Sports Analytics
Research & Methodology
Research notes from EdgeSlate on projection models, probability, calibration, market context and transparent model evaluation.
How to Calculate Expected Value (EV) in Sports Analytics
Stop guessing and start quantitative sports research. Learn how to calculate Expected Value (EV) to determine if a position carries a mathematical advantage over the market maker.
Reading the Slate: How EdgeSlate's AI Projections Add Market Context
How EdgeSlate's independent AI Projections give you a separate daily read on the slate, alongside our core Power and Premium Projections.
The Impact of Weather on NFL Unders
Understand exactly how wind, temperature, and precipitation drive line movement and when the projection leans toward the under in football.
Understanding Line Movement and Steam
Learn how to track sharp money, interpret reverse line movement, and understand why market makers change their odds.
Position Sizing with the Kelly Criterion
Protect your capital and maximize compound growth by utilizing the Kelly Criterion sizing model in your sports analytics portfolio.
Why Fading the Public Works
Explore the psychology of recreational analysts, market maker pricing, and how quantitative models identify structural market inefficiencies.
Why Weather & Stadium Factors Matter More Than You Think
Learn how wind direction at Wrigley Field, altitude at Coors Field, and freezing temperatures in the NFL dramatically alter expected value.
Contrarian Analysis: How to Identify Fake Line Movement
Learn how to spot reverse line movement, differentiate sharp money from public bias, and avoid falling for fake steam in the sports analytics market.
Mastering In-Play Analysis: How Real-Time Data Reveals Model-vs-Market Gaps
In-play analysis surfaces notable model-vs-market gaps in sports analytics. Learn how algorithmic tracking tracks market overreactions.
Steam Chasing vs. Originating: Building a Long-Term market strategy
Explore the two main paths to long-term model evaluation in sports analytics: Top-Down (Steam Chasing) vs. Bottom-Up (Originating projections).
How We Built Our MLB NRFI Model — A Public Methodology Walkthrough
Take a peek under the hood of EdgeSlate's proprietary No Run First Inning (NRFI) model. We reveal the exact data points and statistical modeling techniques we use.
Action Network PRO vs OddsJam vs EdgeSlate — A Brutally Honest Comparison
We break down the features, pricing, and true value of the industry's top sports analytics software platforms. Find out which tool is right for you.
Why 'Guarantees of the Day' Are a Scam — And What Real Analytics Looks Like
Exposing the predatory 'tout' industry. Learn why any handicapper promising guaranteed winners or 'Max Whale Guarantees' is mathematically lying to you.
Fractional Kelly Criterion: How Quantitative Bettors Maximize Growth While Eliminating Ruin
Learn the exact mathematics of Kelly position sizing, why full Kelly leads to brutal drawdowns, and how half-Kelly and quarter-Kelly fractions build sustainable compound growth.
Player Prop Projection Modeling: Usage Elasticity, Minutes Distributions, and Poisson Splits
A deep dive into how quantitative models forecast player performance across NBA, MLB, and NFL through usage shifts, possession pace adjustments, and non-linear distributions.