NBA analytics blog

• True Shooting Percentage (TS%) Explained, With an Example — What true shooting percentage measures, the formula behind it, why free throws count as 0.44 of a possession, and how TS% compares with FG% and eFG%.
• Player Efficiency Rating (PER) Explained: Formula and Limits — How John Hollinger's PER turns a box score into one per-minute number, how it is pace-adjusted and scaled so the league average is 15, and where it fails.
• How We Backtest an NBA Model Without Fooling Ourselves — Walk-forward testing, frozen settings and four untouched seasons: the rules that keep an NBA projection model honest.
• What a 70% Win Probability Really Means — A 70% favourite loses three games in ten. Calibration, not bold calls, is what makes a probability useful.
• How NBA Totals Are Projected: Pace First, Then Efficiency — Possessions times points per possession: the two numbers behind every projected NBA total, and why league scoring trends matter.
• Offensive, Defensive and Net Rating, Explained — Why points per 100 possessions beat points per game, and how opponent adjustment turns raw ratings into team strength.
• The Four Factors: Shooting, Turnovers, Rebounding, Free Throws — Dean Oliver's four factors explain most of the gap between good and bad NBA teams. Here is how to read them.
• Usage Rate and Minutes: The Base of Every Player Projection — Minutes drive counting stats more than anything else. How we project minutes, and why our player projections are only modestly better than season averages.
• Measuring Player Impact: Box Score, On/Off and Minutes — How HoopVantage turns box scores and on/off margins into a points-per-48 impact number for every player.
• Rest, Back-to-Backs, Travel and Altitude: What the Data Shows — How much a back-to-back, a long trip or a night in Denver moves an NBA projection, measured on a decade of games.

Projections are statistical estimates for entertainment and analysis only. Nothing here guarantees any result.