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Opening point spread

As explained in Statistics Slam Dunk

An opening point spread is the initial bookmaker-implied margin between two teams when wagering markets open.

An opening point spread is the predicted scoring margin set before wagers are placed. It expresses the same matchup from either team’s side: Indiana -7 means Indiana is expected to win by seven, while Memphis +7 means Memphis is expected to lose by seven. A positive spread identifies the underdog, which covers by winning outright or losing by fewer points than the spread; a negative spread identifies the favorite, which covers by winning by more. section 17.0 section 17.3

The opening spread is designed to encourage roughly equal wagering on both teams. As bets arrive, it can move before becoming the closing spread. Comparing the opening and closing values with the final scoring margin turns the line into a practical question: did later wagering produce a prediction nearer the result? section 17.0 section 17.5

For game-level analysis, prepare these measures:

  • Margin: road-team points minus home-team points. section 17.4.3
  • Opening-spread error: the absolute difference between margin and the home opening spread. section 17.4.3
  • Closing-spread error: the equivalent absolute difference using the home closing spread. section 17.4.3
  • Spread movement: up, down, or same according to whether the closing spread’s absolute value increased, decreased, or stayed unchanged. section 17.4.3

Indiana versus Memphis

Indiana opened at -7 and Memphis at +7, then the line closed at Indiana -7.5. Indiana won 111–83, so both teams’ final margin was far beyond the spread; for this game, the closing spread was nearer the result than the opening spread. section 17.0

oddsdf4 %>%
 mutate(diff_margin_openspreadH = abs(margin - openspreadH)) -> oddsdf4

This R transformation creates the absolute opening-spread error used to compare the bookmaker’s initial margin with the final game margin. section 17.4.3

Across the 2018–19 NBA regular season, the reported comparisons find closing spreads nearer final margins than opening spreads overall and in most months. The stated interpretation is that wagering crowds added information beyond the initial odds, particularly early in the season; this is an observational comparison rather than a guarantee for any one game. section 17.5 section 17.6

How Statistics Slam Dunk covers it

How deep it goes
Defines cover logic and builds absolute-error and movement variables, then compares aggregate results without modeling line formation.
Examples
Uses Indiana–Memphis and all 1,230 2018–19 regular-season games, with R-derived margin and spread-error fields.
What it assumes
Introduces spread signs and covering from zero while using R, dplyr, tidyr, and SmartEDA for analysis.
What you can do after
Evaluate/understand whether opening or closing NBA spreads are nearer observed final margins using grouped error comparisons.
How it connects
Connects opening point spreads to closing point spreads, final margins, over/under totals, exploratory data analysis, and wisdom of crowds.
Cover of Statistics Slam Dunk
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.