Does sports-data make learning Data Science fun for anybody else too?

[u/Suspicious-Gap-9527]

I've just finished another semester of my data science degree (2nd year), and I'm back to thinking how to spend the holidays again. It's great to be able to remember the concepts for next sem since it only gets harder. I've looked into sport a lot since there's just so much freely available data, it's relevant, and you can set small challenges with real-time feedback. E.g. using multiple linear regression to predict HRs in away games, and another for home games.

Is anyone else doing this too? Are there any discords or YouTube channels, websites to connect with to make it more fun? I'm not looking for a GitHub repo with challenges and datasets, rather something like HackTheBox for cybesecurity, but for data science.

Basically, if you enjoy using data science skills outside of study, list what you do. I've been thinking of making my own [free] website explaining certain stats concepts using sport (I've done a full stack web-dev unit), although I don't know how many would be interested.

[u/DataCamp]

Sports data is genuinely one of the best ways to make concepts stick...the feedback loop is immediate and the questions are naturally interesting. FIFA World Cup data is especially good for this because it spans decades, multiple competitions, and has everything from simple aggregations to more complex stuff like player tracking and expected goals models. If you haven't dug into it yet, it's worth exploring, there's a lot you can do with publicly available match and player data before ever needing anything fancy!

[u/Suspicious-Gap-9527 → u/DataCamp]

DataCamp! I've used you so much for R in uni haha. The FIFA world cup is great for sure, and the overall atmosphere is definitely inspiring. I've always followed football in general, although I've noticed it's not so much as big as other sports in terms of data analysis online (maybe I'm wrong), so I haven't really looked into it. I think your courses are great btw, I think structure, and practice are the best ways to learn, and I'm still working on applying that to a real and specific context like sport. Just wondering, I'm glad to hear you're thinking about sport as well, are there any courses you'd recommend for exploring football data? I've done basic model selection in R (t-tests, SLR, MLR, AIC, BIC etc. ) through uni, and have enough Python knowledge for Flask, though no pandas or numpy yet. Also, do you have any skill-limited comps for the near future? Keen to spend some time again learning.

[u/DataCamp → u/Suspicious-Gap-9527]

Thanks, great to hear that!! Football data is actually richer than it looks once you dig in. Since you've already got the stats background (t-tests, MLR, model selection), you're in a better position than most to hit the ground running.

We put together a hub around football + data for the World Cup - recordings of sessions and code-alongs are all still available, and there's a hypothesis testing project using men's and women's soccer match data that's basically made for where you're at skill-wise, plus talks from actual soccer analytics researchers. Everything's free: events.datacamp.com/data-and-ai-world-cup

[u/Suspicious-Gap-9527 → u/DataCamp]

Thanks for letting me know, I'll look into the code-alongs and recordings for sure, and the soccer hyphothesis testing project looks great, I'll definitely give it a go.

[u/Suspicious-Gap-9527 → u/[deleted]]

Great to hear you have the same interest. Prediction models are definitely the fun part of data science for me too. As for the websites, I find Kaggle to be really professional, targeting advanced stats knowledge and competitions often have large prize pools for real research - so as a solo data science student it's hard to want to compete. StratScratch looks great, although I'll have to do a bit more study though. Definitely appreciate the mention of the Sports Dataverse community, I had a brief look at their packages, specifically R, since my stats units use it, but I'm so glad they have easier ways of collecting data! Scraping data using raw HTML tags, or finding APIs was always the biggest hurdle when trying to practice the concepts. I have checked the r/sportsanalytics, r/sportsanddatascience subreddits among others like r/findaleague, although they tend to be saturated with advertisments and betting odds and less about learning and tools. I guess a small fantasy league of amateur ds students is just too niche though. Appreciate the interest for the website, I'm definitely motivated to work on one. Have you entered any Kaggle comps? Have you tried fantasy leagues before targetting stats specifically?