Why it's important to understand the underlying distribution of a data set.

Simple tactics to make a data visualization tell a story.

A prioritized list of programming languages and tools to learn for product analytics.

Outlining the differences between Data Scientist roles and Data Analyst roles.

A list of recommended sports analytics books, categorized by sport.

A list of free sports data sources, including data sets and R/Python scraping libraries.

Applying the metric framework described in previous posts to several sports metrics.

What are "blind spots" in a sports analytics metric?

Explaining what it means when a sports analytics metric is "adjusted for opportunity"

Comparing style metrics and productivity metrics in sports analytics.

Outlining the key languages and tools to learn for sports analytics.

Comparing predictive metrics to descriptive metrics in sports analytics.

Comparing proxy metrics to factual metrics in sports analytics.

Outlining a framework for thinking about sports analytics metrics.

Media members use analytics to support the storytelling process.

Leagues help orchestrate analytics development among member teams and use data to inform league-level decisions like scheduling and rule changes.

Teams use analytics to make decisions related to in-game strategy, player personnel strategy, and sports science (Part 2 of 2)

Teams use analytics to make decisions related to in-game strategy, player personnel strategy, and sports science (Part 1 of 2)

Sports analytics is useful because humans have mental and situational limits.

A guide to starting a college or high school sports analytics club.

A list of sports analytics conference video archives.

A list of resources for learning how to code specifically for sports analytics.