It is more than obvious that sport analytics is booming thanks to the technology breakthrough, algorithm advancement, and upgraded analytical tools.
If you are a fan of sport analytics… this thought probably has emerged in your mind: how can I be able to perform analytics on sport data?
Let me start with some analytical tools. Either R or Python would be great. These open-resource tools have a wide spectrum of packages allowing you to address various sport analytics issues from fundamental data wrangling to cutting-edge deep learning. As time goes by, more diverse and powerful packages would be developed or updated, so you don’t need to keep learning new tools to address different aspects of sport analytics. As a follow-up question: which one should I eventually choose, R or Python? Here is some information that may help you make a final selection. R comparatively has more well-developed packages in data analytics and has a more convenient IDE (i.e., RStudio) than Python IDEs (e.g., Spyder or PyCharm). But if you’re also interested in other programming fields like web design and app development in addition to sport analytics, Python is a better option given its generic nature. Please note this is status quo. Both R and Python have been updating themselves. For example, in recent years, R added more packages in deep learning and text mining; meanwhile, the data analytics arena of Python also got richer and richer.
A main point distinguishing sport analytics from general data analytics is its contextualized attributes, such as analytics purposes, data features, analytics criteria, and result interpretations. To gain a better understanding on these contextulized attributes, you need to know your target sport program(s) in depth. So where to start to gain these insights? Observing training and games, communicating with coaches and players, learning from experienced analysts in that specific field are highly valuable. As a Miamian, working for varsity teams on campus would be a great start. Don’t forget to educate yourself via industry conferences and theoretical courses such as sport economics, sport psychology, athletic training, coaching, etc.
Having systematic trainings is crucial for a successful run in sport analytics. Oftentimes, un-rigorous analytics are worse than no analytics, which is largely amplified in the field of sport analytics given its high stake. Therefore, going through systematic trainings in algorithm, probability, measurement, and algebra are necessary. You should also pay attention to the technology sector which has been reshaping the landscape of sports analytics. For example, movement tracking systems (e.g., SportUV, Rapsodo, and K-motion) and health mentoring equipment have elevated sport performance analytics to another level.
Lastly, I would like say that conducting sport analytics professionally is a niche field, whereas sport analytics education is for all practitioners, as well as for those working in related industries. No matter if you are inspired to work in sport marketing, management, media, finance, or coaching, you should be equipped with fundamental sports analytics knowledge which enables you to digest analytics reports, generate scientific insights, communicate with analytics departments/groups, and eventually come up with sound data-driven decisions.
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