I turned my WNBA TikTok into a college course

You probably know me as a girl who makes data-driven TikTok videos and Instagram reels related to the WNBA and women’s basketball, but by day I’m actually a Math PhD student in my sixth and final year (hopefully) at the University of Washington. (I’m actually in my eleventh year at UW if you count undergrad 😵‍💫)

I’ve had the pleasure of getting to TA and occasionally teach many mathematics courses throughout my time as a grad student. In the summer of 2023 I wrote and recorded a series of homework help videos for Math 318 Advanced Linear Algebra: Tools and Applications, and I was awarded the Excellence in Teaching Award from the math department for the 2023-24 school year for my efforts :)

Thanks to all the stuff going on with the US Department of Education, it’s probably no surprise to hear that there have been lots and lots of budget cuts at universities, and especially within the math department at UW.

These funding issues are annoying, but they actually led to me creating my own course this past summer! The department was very up front about the fact they were going to be offering limited jobs in Summer 2025, so I needed to either find an internship or fill out a competitive application for the chance to get one of those teaching positions.

Then I learned we could create our own course. Since it would bring in extra credit dollars to the department, I felt like this was the safest route, plus it would be good for my career development. And don’t tell my advisor, but I honestly enjoy teaching math more than I do researching it 😏

I put together a proposal, pitched it to the department, and somehow they said yes! (I later learned that the committee of professors who approved my proposal thought my course was pretty cool hehe)

My goal was to create a course that basically encapsulated everything I do with my TikTok: data analysis, storytelling, math, communication, all of it. I figured if anyone was positioned to teach data storytelling through sports, it was someone who spends their nights figuring out how to make WNBA analytics get views on TikTok. I wanted to create something for math majors who were curious about real-world data and for aspiring data scientists who needed practice explaining their work to normal humans. It was supposed to be a class that students of varying mathematical levels could participate in and learn something from.

Here's the official description I submitted:

This course bridges the gap between mathematical analysis and communication, using sports statistics and analytics as our playground. Students will develop technical skills in Excel/R/Python as well as mathematical exposition skills, learning to transform raw sports statistics into compelling narratives for non-technical audiences. Through weekly readings from Mathletics: How Gamblers, Managers, and Fans Use Mathematics in Sports, hands-on work with real-world datasets, and a culminating video project, students will develop fundamental data literacy and practice the crucial skill of making complex mathematical concepts accessible to general audiences. While sports provide our primary lens, the course emphasizes universally applicable skills in data analysis, visualization, and communication that prepare students for bridging technical and non-technical audiences across any sector, an essential skill for any role in modern data-driven careers.

Course learning objectives:

Learners will be able to…

Prerequisites: At least one previous reasoning course and interest in sports or popular mathematics would be great! This course is open to math majors looking to get their feet wet working with real-world data, and to students with a deeper data science background to develop the skill of communicating their analysis to a general audience.

Text and materials:

My vision was to have the course centered mainly around mathematical communication, but with sports analytics as the medium. With a wide variety of students and majors enrolling in the course, the students were able to choose their own adventure when it came to learning specific sports analytics and math ideas from Mathletics, then as a class and in our assignments we focused on the effective communication and explanation of the ideas learned.

Basically: teach students to turn sports stats into stories people actually want to hear.

The course began in June 2025 with 21 students, split almost exactly 50-50 between juniors and seniors. About 13 were math majors (most of them double majoring with another STEM field), so I ended up with the mix I'd hoped for. UW doesn't offer an undergrad "data science" major on its own, but a few students had it as a concentration within their primary major.

Here's how the class actually worked in practice:

The course met three days a week (MWF) for an hour each session in the afternoon, which gave us a nice rhythm without overwhelming everyone (including me). Mondays and Wednesdays were mainly me lecturing, while Fridays were dedicated workshop days where students could work on projects, explore Mathletics, practice Excel or coding skills, and ask questions. I told students to bring their laptops on Fridays, and the vibe was more hands-on and collaborative than a standard lecture.

The course was “hybrid,” with Panopto recordings posted after class, but I emphasized it would be best experienced in person. Class participation wasn't explicitly graded, but I encouraged everyone to show up engaged. This turned out the be the ultimate challenge for the course however, as class attendance fluctuated a lot during the quarter. I was always understanding of students who needed to watch the lecture recording instead of coming to class in-person (many of them had summer jobs or other obligations), but sometimes it was strange to be lecturing to a large room with only a few students.

The big highlight of the class was field trip to a Seattle Storm game on July 16th! It happened to fall during class time anyway since it was a noon game for Kid's Day, and I was able to get the cost covered for everyone. We took the bus and light rail together, which was honestly one of my favorite parts of the whole course. Even though not every student was able to attend, it was still a great time, and allowed students to practice their data storytelling skills on an actual game they had experienced!

Each week followed a pretty consistent structure:

Readings: Students had weekly reading assignments from Mathletics where they could choose their own adventure by picking chapters that interested them based on their sport preferences or the math concepts they wanted to explore. This let math majors dive deeper into technical content while students with less math background could focus on concepts that felt more accessible.

Hands-on exercises: Students worked with real sports datasets from Sports Reference and SportsDataverse packages, practicing data analysis in Excel, R, or Python (their choice). These exercises built toward the final project and gave everyone practice transforming raw stats into something meaningful.

Class discussions and reflections: We had discussions via Canvas where students reflected on the readings and shared what they were learning. This helped create a community around the material even outside of class time.

Friday workshops: On workshop days, I had students submit exit tickets through Canvas quizzes to check in on what they were working on and what questions they had.

Project milestones: Throughout the quarter, students hit checkpoints for their final project and participated in peer critiques to give each other feedback.

My grading philosophy would be relatively flexible, focused more on giving constructive feedback than on marking students down arbitrarily. Since I did not have a TA to dump grading onto, having the students complete peer-review assignments was also essential to giving students the feedback they needed.

Most students gravitated toward Excel because it was the most accessible, but many who already had coding experience used Python. Only 1-2 students used R even though it's my favorite (rip). I gave tutorials on all three tools and encouraged students to try something new they hadn't worked with before, but ultimately let them choose what felt most comfortable for their skill level.

I tried to give the same self-directed vibe with the Mathletics reading assignments. Students could stretch their thinking and pick chapters that interested them based on their sport preferences or the math concepts they wanted to explore. In theory, this sounds great! In practice, I found that a lot of students kept gravitating toward the earlier and shorter chapters even though it seemed like they were capable of tackling more challenging material.

​If I taught this course again, I'd probably be more explicit in what I assign and give students less open-ended choice. Maybe I'd offer easy/medium/hard options with specific chapters assigned to each difficulty level, and make students pick from one of those three tracks instead of just hoping they'd pick a good chapter on their own. That way we'd be more cohesive as a class in our Canvas discussions, and students would still have some agency while being gently pushed toward material that actually challenges them.​

One example of how the course structure played out was the first two homework assignments, which set the tone for the whole quarter.​

HW1 was intentionally vague. I asked students to pick their favorite chapter from (or really, any chapter they wanted to try), read it carefully, and then explain the key concepts to an audience without a math background. They could submit it however they wanted; typed explanation, video recording, handwritten notes, or slides. I kept the instructions minimal on purpose because I wanted to see what students' baseline math communication looked like before we'd talked about how to do it “well.”

Some students wrote short, dense, technical paragraphs. Others made slides that assumed too much prior knowledge or didn’t define necessary terms clearly (or at all). A few tried video explanations that were a little all over the place. None of them were bad, but they were exactly what I needed to see!

Then in week 2, we spent class time discussing principles of effective mathematical communication: how to hook your audience, how to structure an explanation clearly, how to use examples and illustrations effectively. Students wrote a reflection on what they learned and how they'd apply it to improve their HW1 submission.​

HW2 was where the magic happened. Students redid their chapter explanation (either the same chapter or a new one) using everything they'd learned. This time, their explanations needed to include:

After submitting, students completed anonymous peer reviews where they gave feedback on what worked, what they enjoyed, and what could be improved. The peer review component was important since it forced students to read someone else's explanation critically and think about what makes math communication effective from a reader's perspective.

The difference between HW1 and HW2 submissions was night and day. They started thinking about clarity, structure, and storytelling instead of just dumping information on the page. That iterative process (draft, learn, revise, get feedback) is exactly how I approach my TikTok videos, and it was cool to see students internalize that same workflow.

Around week 4 of 9, we shifted gears from Mathletics to focus on data visualization and storytelling principles using Cole Nussbaumer Knaflic's Storytelling with Data.

I assigned the introduction and first chapter as reading, then asked students to write a 3-5 sentence reflection on what resonated with them. I gave them some guiding questions to consider: Why is understanding your audience so critical before making any chart? How does the distinction between exploratory vs. explanatory analysis change what you present? What is the "Big Idea" and how can it help clarify your message?

The goal was to get students thinking about data visualization not as a technical exercise but as a communication tool. You don't just make a chart because you have data. You make a chart to help your audience understand something specific.

Over the next couple of weeks, we dove into the core principles from the book:

Students practiced these principles by critically evaluating existing sports data visuals (some good, some not) and then applying what they learned to design their own visualizations using real sports datasets. The assignment structure forced them to think through why they were choosing a specific chart type and how it supported their message without adding unnecessary complexity. By the time we finished the “Storytelling with Data” unit, they were making clean, focused visuals that actually communicated a point. They understood that less is more, and that a good chart tells a story without needing a paragraph of explanation.

The culminating assignment for the course was a 4-5 minute video where students communicated a mathematical concept to a general audience using sports as the context. Basically, I asked them to make their own version of what I do on TikTok, but with a bit more time to work with.

The project requirements were straightforward: pick a math or stats concept (from Mathletics or another class), apply it to a real sports context using actual data, and tell a story that makes sense to someone who isn't a mathematician. Students could choose their topic however they wanted. I told them they could start with a math concept and find a sports application, or start with a sports question and figure out what math would answer it. Either way, the audience needed to clearly understand what mathematical idea was being taught.

Students had to gather and analyze real sports data using Excel, R, or Python, create clear visualizations, and develop a narrative that felt engaging to sports fans. I emphasized keeping it accessible: avoid jargon, explain formulas in plain language, and focus on intuition and real-world impact. The target audience was someone who'd taken calculus but not much else.

For the format, students could do a narrated slideshow (Google Slides or PowerPoint with screen recording) or any video format they felt comfortable with. I even looked into AI voiceover tools for students who were uncomfortable recording their own voice. The key was to focus on the script and charts first, then worry about the actual video production.

​We used the Friday workshop days starting in week 6 as checkpoints for the project, so students were building toward it incrementally rather than scrambling at the end. In the final week of class, students watched 2-3 of their classmates' videos and gave constructive peer feedback, which created this nice community moment where everyone got to see what their peers had been working on.

The projects turned out great! Students tackled everything from Pythagorean expectation in college softball to expected value and probabilities in football to expected threat (xT) in soccer.

A few students got really creative with their storytelling, and went beyond just a narrated powerpoint. One framed their whole video as answering a question a casual fan might ask, and another used game footage and graphics to make the math feel dynamic and visual.

Watching students take everything we'd learned about data analysis, visualization, and communication and synthesize it into a cohesive, accessible video was genuinely the highlight of teaching this course. It proved that you can teach complex mathematical concepts through sports in a way that's rigorous, engaging, and actually fun.

I gotta be honest, I was nervous to read the course evaluations. I didn’t even read them until I started writing this post. This was my first time designing a syllabus from scratch, and I had no idea if what I was doing actually made sense to anyone besides me.

Turns out, it went pretty well! The course got a 4.8/5.0 overall rating, with 89% of students rating it as "excellent". Students particularly appreciated the course organization, my explanations and use of examples, and the grading structure. The enthusiasm rating was also high, which honestly made me feel seen because I was very enthusiastic about this course. Only about half the students filled out the survey, but that’s actually pretty good from my experience!

Without pulling exact quotes (for privacy), the feedback consistently highlighted a few key themes:

Communication as a mathematical skill. Multiple students mentioned that this was the first UW math class that explicitly taught them how to communicate math to non-technical audiences, and they valued filling that gap in their education. One student called it a "crucial skill for mathematicians" that most courses don't cover.

Applied math in the real world. Students appreciated seeing how theoretical concepts like statistical algorithms could be applied to real-world applications like sports. The freedom to gear assignments toward their own sports interests made the learning feel personally relevant.

My teaching approach. Students specifically called out the constant feedback, the good and bad examples I showed them, my availability for questions, and the fact that I genuinely cared about what I was teaching. how nice :)

Pacing was too slow for some students. A few mentioned the pace felt slower than what they were used to, and while they appreciated the extra time to dive deep, it was hard to stay engaged at times. Summer quarter is already condensed, so I probably could have pushed the pace a bit more.

Not enough hands-on coding. One student specifically mentioned wanting more advanced coding applications and simple projects earlier in the course rather than saving it all for the final video project. That's fair, and I could have incorporated the technical skills earlier and more often in the quarter.

More structured coding tutorials. I gave tutorials on Excel, R, and Python, but they were pretty basic. I'd love to do more live-coding sessions or guided mini-projects to build confidence and increase the pace of the course.

Guest speakers. Having a sports analyst or data journalist come talk about their work would have been amazing for showing students real career paths. I did have one UW librarian come guest speak as an intro to data visualization and a soft intro to library resources, but I would definitely like to invite more speakers to make the lectures even more interesting and sports-specific.

More emphasis on certain tools. Most students used Excel because it felt accessible, but I wish I'd incentivized more students to try R or Python. Maybe I'd offer bonus points for trying a new tool or require at least one assignment in a coding language.

The Friday workshop structure. Students need unstructured time to explore, ask questions, and just play with data. And I also need a break from blabbing in front of a projector for an hour at a time.

The final video project. Asking students to create a 4-5 minute explainer forced them to synthesize everything they learned about data, storytelling, and communication. It was the perfect capstone!

The flexibility and care. Students appreciated that I was supportive, available, and genuinely invested in their learning. That's not changing.

Would I teach this again? Ye. Version 2.0 would have more structured reading tracks, a faster pace, more hands-on coding, and maybe a guest speaker or two. But the unique core of the course (using sports as a lens for teaching data communication) is something I'm really proud of.

I don't know if any students continued creating sports analytics content after the course ended, but I hope the skills stuck with them regardless of what they do next!

Teaching this course taught me just as much as it taught my students. I've spent years making TikTok videos where I have 60 seconds to hook someone, explain a concept, and make them care about WNBA stats, but stretching that same philosophy across an entire quarter forced me to really articulate why data storytelling matters and how to teach it.

It reinforced something I already believed: that math isn't just about getting the right answer. It's about showing your work in a way that makes other people understand why the answer matters. Sports analytics gave us a shared language for that, but the skills students learned (clarity, storytelling, visual communication) apply to any field.

If you're reading this and you're interested in sports analytics education, data communication, or creating courses that blend technical skills with storytelling, I'd love to connect! I'm hoping to teach this course again (or something like it), and I'm always open to collaborations, guest speaking, or just nerding out about how we can make data feel less intimidating and more human. You can find me on TikTok and Instagram @wnbadata, or reach out via email.