In my PhD thesis defense, I will defend the following 10 propositions based on my research into tactical behavior in professional soccer:

#football #Analytics #research #DataScience #MachineLearning Not every pass can be an assist - my research

In #football #Analytics we often refer to the analysis of attacks as sequence analysis, implying an attack can be modeled as a cascading sequence of events. A thread on why the term “sequence analysis” can be misleading.

#DataScience #Research Despite the popular analogy (often used to describe strategic moves), #football is actually nothing like a game of chess. In chess, one piece can be moved at a time, after which the opponent reacts. In #football, 22 pieces can be moved simultaneously.

“Data don’t lie”. But it typically requires a process of defining #research questions, hypotheses, methodology, interpreting and #dataviz that can introduce subjectivity and #bias. Scientific rigor and objectivity are key in #DataScience. Some #Tips for #DataScientists 🧵 Don’t dive straight into a dataset, domain knowledge is critical. Good #Science requires a theoretical understanding of a topic while #ignorance introduces bias. Sound domain knowledge enables you to ask the right questions and give relevant answers with #DataScience

Tactical behavior in #Football has a spatial and a temporal component, and results from interaction with the opponent. It’s key to account for all these aspects in data-driven tactical analysis, as well as to respect the complexity of the temporal and spatial dimensions 🧵 Two years ago I published a systematic review in @EurJSportSci on using big data in #soccer for tactical performance analysis that illustrates the associated challenges and provides a data-driven scientific framework. #DataScience tinyurl.com/mrxky6ca

Preparing for a technical interview for a #DataScience position? These are some of the questions that typically allow me as an interviewer to quickly distinguish between juniors and mediors, including some quick tips 🧵. #Python #pythonprogramming #DataScientist #Jobs All questions about SQL. Not the hardest thing to learn, but many #DataScientists only start to learn the value of SQL when they actually become part of a dev team. I’m not only talking about SELECT * FROM table, but also about joins, truncates, partitions and constraints.

#DataScientist in a software dev team and #pythonprogramming code for production pipelines? You should think carefully about scalability and integration. One of the things to consider is datatypes, here are some helpful tips 🧵 #Python is a dynamically typed language, but that doesn't mean you shouldn't care about types. Know you dtypes, from "str" to "bool" to "int8" to "float64", and understand their memory footprint and restrictions. Especially when working with larger objects, choose wisely.

Yesterday I shared a small thread about getting into #DataScience. Today I’ll build on that and share a bit about my own journey into sports analytics, specifically as a #DataScientist in the #football industry. 🧵

https://twitter.com/FlorisgoesF/status/1508453394536620040

My path began with a MSc in Sport & Movement Science @VU_FBW. It’s not computer science or anything, but it does involve quite some #Math, #Statistics and #Physics, as well as a course in programming. Mainly it learned me Science, and gave me a lot of domain knowledge in sports.

Many young people ask me how they can become a #DataScientist, specifically in #football. Lately I have also seen a lot of posts on how to get into #DataScience in (1)50 days or so, which is a joke imo. Here is my realistic take on it. Warning: it will be closer to 1500 days. 🧵 #DataScience is an umbrella of roles & fields that require different competencies. But they all have two things in common: you have to know #Science and you have to be able to work with #data. The first requires learning to do research, the second learning to do #programming.