Why GamePlan? Well...
If you've ever seen a soccer game, you know things move at a million miles a minute. The score can be frozen at 0-0 down to the last second of a game, yet I find myself on the edge of my seat every time. While it can be exhilarating to watch, it also means there is no shortage of events and actions to be analyzed in order to better understand the result of a game, the promise of a player, or the future of a team. GamePlan helps you analyze before and after games so that you can be on the ball for the next match you watch.
How does GamePlan help?
With live sports, you can't completely eliminate the unexpected - any why would you want to?! Nonetheless, it is really fun to be right, especially when rivalries are on the line. GamePlan can be used in a variety of contexts, so whether you are looking to understand why exactly your team keeps ruining your weekend, or dive deeper into what the postseason may bring, GamePlan's statistics and predictive analytics can help you out.
When building GamePlan,
I was excited to see how I could present data in a visually appealing format. After all, anyone can make pages upon pages of tables and linear regression models - I think it is important to make models people want to look at! By placing emphasis on the visualization of sports data, I really had to consider what makes for appealing design, while maintaining enough clarity and accuracy to maintain the integrity of the analysis.
I surprised myself
By discovering so many new applications for technologies I thought I knew well! Although I have already used python and its various libraries in many other ways, I always love to discover something new. My favorite part of working on this project was adding the "Locker Room" page, and I am super happy with how the roster images turned out!
My biggest takeaway
Is that you can never have too much data! With limited access to official data, and no access to affordable APIs to collect data, I felt stuck. Fortunately, I was able to manually collect data and compile it effectively, but I would've loved to be able to perform more advanced calculations. Unfortunately, I can't quite achieve a professional analysis when I can't access an amateur amount of data. However, this was an important lesson in using the resources as my disposal, and it really reminded me of the importance of considering the sources and origins of datasets.
Up next for GamePlan
As I continue to work on GamePlan, I intend to implement a web scraping feature to ensure that the most current data is utilized, helping users to achieve the most accurate analysis and predictions. Especially with the 2025 MLS season underway, I look forward to adding additional features such as bracket building and in depth analysis of the top positional players from week to week. I can't wait to see how GamePlan grows!
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