What type of model should I start with for sports betting?

Start with logistic regression. It's simple, interpretable, and surprisingly effective for binary outcome prediction (win/loss, over/under). Logistic regression outputs probabilities, which is exactly what you need, you're ultimately comparing your model's probability to the implied probability from the odds. More complex models like random forests or neural networks can outperform logistic regression, but they require more data, more tuning, and are harder to debug.

What data do I need to build a sports betting model?

At minimum: historical game results, team/player statistics, and odds data (opening lines and closing lines). For team sports like the NFL or NBA, useful features include efficiency metrics (points per possession), strength of schedule, home/away splits, rest days, and injury reports. For player-based sports like tennis or golf, surface/course-specific performance and recent form windows are critical. Most data is available from free public sources or affordable APIs.

What is backtesting and why is it important?

Backtesting evaluates your model's performance on historical data it wasn't trained on. You train the model on data from seasons 1-3, then test it on season 4. This simulates real-world conditions where you're always predicting the future. Without proper backtesting, you risk overfitting, building a model that explains past results perfectly but fails on new data. Walk-forward validation, where you retrain the model as new data arrives, is the gold standard.

How do I know if my model is well-calibrated?

A calibrated model means that when it predicts a 70% probability, the event actually occurs about 70% of the time across many predictions. Test calibration by grouping predictions into probability bins (50-55%, 55-60%, etc.) and comparing predicted probabilities to actual outcomes. Plot these on a calibration curve, a perfectly calibrated model follows the diagonal. Most raw models are overconfident and need Platt scaling or isotonic regression to fix.

How do I convert model probabilities into bets?

Compare your model's probability to the implied probability from the odds. If your model gives Team A a 60% chance and the odds imply 52%, you have an estimated 8% edge. Then apply Kelly Criterion (or fractional Kelly) to determine bet size: edge / odds = optimal fraction of bankroll. Only bet when your model's edge exceeds a minimum threshold (typically 3-5%) to account for model uncertainty. Track CLV to validate whether your model is actually capturing real edges.

Building Your Own Forecasting Model

Every sportsbook uses models. Every sharp betting syndicate uses models. If you're relying on gut feeling, public consensus, or “expert picks,” you're bringing a knife to a gunfight. The good news: building a competent forecasting model doesn't require a PhD in statistics. It requires structured thinking, clean data, and disciplined testing. This guide walks through each step from data collection to live deployment for OwnTheLines players.

Step 1: Data Collection

Your model is only as good as your data. For team sports (NFL, NBA, MLB), start with game-level results and team statistics going back at least 3–5 seasons. Key metrics include offensive and defensive efficiency (points per 100 possessions in the NBA, yards per play in the NFL), pace, turnover rates, and home/away splits. For individual sports (tennis, golf), you need player-level performance metrics segmented by surface or course.

Recommended Data Sources by Sport

SportFree SourcesKey Metrics

NFLPro Football Reference, nflfastREPA/play, success rate, DVOA

NBABasketball Reference, NBA APINet rating, eFG%, pace

MLBFanGraphs, Baseball SavantwRC+, FIP, xwOBA

TennisTennis Abstract, Jeff SackmannServe %, return %, surface splits

GolfData Golf, PGA Tour statsSG components, course history

Equally important: odds data. You need historical opening lines, closing lines, and results to backtest properly. Closing line data is essential for CLV analysis, the most reliable metric for evaluating whether your model captures genuine edges.

Step 2: Feature Selection

Feature selection is choosing which variables to feed into your model. The biggest beginner mistake is throwing in every stat available. More features doesn't mean better predictions, it means more noise and higher overfitting risk. Start with 3–5 features that have strong theoretical justification.

For an NFL point-spread model, a strong starting set might be: (1) offensive EPA per play, (2) defensive EPA per play, (3) home-field advantage constant, (4) rest differential, and (5) a strength-of-schedule adjustment. Test each feature's marginal contribution, if adding a feature doesn't meaningfully improve out-of-sample accuracy, remove it.

Step 3: Model Training and Backtesting

Train your logistic regression (or chosen algorithm) on historical data, but always reserve held-out data for testing. The gold standard is walk-forward validation: train on seasons 1–3, test on season 4. Then retrain on seasons 1–4 and test on season 5. This mimics real-world conditions where your model learns from the past and predicts the future, never the other way around.

Walk-Forward Validation Example

FoldTraining DataTest Data

Fold 12019–2021 Seasons2022 Season

Fold 22019–2022 Seasons2023 Season

Fold 32019–2023 Seasons2024 Season

Fold 42019–2024 Seasons2025 Season

Key metrics to evaluate: (1) log-loss (measures probability accuracy), (2) calibration (do 60% predictions hit 60% of the time?), (3) AUC-ROC (overall discrimination ability), and (4) simulated betting ROI against closing lines. A model with good log-loss and calibration but negative ROI means the market is already pricing in the same information.

Step 4: Calibration and Deployment

Most raw model outputs are poorly calibrated, they tend to be overconfident, predicting 75% when the true probability is 65%. Apply Platt scaling (fitting a logistic function to your predictions) or isotonic regression to correct this. Well-calibrated probabilities are essential because your bet sizing depends on accurate edge estimation.

Once calibrated, convert probabilities to edges by comparing to the market's implied probability. Set a minimum edge threshold (3–5%) below which you don't bet, to account for model uncertainty. Size bets using fractional Kelly, typically 1/4 to 1/3 Kelly to manage variance. Track your CLV religiously: if you're consistently beating the closing line, your model is finding real edges.

For the mathematical framework behind bankroll sizing, see Bankroll Management 101. For a deeper dive into the sample sizes needed to validate your model, explore Statistical Variance.