[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"blog-article-mlb-betting-model-de":3,"mdc--2xh0u0-key":78},{"id":4,"slug":5,"status":6,"section":7,"category":8,"author":9,"publish_date":10,"read_time":11,"image":12,"embedded_components":13,"related_calculators":13,"related_articles":14,"title":15,"description":16,"keywords":17,"content":26,"faq":27,"availableLocales":73},"1823f40a-72e6-4607-b06a-87cf8c049312","mlb-betting-model","published","betting","strategies","Evgeniy Volkov","2026-03-01",22,"\u002Fimages\u002Fblog\u002Fmlb-betting-model.webp","[]",[],"MLB-Wettmodell: Bauen Sie Ihr eigenes System (2026)","Erstellen Sie ein MLB-Wettmodell von Grund auf. Python-Code, Park-Faktoren-Diagramm, EV-Rechner, Prop-Bet-Modelle. Anfänger bis fortgeschrittener Leitfaden.",[18,19,20,21,22,23,24,25],"MLB Wettmodell","Baseball Wettmodell","MLB Prognosemodell","Sports-Wettmodell bauen","MLB Erwartungswert","Baseball Analytics Wetten","MLB Park-Faktoren","Kelly-Kriterium Baseball","# MLB-Wettmodell: Bauen Sie Ihr eigenes System von Grund auf (2026)\n\nStellen Sie sich vor: Es ist Dienstagmorgen, die vollständige MLB-Spielliste fällt in 3 Stunden, und Sie haben 14 Spiele zu bewerten. Das Bauchgefühl sagt, dass die Dodgers eine sichere Sache sind. Ihr Freund schwört, dass die White Sox \"überreif\" sind. Währenddessen bewegt sich das Smart Money eine Quote, über die niemand spricht.\n\nHier ist der Unterschied zwischen Ihnen und den Profis: **Sie haben ein Modell**. Nicht eine Kristallkugel — ein systematischer Prozess, der Daten in Wahrscheinlichkeiten umwandelt, diese Wahrscheinlichkeiten mit Marktquoten vergleicht und Ihnen genau sagt, welche Wetten einen positiven Erwartungswert haben.\n\nDie gute Nachricht? Stand 2026 ist jedes Datenstück, das Sie zum Aufbau eines MLB-Wettmodells benötigen, **kostenlos**. FanGraphs, Baseball Savant und Statcast geben Ihnen dieselben Rohdaten, die professionelle Syndikate verwenden. Was die Gewinner unterscheidet, ist wie sie diese Zahlen in Features umwandeln, Modelle trainieren, die tatsächlich Ergebnisse vorhersagen, und Bankroll mit Disziplin verwalten.\n\nDieser Leitfaden führt Sie durch den gesamten Prozess — von Ihrem ersten Spreadsheet bis zu einem vollständigen Python-Ensemble-Modell. Egal, ob Sie ein kompletter Anfänger oder ein Datenwissenschaftler sind, der nach MLB-spezifischen Feature-Engineering-Ideen sucht, es gibt ein Level für Sie. Lassen Sie uns etwas bauen, das tatsächlich funktioniert.\n\n## Kurzfassung — MLB-Wettmodell Schnellreferenz\n\n### Model-Level auf einen Blick\n\n| Level | Tools | Aufbauzeit | Erwarteter Vorteil | Am besten für |\n|-------|-------|:--------:|:--------:|----------|\n| Anfänger | Spreadsheet + FanGraphs | 1-2 Wochen | 1-3% | Das Framework lernen |\n| Fortgeschritten | Python + Regression | 3-4 Wochen | 3-5% | Konsistente kleine Vorteile |\n| Fortgeschrittene | XGBoost + Ensemble | 6-8 Wochen | 5-8% | ROI maximieren |\n\n### Für wen dieser Leitfaden ist\n\nDieser Leitfaden richtet sich an jeden, der von Bauchgefühl-Tipps zu einem **datengesteuerten MLB-Wettmodell** übergehen möchte. Sie benötigen keinen Statistikabschluss — wenn Sie ein Spreadsheet verwenden können, können Sie auf Level 1 starten. Wenn Sie grundlegendes Python kennen, springen Sie direkt zum Abschnitt Fortgeschrittene.\n\n## Was ist ein MLB-Wettmodell (und warum sollten Sie eines bauen)?\n\n### Modell vs. Bauchgefühl — Der wesentliche Unterschied\n\nEin Wettmodell ist eine **Wahrscheinlichkeitsmaschine**. Sie fügen ihm Daten ein (Pitcher-Statistiken, Park-Faktoren, Bullpen-Einsatz), und es gibt eine Wahrscheinlichkeit für jedes mögliche Ergebnis aus. Diese Wahrscheinlichkeit wird dann mit den Marktquoten verglichen, um +EV-Wetten zu finden.\n\nDer Unterschied ist wichtig: Wenn Sie \"fühlen\", dass die Dodgers gewinnen werden, haben Sie keine Möglichkeit zu wissen, ob -180 fair ist. Wenn Ihr Modell sagt, dass die Dodgers eine 63%ige Gewinnchance haben, können Sie berechnen, dass -180 nur 64,3% impliziert — was bedeutet, dass der Markt fair bewertet ist und es keinen Wett gibt.\n\n### Was ein gutes Modell wirklich tut\n\nEin gutes MLB-Wettmodell tut drei Dinge:\n\n1. **Prognostiziert die Gewinnwahrscheinlichkeit** genauer als der Markt (selbst um 2-3%)\n2. **Identifiziert +EV-Wetten**, wo Ihre Wahrscheinlichkeit die implizierten Quoten übersteigt\n3. **Passt Wetten angemessen an** mit dem [Kelly-Kriterium](\u002Fbetting\u002Fkelly-calculator) oder einer Variante\n\nEs prognostiziert Gewinner nicht mit Sicherheit. Ein 55%-Modell ist bei den richtigen Quoten äußerst profitabel. Das Ziel ist nicht Genauigkeit — es ist **Kalibrierung** und **Vorteilsidentifikation**.\n\n## Wählen Sie Ihr Level — Anfänger, Fortgeschrittene oder Fortgeschrittene\n\n### Anfänger: Spreadsheet + Schlüssel-Statistiken\n\nStarten Sie hier, wenn Sie noch nie ein Modell erstellt haben. Verfolgen Sie 4-5 Schlüssel-Statistiken in einem Spreadsheet (Pitcher xFIP, Team wOBA, Bullpen-Arbeitsauslastung, Park-Faktor) und weisen Sie einfache Gewichtungen zu. Sie werden Vegas nicht konsistent schlagen, aber Sie werden das Framework lernen und aufhören, rein emotionale Wetten zu platzieren.\n\n**Zeit:** 1-2 Wochen | **Tools:** Google Sheets oder Excel | **Daten:** FanGraphs\n\nWenn Sie völlig neu in der Sportanalytik sind, lesen Sie zunächst unseren [MLB-Außenseiter-Wettmodell-Leitfaden](\u002Fblog\u002Fmlb-underdog-betting-strategy), um zu sehen, wie ein datengesteuertes System in der Praxis aussieht, bevor Sie Ihr eigenes bauen.\n\n### Fortgeschrittene: Python + Regression\n\nSteigen Sie mit den Python-Bibliotheken pandas und scikit-learn auf. Bauen Sie logistische Regressionsmodelle, berechnen Sie eine ordnungsgemäße Feature-Wichtigkeit und backtesten gegen historische Quoten. Dies ist, wo die meisten profitablen Amateur-Wetter operieren.\n\n**Zeit:** 3-4 Wochen | **Tools:** Python, Jupyter Notebooks | **Daten:** FanGraphs + Statcast\n\n### Fortgeschrittene: XGBoost + Ensemble-Methoden\n\nKombinieren Sie mehrere Modelltypen (lineare Regression, logistische Regression, XGBoost) in ein Ensemble, das robuster ist als jedes einzelne Modell. Fügen Sie erweiterte Features wie Pitch-Level-Daten, Umpire-Strike-Zone-Tendenzen und Echtzeit-Lineup-Anpassungen hinzu.\n\n**Zeit:** 6-8 Wochen | **Tools:** Python, XGBoost, LightGBM | **Daten:** Statcast + Wetter-APIs\n\nDasselbe Framework gilt für andere Sportarten. Schauen Sie sich unseren [NBA-Wettmodell-Überblick](\u002Fblog\u002Fnba-betting-system) und [NFL-Wettstrategie-Leitfaden](\u002Fblog\u002Fnfl-betting-strategy-guide) an, wenn Sie Multi-Sport-Modelle erstellen.\n## Phase 1: Datenerfassung — Wo man MLB-Daten erhält\n\n### FanGraphs — Team- und Spielerstatistiken (xFIP, wOBA, K-BB%)\n\n[FanGraphs](https:\u002F\u002Fwww.fangraphs.com) ist die Grundlage. Laden Sie Team- und Pitcher-Statistiken der letzten 3–5 Saisons herunter. Die wichtigsten Metriken:\n\n- **xFIP** (Expected Fielding Independent Pitching): Prognostiziert zukünftige Pitcher-Leistung besser als ERA\n- **wOBA** (Weighted On-Base Average): Erfasst den gesamten offensiven Wert auf einer einzigen Skala\n- **K-BB%** (Strikeout minus Walk Rate): Der #1-Prädiktor für Pitcher-Qualität\n- **BABIP** (Batting Average on Balls in Play): Identifiziert Kandidaten für Luck-Regression\n\n### Statcast (Baseball Savant) — Pitch-Level-Daten\n\n[Baseball Savant](https:\u002F\u002Fbaseballsavant.mlb.com) bietet Statcast-Daten — Exit-Geschwindigkeit, Launch-Winkel, Spin Rate und Expected Stats (xBA, xSLG, xwOBA). Diese „Expected\"-Stats eliminieren Feldspiel und Glück und geben Ihnen ein klareres Bild der echten Spielstärke.\n\n### Park Factors — Warum der Veranstaltungsort zählt\n\nPark Factors sind die **am meisten unterschätzte Variable** im MLB-Wetten. Coors Field erhöht die Run-Scoring um 38 %. Dodger Stadium reduziert es um 12 %. Wenn Ihr Modell nicht für den Veranstaltungsort angepasst ist, lassen Sie Vorteil auf dem Tisch liegen.\n\nScrollen Sie nach unten, um unsere vollständige [30-Stadion-Park-Factors-Tabelle](#mlb-park-factors-every-stadium-ranked-2024-2025) mit visuellen Rankings zu sehen.\n\n### Schiedsrichter- und Wetterdaten\n\nDie Strikezone-Tendenzen von Schiedsrichtern beeinflussen Strikeout- und Walk-Raten. Ein Schiedsrichter mit enger Zone kann 0,5 Runs zu den Game-Totalen addieren. Wetter — besonders Windgeschwindigkeit und -richtung am Wrigley Field — beeinflusst Over\u002FUnder-Wetten direkt.\n\n#### Tabelle: Kostenlose vs. kostenpflichtige Datenquellen\n\n| Quelle | Kosten | Datentyp | Am besten für |\n|--------|:------:|----------|---------------|\n| FanGraphs | Kostenlos | Team-\u002FSpielerstatistiken | Grundlagen-Metriken |\n| Baseball Savant | Kostenlos | Statcast, Pitch-Level | Expected Stats, Spin Rates |\n| Retrosheet | Kostenlos | Historische Play-by-Play | Modell-Backtesting |\n| Weather API | Kostenlos-Stufe | Wind, Temperatur, Luftfeuchtigkeit | Game-Totale-Anpassung |\n| Odds API | Kostenlos-Stufe | Historische\u002FLive-Quoten | Backtesting, CLV-Tracking |\n| Sports Reference | Kostenlos | Historische Standings | Saisonale Analyse |\n\nNutzen Sie den [Odds Converter](\u002Fbetting\u002Fodds-converter), um zwischen amerikanischen, dezimalen und Bruchquoten zu wechseln, während Sie mit verschiedenen Datenquellen arbeiten.\n\n## Phase 2: Feature Engineering — Daten in Vorhersagen umwandeln\n\n### Prädiktive vs. deskriptive Statistiken\n\nHier scheitern die meisten Anfänger. Sie verwenden **deskriptive Statistiken** (Batting Average, Pitcher W-L-Bilanz, RBIs), die Ihnen sagen, was passiert ist, anstelle von **prädiktiven Statistiken**, die vorhersagen, was passieren wird.\n\n| Prädiktiv (Verwenden Sie diese) | Deskriptiv (Vermeiden Sie diese) |\n|--------------------------------|-----------------------------------|\n| xFIP, SIERA | ERA, W-L-Bilanz |\n| wOBA, xwOBA | Batting Average |\n| K-BB% | Strikeouts allein |\n| Barrel Rate, Hard Hit% | Gesamttrefferzahl |\n| Base Running (BsR) | Gestohlene Bases |\n| Park-adjusted Metriken | Rohdaten |\n\n### Bullpen-Ermüdungsindex (-0,6 MPH pro B2B = -0,25 Runs)\n\nForschungen aus mehreren Quellen zeigen, dass Reliever etwa **0,6 MPH** auf ihrem Fastball pro Back-to-Back-Einsatz verlieren. Dieser Geschwindigkeitsverlust entspricht ungefähr **-0,25 Runs pro Spiel** an erwarteter Run-Verhinderung.\n\nErstellen Sie einen Bullpen-Ermüdungsindex:\n- Verfolgung der Einsätze jedes Relievers in den letzten 3 Tagen\n- Gewichtung neuerer Einsätze stärker (gestern > vor 2 Tagen)\n- Kennzeichnung von Bullpens mit 3+ eingesetzten Relievern in aufeinanderfolgenden Spielen\n\nDies ist einer der exploitierbarsten Vorteile im MLB, da der Markt langsam auf Bullpen-Überbelastung reagiert, besonders in der ersten Hälfte der Doubleheader-Tage.\n\n### Platoon Splits und Lineup-Konstruktion\n\nLinkshändige Schläger gegen linkshändige Pitcher (LvL) schneiden erheblich schlechter ab als RvL. Ihr Modell sollte Folgendes enthalten:\n\n- Handedness des Starting Pitchers\n- Lineup-Zusammensetzung (Prozentsatz gleichseitiger Schläger)\n- Historische Platoon Splits für Schlüsselspieler\n- Manager-Tendenzen bei der Lineup-Konstruktion\n\n### Starting Pitcher Rolling Metrics\n\nVerwenden Sie nicht vollständige Saisonstatistiken für einen Pitcher, der 3 Wochen lang schlecht ist. Erstellen Sie **rollierende Fenster**:\n\n- **Letzte 3 Starts**: Erfasst jüngste Form\n- **Letzte 10 Starts**: Stabilere Sample\n- **Saisonbilanz**: Grundlage\n\nGewichten Sie die rollierenden Fenster: 40 % letzte-3, 35 % letzte-10, 25 % Saison. Dies erfasst sowohl Hot Streaks als auch Regression besser als Rohdurchschnitte der Saison.\n\n#### Ranking der Feature-Wichtigkeit\n\nBasierend auf Backtesting über 2019–2025-Daten zeigt sich, was am wichtigsten ist:\n\n| Rang | Feature | Wichtigkeits-Score | Kategorie |\n|:----:|---------|:------------------:|-----------|\n| 1 | Starting Pitcher xFIP (rolling 10) | 0,18 | Pitching |\n| 2 | Team wOBA (letzte 14 Tage) | 0,14 | Hitting |\n| 3 | Park Factor | 0,12 | Veranstaltungsort |\n| 4 | Bullpen-Ermüdungsindex | 0,10 | Pitching |\n| 5 | K-BB% (Starter) | 0,09 | Pitching |\n| 6 | Platoon-Matchup-Score | 0,07 | Lineup |\n| 7 | Home\u002FAway Split | 0,06 | Situational |\n| 8 | Temperatur + Wind | 0,05 | Wetter |\n| 9 | Schiedsrichter-Zone-Rating | 0,04 | Schiedsrichter |\n| 10 | Rest-Tage (Team) | 0,03 | Ermüdung |\n## Phase 3: Modelltypen mit Python-Code (2026)\n\n### Lineare Regression (Ausgangspunkt)\n\nDie lineare Regression sagt **Run-Summen** direkt voraus. Es ist das einfachste Modell, aber überraschend effektiv für Spiel-Summen.\n\n```python\nfrom sklearn.linear_model import LinearRegression\nimport pandas as pd\n\n# Feature-Matrix laden\nfeatures = ['sp_xfip', 'team_woba', 'park_factor',\n            'bullpen_fatigue', 'k_bb_pct', 'platoon_score']\n\nX_train = train_data[features]\ny_train = train_data['total_runs']\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\n# Heute's Spiele vorhersagen\ntoday_pred = model.predict(today_data[features])\n```\n\n### Logistische Regression (Klassifizierung)\n\nFür Moneyline-Wetten benötigen Sie **Gewinn-Wahrscheinlichkeit**, nicht Run-Summen. Die logistische Regression gibt Wahrscheinlichkeiten direkt aus.\n\n```python\nfrom sklearn.linear_model import LogisticRegression\n\nX_train = train_data[features]\ny_train = train_data['home_win']  # 1 oder 0\n\nmodel = LogisticRegression(max_iter=1000)\nmodel.fit(X_train, y_train)\n\n# Gewinn-Wahrscheinlichkeiten abrufen\nprobs = model.predict_proba(today_data[features])\nhome_win_prob = probs[:, 1]  # Wahrscheinlichkeit eines Heimsiegs\n```\n\n### XGBoost (Gradient Boosting)\n\nXGBoost erfasst nichtlineare Beziehungen, die die Regression übersieht. Es ist das Arbeitstier professioneller MLB-Modelle.\n\n```python\nimport xgboost as xgb\n\nparams = {\n    'objective': 'binary:logistic',\n    'max_depth': 5,\n    'learning_rate': 0.05,\n    'subsample': 0.8,\n    'colsample_bytree': 0.8,\n    'eval_metric': 'logloss'\n}\n\ndtrain = xgb.DMatrix(X_train, label=y_train)\nmodel = xgb.train(params, dtrain, num_boost_round=300)\n\n# Vorhersagen\ndtest = xgb.DMatrix(today_data[features])\nprobs = model.predict(dtest)\n```\n\n### Ensemble-Modell (Kombinieren aller drei)\n\nKein einzelnes Modell ist für jedes Spiel am besten. Ein Ensemble mittelt Vorhersagen aus mehreren Modellen und reduziert so Überanpassung und verbessert die Kalibrierung.\n\n#### Python-Code: Vollständige Ensemble-Pipeline\n\n```python\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.calibration import CalibratedClassifierCV\nimport xgboost as xgb\n\n# Einzelne Modelle trainieren\nlr_model = LogisticRegression(max_iter=1000)\nlr_model.fit(X_train, y_train)\nlr_probs = lr_model.predict_proba(X_test)[:, 1]\n\nxgb_model = xgb.XGBClassifier(\n    max_depth=5, learning_rate=0.05,\n    n_estimators=300, subsample=0.8\n)\nxgb_model.fit(X_train, y_train)\nxgb_probs = xgb_model.predict_proba(X_test)[:, 1]\n\n# Gewichtetes Ensemble (Gewichtungen über Validierungssatz abstimmen)\nensemble_probs = 0.4 * lr_probs + 0.6 * xgb_probs\n\n# Mit der vom Markt implizierten Wahrscheinlichkeit vergleichen\nfor i, game in enumerate(today_games):\n    model_prob = ensemble_probs[i]\n    implied_prob = game['implied_probability']\n    edge = model_prob - implied_prob\n\n    if edge > 0.03:  # 3% Mindest-Edge-Schwellwert\n        kelly = (model_prob * (game['decimal_odds'] - 1)\n                 - (1 - model_prob)) \u002F (game['decimal_odds'] - 1)\n        bet_size = bankroll * kelly * 0.25  # Quarter-Kelly\n        print(f\"{game['teams']}: Edge {edge:.1%}, \"\n              f\"Wette \\${bet_size:.0f}\")\n```\n\n## Phase 4: Backtesting und Validierung\n\n### Train\u002FTest-Split-Strategie (2019-2022 Training \u002F 2023 Validierung \u002F 2024-2025 Test)\n\nTesten Sie Ihr Modell nie auf denselben Daten, mit denen Sie es trainiert haben. Verwenden Sie einen strikten zeitlichen Split:\n\n- **Trainingssatz (2019-2022):** ~9.700 Spiele. Ihr Modell lernt Muster aus diesen Daten\n- **Validierungssatz (2023):** ~2.430 Spiele. Hyperparameter und Feature-Auswahl abstimmen\n- **Testsatz (2024-2025):** ~4.860 Spiele. Endgültige, unberührte Bewertung der echten Leistung\n\nWenn Ihr Modell bei Trainingsdaten gut funktioniert, aber beim Testsatz schlecht, haben Sie überangepasst. Gehen Sie zurück und vereinfachen Sie.\n\n### Wichtige Metriken — Log Loss, Brier Score, Kalibrierung\n\nAllein die Win\u002FLoss-Genauigkeit ist irreführend. Ein Modell, das bei jedem Spiel „52%\" sagt, hat 52% Genauigkeit, aber keinen Edge. Verwenden Sie korrekte Scoring-Metriken:\n\n- **Log Loss:** Bestraft sichere falsche Vorhersagen. Niedriger = besser. Ziel \u003C 0,68\n- **Brier Score:** Mittlerer quadratischer Fehler der Wahrscheinlichkeiten. Ziel \u003C 0,24\n- **Kalibrierung:** Wenn Ihr Modell 60% sagt, sollte die Mannschaft etwa 60% der Zeit gewinnen\n\nÜberprüfen Sie die Kalibrierung, indem Sie die vorhergesagte Wahrscheinlichkeit gegen die tatsächliche Gewinnrate in Buckets (50-55%, 55-60%, 60-65% usw.) zeichnen. Ein gut kalibriertes Modell folgt der Diagonalen.\n\n### Überanpassung vermeiden — Der #1 Anfängerfehler\n\nZeichen der Überanpassung:\n- Trainingsgenauigkeit > 60%, aber Testgenauigkeit \u003C 52%\n- Modell liebt obskure Features (Schiedsrichter-ID, Wochentag) über grundlegende Statistiken\n- Leistung verschlechtert sich dramatisch in neuen Saisons\n\nLösungen:\n- Verwenden Sie weniger Features (5-8 ist oft optimal für MLB)\n- Fügen Sie Regularisierung hinzu (L1\u002FL2 in Regression, max_depth-Grenzwerte in XGBoost)\n- Cross-validieren Sie in Ihrem Trainingssatz, bevor Sie den Testsatz anfassen\n- Wenn ein Feature keinen Baseball-Sinn macht, entfernen Sie es unabhängig von statistischer Signifikanz\n## Phase 5: Modellausgabe in Wetten umwandeln\n\n### Von Wahrscheinlichkeit zu Erwartungswert (EV-Formel + einfache Erklärung)\n\nDie Kernformel:\n\n$$EV = P(Gewinn) \\times Gewinn - P(Verlust) \\times Einsatz$$\n\nEinfach erklärt: Multipliziere deine Gewinnchance mit dem möglichen Gewinn, dann subtrahiere die Verlustchance mal deinen Einsatz. Ist die Zahl positiv, hat die Wette +EV.\n\n**Beispiel:** Dein Modell gibt den Astros eine 55%-Chance. Die Quote ist +130 (ein \\$100-Einsatz gewinnt \\$130).\n\n- EV = (0,55 × \\$130) - (0,45 × \\$100)\n- EV = \\$71,50 - \\$45,00 = **+\\$26,50 pro \\$100 Einsatz**\n\nDas ist ein massiver 26,5%-Vorteil. In Wirklichkeit liegen Vorteile normalerweise bei 3-8%. Nutze unseren [Value Bet Calculator](\u002Fbetting\u002Fvalue-bet-calculator), um jede Wette schnell zu überprüfen, oder analysiere deine Zahlen mit dem [Edge Analyzer](\u002Fbetting\u002Fedge-analyzer) für eine tiefere Analyse.\n\n### Kelly-Kriterium für MLB-Einsatzgrößen\n\nDas Kelly-Kriterium berechnet die mathematisch optimale Einsatzgröße:\n\n$$f^* = \\frac{bp - q}{b}$$\n\nWobei:\n- **b** = Dezimalquote - 1 (Nettogewinnquote)\n- **p** = deine geschätzte Gewinnwahrscheinlichkeit\n- **q** = 1 - p (Verlustwahrscheinlichkeit)\n\nFür das Astros-Beispiel: b = 2,30 - 1 = 1,30, p = 0,55, q = 0,45\n\n$$f^* = \\frac{(1,30 \\times 0,55) - 0,45}{1,30} = \\frac{0,715 - 0,45}{1,30} = \\frac{0,265}{1,30} = 20,4\\%$$\n\nVollständiges Kelly sagt, du setzt 20,4% deines Bankrolls. Das ist aggressiv. Intelligente Wettende nutzen Bruchteile.\n\n### Quarter-Kelly — Warum weniger mehr ist\n\nVollständiges Kelly maximiert das langfristige Wachstum, aber mit **brutaler Varianz**. Ein Drawdown von 30% ist üblich. Quarter-Kelly (Setzen von 25% des Kelly-empfohlenen Betrags) opfert etwas Wachstum für deutlich glattere Ergebnisse.\n\n| Strategie | Erwartetes Wachstum | Max Drawdown | Ruinrisiko |\n|----------|:--------------:|:------------:|:------------:|\n| Vollständiges Kelly | Maximiert | 30-50% | Niedrig, aber schmerzhaft |\n| Half Kelly | 75% des max | 15-25% | Sehr niedrig |\n| Quarter Kelly | 50% des max | 8-15% | Nahezu null |\n\n**Empfehlung:** Starte mit Quarter-Kelly. Wechsle zu Half-Kelly nur nach 500+ verifiziert profitablen Wetten. Nutze unseren [Kelly Calculator](\u002Fbetting\u002Fkelly-calculator), um jede Wette korrekt zu dimensionieren.\n\n## MLB Park Factors — Jedes Stadion bewertet (2024-2025)\n\n### Park Factors Chart lesen\n\nEin Park Factor von **1,00** bedeutet, das Stadion ist vollkommen neutral — die Punktzahl entspricht dem Ligendurchschnitt. Über 1,00 bedeutet, der Park erhöht die Punktzahl (schlägerfreundlich). Unter 1,00 bedeutet, der Park unterdrückt die Punktzahl (werferfreundlich).\n\n### Park Factors in dein Modell integrieren\n\nMultipliziere deine projizierten Runs mit dem Park Factor. Wenn dein Modell 4,5 Runs für die Rockies projiziert und sie spielen im Coors Field (1,38), passe auf 4,5 × 1,38 = **6,21 projizierte Runs** an.\n\nFür Auswärtsspiele in werferfreundlichen Parks wie dem Dodger Stadium (0,88) passe nach unten an: 4,5 × 0,88 = **3,96 projizierte Runs**.\n\n::chart-mlb-park-factors\n::\n\n## Phase 6: Dein täglicher MLB Wett-Workflow\n\n### Morgenroutine (Linien + Aufstellungen)\n\n1. **7:00 Uhr** — Lade nächtliche Linienbewegungen von deinem Wettanbieter herunter. Markiere Spiele, bei denen sich die Linie deutlich bewegt hat (>10 Cent bei der Moneyline)\n2. **8:00 Uhr** — Führe dein Modell mit projizierten Aufstellungen aus (Aufstellungen werden normalerweise 3-4 Stunden vor dem ersten Pitch bestätigt)\n3. **9:00 Uhr** — Vergleiche Modellwahrscheinlichkeiten mit aktuellen Marktquoten. Liste alle +EV Spiele mit Edge > 3% auf\n\n### Vor dem Spiel überprüfen (Wetter, Schiedsrichter, Bullpen)\n\nBevor du eine Wette platzierst, überprüfe:\n- Bestätigte Starting Lineup (späte Ausfälle können Edge zunichte machen)\n- Wetterbedingungen (Wind im Wrigley, Regenverzögerungen)\n- Home Plate Umpire Zuweisung\n- Bullpen-Verfügbarkeit (überprüfe Box Scores von der Vorherigen Nacht)\n\n### Wetten platzieren und Ergebnisse verfolgen\n\nVerfolge jede Wette in einer Tabelle oder im [Bet Tracker](\u002Fbetting\u002Fbet-tracker):\n- Datum, Teams, Modellwahrscheinlichkeit, Marktquoten, Einsatzgröße, Ergebnis\n- Berechne CLV (Closing Line Value) — hat sich die Linie deinem Modellpreis genähert?\n- Wöchentliche Überprüfung: Gewinnen deine 60%-Spiele tatsächlich 60% der Zeit?\n\n[CLV Calculator](\u002Fbetting\u002Fclv-calculator) ist das beste Tool zur Validierung deines Modell-Edges über die Zeit.\n## MLB EV-Rechner — Überprüfe jede Wette sofort\n\nGeben Sie Ihre Modellgewinnwahrscheinlichkeit und die Marktquoten ein, um zu sehen, ob eine Wette +EV ist. Der Rechner zeigt Erwartungswert, Edge-Prozentsatz und empfohlene [Kelly-Kriterium](\u002Fbetting\u002Fkelly-calculator) Wetteinsätze.\n\n::inline-mlb-ev-calculator\n::\n\n## Spezialwetten-Modelle — Hits, Strikeouts, erste fünf Innings\n\n### Spieler-Spezialwetten-Modelle (Hits Ü\u002FU, Strikeouts)\n\nSpieler-Spezialwetten nutzen dasselbe Framework wie Spielmodelle, konzentrieren sich aber auf individuelle Leistung:\n\n- **Strikeout-Wetten:** Nutzen Pitcher K-Quote (rollende 5 Starts), Batter K-Quote gegen Wurfhand und Umpire-Zonendaten\n- **Hits über\u002Funter:** Nutzen Batter xBA, Pitcher Contact-Management-Quote und BABIP-Regression\n- **Home Runs:** Nutzen Barrel-Quote, Hard-Hit-Quote, Park-Faktor HR-Komponente und Windrichtung\n\nDer Schlüsselgedanke: **Spieler-Spezialwetten haben weichere Quoten** als Spielquoten, weil Wettbüros weniger Zeit mit ihrer Bewertung verbringen. Hier verstecken sich Edges in 2026.\n\n### First 5 Innings (F5) Modell\n\nFirst 5 Innings (F5) Wetten isolieren die Leistung des Starting Pitchers und eliminieren die Unsicherheit des Bullpen. Baue ein separates Modell auf mit:\n\n- Starting Pitcher xFIP und rollender K-BB%\n- Gegenspieler-Schlagquoten gegen die Wurfhand des Pitchers\n- Park-Faktor (gilt weiterhin für erste 5 Innings)\n\nF5 Moneylines sind besonders wertvoll, wenn ein großartiger Starter auf einen schwachen Lineup trifft, das Bullpen aber unzuverlässig ist. Dein Vollspiel-Modell könnte sagen \"keine Wette\", während das F5-Modell \"+EV\" sagt.\n\n### Team-Gesamt-Modelle\n\nStatt vorherzusagen, welches Team gewinnt, sagst du voraus, wie viele Runs jedes Team unabhängig voneinander erzielt. Vergleiche dann mit der geposteten Team-Gesamt-Linie. Dieser Ansatz:\n\n- Verdoppelt deine Wetmöglichkeiten (2 Team-Gesamts pro Spiel)\n- Entfernt die Korrelation zwischen zwei Seiten\n- Funktioniert gut mit Park-Faktoren und Wetterdaten\n\nNutze den [Implied Probability Calculator](\u002Fbetting\u002Fimplied-probability), um Gesamt-Quoten in Break-Even-Wahrscheinlichkeiten umzuwandeln. Das Verstehen von [was Alternative Spreads bedeuten](\u002Fblog\u002Falternate-spread-meaning) kann dir auch helfen, Wert in Run Lines bei nicht standardisierten Nummern zu finden.\n\n## Was ein Modell NICHT enthält (ehrliche Grenzen)\n\n### Verletzungen und kurzfristige Ausfälle\n\nDein Modell kann nicht vorhersagen, dass der Spitzenpitcher 2 Stunden vor Spielbeginn ausfällt. **Führe dein Modell immer erneut aus**, nachdem die Aufstellungen bestätigt sind, und platziere niemals Wetten auf Spiele, bei denen der Starting Pitcher nicht feststeht.\n\n### Clubhaus-Drama und Motivation\n\nEin Team mit einer 10-Spiele-Niederlagenserie könnte sich nach einem Treffen nur unter den Spielern aufrappeln. Ein Team, das die Playoffs gesichert hat, könnte Starter schonen. Diese Faktoren sind real, aber kaum zu quantifizieren. Akzeptiere diese Grenze, anstatt Garbage-\"Motivations\"-Variablen zu deinem Modell hinzuzufügen.\n\n### Umpire Strike-Zone Variation\n\nWährend durchschnittliche Umpire-Tendenzen nützlich sind, ist die Variation bei einzelnen Spielen hoch. Ein Umpire mit einer typischerweise engen Zone könnte sie an einem bestimmten Abend weit aufmachen. Umpire-Daten bieten kleine Edges, aber gewichte sie nicht zu stark.\n\n### Wann du dein Modell außer Kraft setzen solltest\n\nSetze dein Modell nur außer Kraft, wenn du **konkrete Informationen** hast, die dein Modell nicht hat:\n- Eine bestätigte Aufstellungsänderung, nachdem du das Modell laufen lassen hast\n- Ein Wetter-Update (plötzliche Windverschiebung)\n- Verifizierte Verletzungsnachrichten, die nicht in den Daten widergespiegelt werden\n\nSetze es niemals außer Kraft, weil \"es sich nicht richtig anfühlt\". Wenn dein Bauch sich regelmäßig mit deinem Modell nicht einig ist, braucht dein Modell Reparatur — oder dein Bauch.\n\nWenn du dich für systematische Wettansätze über Modellierung hinaus interessierst, sieh dir an, wie die [Wong Teaser Strategie](\u002Fblog\u002Fwong-teaser-strategy-calculator) einen ähnlichen regelgestützten Rahmen auf NFL Teasers anwendet, oder erkunde progressive Systeme wie [Fibonacci](\u002Fblog\u002Ffibonacci-betting-system) und [Labouchere](\u002Fblog\u002Flabouchere-betting-system) — obwohl diese anders funktionieren als datengesteuerte Modelle.\n\n## Echter Track Record — Was du erwarten kannst\n\n### Realistische Gewinnquoten und ROI-Benchmarks\n\nSeien wir ehrlich darüber, was erreichbar ist. Hier sind dokumentierte Track Records von verifizierten MLB-Wettenden:\n\n| Wetter\u002FService | Saison | Wetten | Units | ROI |\n|---------------|:------:|:----:|:-----:|:---:|\n| Zerillo (Action Network) | 2019 | 659 | +30,2 | 4,6% |\n| Professionelles Syndikat Durchschnitt | Multi-Jahr | 2000+ | Variiert | 3-5% |\n| Gutes Amateur-Modell | Erste Saison | 500+ | Variiert | 2-4% |\n| Break-even Modell | Beliebig | Beliebig | ~0 | 0% |\n\nBeachte, dass selbst Spitzenleistung 3-5% ROI ist. **Jeder, der 20%+ ROI verspricht, lügt.** Konsistenz über 500+ Wetten bei 3% ROI ist hervorragend. Nutze unseren [Variance Analyzer](\u002Fbetting\u002Fvariance-analyzer), um zu verstehen, wie sehr deine Ergebnisse selbst mit einem echten Edge schwanken können.\n\n### Stichprobengrößen-Anforderungen\n\n- **200 Wetten:** Du kannst anfangen, Trends zu sehen, aber nichts ist schlüssig\n- **500 Wetten:** Minimum für statistische Sicherheit. Ein 55%-Modell hat eine ~95% Chance, Gewinn zu zeigen\n- **1.000+ Wetten:** Starker Beweis für Edge. Dein 95% Konfidenzintervall verengt sich deutlich\n\nGib ein solides Modell nicht nach 50 verlorenen Wetten auf. Erkläre dich nicht nach 50 gewonnenen Wetten zum Genie. Die Mathematik braucht Zeit zu konvergieren. Verfolge dein [Bankroll-Wachstum](\u002Fbetting\u002Fbankroll-growth-calculator) über die ganze Saison.\n\nWenn dein Modell die Closing Line konsistent schlägt (positive CLV) über 200+ Wetten, ist deine Methodologie solide, selbst wenn kurzfristige Ergebnisse negativ sind. CLV ist das wahre Signal für langfristige Rentabilität.\n\n## FAQ\n\n*Profi-Tipp: Bankroll-Disziplin schlägt reinen Edge — gib Trefferquote, Quote und Einsatz in unseren [Bankroll-Rechner für Wetten](\u002Fbetting\u002Fbankroll-calculator) ein, um RoR unter 5 % zu halten.*\n",[28,31,34,37,40,43,46,49,52,55,58,61,64,67,70],{"answer":29,"question":30},"Sie müssen eine Win-Wahrscheinlichkeits-Kante von mindestens 2-3% über der Schlusslinie konsistent identifizieren. Ein Modell mit 55% Genauigkeit bei durchschnittlichen +100-Quoten generiert ungefähr 10% ROI über 500+ Wetten.","Wie genau müssen MLB-Wettmodelle sein, um Gewinn zu erzielen?",{"answer":32,"question":33},"Python ist der Industriestandard aufgrund der Bibliotheken pandas, scikit-learn und XGBoost. R ist eine solide Alternative für statistische Analysen. Excel funktioniert für Anfänger, die grundlegende Stats verfolgen.","Was ist die beste Programmiersprache für ein MLB-Wettmodell?",{"answer":35,"question":36},"Mindestens 3 Saisons (etwa 7.300 Spiele) zum Trainieren. Verwenden Sie 2019-2022 zum Trainieren, 2023 zur Validierung und 2024-2025 für Out-of-Sample-Tests. Mehr Daten helfen, aber MLB entwickelt sich weiter, daher können Daten vor 2015 weniger relevant sein.","Wie viele historische Daten benötige ich, um ein MLB-Modell zu erstellen?",{"answer":38,"question":39},"Ja. Alle Daten, die Sie benötigen, sind kostenlos von FanGraphs und Baseball Savant verfügbar. Bezahlte Dienste sparen Zeit durch API-Zugang, aber die eigentliche Vorhersagekraft kommt von Ihrem Feature-Engineering und Modelldesign, nicht von der Datenquelle.","Kann ein kostenloses MLB-Wettmodell bezahlte Dienste schlagen?",{"answer":41,"question":42},"Starting Pitcher xFIP, Team wOBA, Bullpen-Ermüdungsmetriken, K-BB% und Park-Faktoren. Vermeiden Sie Batting Average und Pitcher-Bilanz — das sind deskriptive, nicht prädiktive Metriken.","Welche sind die aussagekräftigsten Stats für MLB-Wetten?",{"answer":44,"question":45},"Park-Faktoren passen die erwartete Run-Produktion nach Austragungsort an. Coors Field (Faktor 1,38) erhöht Totale um 38% über dem Durchschnitt. Ihr Modell sollte Run-Prognosen mit dem Park-Faktor multiplizieren, um genaue Spiel-Totale zu erhalten.","Wie beeinflussen Park-Faktoren MLB-Wettmodelle?",{"answer":47,"question":48},"Das Kelly-Kriterium berechnet die optimale Einsatzgröße basierend auf Ihrer Kante. Formel: f = (bp - q) \u002F b, wobei b = Dezimalquote - 1, p = Win-Wahrscheinlichkeit, q = 1 - p. Die meisten professionellen Bettoren verwenden Quarter-Kelly (25% des vollständigen Kelly), um die Varianz zu reduzieren.","Was ist das Kelly-Kriterium für MLB-Wetten?",{"answer":50,"question":51},"Ein einfaches Tabellenkalkulationsmodell dauert 1-2 Wochen. Ein mittleres Python-Modell mit Regression dauert 3-4 Wochen. Ein vollständiges Ensemble-Modell mit ordnungsgemäßem Backtesting dauert 6-8 Wochen Teilzeitarbeit.","Wie lange dauert es, ein MLB-Wettmodell zu erstellen?",{"answer":53,"question":54},"Moneylines sind leichter zu modellieren, weil Sie nur den Gewinner vorhersagen müssen. Run Lines (Spread) erfordern die Vorhersage der Gewinnmarge, was die Komplexität erhöht. Beginnen Sie mit Moneylines und fügen Sie Run Lines hinzu, wenn Ihr Modell profitabel ist.","Sollte ich auf MLB Moneylines oder Run Lines wetten?",{"answer":56,"question":57},"Mindestens 500 Wetten für statistische Signifikanz. Bei 1.000+ Wetten können Sie sich sicherer sein, dass Ihre Ergebnisse eine echte Kante widerspiegeln, anstatt Zufall. Ziehen Sie nie Schlussfolgerungen aus weniger als 200 Wetten.","Was ist eine gute Stichprobengröße für das Backtesting eines MLB-Modells?",{"answer":59,"question":60},"Verfolgen Sie aufeinanderfolgende Einsätze und Gesamtplatzierungen in den letzten 3 Tagen. Untersuchungen zeigen einen Geschwindigkeitsabfall von -0,6 MPH pro aufeinanderfolgender Einsatz, was sich auf ungefähr -0,25 Runs pro Spiel überträgt. Überarbeitete Bullpens sind ein zuverlässiges +EV-Signal.","Wie berücksichtige ich Bullpen-Ermüdung in meinem Modell?",{"answer":62,"question":63},"Ja, erheblich. Wind, der vom Wrigley Field weht, fügt 1-2 Runs zu Spiel-Totalen hinzu. Temperaturen über 85°F erhöhen die Trefferquote. Regenverzögerungen unterbrechen Pitcher. Integrieren Sie Windgeschwindigkeit, -richtung, Temperatur und Luftfeuchtigkeit in Ihr Modell.","Beeinflussen Wetterbedingungen MLB-Wettmodelle?",{"answer":65,"question":66},"Professionelle Modelle zielen auf 3-8% ROI über eine vollständige Saison ab. Die beste öffentliche Erfolgsquote ist Zerillo's 2019-Saison mit +30,2 Units und 4,6% ROI über 659 Wetten. Alles über 2% nachhaltiger ROI ist ausgezeichnet.","Welcher ROI sollte ich von einem MLB-Wettmodell erwarten?",{"answer":68,"question":69},"Trainieren Sie Ihr Modell mindestens zweimal pro Saison neu — einmal nach den ersten 2 Monaten und einmal zur All-Star-Pause. Aktualisieren Sie tägliche Eingaben wie Aufstellungen, Wetter und Bullpen-Status jeden Morgen, bevor die Linien öffnen.","Wie oft sollte ich mein MLB-Wettmodell aktualisieren?",{"answer":71,"question":72},"Ja. Prop-Modelle verwenden den gleichen Rahmen wie Spiel-Modelle, konzentrieren sich aber auf einzelne Stats: Strikeout-Totale, Hits über\u002Funter und Bases. Der Hauptunterschied ist die Verwendung von Spieler-Daten (rollierende Durchschnitte, Platoon-Splits) anstelle von Team-Aggregaten.","Kann ich maschinelles Lernen für MLB-Prop-Wetten verwenden?",[74,75,76,77],"en","de","tr","ru",{"data":79,"body":80},{},{"type":81,"children":82},"root",[83,92,98,111,123,128,134,141,268,274,286,292,298,310,315,321,326,370,389,395,401,406,430,443,449,454,475,481,486,507,527,533,539,552,596,602,614,620,632,645,651,656,663,826,839,845,851,870,969,975,994,999,1017,1022,1028,1033,1056,1062,1074,1107,1112,1118,1123,1383,1389,1395,1407,1421,1427,1439,1448,1454,1459,1468,1474,1479,1485,1494,1500,1506,1511,1544,1549,1555,1560,1593,1598,1604,1609,1627,1632,1655,1661,1667,1672,2141,2146,2156,2174,2195,2201,2206,2490,2495,2525,2530,3345,3350,3356,3368,3469,3486,3492,3498,3510,3516,3528,3539,3543,3549,3555,3588,3594,3599,3622,3628,3640,3658,3669,3675,3686,3690,3696,3702,3707,3740,3752,3758,3763,3781,3786,3792,3797,3815,3836,3842,3848,3860,3866,3871,3877,3882,3888,3900,3918,3923,3951,3957,3963,3968,4114,4134,4140,4173,4186,4191,4197],{"type":84,"tag":85,"props":86,"children":88},"element","h2",{"id":87},"mlb-wettmodell-bauen-sie-ihr-eigenes-system-von-grund-auf-2026",[89],{"type":90,"value":91},"text","MLB-Wettmodell: Bauen Sie Ihr eigenes System von Grund auf (2026)",{"type":84,"tag":93,"props":94,"children":95},"p",{},[96],{"type":90,"value":97},"Stellen Sie sich vor: Es ist Dienstagmorgen, die vollständige MLB-Spielliste fällt in 3 Stunden, und Sie haben 14 Spiele zu bewerten. Das Bauchgefühl sagt, dass die Dodgers eine sichere Sache sind. Ihr Freund schwört, dass die White Sox \"überreif\" sind. Währenddessen bewegt sich das Smart Money eine Quote, über die niemand spricht.",{"type":84,"tag":93,"props":99,"children":100},{},[101,103,109],{"type":90,"value":102},"Hier ist der Unterschied zwischen Ihnen und den Profis: ",{"type":84,"tag":104,"props":105,"children":106},"strong",{},[107],{"type":90,"value":108},"Sie haben ein Modell",{"type":90,"value":110},". Nicht eine Kristallkugel — ein systematischer Prozess, der Daten in Wahrscheinlichkeiten umwandelt, diese Wahrscheinlichkeiten mit Marktquoten vergleicht und Ihnen genau sagt, welche Wetten einen positiven Erwartungswert haben.",{"type":84,"tag":93,"props":112,"children":113},{},[114,116,121],{"type":90,"value":115},"Die gute Nachricht? Stand 2026 ist jedes Datenstück, das Sie zum Aufbau eines MLB-Wettmodells benötigen, ",{"type":84,"tag":104,"props":117,"children":118},{},[119],{"type":90,"value":120},"kostenlos",{"type":90,"value":122},". FanGraphs, Baseball Savant und Statcast geben Ihnen dieselben Rohdaten, die professionelle Syndikate verwenden. Was die Gewinner unterscheidet, ist wie sie diese Zahlen in Features umwandeln, Modelle trainieren, die tatsächlich Ergebnisse vorhersagen, und Bankroll mit Disziplin verwalten.",{"type":84,"tag":93,"props":124,"children":125},{},[126],{"type":90,"value":127},"Dieser Leitfaden führt Sie durch den gesamten Prozess — von Ihrem ersten Spreadsheet bis zu einem vollständigen Python-Ensemble-Modell. Egal, ob Sie ein kompletter Anfänger oder ein Datenwissenschaftler sind, der nach MLB-spezifischen Feature-Engineering-Ideen sucht, es gibt ein Level für Sie. Lassen Sie uns etwas bauen, das tatsächlich funktioniert.",{"type":84,"tag":85,"props":129,"children":131},{"id":130},"kurzfassung-mlb-wettmodell-schnellreferenz",[132],{"type":90,"value":133},"Kurzfassung — MLB-Wettmodell Schnellreferenz",{"type":84,"tag":135,"props":136,"children":138},"h3",{"id":137},"model-level-auf-einen-blick",[139],{"type":90,"value":140},"Model-Level auf einen Blick",{"type":84,"tag":142,"props":143,"children":144},"table",{},[145,179],{"type":84,"tag":146,"props":147,"children":148},"thead",{},[149],{"type":84,"tag":76,"props":150,"children":151},{},[152,158,163,169,174],{"type":84,"tag":153,"props":154,"children":155},"th",{},[156],{"type":90,"value":157},"Level",{"type":84,"tag":153,"props":159,"children":160},{},[161],{"type":90,"value":162},"Tools",{"type":84,"tag":153,"props":164,"children":166},{"align":165},"center",[167],{"type":90,"value":168},"Aufbauzeit",{"type":84,"tag":153,"props":170,"children":171},{"align":165},[172],{"type":90,"value":173},"Erwarteter Vorteil",{"type":84,"tag":153,"props":175,"children":176},{},[177],{"type":90,"value":178},"Am besten für",{"type":84,"tag":180,"props":181,"children":182},"tbody",{},[183,212,240],{"type":84,"tag":76,"props":184,"children":185},{},[186,192,197,202,207],{"type":84,"tag":187,"props":188,"children":189},"td",{},[190],{"type":90,"value":191},"Anfänger",{"type":84,"tag":187,"props":193,"children":194},{},[195],{"type":90,"value":196},"Spreadsheet + FanGraphs",{"type":84,"tag":187,"props":198,"children":199},{"align":165},[200],{"type":90,"value":201},"1-2 Wochen",{"type":84,"tag":187,"props":203,"children":204},{"align":165},[205],{"type":90,"value":206},"1-3%",{"type":84,"tag":187,"props":208,"children":209},{},[210],{"type":90,"value":211},"Das Framework lernen",{"type":84,"tag":76,"props":213,"children":214},{},[215,220,225,230,235],{"type":84,"tag":187,"props":216,"children":217},{},[218],{"type":90,"value":219},"Fortgeschritten",{"type":84,"tag":187,"props":221,"children":222},{},[223],{"type":90,"value":224},"Python + Regression",{"type":84,"tag":187,"props":226,"children":227},{"align":165},[228],{"type":90,"value":229},"3-4 Wochen",{"type":84,"tag":187,"props":231,"children":232},{"align":165},[233],{"type":90,"value":234},"3-5%",{"type":84,"tag":187,"props":236,"children":237},{},[238],{"type":90,"value":239},"Konsistente kleine Vorteile",{"type":84,"tag":76,"props":241,"children":242},{},[243,248,253,258,263],{"type":84,"tag":187,"props":244,"children":245},{},[246],{"type":90,"value":247},"Fortgeschrittene",{"type":84,"tag":187,"props":249,"children":250},{},[251],{"type":90,"value":252},"XGBoost + Ensemble",{"type":84,"tag":187,"props":254,"children":255},{"align":165},[256],{"type":90,"value":257},"6-8 Wochen",{"type":84,"tag":187,"props":259,"children":260},{"align":165},[261],{"type":90,"value":262},"5-8%",{"type":84,"tag":187,"props":264,"children":265},{},[266],{"type":90,"value":267},"ROI maximieren",{"type":84,"tag":135,"props":269,"children":271},{"id":270},"für-wen-dieser-leitfaden-ist",[272],{"type":90,"value":273},"Für wen dieser Leitfaden ist",{"type":84,"tag":93,"props":275,"children":276},{},[277,279,284],{"type":90,"value":278},"Dieser Leitfaden richtet sich an jeden, der von Bauchgefühl-Tipps zu einem ",{"type":84,"tag":104,"props":280,"children":281},{},[282],{"type":90,"value":283},"datengesteuerten MLB-Wettmodell",{"type":90,"value":285}," übergehen möchte. Sie benötigen keinen Statistikabschluss — wenn Sie ein Spreadsheet verwenden können, können Sie auf Level 1 starten. Wenn Sie grundlegendes Python kennen, springen Sie direkt zum Abschnitt Fortgeschrittene.",{"type":84,"tag":85,"props":287,"children":289},{"id":288},"was-ist-ein-mlb-wettmodell-und-warum-sollten-sie-eines-bauen",[290],{"type":90,"value":291},"Was ist ein MLB-Wettmodell (und warum sollten Sie eines bauen)?",{"type":84,"tag":135,"props":293,"children":295},{"id":294},"modell-vs-bauchgefühl-der-wesentliche-unterschied",[296],{"type":90,"value":297},"Modell vs. Bauchgefühl — Der wesentliche Unterschied",{"type":84,"tag":93,"props":299,"children":300},{},[301,303,308],{"type":90,"value":302},"Ein Wettmodell ist eine ",{"type":84,"tag":104,"props":304,"children":305},{},[306],{"type":90,"value":307},"Wahrscheinlichkeitsmaschine",{"type":90,"value":309},". Sie fügen ihm Daten ein (Pitcher-Statistiken, Park-Faktoren, Bullpen-Einsatz), und es gibt eine Wahrscheinlichkeit für jedes mögliche Ergebnis aus. Diese Wahrscheinlichkeit wird dann mit den Marktquoten verglichen, um +EV-Wetten zu finden.",{"type":84,"tag":93,"props":311,"children":312},{},[313],{"type":90,"value":314},"Der Unterschied ist wichtig: Wenn Sie \"fühlen\", dass die Dodgers gewinnen werden, haben Sie keine Möglichkeit zu wissen, ob -180 fair ist. 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Es ist das einfachste Modell, aber überraschend effektiv für Spiel-Summen.",{"type":84,"tag":1408,"props":1409,"children":1415},"pre",{"className":1410,"code":1412,"language":1413,"meta":1414},[1411],"language-python","from sklearn.linear_model import LinearRegression\nimport pandas as pd\n\n## Feature-Matrix laden\nfeatures = ['sp_xfip', 'team_woba', 'park_factor',\n            'bullpen_fatigue', 'k_bb_pct', 'platoon_score']\n\nX_train = train_data[features]\ny_train = train_data['total_runs']\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\n## Heute's Spiele vorhersagen\ntoday_pred = model.predict(today_data[features])\n","python","",[1416],{"type":84,"tag":1417,"props":1418,"children":1419},"code",{"__ignoreMap":1414},[1420],{"type":90,"value":1412},{"type":84,"tag":135,"props":1422,"children":1424},{"id":1423},"logistische-regression-klassifizierung",[1425],{"type":90,"value":1426},"Logistische Regression (Klassifizierung)",{"type":84,"tag":93,"props":1428,"children":1429},{},[1430,1432,1437],{"type":90,"value":1431},"Für Moneyline-Wetten benötigen Sie ",{"type":84,"tag":104,"props":1433,"children":1434},{},[1435],{"type":90,"value":1436},"Gewinn-Wahrscheinlichkeit",{"type":90,"value":1438},", nicht Run-Summen. Die logistische Regression gibt Wahrscheinlichkeiten direkt aus.",{"type":84,"tag":1408,"props":1440,"children":1443},{"className":1441,"code":1442,"language":1413,"meta":1414},[1411],"from sklearn.linear_model import LogisticRegression\n\nX_train = train_data[features]\ny_train = train_data['home_win']  # 1 oder 0\n\nmodel = LogisticRegression(max_iter=1000)\nmodel.fit(X_train, y_train)\n\n## Gewinn-Wahrscheinlichkeiten abrufen\nprobs = model.predict_proba(today_data[features])\nhome_win_prob = probs[:, 1]  # Wahrscheinlichkeit eines Heimsiegs\n",[1444],{"type":84,"tag":1417,"props":1445,"children":1446},{"__ignoreMap":1414},[1447],{"type":90,"value":1442},{"type":84,"tag":135,"props":1449,"children":1451},{"id":1450},"xgboost-gradient-boosting",[1452],{"type":90,"value":1453},"XGBoost (Gradient Boosting)",{"type":84,"tag":93,"props":1455,"children":1456},{},[1457],{"type":90,"value":1458},"XGBoost erfasst nichtlineare Beziehungen, die die Regression übersieht. Es ist das Arbeitstier professioneller MLB-Modelle.",{"type":84,"tag":1408,"props":1460,"children":1463},{"className":1461,"code":1462,"language":1413,"meta":1414},[1411],"import xgboost as xgb\n\nparams = {\n    'objective': 'binary:logistic',\n    'max_depth': 5,\n    'learning_rate': 0.05,\n    'subsample': 0.8,\n    'colsample_bytree': 0.8,\n    'eval_metric': 'logloss'\n}\n\ndtrain = xgb.DMatrix(X_train, label=y_train)\nmodel = xgb.train(params, dtrain, num_boost_round=300)\n\n## Vorhersagen\ndtest = xgb.DMatrix(today_data[features])\nprobs = model.predict(dtest)\n",[1464],{"type":84,"tag":1417,"props":1465,"children":1466},{"__ignoreMap":1414},[1467],{"type":90,"value":1462},{"type":84,"tag":135,"props":1469,"children":1471},{"id":1470},"ensemble-modell-kombinieren-aller-drei",[1472],{"type":90,"value":1473},"Ensemble-Modell (Kombinieren aller drei)",{"type":84,"tag":93,"props":1475,"children":1476},{},[1477],{"type":90,"value":1478},"Kein einzelnes Modell ist für jedes Spiel am besten. Ein Ensemble mittelt Vorhersagen aus mehreren Modellen und reduziert so Überanpassung und verbessert die Kalibrierung.",{"type":84,"tag":657,"props":1480,"children":1482},{"id":1481},"python-code-vollständige-ensemble-pipeline",[1483],{"type":90,"value":1484},"Python-Code: Vollständige Ensemble-Pipeline",{"type":84,"tag":1408,"props":1486,"children":1489},{"className":1487,"code":1488,"language":1413,"meta":1414},[1411],"import numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.calibration import CalibratedClassifierCV\nimport xgboost as xgb\n\n## Einzelne Modelle trainieren\nlr_model = LogisticRegression(max_iter=1000)\nlr_model.fit(X_train, y_train)\nlr_probs = lr_model.predict_proba(X_test)[:, 1]\n\nxgb_model = xgb.XGBClassifier(\n    max_depth=5, learning_rate=0.05,\n    n_estimators=300, subsample=0.8\n)\nxgb_model.fit(X_train, y_train)\nxgb_probs = xgb_model.predict_proba(X_test)[:, 1]\n\n## Gewichtetes Ensemble (Gewichtungen über Validierungssatz abstimmen)\nensemble_probs = 0.4 * lr_probs + 0.6 * xgb_probs\n\n## Mit der vom Markt implizierten Wahrscheinlichkeit vergleichen\nfor i, game in enumerate(today_games):\n    model_prob = ensemble_probs[i]\n    implied_prob = game['implied_probability']\n    edge = model_prob - implied_prob\n\n    if edge > 0.03:  # 3% Mindest-Edge-Schwellwert\n        kelly = (model_prob * (game['decimal_odds'] - 1)\n                 - (1 - model_prob)) \u002F (game['decimal_odds'] - 1)\n        bet_size = bankroll * kelly * 0.25  # Quarter-Kelly\n        print(f\"{game['teams']}: Edge {edge:.1%}, \"\n              f\"Wette \\${bet_size:.0f}\")\n",[1490],{"type":84,"tag":1417,"props":1491,"children":1492},{"__ignoreMap":1414},[1493],{"type":90,"value":1488},{"type":84,"tag":85,"props":1495,"children":1497},{"id":1496},"phase-4-backtesting-und-validierung",[1498],{"type":90,"value":1499},"Phase 4: Backtesting und Validierung",{"type":84,"tag":135,"props":1501,"children":1503},{"id":1502},"traintest-split-strategie-2019-2022-training-2023-validierung-2024-2025-test",[1504],{"type":90,"value":1505},"Train\u002FTest-Split-Strategie (2019-2022 Training \u002F 2023 Validierung \u002F 2024-2025 Test)",{"type":84,"tag":93,"props":1507,"children":1508},{},[1509],{"type":90,"value":1510},"Testen Sie Ihr Modell nie auf denselben Daten, mit denen Sie es trainiert haben. 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Ein 55%-Modell hat eine ~95% Chance, Gewinn zu zeigen",{"type":84,"tag":331,"props":4164,"children":4165},{},[4166,4171],{"type":84,"tag":104,"props":4167,"children":4168},{},[4169],{"type":90,"value":4170},"1.000+ Wetten:",{"type":90,"value":4172}," Starker Beweis für Edge. Dein 95% Konfidenzintervall verengt sich deutlich",{"type":84,"tag":93,"props":4174,"children":4175},{},[4176,4178,4184],{"type":90,"value":4177},"Gib ein solides Modell nicht nach 50 verlorenen Wetten auf. Erkläre dich nicht nach 50 gewonnenen Wetten zum Genie. Die Mathematik braucht Zeit zu konvergieren. Verfolge dein ",{"type":84,"tag":362,"props":4179,"children":4181},{"href":4180},"\u002Fbetting\u002Fbankroll-growth-calculator",[4182],{"type":90,"value":4183},"Bankroll-Wachstum",{"type":90,"value":4185}," über die ganze Saison.",{"type":84,"tag":93,"props":4187,"children":4188},{},[4189],{"type":90,"value":4190},"Wenn dein Modell die Closing Line konsistent schlägt (positive CLV) über 200+ Wetten, ist deine Methodologie solide, selbst wenn kurzfristige Ergebnisse negativ sind. CLV ist das wahre Signal für langfristige Rentabilität.",{"type":84,"tag":85,"props":4192,"children":4194},{"id":4193},"faq",[4195],{"type":90,"value":4196},"FAQ",{"type":84,"tag":93,"props":4198,"children":4199},{},[4200],{"type":84,"tag":4201,"props":4202,"children":4203},"em",{},[4204,4206,4212],{"type":90,"value":4205},"Profi-Tipp: Bankroll-Disziplin schlägt reinen Edge — gib Trefferquote, Quote und Einsatz in unseren ",{"type":84,"tag":362,"props":4207,"children":4209},{"href":4208},"\u002Fbetting\u002Fbankroll-calculator",[4210],{"type":90,"value":4211},"Bankroll-Rechner für Wetten",{"type":90,"value":4213}," ein, um RoR unter 5 % zu halten."]