Our Methodology
How we calculate win probabilities—and why you should trust the numbers
Our Approach
Sports prediction is fundamentally a problem of incomplete information. You can't know everything that affects the outcome of a game—locker room dynamics, a player's sleep quality, whether the ref had a bad morning. What you can do is measure what's measurable and weight it based on how predictive it's actually been.
That's what we do. We build probabilistic models that take every quantifiable factor—team strength, home field, rest days, weather, injuries—and combine them into a single win probability. The number isn't a prediction; it's our best estimate of each team's chances given what we know right now.
We show our work because you should be able to see what's driving the number. If you think we're underweighting a factor, that's useful information for you.
The Models
Elo Ratings
Every team starts with a baseline rating. Win and you gain points; lose and you drop. The amount transferred depends on the expected outcome—beating a strong team is worth more than beating a weak one. This system, originally designed for chess, has proven remarkably effective across sports because it captures relative team strength without overreacting to single results.
Based on: Elo (1978), Bradley & Terry (1952)
Logistic Regression
We use logistic regression to convert rating differences and contextual factors into probabilities. The aim is calibration: when we say a team has a 70% chance, we want those teams to win about 70% of the time. We don't just assert that; we track how close we actually get, by confidence band, on our accuracy page. The model learns weights for each factor from thousands of past games.
Factor Weighting
Not all factors matter equally, and what matters varies by sport. Home field is worth about 3 points in NFL, but the impact of weather depends on the matchup. Back-to-backs tank NBA performance but don't exist in football. We weight each factor based on its historical predictive power in that specific sport, updating weights as we gather more data.
Based on: Schwartz & Barsky (1977) on home advantage, Massey (1997) on sports ratings
Academic Foundations
Our methodology draws on decades of research in sports analytics, statistics, and decision science. Here are the foundational works that inform our approach:
- Elo, A. E. (1978). The Rating of Chess Players, Past and Present. Arco Publishing. Link
- Bradley, R. A., & Terry, M. E. (1952). Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons. Biometrika, 39(3/4), 324–345. doi:10.2307/2334029
- Schwartz, B., & Barsky, S. F. (1977). The Home Advantage. Social Forces, 55(3), 641–661. doi:10.2307/2577461
- Brier, G. W. (1950). Verification of Forecasts Expressed in Terms of Probability. Monthly Weather Review, 78(1), 1–3. doi:10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2
- Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail—but Some Don't. Penguin Press. Link
- Massey, K. (1997). Statistical Models Applied to the Rating of Sports Teams. Bluefield College. Link
Data Sources
Good models need good data. We pull from multiple sources and cross-validate to ensure accuracy:
Game Data
Official league APIs, verified third-party providers, and historical databases. Every score, stat, and schedule confirmed against multiple sources.
Injury Reports
Official team injury designations, updated multiple times daily during game weeks. We track practice participation and game-time decisions.
Weather Data
National Weather Service forecasts for outdoor venues, updated hourly as game time approaches. Temperature, wind, and precipitation all factor in.
Validation
Automated checks flag inconsistencies. Manual review catches edge cases. If data looks wrong, we investigate before using it.
Model Accuracy
We hold ourselves accountable. Here's how we measure whether our probabilities are actually accurate:
Figures are rolling averages. See live accuracy data →
Calibration
Calibration asks a simple question: when we say a team has a 70% chance, do those teams actually win about 70% of the time? We test it by bucketing every prediction and comparing predicted vs. actual win rates. A perfectly calibrated model traces the diagonal. Ours holds up well on near toss-ups and on our most confident calls, and runs a little overconfident in the middle bands, which is exactly what the accuracy page shows in full.
Brier Score
The Brier score measures the accuracy of probabilistic predictions—it's the mean squared error between predicted probabilities and actual outcomes (0 or 1). Lower is better. A score of 0 means perfect predictions; 0.25 is no better than flipping a coin. We track our Brier scores by sport and season.
Based on: Brier (1950)
Win Rate by Confidence
We bucket games by how confident we were. Near toss-ups land close to a coin flip, as they should. The more confident we are, the higher our hit rate climbs, and our 75%+ calls are where we're strongest. The live figures for each band are in the cards above and on our accuracy page.
What We Can't Measure
No model captures everything. Revenge games, contract years, coaching changes mid-season, locker room drama—these matter but resist quantification. We also can't predict surprise inactives until they're announced. A 35% underdog still wins 35% of the time; that's not an error, it's uncertainty. Use our numbers as one input among many.
See the methodology in action
Every game page shows the factor breakdown driving our probabilities.
Learn More
Learn about specific aspects of our analysis: