The aim of this “guidebook” is to introduce many concepts from statistics and explain them in an easy-to-digest way… all while discussing the context of football and football analysis.

I will cover many topics, from the basics of descriptive statistics (means, medians, etc.) to the more advanced concepts (regression analyses, k-means clustering) and much in between.

This won’t be a comprehensive “textbook” by any means and won’t include much math either. Statistics knowledge is very approachable when presented correctly and specifically for football, and that’s my goal with this. This won’t teach you how to pen-and-paper prove the normal distribution is related to the binomial distribution… but it will teach you what those are, how they appear in football, and what we can use them for when analyzing football (that’s just one example).

So, please don’t be scared by the term “statistics”. A strong foundation in statistics, not the mathematical side but the theoretical side, is vital to bring your analyses to the next level. The goal of this guidebook is to give you some intro level knowledge on many aspects of statistics relevant to football as well as some tools for how to apply that new knowledge using a combination of Tableau, Python, and R.

I will be updating this as I finish more sections. This page acts as the cover page & table of contents, so feel free to bookmark this and come back to check when I add more pages.

Table of Contents

• Averages/Means and Medians
  • Application: Scatter Plots
• Averages Beyond Basics: Moving Averages
  • Application: Moving average analysis
• Percentiles
  • Application: Radar/”Pizza” Plots
• Distributions (& Standard Deviations)
  • Application: Richer info then just percentile
  • Application: League analysis
• Metric Adjustments: Per 90, Possession, & More!
  • Application: The right metric for your case
  • Application: Richer insight from “non-traditional” adjustments
• Beyond Basics: Variable Selection
  • Application: Data matching with you specific question
• Correlation (& not causation…)
  • Application: “Better” scatter plots
• Correlation Beyond Basics: Residuals
  • Application: Player residual analysis
• Intro to Statistical Tests
  • Application: Proving your point 😉
  • Application: Showing differences exist
• Linear Regression Basics
  • Application: Deeper studies & tests
• K-Means Clustering
  • Application: Finding similar players
• Extra: Asking the Right Question
  • Application: Player Identification