Learn sports analytics
In-depth guides to how prediction models work, what the numbers mean and how to judge them. Analytics only: no wagering, no guaranteed outcomes.
20 articles
How Sports Prediction Models Work
A plain-English tour of a sports prediction model: the data it learns from, the features it builds, how it turns them into a probability, and how it is tested before anyone relies on it.
Read articleWhat Does a 70% Win Probability Actually Mean?
A 70% win probability is not a prediction that a team will win. It is a statement about frequency. Here is how to read it, how to check it, and the mistakes people make with it.
Read articleUnderstanding Brier Score in Sports Predictions
The Brier score measures how good probability forecasts are, not just whether picks were right. Worked examples, benchmarks and the common traps when comparing models.
Read articleWhy Sports Prediction Models Get Games Wrong
Some misses are the model working as intended; others reveal real weaknesses. How to tell the difference, with examples from EdgeIQ's own evaluation.
Read articleHow Model Calibration Improves Sports Forecasts
Calibration makes a model's percentages trustworthy. What it is, how it is measured with reliability charts, the methods used to fix it, and why it can backfire on small samples.
Read articleNFL Analytics: Key Metrics Behind Game Predictions
Which numbers actually carry signal in NFL game prediction, why a 17-game season makes everything harder, and how EdgeIQ's NFL engine weighs team strength, form, venue and rest.
Read articleNBA Analytics: What Factors Matter Most?
Basketball is the most predictable of the major team sports. Why that is, which factors matter most — pace, efficiency, rest and schedule — and where NBA models still struggle.
Read articleMLB Analytics and the Challenge of Predicting Baseball
Baseball has the richest statistics in sports and some of the least predictable single games. Why even great teams lose four in ten, and how that shapes MLB projections.
Read articleNHL Analytics: Understanding Hockey Prediction Models
Hockey is low-scoring, fast and heavily influenced by goaltending and luck. How that shapes NHL prediction models, why probabilities stay close to 50%, and what inputs matter.
Read articleUnderstanding Home-Field Advantage With Data
Home teams win more often in every major sport, but by different amounts and for debated reasons. How models measure and use home advantage, including EdgeIQ's league-by-league settings.
Read articleHow Injuries Can Affect Sports Predictions
Why player availability can move a game more than any team statistic, how different sports feel injuries differently, and how to read a projection when late news breaks.
Read articleStrength of Schedule Explained
A team's record means little without knowing who it played. How strength of schedule is calculated, why it matters most in college sports, and how EdgeIQ uses it without leaking future results.
Read articlePrediction Accuracy vs Probability Calibration
Accuracy asks whether the favorite won. Calibration asks whether the percentages were honest. Why you need both, how they can disagree, and which to trust when they do.
Read articleHow to Evaluate a Sports Prediction Model
A practical checklist for judging any sports prediction source: sample size, chronological testing, baselines, probability quality, transparency and the red flags that should make you walk away.
Read articleWhy Historical Performance Doesn't Guarantee Future Results
Backtests describe the past. Regression to the mean, changing conditions, small samples and selection effects all explain why a strong record can fade — and how to set realistic expectations.
Read articleHow EdgeIQ's NFL Predictions Work
A plain-language walkthrough of how EdgeIQ turns NFL schedules, team performance and recent form into a projected score, a win probability and a published accuracy record.
Read articleHow to Read an NFL Prediction: Probability, Score and Margin
Win probability, projected score and projected margin each answer a different question. Here is what every number on an EdgeIQ NFL game page means and how they fit together.
Read articleWhat Model Accuracy and Calibration Actually Mean
Accuracy counts how often a sports model is right. Calibration checks whether its confidence is honest. You need both to judge a prediction model — here is how to read each one.
Read articleComparing Model Projections With Market Prices
Sportsbook prices are a crowd forecast with a built-in margin. Here is how to strip that margin out, convert prices to probabilities and compare them with a model projection — as research, not as a wager.
Read articleHow EdgeIQ Picks the NFL Games Worth Watching
EdgeIQ surfaces upcoming NFL games where the model is most confident and has a verified track record at that confidence level. Here are the exact rules behind the list.
Read articleStill have questions?
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EdgeIQ articles are edited by Patrick C. We explain methods, attribute external sources where used, and update articles when the underlying methods change. Read about our editorial approach.