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FanGraphs Prep: Strikeouts, ERA, and the Relationship Between Variables

This is the latest in a series of baseball-themed lessons we’re calling FanGraphs Prep. In light of so many parents suddenly having their school-aged kids learning from home, we hope is that these units offer a thoughtfully designed, baseball-themed supplement to the school work your student might already be doing.

Overview:

A four-day unit that uses strikeouts, walks, and home runs to describe relationships between variables and predictive logic.

Many statistics in baseball are inter-related. We examined the relationship between runs and wins a few weeks ago. Today, we’ll learn about a few more of these relationships and how to think predicatively about them.

Learning Objectives:

  • Make a hypothesis about the relationship between two variables
  • Create a scatter plot using a dataset containing multiple variables
  • Estimate and calculate a trend line
  • Evaluate a hypothesis using data
  • Describe the relationship between variables

Target Grade Level: 7-9

Daily Activities

Day 1
ERA, or earned run average, measures how many runs a pitcher gives up per nine innings. It’s measured in runs — the only thing this statistic cares about is how many innings a pitcher throws and how many earned runs they surrender. But we can look at other statistics as well: what percentage of opposing batters a pitcher strikes out, what percentage they walk, and what percentage of opposing batters hit home runs.

Come up with a hypothesis about how these three statistics relate to ERA. Do you think that pitchers who strike out more batters allow fewer runs on average, or more? Why? Do the same for each of strikeout rate, walk rate, and home run rate. Read the rest of this entry »