• When launching the lesson, it’s helpful to play up the fact that the “Astrological Law of Sports” is not only surprising, but it’s also astonishingly widespread across varying sports and across the globe. This helps invest students in the context of the lesson and the Discussion Question.
• Rounding should usually be determined by context. However, when calculating proportions, using three significant figures is often sufficient (e.g. 0.273 or 27.3%). This gives students a good rule of thumb for their work.
• Having students draw their own bar charts and other representations once or twice is helpful for learning the concepts behind each visualization. However, drawing graphs takes up a lot of class time, so it’s best to focus student practice on interpretation. Students will begin to use the free online tool CODAP in Lesson 1.4, which will allow them to efficiently visualize data throughout the course.
• When showing the graphs comparing the birthdays of the Italian population with the birthdays of Italian soccer players, ask students what these graphs might look like if the y-axes used raw counts, instead of percentages. Of course, because the sample sizes are very different (there are far more Italians than there are Italian professional soccer players), the graphs would no longer be comparable. This is why percentages are called relative frequencies - they allow us to relate the information across sample sizes.
• Students will use our “big picture - closer look - zooming in” framework to describe quantitative distributions in Lesson 1.3. While not introduced in this lesson, using this framework informally in Lesson 1.2 can lay the foundation for students to see its usefulness. When discussing or creating graphs about the birth months of Italian soccer players or the whole Italian population, asking “What’s the big picture here?” and “What are the details when we zoom in?” will ease the transition to the protocol they see in the next lesson.

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: Ten-Minute Talk. The lesson provides space for students to jot down their thoughts on the prompt, before discussing with a partner and engaging in whole group discussion. You can find more background on implementing a Ten-Minute talk here.

Purpose & Background: The goal of this Lesson Starter is to provide an enticing hook for the lesson’s Key Question: Why are more elite athletes born in January - June? With the rise of social media, the popularity of astrological signs has surged in recent years. So, introducing the lesson context through astrological signs provides an excellent conversation starter. Generally, studies haven’t found any consistent associations between Zodiac signs and personal traits, and students may express skepticism that birth month can determine personal traits. This makes the trend explored in the lesson – that elite athletes tend to be born in the first half of the year – all the more remarkable and intriguing. We used ChatGPT to search online for the most common traits associated with each astrological sign. The results are displayed in the Lesson Starter. Note: Some students may hold strong beliefs about the predictive value of astrological signs that should be treated gently and with respect (see our recommended classroom norms). For the purposes of this lesson, neither view (belief or skepticism) is problematic.

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.

• At first, some students may draw a blank on the discussion question, especially those with little experience in youth sports. Expanding on the hint in the following ways may be helpful:
• “If everyone born in 2015 is in the same group, would players born in January 2015 be the oldest or youngest in the cohort?”
• “Which players might tend to display the most athleticism in a youth group: the oldest players or the youngest players? Why?”
• “Which players might tend to get the most attention and coaching: the more athletic ones or the less athletic ones? Why?”
• The phenomenon described in the Discussion Question is known in academic literature as the “relative age effect.” Numerous journal articles have been written on the relative age effect, in contexts that include education, the workplace, and psychology. Although the exact causal mechanism discussed here (older kids getting better opportunities due to initial age advantage) cannot be fully proven without an experiment, the prevalence of the relative age effect across many sports and other contexts provides substantial evidence for this explanation.

• This lesson utilizes the 12 zodiac signs of Western astrology as an enticing hook to draw students into the lesson. Of course, there are a multitude of other astrological traditions that can be referenced. Ultimately, as students discover, the trends have more to do with how youth leagues group athletes, rather than anything astrological.
• The phenomenon described in the Discussion Question is known in academic literature as the “relative age effect.” Numerous journal articles have been written on the relative age effect, in contexts that include education, the workplace, and psychology. Although the exact causal mechanism discussed here (older kids getting better opportunities due to initial age advantage) cannot be fully proven without an experiment, the prevalence of the relative age effect across many sports and other contexts provides substantial evidence for this explanation.
• The relative age effect was most popularized by Malcolm Gladwell in his book Outliers (2008). The example Gladwell discussed focused on hockey. The lesson focuses on Italian soccer due to the strength of the relative age effect in this context and the readily available data from this journal article. However, the relative age effect has also been observed for Handball, Rugby, Track & Field, Skiing, Hockey, Tennis, and the Olympics.

• Generally, bar charts are preferred to pie charts among most statisticians. The human eye is much more perceptive of the relative size of bar heights than the relative size of sector areas. See this demonstration as an example.
• In addition, pie charts become very difficult to read when a variable has more than five or six categories, whereas bar charts are still fairly readable with a high number of categories.