High School Research Student Cayden Recreates the Grand Slam Between the Top 10 Male Tennis Players
10 minute read
Cayden is a sophomore from San Mateo, who developed a tennis match-up simulator with his mentor, Nick. He spent hours coding in Python to create a logistic regressions model that could determine the player with most wins out of the top 10 male players in the Grand Slam. After even more research, he was able to choose a journal perfect for him to submit his amazing paper to! You can read more about Cayden’s Polygence experience in the interview below.
I heard great things about Polygence from several of my friends who told me that they received amazing guidance while working on their projects. When I researched more about it, I was really attracted to the idea of being mentored by a young professional and how many possibilities there were for my project. I really wanted to support my academics at school with a project that would be a bit more interesting than just school work and my regular classes, something that wasn’t even offered at school. Plus, Polygence is more than just opening a book, studying, and test-taking. It’s about application of knowledge and using what I know to contribute something new to the world, which I like.
I created a logistic regressions model through Python that was able to predict tennis outcomes of matches from the top 10 rated male players in the Grand Slam Tennis tournament. I was really interested in seeing who would defeat whom, or at least what the probability was that one player would win against another. Yet, data and analytics in sports have been dramatically affected by COVID, so I chose data sets from before COVID play to prevent any hidden variables, specifically 2015 to 2019. I also thought putting this all in a research paper would be the best project for me to do because it’d be a new experience and I really enjoy writing.
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I was pretty surprised by the player the regression determined was most likely to win. The players I thought would’ve been predicted for most wins were one of the “big three” in men’s tennis: Novak Djokovic, Rafael Nadal, and Roger Federer since they’ve had the most successful careers in men's singles. That's why many people would argue that they are the best, but in the model, it was interesting because none of them were predicted to be the big winner. It was actually Milos Raonic, who’s not as well known, but has a very high first serve win percentage, which I think is why he was predicted by the model to win the most.
It’s a win for the underdogs! Did you already know that this was the project you wanted to do when you first started with Polygence?
Going into the process, I had three project choices that I thought of. I was interested in tennis, but also thought of coding a formula to create a playlist for someone based on their mood and musical preferences, or an animal index since I really love nature and conservation. Yet, I began my project when I was finishing up my first varsity tennis season and really loved it. So, I thought that doing the project on tennis would be a bit more personal than the others since it’s such a passion of mine. The simulation part came about from my own experience working in data analytics, which I thought would be the perfect field for researching tennis. The project really took shape once I was matched with my mentor, who studies sports analytics and was able to provide excellent guidance on integrating my passion for sports with data science.
I studied artificial intelligence through the program, Inspirit AI, where we completed research used to develop our projects. I learned about disaster relief efforts with AI and using social media to lower casualties by increasing emergency response time. Yet, I hadn’t done anything requiring the same depth of knowledge or rigor as my project with Polygence did. Polygence allowed me to complete much more in-depth research and to explore my career interests more.
I would say the coding and designing were probably the most difficult parts of this whole process. In terms of coding, even though I've worked on computer science and robotics in the past, coding for data analytics is different. It requires more libraries and functions than computer science, so there was a learning curve for me. Even if the syntax might have been similar to what I knew, I still had to troubleshoot bugs in the code and think through how it should work. The design part was also difficult because even though you may have an idea in your head, writing it into the code or on an Excel sheet is an entirely different process. So, putting it all together took some time to create since I wanted it to be nice and easy to read.
My favorite part of the project was seeing the end result. After the struggles that I worked to overcome in the code, I was thrilled when everything finally worked! As I mentioned before, I was surprised to see the prediction outcome, but it added to the excitement. It was satisfying to see that the research wasn't just theoretical, but was also very practical because many people could use the data and create a similar model. Plus, because tennis was something I played every day, I was really excited to see how I could match my interest in athletics with math and coding, using both points of knowledge together to create something I was proud of.
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I’m sure that was really satisfying. Can you walk me through a typical session between you and your mentor?
We’d start the session by reviewing the agenda for the day and materials we had both read beforehand. I typically sent questions ahead of time to my mentor which he would address at the start. He’d then ask me a question to really get me thinking and guide me towards coming to conclusions on my own. The rest of the session was spent adding to the paper or working on the code and designing the data’s presentation. As the meeting closed out, we would always talk about what I had to do before the next session to keep on track, which was a way for us to make sure we had enough time to continue on without any major issues in our way.
I would say the most interesting thing I learned was how to simplify complex processes. I realized that it’s really easy to get in a technical mindset and communicate ineffectively because of using jargon that most people wouldn’t know. I realized how important it was to not only communicate what I was learning, but to effectively communicate what I was learning. Through writing my paper, I really developed a deep understanding of the content and now, feel like I can explain it in a simple way.
I recently submitted my paper for publishing in late September, so I am waiting to hear about the next steps. I started by researching several different high school science journals, and then looked into the background of the board members for each publication since it was a big deal to me to ensure that it had well-respected members of the science and mathematics community. I also read previous issues to make sure that the works already showcased in the journals were something that I fit into and wanted to be a part of. The last part was me making sure I was carefully following the formatting requirements of each journal since they’re all different.
We’re all wishing you the best of luck! What advice would you give to those who want to write a paper?
Writers will eventually get tired of editing. Yet, I think they should keep editing as much as possible because, in the end, they'll be amazed by the things that they can do. During those frustrating times where you feel stuck and sick of editing, trust in your mentor and trust in the process. You’ll know you’re finished once you and your mentor both read over your paper one last time, and you just look at each other thinking, “Wow, we did it!” Making it to that point is worth it because it’ll leave you proud and with many incredible skills that you’d never imagined you’d have.
Those are some wise words. What advice would you give to other students thinking about going into data analysis?
It's really important to keep up your math because it’s essential for the field. For many students, it might feel like a math or coding course doesn’t go deep enough to help you reach your goals, so it's important to research it out-of-school to find that deeper level of understanding. It personally helps me to watch YouTube videos about data analysis and to read the science section of The New York Times and The San Jose Mercury News. I also took a summer data and analytics program through Georgetown University called “HOYA Summer High School Sessions.” During the process, I learned the importance of cross-curricular skills too. Many people might think, “I’m interested in STEM, why would my English class be important?” In reality, all of the skills I learned in English class over the years were important in helping me clearly communicate my knowledge. It would’ve been really difficult to finish this project if I hadn’t had a strong foundation in writing.
One thing I learned during the program is: if you can dream of an idea, then you can always do it. It doesn’t matter how large the gap between current skill set and required skill set is, with enough patience and perseverance the sky's the limit. If there's something that you want to learn about, then go for it. If it becomes challenging, as long as you're passionate about it, you'll still be able to get through it. Also, don't wait. If you feel like you're too young or you don't know enough, your mentor will guide you. So, if you can, it's important to start as early as possible.
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