Share this page!

Jan Van Haaren Profile picture
Twitter logo
Nov 2 • 21 tweets • 16 min read
🧮 I computed the most likely opponent for each club in the UEFA Champions League round of 16. Taking into account that clubs from the same group and same association cannot face each other, my constraint solver produced 3,876 possible draws. ⬇️ #UCL #UCLdraw
Bayern Munich's (@FCBayern) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Liverpool - 39.9%
2. Milan - 23.9%
3. Club Brugge & Paris Saint-Germain - 18.1%
Benfica's (@SLBenfica) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Liverpool - 19.8%
2. Inter Milan - 14.4%
3. Borussia Dortmund & Eintracht Frankfurt - 13.8%
5. Milan - 13.7%
6. RB Leipzig - 13.2%
7. Club Brugge - 11.2%
Chelsea's (@ChelseaFC) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Inter Milan - 18.7%
2. Borussia Dortmund & Eintracht Frankfurt - 18.0%
4. RB Leipzig - 17.4%
5. Club Brugge & Paris Saint-Germain - 13.9%
Manchester City's (@ManCity) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Inter Milan - 18.9%
2. Eintracht Frankfurt - 18.0%
3. RB Leipzig - 17.4%
4. Milan - 17.3%
5. Club Brugge & Paris Saint-Germain - 14.2%
Napoli's (@sscnapoli) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Borussia Dortmund & Eintracht Frankfurt - 22.3%
3. RB Leipzig - 21.4%
4. Club Brugge & Paris Saint-Germain - 17.0%
Porto's (@FCPorto) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Liverpool - 19.8%
2. Inter Milan - 14.4%
3. Borussia Dortmund & Eintracht Frankfurt - 13.8%
5. Milan - 13.7%
6. RB Leipzig - 13.2%
7. Paris Saint-Germain - 11.2%
Real Madrid's (@realmadrid) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Liverpool - 20.6%
2. Inter Milan - 14.7%
3. Borussia Dortmund & Eintracht Frankfurt & Milan - 14.0%
6. Club Brugge & Paris Saint-Germain - 11.4%
Tottenham Hotspur's (@SpursOfficial) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Inter Milan - 18.9%
2. Borussia Dortmund - 18.0%
3. RB Leipzig - 17.4%
4. Milan - 17.3%
5. Club Brugge & Paris Saint-Germain - 14.2%
Borussia Dortmund's (@BVB) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Napoli - 22.3%
2. Chelsea & Tottenham Hotspur - 18.0%
4. Real Madrid - 14.0%
5. Benfica & Porto - 13.8%
Club Brugge's (@ClubBrugge) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Bayern Munich - 18.1%
2. Napoli - 17.0%
3. Manchester City & Tottenham Hotspur - 14.2%
5. Chelsea - 13.9%
6. Real Madrid - 11.4%
7. Benfica - 11.2%
Eintracht Frankfurt's (@Eintracht) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Napoli - 22.3%
2. Chelsea & Manchester City - 18.0%
4. Real Madrid - 14.0%
5. Benfica & Porto - 13.8%
Inter Milan's (@Inter) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Manchester City & Tottenham Hotspur - 18.9%
3. Chelsea - 18.7%
4. Real Madrid - 14.7%
5. Benfica & Porto - 14.4%
Liverpool's (@LFC) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Bayern Munich - 39.9%
2. Real Madrid - 20.6%
3. Benfica & Porto - 19.8%
Milan's (@acmilan) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Bayern Munich - 23.9%
2. Manchester City & Tottenham Hotspur - 17.3%
4. Real Madrid - 14.0%
5. Benfica & Porto - 13.7%
Paris Saint-Germain's (@PSG_inside) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Bayern Munich - 18.1%
2. Napoli - 17.0%
3. Manchester City & Tottenham Hotspur - 14.2%
5. Chelsea - 13.9%
6. Real Madrid - 11.4%
7. Porto - 11.2%
RB Leipzig's (@RBLeipzig) potential opponents in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw

1. Napoli - 21.4%
2. Chelsea & Manchester City & Tottenham Hotspur - 17.4%
5. Benfica & Porto - 13.2%
This table shows the proportion of potential draws in which a given group winner faces a given group runner-up in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw This table shows the proportion of potential draws in which
This table shows the proportion of potential draws in which a given group runner-up faces a given group winner in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw This table shows the proportion of potential draws in which
This table shows the number of potential draws in which a given group winner faces a given group runner-up in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw This table shows the number of potential draws in which a gi
This table shows the number of potential draws in which a given group runner-up faces a given group winner in the UEFA Champions League round of 16. ⬇️ #UCL #UCLdraw This table shows the number of potential draws in which a gi

• • •

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

Keep Current with Jan Van Haaren

Jan Van Haaren 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 @JanVanHaaren

Jan Van Haaren Profile picture
Sep 10, 2021
🧵 Computing and visualizing advanced soccer analytics metrics is becoming extremely straightforward. Access to event data and basic programming skills suffice nowadays as free open-source software libraries take care of most of the heavy lifting for you! (1/20)
The @PySport open-source website (opensource.pysport.org) provides an excellent starting point. The website currently lists no fewer than 2 open-data repositories and 44 software libraries: 24 Python libraries, 19 R libraries and 1 Haskell library. (2/20)
Having joined a soccer club ten weeks ago, powerful Python libraries have enabled me to produce a series of performance and style metrics for match analysis, player evaluation and player recruitment purposes in virtually no time. This thread touches upon my favorite tools! (3/20)
Read 20 tweets
Jan Van Haaren Profile picture
Dec 7, 2019
Last month I had the pleasure of giving a keynote talk at @BarcaInnoHub's Barça Sports Analytics Summit. I presented a few event-data based football metrics that we developed for player recruitment. My presentation slides are available on Google Drive.

docs.google.com/presentation/d…
🗣️ "Working with many football organizations across the globe, @SciSportsNL primarily focuses on helping football clubs to identify appropriate transfer targets. Football clubs' recruitment departments are typically faced with a multitude of questions which they need to answer."
🗣️ "To help answer the scouts' questions, we developed a whole range of performance and style metrics for football players. These metrics enable us to compose a data-driven profile for about any professional or semi-professional football player in the world."
Read 23 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!

:(