position z5
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},
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'Ausgewogen': {
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'z1': [1, 2, 3], # Top 3 jeder Position
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'z2': [3, 4, 5],
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'z3': [4, 5, 6, 7],
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'z4': [6, 7, 8],
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'z5': [8, 9, 10]
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},
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'Optimal': {} # Wird basierend auf tatsächlichen Daten gefüllt
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}
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# Optimal-Strategie basierend auf echten Top-3 pro Position
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for pos in positions:
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top_3_groups = recommendations[pos]['top_groups']
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strategies['Optimal'][pos] = top_3_groups
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for strategy_name, strategy in strategies.items():
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if strategy: # Nur wenn Strategie gefüllt ist
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print(f"\n{strategy_name}-Strategie:")
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total_combinations = 1
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coverage_per_position = []
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for pos in positions:
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recommended_groups = strategy[pos]
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pos_stats = position_stats[pos]['all_groups']
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total_pos = len(df)
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# Abdeckung dieser Gruppen
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coverage = sum(pos_stats.get(group, 0) for group in recommended_groups)
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coverage_pct = (coverage / total_pos) * 100
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coverage_per_position.append(coverage_pct)
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total_combinations *= len(recommended_groups)
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print(f" {pos}: Gruppen {recommended_groups} ({coverage_pct:.1f}% Abdeckung)")
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avg_coverage = np.mean(coverage_per_position)
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print(f" Durchschnittliche Abdeckung: {avg_coverage:.1f}%")
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print(f" Mögliche Kombinationen: {total_combinations:,}")
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# 7. Heiße und kalte Zahlen
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print(f"\n🔥❄️ HEISSE UND KALTE GRUPPEN:")
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print("="*35)
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# Alle Gruppen über alle Positionen sammeln
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all_groups = []
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for pos in positions:
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all_groups.extend(df[pos].tolist())
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group_total_counts = Counter(all_groups)
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total_appearances = len(all_groups)
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expected_per_group = total_appearances / 10 # 10 Gruppen
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print(f"Erwartete Häufigkeit pro Gruppe: {expected_per_group:.1f}")
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print(f"\nHeisse Gruppen (über Erwartung):")
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hot_groups = []
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for group in range(1, 11):
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actual = group_total_counts.get(group, 0)
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deviation = actual - expected_per_group
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if deviation > 0:
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hot_groups.append((group, actual, deviation))
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hot_groups.sort(key=lambda x: x[2], reverse=True)
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for group, count, deviation in hot_groups:
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percentage = (count / total_appearances) * 100
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print(f" Gruppe {group}: {count} (+{deviation:.1f}, {percentage:.1f}%)")
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print(f"\nKalte Gruppen (unter Erwartung):")
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cold_groups = []
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for group in range(1, 11):
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actual = group_total_counts.get(group, 0)
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deviation = actual - expected_per_group
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if deviation < 0:
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cold_groups.append((group, actual, deviation))
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cold_groups.sort(key=lambda x: x[2])
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for group, count, deviation in cold_groups:
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percentage = (count / total_appearances) * 100
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print(f" Gruppe {group}: {count} ({deviation:.1f}, {percentage:.1f}%)")
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# 8. Export der Empfehlungen
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print(f"\n💾 EMPFEHLUNGEN EXPORT:")
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print("="*25)
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# Erstelle Empfehlungs-DataFrame
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recommendation_data = []
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# Für jede Position die besten Empfehlungen
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for pos in positions:
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pos_recommendations = recommendations[pos]
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for group in pos_recommendations['top_groups']:
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prob = (position_stats[pos]['all_groups'][group] / len(df)) * 100
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recommendation_data.append({
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'position': pos,
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'gruppe': group,
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'wahrscheinlichkeit_prozent': prob,
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'anzahl_auftreten': position_stats[pos]['all_groups'][group],
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'empfehlung_rang': pos_recommendations['top_groups'].index(group) + 1
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})
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rec_df = pd.DataFrame(recommendation_data)
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output_file = "/Users/sebastianfrohlich/Library/Mobile Documents/com~apple~CloudDocs/Jupyter Notebooks/Eurojackpot/treffer_empfehlungen_umschluesselt.csv"
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rec_df.to_csv(output_file, sep=';', index=False)
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print(f"✅ Empfehlungen gespeichert: treffer_empfehlungen_umschluesselt.csv")
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return position_stats, recommendations
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if __name__ == "__main__":
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analyze_hit_probabilities()
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