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Salary

As explained in Statistics Slam Dunk

A player salary is the monetary compensation paid by a sports team to an individual player, typically stated as an annual amount.

A player salary is the monetary compensation paid by a sports team to an individual player, usually stated as an annual amount. In roster planning, salaries become a budget variable: a team may need to keep total annual salaries below a fixed limit while satisfying roster and age requirements. section 4.6

Salary data supports several practical analytical tasks:

  • Compare player compensation across seasons, teams, or roster positions. section 12.3 section 12.4
  • Check whether a proposed roster stays within a $90 million collective salary limit. section 4.6
  • Prepare salary records for inequality analysis by removing unused fields, checking missing teams, and excluding sparse early seasons. section 12.4

Evaluating a free-agent budget

A fictional NBA team is choosing five free agents: one point guard, shooting guard, center, power forward, and small forward. The selected players' annual salaries must total no more than $90 million, while their average age must be 30 or less. A salary table includes player names, ages, positions, annual salaries, and win shares, making the budget a concrete roster-building calculation. section 4.6

How Statistics Slam Dunk covers it

How deep it goes
Treats salary as a dataset variable, then covers selection, summary statistics, duplicate checks, missing teams, and seasonal filtering.
Examples
Uses an NBA free-agent budget, historical salary records, team-season summaries, and Denver Nuggets and Chicago Bulls roster checks.
What it assumes
Assumes basic player, team, position, and season concepts while teaching salary-focused data checks through concrete NBA records.
What you can do after
Apply salary data in R by importing, summarizing, filtering, ranking, and checking records for team and season analysis.
How it connects
Connects salary to constrained optimization, data wrangling, descriptive statistics, inequality, and duplicate-record detection.

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.