Marc Gutierrez @MarcGutzPe
Physics & Complex Systems | Sport Analytics | Now into Computer Vision | PhD Student @la_UPC | Former Data Scientist @FCBarcelona_es Barcelona, Spain Joined August 2021-
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The worst part of academically returning to football is having to spell it as soccer.
Today in @SciReports, the graph imputer, a new model that can predict where players will run, on and off-screen, using past and future available observations of subsets of players: dpmd.ai/multiagent-foo… #AISports 1/
Worthwhile to take a look!
Glad to confirm I'll be heading London tomorrow to attend @OptaPro Pro Forum! Looking forward to seeing the findings of these projects. Early morning flights force me to take many coffees, feel free to stop by and have some analytics chatting! statsperform.com/resource/prese…
🧵 Progressive passing is affected by team shape and quality As such - similarly to possession adjusting metrics, I've normalised progressive passes based on "how easy" it is to be progressive in that team - so scaling players' numbers by the rest of their team's progression
This is some serious stuff right here!
Update: I'm thrilled to announce that I'll be taking on a new role as Data Scientist at FC Barcelona (@BarcaInnoHub). Grateful to @SergioMinuto90 and the rest of the staff for the opportunity given. Exciting times are ahead!
⚽BASES DEL JUEGO DE POSICION⚽ En este hilo analizaremos como se desarrolla uno de los MdJ más complejos y completos. Algunos ejemplo de equipos que han utilizado este MdJ de manera efectiva: City (Guardiola), Selección Española (Luis Enrique) o la Selección Italiana (Mancini)
markstats @markrstats
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1K Followers 2K Following Assistant Professor in Management Information Systems - @UCDDublin, @UCDNCRA. Interests: Sports Analytics, Natural Computing, Ai, Complex & Adaptive Systems.I'm surprised how many people aren't using AI Chrome extensions yet. Stop limiting yourself to just ChatGPT. Here are 5 new AI chrome extensions that will turn you into a productivity machine:
Durante las últimas temporadas de @ACBCOM el uso del challenge ha sido vital para el desenlace de partidos, y pedirlos en las situaciones adecuadas es clave. Sin embargo, por fácil que parezca, no es un problema trivial. Vamos a ver qué dicen los números 🧵
Hugging Face ❤️ Computer Vision 👁🤗 We've been taking some giant leaps in our ecosystem dedicated to Computer Vision but it remains unclear what all we have to offer. Our latest blog post provides you with a concrete overview of "State of CV at 🤗" ⤵️ huggingface.co/blog/cv_state
You can't "train" a model. The model always exists. It existed before you were born and it exists after your death. You can only find the model. "Training" is just your way of looking for the model's location in the infinite hypothesis space and binding its essence to silicon
It's always refreshing to see how people from the IPCV group of @DTIC_UPF are still researching the field of Computer Vision in Sports. Great work by Alejandro Cartas (+ @ColomaBallester and Gloria), who also put together a detailed Git repo to train an Action Spotting network🔥
I am happy to announce that our paper on Action Spotting in Soccer Videos using Graphs is now freely available! @ColomaBallester, Gloria Haro Check it out!! 👇👇👇 Paper: dl.acm.org/doi/10.1145/35… Project page: ipcv.github.io/SoccerActionSp… Code: github.com/IPCV/soccer_ac…
Terminan 3 meses de estancia en el @VillarrealCF en los que ir a trabajar no ha sido un trabajo, sino un sueño.
Python 3.11 is out! 🎉 This is one of the most exciting releases in a while, including significant speed upgrades and better error messages. Here's what's new:
This may revolutionize data science: we introduce TabPFN, a new tabular data classification method that takes 1 second & yields SOTA performance (better than hyperparameter-optimized gradient boosting in 1h). Current limits: up to 1k data points, 100 features, 10 classes. 🧵1/6
I've spent 100's of hours training computer vision models. I've revisited my code and notes from those projects and distilled them into a repeatable process that anyone can follow. Here are the 7 steps I take every time I train a vision model:
So excited about this!
We are also excited to welcome Sergio Llana Pérez as a Data Scientist to Zelus. Sergio has a wealth of experience building data algorithms and model pipelines, most recently as the Head of Sports Analytics at FC Barcelona. Welcome, Sergio!
Personal update: today is my last day at FC Barcelona after 4 beautiful years. Very proud to have been part of the club's Sports Analytics department from the early stages. I want to thank my teammates, coaches, and all the other inspiring people I have been lucky to work with.
It feels surreal to say this but I’ve accepted a job offer as a coach at @VillarrealCF starting next week. I’m so excited to work in a full time position for a club I’ve grown to love and become a proud supporter of. My hard work and determination has finally paid off! 🇪🇸⚽️
"Statistical physics of exchangeable sparse simple networks, multiplex networks, and simplicial complexes" is now published !! Check it out!! journals.aps.org/pre/abstract/1… GitHub code:github.com/ginestrab/Exch…
Excited to be attending tomorrow's @OptaPro Pro Forum in London. Looking forward to hearing about the papers to be presented and discuss analytics afterwards! statsperform.com/resource/prese…
I just landed in Boston! 4 fascinating days await me. I'm going to seize the day. I'm at @MIT to have a meeting and try to sketch "the next" in the exciting world of sports analytics March 2 Scientific Meetings March 3 sportssummit.mit.edu March 4-5 sloansportsconference.com
Good news! The full document of my Ph.D. thesis it's now available at hdl.handle.net/10803/673529 You can read more about the details of our work in this thread x.com/javiondata/sta… Below, a quick description of which chapters to read depending on your interest and time.
I'm glad to share that I've successfully defended my Ph.D. thesis in Artificial Intelligence, under the inspiring direction of @LukeBornn and the support of @FCBarcelona coaches. Here's a thread with details of our quest for a comprehensive analysis framework (toolbox) in soccer
You should always work on improving your model evaluation skills. Model evaluation is the worst taught skill in machine learning, and I believe the best way of improving is through practice. But, this paper from @rasbt is by far my favorite single written resource: