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the importance of de-averaging metrics
September 27, 2022
Why it's important to understand the underlying distribution of a data set.
data visualization as storytelling
September 20, 2022
Simple tactics to make a data visualization tell a story.
programming languages & tools to learn for product analytics
September 13, 2022
A prioritized list of programming languages and tools to learn for product analytics.
data analyst vs. data scientist: what's the difference?
May 17, 2022
Outlining the differences between Data Scientist roles and Data Analyst roles.
sports analytics reading list
June 15, 2021
A list of recommended sports analytics books, categorized by sport.
free sports data sources
March 9, 2021
A list of free sports data sources, including data sets and R/Python scraping libraries.
sports analytics 101: metric framework examples
February 10, 2021
Applying the metric framework described in previous posts to several sports metrics.
sports analytics 101: blind spots
January 27, 2021
What are "blind spots" in a sports analytics metric?
sports analytics 101: adjusting for opportunity
January 14, 2021
Explaining what it means when a sports analytics metric is "adjusted for opportunity"
sports analytics 101: productivity vs. style
January 6, 2021
Comparing style metrics and productivity metrics in sports analytics.
languages and tools to learn for sports analytics
December 16, 2020
Outlining the key languages and tools to learn for sports analytics.
sports analytics 101: descriptive vs. predictive
December 8, 2020
Comparing predictive metrics to descriptive metrics in sports analytics.
sports analytics 101:: facts and proxies
December 1, 2020
Comparing proxy metrics to factual metrics in sports analytics.
sports analytics 101: a framework for metrics
November 19, 2020
Outlining a framework for thinking about sports analytics metrics.
sports analytics 101: media use cases
November 12, 2020
Media members use analytics to support the storytelling process.
sports analytics 101: league use cases
October 30, 2020
Leagues help orchestrate analytics development among member teams and use data to inform league-level decisions like scheduling and rule changes.
sports analytics 101: team use cases (part 2)
October 23, 2020
Teams use analytics to make decisions related to in-game strategy, player personnel strategy, and sports science (Part 2 of 2)
sports analytics 101: team use cases (part 1)
October 15, 2020
Teams use analytics to make decisions related to in-game strategy, player personnel strategy, and sports science (Part 1 of 2)
sports analytics 101: the case for sports analytics
October 8, 2020
Sports analytics is useful because humans have mental and situational limits.
how to start a sports analytics club
September 28, 2020
A guide to starting a college or high school sports analytics club.
where to watch: sports analytics conference video archives
September 17, 2020
A list of sports analytics conference video archives.
coding for sports analytics: resources to get started
September 15, 2020
A list of resources for learning how to code specifically for sports analytics.