• When playing the initial clip of Brad Pitt (“There are rich teams and there are poor teams. Then there’s 50 feet of crap. And then there’s us.”), pause to have students consider how player talent may vary between the rich teams and the poor teams – and whether that’s “fair” in their eyes. This helps motivate the analysis of the distribution of team payrolls throughout the lesson.
• This lesson also introduces the Big Picture - Closer Look - Zooming In strategy to describe a distribution. It is not designed to be a checklist, but rather a framework that students can use in a wide variety of situations to describe what they see or conclusions they make. Describing it as a way to organize their thinking, like a funnel for their analysis, may support this perspective. The icons associated with each part of the framework (as seen in the lesson slide deck and video) are also listed on the lesson handout. Students should not limit their responses to align with the icons. Instead, the intent behind the icons is to help them to gradually narrow their thinking.
• Consistent reinforcement of key features of graphs, such as title, axis labels, and axis scale, will prompt students to be mindful of these necessary features when reading or creating graphs.
• The exact minimum and maximum values are not visible in a histogram. So, in the guided notes, students describe the spread for a histogram as the distance between the lowest bin of values and highest bin of values. Advise students that for dotplots – where the exact minimum and maximum data values are visible – they should report the spread as the distance between the minimum and maximum.
• Students will not see the term 'outlier' until lesson 1.6, but they may informally recognize extreme values in the data. If the term surfaces organically, recognize it with the qualification that they will define it more precisely later.

First, download this lesson's slide deck and handout key to see the prompt and sample responses for the Lesson Starter. Then, check out the additional background notes below.

Instructional routine: Notice & Wonder. The lesson handout provides a Notice & Wonder T-frame for students to capture their notes and ideas. It is important that students recognize the difference between a noticing (observation) and a wondering (question that comes to mind). You can find more background on implementing a Notice & Wonder here.

Purpose & Background: The goal of this Lesson Starter is to get students to recognize extreme variation and call it out. The prompt introduces students to the dataset that they will explore throughout the lesson. Without the context of Moneyball that will frame the learning, they may be more likely to hone in on the disparities in payrolls and what they might mean. The recognition of the Yankees as the best resourced team will likely come from a student Noticing and will position students to appreciate the challenges of the A’s when their story comes into play. Seeing the whole dataset will also provide the broader perspective necessary for the lesson’s Discussion Question.

First, download this lesson's handout key and read through its Discussion Question section. Then, check out our model discussion norms and the additional background notes below.

• For part (b) of the Discussion Question, another point that the “yes” side might raise: if a team is consistently not competitive, their fan base will get tired of seeing the losses and will tune out. For the fandom of the sport to thrive, you’d want as many teams as possible to be competitive. Making team payrolls more equal helps this outcome.
• To help students visualize the distribution before and after the luxury tax, you can use these extra slides: ppt, pdf.

• While embedded in baseball, the story of this lesson is more about the ‘haves’ and the ‘have nots’. No understanding of the game is actually required to dive deeply into the context of the lesson. While some students may be excited to see the context, assure students who don’t follow baseball that they, too, will find the overall investigation interesting.
• To help students make connections between payrolls and team quality throughout the lesson, ask them early in the lesson: “Which teams would you expect to get the best players? Why?”
• The field of statistical analysis for baseball is called sabermetrics, named for the Society of American Baseball Research (SABR). The movie Moneyball helped popularize sabermetrics, and it has grown into a deep field of research and study. The second movie clip in the lesson gives motivation for sabermetrics, displays some real sabermetric formulas, and mentions Bill James – a famous early pioneer of sports statistics. Of course, students do not need to know anything about sabermetrics to fully participate in the lesson. But it’s a great first look for students interested in exploring the field of sports statistics as a career.

• For practicing data scientists, one of the first steps of any analysis process (after retrieving and cleaning data) is visualizing data. Because models can be heavily influenced by undetected skewness or unexpectedly wide spread, going straight into calculations or model building before “getting to know the data” by visualizing and exploring it can lead to missteps. This step of visualizing and exploring data is sometimes called Exploratory Data Analysis or EDA.
• Stem-and-leaf plots were especially useful in the pre-computer era. However, they aren’t used in the field much anymore, and we have excluded them from this course. For small data sets, dotplots are most common. For larger data sets, histograms and boxplots (covered in a later lesson) are most common.