As I slowly but surely level up in Pókemon Go I begin to notice all of the data contained within the game. Each type of Pókemon carries myriad numeric data (image, left) and you can check your Pókedex to see how many times you have seen and caught each type. I began to wonder if my personal data supported Dave Their’s graphic regarding Pókemon rarity. I went through my Pókedex and recorded the number, times seen, and times caught for every type of Pókemon visible in my Pókedex. I placed this data directly into a Google Sheet from Bulbasaur (#001) to Dratini (#147). From this data I was able to rank the Pókemon in my possession from most to least frequently caught and calculate my success rate for each type. The hardest part of doing this was not the data entry, but the navigation within Pókemon Go’s interface from screen to screen to get the numbers, but it didn’t take long. The bar graph (image, below) is a sample of a basic presentation that can be created using any learner’s existing Pókemon data.
User generated data from catching Pókemon seems like an engaging and personal(ized) way to address math standards. The Common Core State Standards for Measurement and Data call for students to draw a scaled bar graph in Grade 3, going so far as to say, “For example, draw a bar graph in which each square in the bar graph might represent 5 pets.” Based on the example prompt, how many squares would I need to represent the number of Rattata I have caught? Would I need more, less, or the same number of squares to represent the number of Paras I have caught?
The CCSS for Statistics and Probability call for students to summarize numerical data sets starting in Grade 6. In making generalizations about a data set in Grade 7, students could be directed to Dave Their’s graphic. Imagine the rich opportunities for a class discussion regarding having only one user’s data, whether or not the data is random, and if it can really be used to draw any conclusions as to the accuracy of existing models such as Their’s graphic.
In a glance at the Next Generation Science Standards Science and Engineering Practices (SEPs), I note that I have participated in the following SEPs during this activity:
- Asking questions: Does my Pókemon Go capture data support David Their’s graphic?
- Developing and using models: I developed my bar graph and used it to assist me in answering my question.
- Planning and carrying out investigations: I planned the steps I would take to get the data organized for ease of visual comparison and then I did the work.
- Analyzing and interpreting data: I see that my top five Pókemon capture types are listed by Their as being “everywhere” or “virtually everywhere.” The only Pókemon in my list that is considered “uncommon” by Their is Geodude, and I live in the land of rocks, so it makes sense that I would catch more of them. My sampling is not random.
- Using mathematics: The math I used was very basic. Though I have caught 27 Rattata I have actually seen 30, with a catch rate of 90%. There is potential for more math here limited only by one’s imagination!
Leslie Farooq 
I come here searching for Pókemon, GO Math –
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Category theory, which offers within an abstract way with mathematical set ups and connections between them, continues to be in development.
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