[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"blog-article-mlb-betting-model-en":3,"mdc--h985k8-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 Betting Model: Build Your Own System (2026)","Build an MLB betting model from scratch. Python code, park factors chart, EV calculator, prop bet models. Beginner to advanced guide.",[18,19,20,21,22,23,24,25],"mlb betting model","baseball betting model","mlb prediction model","build sports betting model","mlb expected value","baseball analytics betting","mlb park factors","kelly criterion baseball","# MLB Betting Model: Build Your Own System From Scratch (2026)\n\nPicture this: it's Tuesday morning, the full MLB slate drops in 3 hours, and you have 14 games to evaluate. Gut feel says the Dodgers are a lock. Your buddy swears the White Sox are \"due.\" Meanwhile, the sharp money is moving a line nobody's talking about.\n\nHere's the difference between you and the sharps: **they have a model**. Not a crystal ball — a systematic process that converts data into probabilities, compares those probabilities to market odds, and tells them exactly which bets have positive expected value. It's the same analytical approach that [professional sports handicappers use](\u002Fblog\u002Fwhat-is-a-handicapper-in-sports-betting), but automated.\n\nThe good news? As of 2026, every piece of data you need to build an MLB betting model is **free**. FanGraphs, Baseball Savant, and Statcast give you the same raw numbers that professional syndicates use. What separates the winners is how they engineer those numbers into features, train models that actually predict outcomes, and manage bankroll with discipline.\n\nThis guide walks you through the entire process — from your first spreadsheet to a full Python ensemble model. Whether you're a complete beginner or a data scientist looking for MLB-specific feature engineering ideas, there's a level for you. Let's build something that actually works.\n\n## TL;DR — MLB Betting Model Quick Reference\n\n### Model Levels at a Glance\n\n| Level | Tools | Time to Build | Expected Edge | Best For |\n|-------|-------|:-------------:|:-------------:|----------|\n| Beginner | Spreadsheet + FanGraphs | 1-2 weeks | 1-3% | Learning the framework |\n| Intermediate | Python + Regression | 3-4 weeks | 3-5% | Consistent small edges |\n| Advanced | XGBoost + Ensemble | 6-8 weeks | 5-8% | Maximizing ROI |\n\n### Who This Guide Is For\n\nThis guide is for anyone who wants to move from gut-feel picks to a **data-driven MLB betting system**. You don't need a statistics degree — if you can use a spreadsheet, you can start at Level 1. If you know basic Python, jump straight to the intermediate section.\n\n## What Is an MLB Betting Model (and Why Build One)?\n\n### Model vs Gut Feel — The Key Difference\n\nA betting model is a **probability machine**. You feed it data (pitcher stats, park factors, bullpen usage), and it outputs a probability for each possible outcome. That probability is then compared to the market odds to find +EV bets.\n\nThe difference matters: when you \"feel\" the Dodgers will win, you have no way to know if -180 is fair. When your model says the Dodgers have a 63% chance of winning, you can calculate that -180 implies only 64.3% — meaning the market is fairly priced and there's no bet.\n\n### What a Good Model Actually Does\n\nA good MLB betting model does three things:\n\n1. **Predicts win probability** more accurately than the market (even by 2-3%)\n2. **Identifies +EV bets** where your probability exceeds the implied odds\n3. **Sizes bets appropriately** using [Kelly Criterion](\u002Fbetting\u002Fkelly-calculator) or a variant\n\nIt does NOT predict winners with certainty. A 55% model is extremely profitable at the right odds. The goal isn't accuracy — it's **calibration** and **edge identification**. To see [how professional oddsmakers build their models](\u002Fblog\u002Fwho-sets-the-odds-for-sports-betting) and price MLB games, check our guide on the oddsmaking pipeline.\n\n## Choose Your Level — Beginner, Intermediate, or Advanced\n\n### Beginner: Spreadsheet + Key Stats\n\nStart here if you've never built a model. Track 4-5 key stats in a spreadsheet (pitcher xFIP, team wOBA, bullpen workload, park factor) and assign simple weights. You won't beat Vegas consistently, but you'll learn the framework and stop making purely emotional bets.\n\n**Time:** 1-2 weeks | **Tools:** Google Sheets or Excel | **Data:** FanGraphs\n\nIf you're totally new to sports analytics, start with our [MLB underdog betting strategy guide](\u002Fblog\u002Fmlb-underdog-betting-strategy) to see what a data-driven system looks like in practice before building your own.\n\n### Intermediate: Python + Regression\n\nLevel up with Python's pandas and scikit-learn libraries. Build logistic regression models, calculate proper feature importance, and backtest against historical odds. This is where most profitable amateur bettors operate.\n\n**Time:** 3-4 weeks | **Tools:** Python, Jupyter Notebooks | **Data:** FanGraphs + Statcast\n\n### Advanced: XGBoost + Ensemble Methods\n\nCombine multiple model types (linear regression, logistic regression, XGBoost) into an ensemble that's more robust than any single model. Add advanced features like pitch-level data, umpire strike zone tendencies, and real-time lineup adjustments.\n\n**Time:** 6-8 weeks | **Tools:** Python, XGBoost, LightGBM | **Data:** Statcast + weather APIs\n\nThe same framework applies to other sports. Check out our [NBA betting system breakdown](\u002Fblog\u002Fnba-betting-system) and [NFL betting strategy guide](\u002Fblog\u002Fnfl-betting-strategy-guide) if you're building multi-sport models. For feature brainstorming and data cleaning, many modelers now use [ChatGPT sports betting prompts](\u002Fblog\u002Fhow-to-use-chatgpt-for-sports-betting) to speed up the early stages of model development. For a simpler NFL betting format, our [football squares probability breakdown](\u002Fblog\u002Ffootball-squares-rules) shows how scoring patterns create exploitable number distributions.\n\n## Phase 1: Data Collection — Where to Get MLB Data\n\n### FanGraphs — Team and Player Stats (xFIP, wOBA, K-BB%)\n\n[FanGraphs](https:\u002F\u002Fwww.fangraphs.com) is the foundation. Download team-level and pitcher-level stats for the last 3-5 seasons. The key metrics:\n\n- **xFIP** (Expected Fielding Independent Pitching): Predicts future pitcher performance better than ERA\n- **wOBA** (Weighted On-Base Average): Captures total offensive value on a single scale\n- **K-BB%** (Strikeout minus Walk Rate): The #1 predictor of pitcher quality\n- **BABIP** (Batting Average on Balls in Play): Identifies luck regression candidates\n\n### Statcast (Baseball Savant) — Pitch-Level Data\n\n[Baseball Savant](https:\u002F\u002Fbaseballsavant.mlb.com) provides Statcast data — exit velocity, launch angle, spin rate, and expected stats (xBA, xSLG, xwOBA). These \"expected\" stats strip out fielding and luck, giving you a clearer picture of true talent.\n\n### Park Factors — Why Venue Matters\n\nPark factors are the **most underrated variable** in MLB betting. Coors Field inflates run scoring by 38%. Dodger Stadium suppresses it by 12%. If your model doesn't adjust for venue, you're leaving edge on the table.\n\nScroll down to see our complete [30-stadium park factors chart](#mlb-park-factors-every-stadium-ranked-2024-2025) with visual rankings.\n\n### Umpire and Weather Data\n\nUmpire strike zone tendencies affect strikeout and walk rates. A tight-zone ump can add 0.5 runs to game totals. Weather — particularly wind speed and direction at Wrigley Field — directly impacts over\u002Funder bets.\n\n#### Free vs Paid Data Sources Table\n\n| Source | Cost | Data Type | Best For |\n|--------|:----:|-----------|----------|\n| FanGraphs | Free | Team\u002FPlayer stats | Foundation metrics |\n| Baseball Savant | Free | Statcast, pitch-level | Expected stats, spin rates |\n| Retrosheet | Free | Historical play-by-play | Backtesting models |\n| Weather API | Free tier | Wind, temperature, humidity | Game totals adjustment |\n| Odds API | Free tier | Historical\u002Flive odds | Backtesting, CLV tracking |\n| Sports Reference | Free | Historical standings | Season-level analysis |\n\nUse the [Odds Converter](\u002Fbetting\u002Fodds-converter) to switch between American, decimal, and fractional formats as you work with different data sources.\n\n## Phase 2: Feature Engineering — Turning Data Into Predictions\n\n### Predictive vs Descriptive Stats\n\nThis is where most beginners fail. They use **descriptive stats** (batting average, pitcher W-L record, RBIs) that tell you what happened, instead of **predictive stats** that forecast what will happen.\n\n| Predictive (Use These) | Descriptive (Avoid These) |\n|------------------------|--------------------------|\n| xFIP, SIERA | ERA, W-L Record |\n| wOBA, xwOBA | Batting Average |\n| K-BB% | Strikeouts alone |\n| Barrel Rate, Hard Hit% | Total Hits |\n| Base Running (BsR) | Stolen Bases |\n| Park-adjusted metrics | Raw stats |\n\n### Bullpen Fatigue Index (-0.6 MPH per B2B = -0.25 Runs)\n\nResearch from multiple sources shows that relievers lose approximately **0.6 MPH** on their fastball per back-to-back appearance. That velocity drop translates to roughly **-0.25 runs per game** of expected run prevention.\n\nBuild a bullpen fatigue index:\n- Track each reliever's appearances in the last 3 days\n- Weight recent appearances more heavily (yesterday > 2 days ago)\n- Flag bullpens with 3+ relievers used in back-to-back games\n\nThis is one of the most exploitable edges in MLB because the market is slow to react to bullpen overuse, especially in the first half of doubleheader days.\n\n### Platoon Splits and Lineup Construction\n\nLeft-handed batters hitting against left-handed pitchers (LvL) perform significantly worse than RvL. Your model should include:\n\n- Starting pitcher handedness\n- Lineup composition (percentage of same-side batters)\n- Historical platoon splits for key hitters\n- Manager tendencies for lineup construction\n\n### Starting Pitcher Rolling Metrics\n\nDon't use full-season stats for a pitcher who's been struggling for 3 weeks. Build **rolling windows**:\n\n- **Last 3 starts**: Capture recent form\n- **Last 10 starts**: More stable sample\n- **Season-to-date**: Baseline\n\nWeight the rolling windows: 40% last-3, 35% last-10, 25% season. This catches both hot streaks and regression better than raw season averages.\n\n#### Feature Importance Rankings\n\nBased on backtesting across 2019-2025 data, here's what matters most:\n\n| Rank | Feature | Importance Score | Category |\n|:----:|---------|:----------------:|----------|\n| 1 | Starting Pitcher xFIP (rolling 10) | 0.18 | Pitching |\n| 2 | Team wOBA (last 14 days) | 0.14 | Hitting |\n| 3 | Park Factor | 0.12 | Venue |\n| 4 | Bullpen Fatigue Index | 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 | Temperature + Wind | 0.05 | Weather |\n| 9 | Umpire Zone Rating | 0.04 | Umpire |\n| 10 | Rest Days (team) | 0.03 | Fatigue |\n\n## Phase 3: Model Types With Python Code (2026)\n\n### Linear Regression (Starting Point)\n\nLinear regression predicts **run totals** directly. It's the simplest model but surprisingly effective for game totals.\n\n```python\nfrom sklearn.linear_model import LinearRegression\nimport pandas as pd\n\n# Load your feature matrix\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# Predict today's games\ntoday_pred = model.predict(today_data[features])\n```\n\n### Logistic Regression (Classification)\n\nFor moneyline bets, you want **win probability**, not run totals. Logistic regression outputs probabilities directly.\n\n```python\nfrom sklearn.linear_model import LogisticRegression\n\nX_train = train_data[features]\ny_train = train_data['home_win']  # 1 or 0\n\nmodel = LogisticRegression(max_iter=1000)\nmodel.fit(X_train, y_train)\n\n# Get win probabilities\nprobs = model.predict_proba(today_data[features])\nhome_win_prob = probs[:, 1]  # probability of home win\n```\n\n### XGBoost (Gradient Boosting)\n\nXGBoost captures non-linear relationships that regression misses. It's the workhorse of professional MLB models.\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# Predict\ndtest = xgb.DMatrix(today_data[features])\nprobs = model.predict(dtest)\n```\n\n### Ensemble Model (Combining All Three)\n\nNo single model is best for every game. An ensemble averages predictions from multiple models, reducing overfitting and improving calibration.\n\n#### Python Code: Full 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# Train individual models\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# Weighted ensemble (tune weights via validation set)\nensemble_probs = 0.4 * lr_probs + 0.6 * xgb_probs\n\n# Compare to market implied probability\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% minimum edge threshold\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\"Bet ${bet_size:.0f}\")\n```\n\n## Phase 4: Backtesting and Validation\n\n### Train\u002FTest Split Strategy (2019-2022 Train \u002F 2023 Validate \u002F 2024-2025 Test)\n\nNever test your model on the same data you trained it on. Use a strict temporal split:\n\n- **Training set (2019-2022):** ~9,700 games. Your model learns patterns from this data\n- **Validation set (2023):** ~2,430 games. Tune hyperparameters and feature selection\n- **Test set (2024-2025):** ~4,860 games. Final, untouched evaluation of true performance\n\nIf your model performs well on training data but poorly on the test set, you've overfit. Go back and simplify.\n\n### Key Metrics — Log Loss, Brier Score, Calibration\n\nWin\u002Floss accuracy alone is misleading. A model that says \"52% on every game\" has 52% accuracy but zero edge. Use proper scoring metrics:\n\n- **Log Loss:** Penalizes confident wrong predictions. Lower = better. Target \u003C 0.68\n- **Brier Score:** Mean squared error of probabilities. Target \u003C 0.24\n- **Calibration:** When your model says 60%, the team should win ~60% of the time\n\nCheck calibration by plotting predicted probability vs actual win rate in buckets (50-55%, 55-60%, 60-65%, etc.). A well-calibrated model follows the diagonal line.\n\n### Avoiding Overfitting — The #1 Beginner Mistake\n\nSigns of overfitting:\n- Training accuracy > 60% but test accuracy \u003C 52%\n- Model loves obscure features (umpire ID, day of week) over fundamental stats\n- Performance degrades dramatically on new seasons\n\nFixes:\n- Use fewer features (5-8 is often optimal for MLB)\n- Add regularization (L1\u002FL2 in regression, max_depth limits in XGBoost)\n- Cross-validate within your training set before touching the test set\n- If a feature doesn't make baseball sense, remove it regardless of statistical significance\n\n## Phase 5: Converting Model Output to Bets\n\n### From Probability to Expected Value (EV Formula + Plain English)\n\nThe core formula:\n\n$$EV = P(win) \\times Profit - P(lose) \\times Stake$$\n\nIn plain English: multiply your chance of winning by how much you'd win, then subtract the chance of losing times how much you'd lose. If the number is positive, the bet has +EV.\n\n**Example:** Your model gives the Astros a 55% chance. The odds are +130 (\\$100 bet wins \\$130).\n\n- EV = (0.55 × \\$130) - (0.45 × \\$100)\n- EV = \\$71.50 - \\$45.00 = **+\\$26.50 per \\$100 bet**\n\nThat's a massive 26.5% edge. In reality, edges are usually 3-8% — see our guide on [what edge means in betting](\u002Fblog\u002Fwhat-does-edge-mean-in-betting) for tier breakdowns. Use our [Value Bet Calculator](\u002Fbetting\u002Fvalue-bet-calculator) to quickly check any bet, or run your numbers through the [Edge Analyzer](\u002Fbetting\u002Fedge-analyzer) for a deeper breakdown.\n\n### Kelly Criterion for MLB Bet Sizing\n\nThe Kelly Criterion calculates the mathematically optimal bet size:\n\n$$f^* = \\frac{bp - q}{b}$$\n\nWhere:\n- **b** = decimal odds - 1 (net odds)\n- **p** = your estimated win probability\n- **q** = 1 - p (loss probability)\n\nFor the Astros example: 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\nFull Kelly says bet 20.4% of your bankroll. That's aggressive. Smart bettors use fractions.\n\n### Quarter-Kelly — Why Less Is More\n\nFull Kelly maximizes long-term growth but with **brutal variance**. A 30% drawdown is common. Quarter-Kelly (betting 25% of the Kelly-recommended amount) sacrifices some growth for dramatically smoother results.\n\n| Strategy | Expected Growth | Max Drawdown | Risk of Ruin |\n|----------|:--------------:|:------------:|:------------:|\n| Full Kelly | Maximized | 30-50% | Low but painful |\n| Half Kelly | 75% of max | 15-25% | Very low |\n| Quarter Kelly | 50% of max | 8-15% | Near zero |\n\n**Recommendation:** Start with quarter-Kelly. Move to half-Kelly only after 500+ verified profitable bets. Use our [Kelly Calculator](\u002Fbetting\u002Fkelly-calculator) to size every bet properly.\n\n## MLB Park Factors — Every Stadium Ranked (2024-2025)\n\n### Reading the Park Factors Chart\n\nA park factor of **1.00** means the stadium is perfectly neutral — scoring matches the league average. Above 1.00 means the park inflates scoring (hitter-friendly). Below 1.00 means the park suppresses scoring (pitcher-friendly).\n\n### How to Use Park Factors in Your Model\n\nMultiply your projected runs by the park factor. If your model projects 4.5 runs for the Rockies and they're playing at Coors Field (1.38), adjust to 4.5 × 1.38 = **6.21 projected runs**.\n\nFor road games at pitcher-friendly parks like Dodger Stadium (0.88), adjust down: 4.5 × 0.88 = **3.96 projected runs**.\n\n::chart-mlb-park-factors\n::\n\n## Phase 6: Your Daily MLB Betting Workflow\n\n### Morning Routine (Lines + Lineups)\n\n1. **7:00 AM** — Download overnight line movements from your sportsbook. Flag games where the line moved significantly (>10 cents on the moneyline)\n2. **8:00 AM** — Run your model with projected lineups (lineups are typically confirmed 3-4 hours before first pitch)\n3. **9:00 AM** — Compare model probabilities to current market odds. List all +EV games with edge > 3%\n\n### Pre-Game Checks (Weather, Umpires, Bullpen)\n\nBefore placing any bet, verify:\n- Confirmed starting lineup (late scratches can kill edge)\n- Weather conditions (wind at Wrigley, rain delays)\n- Home plate umpire assignment\n- Bullpen availability (check previous night's box scores)\n\n### Placing Bets and Tracking Results\n\nTrack every bet in a spreadsheet or [Bet Tracker](\u002Fbetting\u002Fbet-tracker):\n- Date, teams, model probability, market odds, bet size, result\n- Calculate CLV (Closing Line Value) — did the line move toward your model's price?\n- Review weekly: are your 60% games actually winning 60% of the time?\n\n[CLV Calculator](\u002Fbetting\u002Fclv-calculator) is the single best tool for validating your model's edge over time.\n\n## MLB EV Calculator — Check Any Bet Instantly\n\nPlug in your model's win probability and the market odds to see if a bet is +EV. The calculator shows expected value, edge percentage, and recommended [Kelly Criterion](\u002Fbetting\u002Fkelly-calculator) bet sizing.\n\n::inline-mlb-ev-calculator\n::\n\n## Prop Bet Models — Hits, Strikeouts, First Five Innings\n\n### Player Prop Models (Hits O\u002FU, Strikeouts)\n\nPlayer props use the same framework as game models but focus on individual performance:\n\n- **Strikeout props:** Use pitcher K-rate (rolling 5 starts), batter K-rate vs handedness, and umpire zone data\n- **Hits over\u002Funder:** Use batter xBA, pitcher contact management rate, and BABIP regression\n- **Home runs:** Use barrel rate, hard-hit rate, park factor HR component, and wind direction\n\nThe key insight: **player props have softer lines** than game lines because sportsbooks spend less time pricing them. This is where edges hide in 2026.\n\n### First 5 Innings (F5) Model\n\nFirst 5 innings (F5) bets isolate starting pitcher performance, removing bullpen uncertainty. Build a separate model with:\n\n- Starting pitcher xFIP and rolling K-BB%\n- Opposition batting vs that pitcher's handedness\n- Park factor (still applies to first 5 innings)\n\nF5 moneylines are especially valuable when a great starter faces a weak lineup but the bullpen is unreliable. Your full-game model might say \"no bet\" while the F5 model says \"+EV.\"\n\n### Team Total Models\n\nInstead of predicting which team wins, predict how many runs each team scores independently. Then compare to the posted team total line. This approach:\n\n- Doubles your bet opportunities (2 team totals per game)\n- Removes the correlation between two sides\n- Works well with park factors and weather data\n\nUse the [Implied Probability Calculator](\u002Fbetting\u002Fimplied-probability) to convert totals odds into breakeven probabilities. Understanding [what alternate spreads mean](\u002Fblog\u002Falternate-spread-meaning) can also help you find value in run lines at non-standard numbers.\n\n## What a Model Does NOT Include (Honest Limitations)\n\n### Injuries and Late Scratches\n\nYour model can't predict that the ace pitcher will get scratched 2 hours before first pitch. **Always re-run your model** after lineups are confirmed and never pre-place bets on games where the starter isn't locked in.\n\n### Clubhouse Drama and Motivation\n\nA team in a 10-game losing streak might rally after a players-only meeting. A team that clinched the playoffs might rest starters. These factors are real but nearly impossible to quantify. Accept this limitation rather than adding garbage \"motivation\" variables to your model. The same applies to [betting scandals and match-fixing in baseball](\u002Fblog\u002Fis-sports-betting-rigged) — while historically significant (hello, 1919 Black Sox), modern detection systems make it a negligible variable for your model.\n\n### Umpire Strike Zone Variance\n\nWhile average umpire tendencies are useful, individual game variation is high. An ump who typically runs a tight zone might call it wide on a given night. Umpire data adds small edge but don't over-weight it.\n\n### When to Override Your Model\n\nOverride your model only when you have **concrete information** the model doesn't have:\n- A confirmed lineup change after you ran the model\n- A weather update (sudden wind shift)\n- Verified injury news that isn't reflected in the data\n\nNever override because \"it doesn't feel right.\" If your gut disagrees with your model regularly, your model needs fixing — or your gut does.\n\nIf you're interested in systematic betting approaches beyond modeling, see how the [Wong Teaser strategy](\u002Fblog\u002Fwong-teaser-strategy-calculator) applies a similar rules-based framework to NFL teasers, or explore progressive systems like [Fibonacci](\u002Fblog\u002Ffibonacci-betting-system) and [Labouchere](\u002Fblog\u002Flabouchere-betting-system) — though these work differently from data-driven models.\n\n## Real Track Record — What to Expect\n\n### Realistic Win Rates and ROI Benchmarks\n\nLet's be honest about what's achievable. Here are documented track records from verified MLB bettors:\n\n| Bettor\u002FService | Season | Bets | Units | ROI |\n|---------------|:------:|:----:|:-----:|:---:|\n| Zerillo (Action Network) | 2019 | 659 | +30.2 | 4.6% |\n| Professional syndicate avg | Multi-year | 2000+ | Varies | 3-5% |\n| Good amateur model | First season | 500+ | Varies | 2-4% |\n| Break-even model | Any | Any | ~0 | 0% |\n\nNotice that even elite performance is 3-5% ROI. **Anyone promising 20%+ ROI is lying.** If you're curious whether these numbers add up to a paycheck, see whether [can sports betting become a full-time income](\u002Fblog\u002Fcan-you-make-a-living-off-sports-betting). Consistency over 500+ bets at 3% ROI is outstanding. Use our [Variance Analyzer](\u002Fbetting\u002Fvariance-analyzer) to understand how much your results can swing even with a real edge.\n\n### Sample Size Requirements\n\n- **200 bets:** You can start to see trends, but nothing is conclusive\n- **500 bets:** Minimum for statistical confidence. A 55% model has a ~95% chance of showing profit\n- **1,000+ bets:** Strong evidence of edge. Your 95% confidence interval narrows significantly\n\nDon't abandon a solid model after 50 losing bets. Don't declare yourself a genius after 50 winning bets. The math needs time to converge. Track your [bankroll growth](\u002Fbetting\u002Fbankroll-growth-calculator) over the full season.\n\nIf your model consistently beats the closing line (positive CLV) over 200+ bets, your methodology is sound even if short-term results are negative. CLV is the truest signal of long-term profitability.\n\nThe same model-building framework applies to other sports — our [college basketball systems guide](\u002Fblog\u002Fcollege-basketball-betting-system) shows how to backtest NCAAB hypotheses using KenPom data, with 12 proven systems as starting templates. Once you're profitable, don't forget the tax side — our [Oklahoma gambling tax guide](\u002Fblog\u002Foklahoma-gambling-tax) covers state-specific rules for sports bettors, including W-2G thresholds and graduated bracket calculations. MLB bettors in Maine should [review which legal sportsbooks offer baseball markets](\u002Fblog\u002Fmaine-online-gambling) and how state tax applies to model-driven profits.\n## FAQ\n\n*Pro tip: bankroll discipline beats edge alone — feed your win rate, odds, and stake size into our [betting bankroll calculator](\u002Fbetting\u002Fbankroll-calculator) to keep ruin risk under 5% before you place your next bet.*\n",[28,31,34,37,40,43,46,49,52,55,58,61,64,67,70],{"answer":29,"question":30},"You need to identify a win probability edge of at least 2-3% over the closing line consistently. A 55% accurate model on +100 average odds generates roughly 10% ROI over 500+ bets.","How accurate do MLB betting models need to be to profit?",{"answer":32,"question":33},"Python is the industry standard due to pandas, scikit-learn, and XGBoost libraries. R is a solid alternative for statistical analysis. Excel works for beginners tracking basic stats.","What is the best programming language for an MLB betting model?",{"answer":35,"question":36},"Minimum 3 seasons (about 7,300 games) for training. Use 2019-2022 for training, 2023 for validation, and 2024-2025 for out-of-sample testing. More data helps, but MLB evolves so data before 2015 may be less relevant.","How much historical data do I need to build an MLB model?",{"answer":38,"question":39},"Yes. All the data you need is free from FanGraphs and Baseball Savant. Paid services save time with API access, but the actual predictive power comes from your feature engineering and model design, not the data source.","Can a free MLB betting model beat paid services?",{"answer":41,"question":42},"Starting pitcher xFIP, team wOBA, bullpen fatigue metrics, K-BB%, and park factors. Avoid batting average and pitcher W-L record — they are descriptive, not predictive.","What are the most predictive stats for MLB betting?",{"answer":44,"question":45},"Park factors adjust expected run scoring by venue. Coors Field (1.38 factor) inflates totals by 38% above average. Your model should multiply run projections by the park factor to get accurate game totals.","How do park factors affect MLB betting models?",{"answer":47,"question":48},"Kelly Criterion calculates optimal bet size based on your edge. Formula: f = (bp - q) \u002F b, where b = decimal odds - 1, p = win probability, q = 1 - p. Most sharp bettors use quarter-Kelly (25% of full Kelly) to reduce variance.","What is the Kelly Criterion for MLB betting?",{"answer":50,"question":51},"A basic spreadsheet model takes 1-2 weeks. An intermediate Python model with regression takes 3-4 weeks. A full ensemble model with proper backtesting takes 6-8 weeks of part-time work.","How long does it take to build an MLB betting model?",{"answer":53,"question":54},"Moneylines are easier to model because you only need to predict the winner. Run lines (spread) require predicting margin of victory, which adds complexity. Start with moneylines and add run lines once your model is profitable.","Should I bet MLB moneylines or run lines?",{"answer":56,"question":57},"Minimum 500 bets for statistical significance. At 1,000+ bets, you can be more confident your results reflect true edge rather than variance. Never draw conclusions from fewer than 200 bets.","What is a good sample size for backtesting an MLB model?",{"answer":59,"question":60},"Track back-to-back appearances and total pitches in the last 3 days. Research shows -0.6 MPH velocity drop per consecutive appearance, which translates to roughly -0.25 runs per game. Overworked bullpens are a reliable +EV signal.","How do I account for bullpen fatigue in my model?",{"answer":62,"question":63},"Yes, significantly. Wind blowing out at Wrigley Field adds 1-2 runs to game totals. Temperature above 85°F increases scoring. Rain delays disrupt pitchers. Include wind speed, direction, temperature, and humidity in your model.","Do weather conditions affect MLB betting models?",{"answer":65,"question":66},"Professional models target 3-8% ROI over a full season. The best public track record is Zerillo's 2019 season at +30.2 units with 4.6% ROI over 659 bets. Anything above 2% sustained ROI is excellent.","What ROI should I expect from an MLB betting model?",{"answer":68,"question":69},"Re-train your model with new data at least twice per season — once after the first 2 months and once at the All-Star break. Update daily inputs like lineups, weather, and bullpen status every morning before lines open.","How often should I update my MLB betting model?",{"answer":71,"question":72},"Yes. Player prop models use the same framework as game models but focus on individual stats: strikeout totals, hits over\u002Funder, and bases. The key difference is using player-level data (rolling averages, platoon splits) instead of team aggregates.","Can I use machine learning for MLB prop bets?",[74,75,76,77],"en","de","tr","ru",{"data":79,"body":80},{},{"type":81,"children":82},"root",[83,92,98,120,132,137,143,150,277,283,295,301,307,319,324,330,335,378,405,411,417,422,446,459,465,470,491,497,502,523,559,565,571,584,628,634,646,652,664,677,683,688,695,858,871,877,883,902,1001,1007,1026,1031,1049,1054,1060,1065,1088,1094,1106,1139,1144,1150,1155,1415,1421,1427,1439,1453,1459,1471,1480,1486,1491,1500,1506,1511,1517,1526,1532,1538,1543,1576,1581,1587,1592,1625,1630,1636,1641,1659,1664,1687,1693,1699,1704,2114,2119,2129,2147,2176,2182,2187,2468,2473,2503,2508,3123,3128,3134,3146,3247,3264,3270,3276,3288,3294,3306,3317,3321,3327,3333,3366,3372,3377,3400,3406,3418,3436,3447,3453,3464,3468,3474,3480,3485,3518,3530,3536,3541,3559,3564,3570,3575,3593,3613,3619,3625,3637,3643,3656,3662,3667,3673,3685,3703,3708,3736,3742,3748,3753,3899,3927,3933,3966,3979,3984,4013,4019],{"type":84,"tag":85,"props":86,"children":88},"element","h2",{"id":87},"mlb-betting-model-build-your-own-system-from-scratch-2026",[89],{"type":90,"value":91},"text","MLB Betting Model: Build Your Own System From Scratch (2026)",{"type":84,"tag":93,"props":94,"children":95},"p",{},[96],{"type":90,"value":97},"Picture this: it's Tuesday morning, the full MLB slate drops in 3 hours, and you have 14 games to evaluate. Gut feel says the Dodgers are a lock. Your buddy swears the White Sox are \"due.\" Meanwhile, the sharp money is moving a line nobody's talking about.",{"type":84,"tag":93,"props":99,"children":100},{},[101,103,109,111,118],{"type":90,"value":102},"Here's the difference between you and the sharps: ",{"type":84,"tag":104,"props":105,"children":106},"strong",{},[107],{"type":90,"value":108},"they have a model",{"type":90,"value":110},". Not a crystal ball — a systematic process that converts data into probabilities, compares those probabilities to market odds, and tells them exactly which bets have positive expected value. It's the same analytical approach that ",{"type":84,"tag":112,"props":113,"children":115},"a",{"href":114},"\u002Fblog\u002Fwhat-is-a-handicapper-in-sports-betting",[116],{"type":90,"value":117},"professional sports handicappers use",{"type":90,"value":119},", but automated.",{"type":84,"tag":93,"props":121,"children":122},{},[123,125,130],{"type":90,"value":124},"The good news? As of 2026, every piece of data you need to build an MLB betting model is ",{"type":84,"tag":104,"props":126,"children":127},{},[128],{"type":90,"value":129},"free",{"type":90,"value":131},". FanGraphs, Baseball Savant, and Statcast give you the same raw numbers that professional syndicates use. What separates the winners is how they engineer those numbers into features, train models that actually predict outcomes, and manage bankroll with discipline.",{"type":84,"tag":93,"props":133,"children":134},{},[135],{"type":90,"value":136},"This guide walks you through the entire process — from your first spreadsheet to a full Python ensemble model. Whether you're a complete beginner or a data scientist looking for MLB-specific feature engineering ideas, there's a level for you. Let's build something that actually works.",{"type":84,"tag":85,"props":138,"children":140},{"id":139},"tldr-mlb-betting-model-quick-reference",[141],{"type":90,"value":142},"TL;DR — MLB Betting Model Quick Reference",{"type":84,"tag":144,"props":145,"children":147},"h3",{"id":146},"model-levels-at-a-glance",[148],{"type":90,"value":149},"Model Levels at a Glance",{"type":84,"tag":151,"props":152,"children":153},"table",{},[154,188],{"type":84,"tag":155,"props":156,"children":157},"thead",{},[158],{"type":84,"tag":76,"props":159,"children":160},{},[161,167,172,178,183],{"type":84,"tag":162,"props":163,"children":164},"th",{},[165],{"type":90,"value":166},"Level",{"type":84,"tag":162,"props":168,"children":169},{},[170],{"type":90,"value":171},"Tools",{"type":84,"tag":162,"props":173,"children":175},{"align":174},"center",[176],{"type":90,"value":177},"Time to Build",{"type":84,"tag":162,"props":179,"children":180},{"align":174},[181],{"type":90,"value":182},"Expected Edge",{"type":84,"tag":162,"props":184,"children":185},{},[186],{"type":90,"value":187},"Best For",{"type":84,"tag":189,"props":190,"children":191},"tbody",{},[192,221,249],{"type":84,"tag":76,"props":193,"children":194},{},[195,201,206,211,216],{"type":84,"tag":196,"props":197,"children":198},"td",{},[199],{"type":90,"value":200},"Beginner",{"type":84,"tag":196,"props":202,"children":203},{},[204],{"type":90,"value":205},"Spreadsheet + FanGraphs",{"type":84,"tag":196,"props":207,"children":208},{"align":174},[209],{"type":90,"value":210},"1-2 weeks",{"type":84,"tag":196,"props":212,"children":213},{"align":174},[214],{"type":90,"value":215},"1-3%",{"type":84,"tag":196,"props":217,"children":218},{},[219],{"type":90,"value":220},"Learning the framework",{"type":84,"tag":76,"props":222,"children":223},{},[224,229,234,239,244],{"type":84,"tag":196,"props":225,"children":226},{},[227],{"type":90,"value":228},"Intermediate",{"type":84,"tag":196,"props":230,"children":231},{},[232],{"type":90,"value":233},"Python + Regression",{"type":84,"tag":196,"props":235,"children":236},{"align":174},[237],{"type":90,"value":238},"3-4 weeks",{"type":84,"tag":196,"props":240,"children":241},{"align":174},[242],{"type":90,"value":243},"3-5%",{"type":84,"tag":196,"props":245,"children":246},{},[247],{"type":90,"value":248},"Consistent small edges",{"type":84,"tag":76,"props":250,"children":251},{},[252,257,262,267,272],{"type":84,"tag":196,"props":253,"children":254},{},[255],{"type":90,"value":256},"Advanced",{"type":84,"tag":196,"props":258,"children":259},{},[260],{"type":90,"value":261},"XGBoost + Ensemble",{"type":84,"tag":196,"props":263,"children":264},{"align":174},[265],{"type":90,"value":266},"6-8 weeks",{"type":84,"tag":196,"props":268,"children":269},{"align":174},[270],{"type":90,"value":271},"5-8%",{"type":84,"tag":196,"props":273,"children":274},{},[275],{"type":90,"value":276},"Maximizing ROI",{"type":84,"tag":144,"props":278,"children":280},{"id":279},"who-this-guide-is-for",[281],{"type":90,"value":282},"Who This Guide Is For",{"type":84,"tag":93,"props":284,"children":285},{},[286,288,293],{"type":90,"value":287},"This guide is for anyone who wants to move from gut-feel picks to a ",{"type":84,"tag":104,"props":289,"children":290},{},[291],{"type":90,"value":292},"data-driven MLB betting system",{"type":90,"value":294},". You don't need a statistics degree — if you can use a spreadsheet, you can start at Level 1. If you know basic Python, jump straight to the intermediate section.",{"type":84,"tag":85,"props":296,"children":298},{"id":297},"what-is-an-mlb-betting-model-and-why-build-one",[299],{"type":90,"value":300},"What Is an MLB Betting Model (and Why Build One)?",{"type":84,"tag":144,"props":302,"children":304},{"id":303},"model-vs-gut-feel-the-key-difference",[305],{"type":90,"value":306},"Model vs Gut Feel — The Key Difference",{"type":84,"tag":93,"props":308,"children":309},{},[310,312,317],{"type":90,"value":311},"A betting model is a ",{"type":84,"tag":104,"props":313,"children":314},{},[315],{"type":90,"value":316},"probability machine",{"type":90,"value":318},". You feed it data (pitcher stats, park factors, bullpen usage), and it outputs a probability for each possible outcome. That probability is then compared to the market odds to find +EV bets.",{"type":84,"tag":93,"props":320,"children":321},{},[322],{"type":90,"value":323},"The difference matters: when you \"feel\" the Dodgers will win, you have no way to know if -180 is fair. When your model says the Dodgers have a 63% chance of winning, you can calculate that -180 implies only 64.3% — meaning the market is fairly priced and there's no bet.",{"type":84,"tag":144,"props":325,"children":327},{"id":326},"what-a-good-model-actually-does",[328],{"type":90,"value":329},"What a Good Model Actually Does",{"type":84,"tag":93,"props":331,"children":332},{},[333],{"type":90,"value":334},"A good MLB betting model does three things:",{"type":84,"tag":336,"props":337,"children":338},"ol",{},[339,350,360],{"type":84,"tag":340,"props":341,"children":342},"li",{},[343,348],{"type":84,"tag":104,"props":344,"children":345},{},[346],{"type":90,"value":347},"Predicts win probability",{"type":90,"value":349}," more accurately than the market (even by 2-3%)",{"type":84,"tag":340,"props":351,"children":352},{},[353,358],{"type":84,"tag":104,"props":354,"children":355},{},[356],{"type":90,"value":357},"Identifies +EV bets",{"type":90,"value":359}," where your probability exceeds the implied odds",{"type":84,"tag":340,"props":361,"children":362},{},[363,368,370,376],{"type":84,"tag":104,"props":364,"children":365},{},[366],{"type":90,"value":367},"Sizes bets appropriately",{"type":90,"value":369}," using ",{"type":84,"tag":112,"props":371,"children":373},{"href":372},"\u002Fbetting\u002Fkelly-calculator",[374],{"type":90,"value":375},"Kelly Criterion",{"type":90,"value":377}," or a variant",{"type":84,"tag":93,"props":379,"children":380},{},[381,383,388,390,395,397,403],{"type":90,"value":382},"It does NOT predict winners with certainty. A 55% model is extremely profitable at the right odds. The goal isn't accuracy — it's ",{"type":84,"tag":104,"props":384,"children":385},{},[386],{"type":90,"value":387},"calibration",{"type":90,"value":389}," and ",{"type":84,"tag":104,"props":391,"children":392},{},[393],{"type":90,"value":394},"edge identification",{"type":90,"value":396},". To see ",{"type":84,"tag":112,"props":398,"children":400},{"href":399},"\u002Fblog\u002Fwho-sets-the-odds-for-sports-betting",[401],{"type":90,"value":402},"how professional oddsmakers build their models",{"type":90,"value":404}," and price MLB games, check our guide on the oddsmaking pipeline.",{"type":84,"tag":85,"props":406,"children":408},{"id":407},"choose-your-level-beginner-intermediate-or-advanced",[409],{"type":90,"value":410},"Choose Your Level — Beginner, Intermediate, or Advanced",{"type":84,"tag":144,"props":412,"children":414},{"id":413},"beginner-spreadsheet-key-stats",[415],{"type":90,"value":416},"Beginner: Spreadsheet + Key Stats",{"type":84,"tag":93,"props":418,"children":419},{},[420],{"type":90,"value":421},"Start here if you've never built a model. Track 4-5 key stats in a spreadsheet (pitcher xFIP, team wOBA, bullpen workload, park factor) and assign simple weights. You won't beat Vegas consistently, but you'll learn the framework and stop making purely emotional bets.",{"type":84,"tag":93,"props":423,"children":424},{},[425,430,432,437,439,444],{"type":84,"tag":104,"props":426,"children":427},{},[428],{"type":90,"value":429},"Time:",{"type":90,"value":431}," 1-2 weeks | ",{"type":84,"tag":104,"props":433,"children":434},{},[435],{"type":90,"value":436},"Tools:",{"type":90,"value":438}," Google Sheets or Excel | ",{"type":84,"tag":104,"props":440,"children":441},{},[442],{"type":90,"value":443},"Data:",{"type":90,"value":445}," FanGraphs",{"type":84,"tag":93,"props":447,"children":448},{},[449,451,457],{"type":90,"value":450},"If you're totally new to sports analytics, start with our ",{"type":84,"tag":112,"props":452,"children":454},{"href":453},"\u002Fblog\u002Fmlb-underdog-betting-strategy",[455],{"type":90,"value":456},"MLB underdog betting strategy guide",{"type":90,"value":458}," to see what a data-driven system looks like in practice before building your own.",{"type":84,"tag":144,"props":460,"children":462},{"id":461},"intermediate-python-regression",[463],{"type":90,"value":464},"Intermediate: Python + Regression",{"type":84,"tag":93,"props":466,"children":467},{},[468],{"type":90,"value":469},"Level up with Python's pandas and scikit-learn libraries. Build logistic regression models, calculate proper feature importance, and backtest against historical odds. This is where most profitable amateur bettors operate.",{"type":84,"tag":93,"props":471,"children":472},{},[473,477,479,483,485,489],{"type":84,"tag":104,"props":474,"children":475},{},[476],{"type":90,"value":429},{"type":90,"value":478}," 3-4 weeks | ",{"type":84,"tag":104,"props":480,"children":481},{},[482],{"type":90,"value":436},{"type":90,"value":484}," Python, Jupyter Notebooks | ",{"type":84,"tag":104,"props":486,"children":487},{},[488],{"type":90,"value":443},{"type":90,"value":490}," FanGraphs + Statcast",{"type":84,"tag":144,"props":492,"children":494},{"id":493},"advanced-xgboost-ensemble-methods",[495],{"type":90,"value":496},"Advanced: XGBoost + Ensemble Methods",{"type":84,"tag":93,"props":498,"children":499},{},[500],{"type":90,"value":501},"Combine multiple model types (linear regression, logistic regression, XGBoost) into an ensemble that's more robust than any single model. Add advanced features like pitch-level data, umpire strike zone tendencies, and real-time lineup adjustments.",{"type":84,"tag":93,"props":503,"children":504},{},[505,509,511,515,517,521],{"type":84,"tag":104,"props":506,"children":507},{},[508],{"type":90,"value":429},{"type":90,"value":510}," 6-8 weeks | ",{"type":84,"tag":104,"props":512,"children":513},{},[514],{"type":90,"value":436},{"type":90,"value":516}," Python, XGBoost, LightGBM | ",{"type":84,"tag":104,"props":518,"children":519},{},[520],{"type":90,"value":443},{"type":90,"value":522}," Statcast + weather APIs",{"type":84,"tag":93,"props":524,"children":525},{},[526,528,534,535,541,543,549,551,557],{"type":90,"value":527},"The same framework applies to other sports. Check out our ",{"type":84,"tag":112,"props":529,"children":531},{"href":530},"\u002Fblog\u002Fnba-betting-system",[532],{"type":90,"value":533},"NBA betting system breakdown",{"type":90,"value":389},{"type":84,"tag":112,"props":536,"children":538},{"href":537},"\u002Fblog\u002Fnfl-betting-strategy-guide",[539],{"type":90,"value":540},"NFL betting strategy guide",{"type":90,"value":542}," if you're building multi-sport models. For feature brainstorming and data cleaning, many modelers now use ",{"type":84,"tag":112,"props":544,"children":546},{"href":545},"\u002Fblog\u002Fhow-to-use-chatgpt-for-sports-betting",[547],{"type":90,"value":548},"ChatGPT sports betting prompts",{"type":90,"value":550}," to speed up the early stages of model development. For a simpler NFL betting format, our ",{"type":84,"tag":112,"props":552,"children":554},{"href":553},"\u002Fblog\u002Ffootball-squares-rules",[555],{"type":90,"value":556},"football squares probability breakdown",{"type":90,"value":558}," shows how scoring patterns create exploitable number distributions.",{"type":84,"tag":85,"props":560,"children":562},{"id":561},"phase-1-data-collection-where-to-get-mlb-data",[563],{"type":90,"value":564},"Phase 1: Data Collection — Where to Get MLB Data",{"type":84,"tag":144,"props":566,"children":568},{"id":567},"fangraphs-team-and-player-stats-xfip-woba-k-bb",[569],{"type":90,"value":570},"FanGraphs — Team and Player Stats (xFIP, wOBA, K-BB%)",{"type":84,"tag":93,"props":572,"children":573},{},[574,582],{"type":84,"tag":112,"props":575,"children":579},{"href":576,"rel":577},"https:\u002F\u002Fwww.fangraphs.com",[578],"nofollow",[580],{"type":90,"value":581},"FanGraphs",{"type":90,"value":583}," is the foundation. Download team-level and pitcher-level stats for the last 3-5 seasons. The key metrics:",{"type":84,"tag":585,"props":586,"children":587},"ul",{},[588,598,608,618],{"type":84,"tag":340,"props":589,"children":590},{},[591,596],{"type":84,"tag":104,"props":592,"children":593},{},[594],{"type":90,"value":595},"xFIP",{"type":90,"value":597}," (Expected Fielding Independent Pitching): Predicts future pitcher performance better than ERA",{"type":84,"tag":340,"props":599,"children":600},{},[601,606],{"type":84,"tag":104,"props":602,"children":603},{},[604],{"type":90,"value":605},"wOBA",{"type":90,"value":607}," (Weighted On-Base Average): Captures total offensive value on a single scale",{"type":84,"tag":340,"props":609,"children":610},{},[611,616],{"type":84,"tag":104,"props":612,"children":613},{},[614],{"type":90,"value":615},"K-BB%",{"type":90,"value":617}," (Strikeout minus Walk Rate): The #1 predictor of pitcher quality",{"type":84,"tag":340,"props":619,"children":620},{},[621,626],{"type":84,"tag":104,"props":622,"children":623},{},[624],{"type":90,"value":625},"BABIP",{"type":90,"value":627}," (Batting Average on Balls in Play): Identifies luck regression candidates",{"type":84,"tag":144,"props":629,"children":631},{"id":630},"statcast-baseball-savant-pitch-level-data",[632],{"type":90,"value":633},"Statcast (Baseball Savant) — Pitch-Level Data",{"type":84,"tag":93,"props":635,"children":636},{},[637,644],{"type":84,"tag":112,"props":638,"children":641},{"href":639,"rel":640},"https:\u002F\u002Fbaseballsavant.mlb.com",[578],[642],{"type":90,"value":643},"Baseball Savant",{"type":90,"value":645}," provides Statcast data — exit velocity, launch angle, spin rate, and expected stats (xBA, xSLG, xwOBA). 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An ensemble averages predictions from multiple models, reducing overfitting and improving calibration.",{"type":84,"tag":689,"props":1512,"children":1514},{"id":1513},"python-code-full-ensemble-pipeline",[1515],{"type":90,"value":1516},"Python Code: Full Ensemble Pipeline",{"type":84,"tag":1440,"props":1518,"children":1521},{"className":1519,"code":1520,"language":1445,"meta":1446},[1443],"import numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.calibration import CalibratedClassifierCV\nimport xgboost as xgb\n\n## Train individual models\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## Weighted ensemble (tune weights via validation set)\nensemble_probs = 0.4 * lr_probs + 0.6 * xgb_probs\n\n## Compare to market implied probability\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% minimum edge threshold\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\"Bet ${bet_size:.0f}\")\n",[1522],{"type":84,"tag":1449,"props":1523,"children":1524},{"__ignoreMap":1446},[1525],{"type":90,"value":1520},{"type":84,"tag":85,"props":1527,"children":1529},{"id":1528},"phase-4-backtesting-and-validation",[1530],{"type":90,"value":1531},"Phase 4: Backtesting and Validation",{"type":84,"tag":144,"props":1533,"children":1535},{"id":1534},"traintest-split-strategy-2019-2022-train-2023-validate-2024-2025-test",[1536],{"type":90,"value":1537},"Train\u002FTest Split Strategy (2019-2022 Train \u002F 2023 Validate \u002F 2024-2025 Test)",{"type":84,"tag":93,"props":1539,"children":1540},{},[1541],{"type":90,"value":1542},"Never test your model on the same data you trained it on. Use a strict temporal split:",{"type":84,"tag":585,"props":1544,"children":1545},{},[1546,1556,1566],{"type":84,"tag":340,"props":1547,"children":1548},{},[1549,1554],{"type":84,"tag":104,"props":1550,"children":1551},{},[1552],{"type":90,"value":1553},"Training set (2019-2022):",{"type":90,"value":1555}," ~9,700 games. Your model learns patterns from this data",{"type":84,"tag":340,"props":1557,"children":1558},{},[1559,1564],{"type":84,"tag":104,"props":1560,"children":1561},{},[1562],{"type":90,"value":1563},"Validation set (2023):",{"type":90,"value":1565}," ~2,430 games. Tune hyperparameters and feature selection",{"type":84,"tag":340,"props":1567,"children":1568},{},[1569,1574],{"type":84,"tag":104,"props":1570,"children":1571},{},[1572],{"type":90,"value":1573},"Test set (2024-2025):",{"type":90,"value":1575}," ~4,860 games. Final, untouched evaluation of true performance",{"type":84,"tag":93,"props":1577,"children":1578},{},[1579],{"type":90,"value":1580},"If your model performs well on training data but poorly on the test set, you've overfit. Go back and simplify.",{"type":84,"tag":144,"props":1582,"children":1584},{"id":1583},"key-metrics-log-loss-brier-score-calibration",[1585],{"type":90,"value":1586},"Key Metrics — Log Loss, Brier Score, Calibration",{"type":84,"tag":93,"props":1588,"children":1589},{},[1590],{"type":90,"value":1591},"Win\u002Floss accuracy alone is misleading. A model that says \"52% on every game\" has 52% accuracy but zero edge. Use proper scoring metrics:",{"type":84,"tag":585,"props":1593,"children":1594},{},[1595,1605,1615],{"type":84,"tag":340,"props":1596,"children":1597},{},[1598,1603],{"type":84,"tag":104,"props":1599,"children":1600},{},[1601],{"type":90,"value":1602},"Log Loss:",{"type":90,"value":1604}," Penalizes confident wrong predictions. Lower = better. Target \u003C 0.68",{"type":84,"tag":340,"props":1606,"children":1607},{},[1608,1613],{"type":84,"tag":104,"props":1609,"children":1610},{},[1611],{"type":90,"value":1612},"Brier Score:",{"type":90,"value":1614}," Mean squared error of probabilities. Target \u003C 0.24",{"type":84,"tag":340,"props":1616,"children":1617},{},[1618,1623],{"type":84,"tag":104,"props":1619,"children":1620},{},[1621],{"type":90,"value":1622},"Calibration:",{"type":90,"value":1624}," When your model says 60%, the team should win ~60% of the time",{"type":84,"tag":93,"props":1626,"children":1627},{},[1628],{"type":90,"value":1629},"Check calibration by plotting predicted probability vs actual win rate in buckets (50-55%, 55-60%, 60-65%, etc.). 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",{"type":84,"tag":104,"props":3905,"children":3906},{},[3907],{"type":90,"value":3908},"Anyone promising 20%+ ROI is lying.",{"type":90,"value":3910}," If you're curious whether these numbers add up to a paycheck, see whether ",{"type":84,"tag":112,"props":3912,"children":3914},{"href":3913},"\u002Fblog\u002Fcan-you-make-a-living-off-sports-betting",[3915],{"type":90,"value":3916},"can sports betting become a full-time income",{"type":90,"value":3918},". Consistency over 500+ bets at 3% ROI is outstanding. 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