Share this page!

Floris Goes-Smit Profile picture
Twitter logo
Feb 7 • 12 tweets • 2 min read
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
Succes is the product of interaction, not action - my research
The most important players are often overlooked & undervalued - my research
Progress in science, not unlike succes in football comes through interaction - my research
Moving the ball without moving the defense is pointless - my research
The determinants of success are generally hidden in plain sight - my research
Je gaat het pas zien als je het doorhebt. Johan Cruijff
Football is the ballet of the masses. Dmitri Shostakovich
In football, the worst blindness is only seeing the ball. Nelson Falcao Rodrigues
The whole is greater than the sum of the parts. Aristotle
In the following days I will dive into every single one of them to elaborate on how they are of relevance to tactical behavior in attacking play. Stay tuned

• • •

Missing some Tweet in this thread? You can try to force a refresh
　

Keep Current with Floris Goes-Smit

Floris Goes-Smit Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @FlorisgoesF

Floris Goes-Smit Profile picture
Aug 12, 2022
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.
The movements of the 22 pieces are strongly bound together, and #Science has shown that attacking play is not the result of individual, sequential, actions, but rather the product of inter-player & inter-team interaction.
Read 15 tweets
Floris Goes-Smit Profile picture
May 30, 2022
“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
Investigate the alternate hypothesis. Business questions asked to #DataScientists are often directive, as there already is a hypothesis. Don’t confirm this hypothesis without properly investigating the alternate option.
Read 11 tweets
Floris Goes-Smit Profile picture
Apr 5, 2022
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
The most common analysis issue is the fact that spatial and/or temporal complexity is not respected. For example by aggregating data over multiple minutes, or constructing spatial features aggregating 11 player positions into a single variable.
Read 9 tweets
Floris Goes-Smit Profile picture
Apr 4, 2022
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.
Interacting with an API. Make sure you know your requests (GET, POST, PUT, DELETE, PATCH), as well as the #Python requests library.
Read 10 tweets
Floris Goes-Smit Profile picture
Apr 3, 2022
#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.
Loose the strings. 9/10 times strings can be replaced by categoricals (Pandas) or even better by Enums (docs.python.org/3/library/enum…). This can reduce memory footprint of large dataframes with >30%, and improves performance.
Read 8 tweets
Floris Goes-Smit Profile picture
Mar 29, 2022
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. 🧵
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.
I wasn’t planning to become a #DataScientist, but I wanted to work in sports. I did various stints as an embedded sports scientist, mostly internships/part-time, before joining @ZZLEIDENBASKETB. Those jobs involved data & science, but it wasn’t anything close to #DataScience.
Read 13 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us on Twitter!

Send Email!

:(