The “richest match” in football isn’t the World Cup final… 👀💰
Winning the Championship playoff final can be worth over £300 MILLION in long-term value — more than most trophies in world football.
From the FIFA World Cup to the Premier League title, here are the biggest financial rewards in the game ⚽📈
Which one surprised you the most? 👇
Analista Sênior de Dados de Futebol no Manchester United. (MEU SONHO!)
Quero trabalhar com dados no esporte(preferencialmente FUTEBOL e FÓRMULA 1), ai sempre fico olhando as vagas pra ter um norte das skills que preciso saber.
https://t.co/hZ4WJHNJtX
As a Recruitment Analyst every day is different.
Scouting one day, data models the next.
Many football recruitment roles now blend across paths.
Data analyst
Data science
Video scouting
Traditional scouting
New opportunities in the game belong to those who blend scouting and analysis skills seamlessly.
I've witnessed this shift firsthand:
- Hired as an analyst but also done live scouting
- Zero experience scouting before moving to football
- Analysis background opened scouting opportunities
- Small recruitment teams need people you can do both
This is a huge advantage, as being a generalist has 3 distinct advantages:
1. Multiple skills make you valuable to budget-conscious clubs
2. You can handle the entire player evaluation process end-to-end
3. Your career progression accelerates dramatically
Data literacy has become mandatory in any football recruitment role.
Even traditional scouts need to understand performance metrics.
Here's how to position yourself for this new reality:
1. Learn both qualitative scouting frameworks + analysis
2. Gain experience with video and live scouting
3. Contextualise numbers with visual evidence
4. Build communication skills to translate insights
5. Stay flexible as role boundaries continue to shift
In my career, combining data analysis and scouting has opened doors.
What's your experience with the scout-analyst overlap? Are you seeing this blend of skills elsewhere?
I’m a senior data analyst.
If you’re learning:
• Statistics
• SQL
• Excel
• Power BI
•Python
• Data Modeling
• Data Visualization
• AI for data analysis
and looking to break into data analytics. This account shares practical insights on Statistics, SQL, Excel, Python, Power BI, Data Modeling, and Data Visualization, Ai for data analysis - helping you skip the headaches and fast-track your career.
Last season when Slot won the league, data showed that;
• His football was not sustainable.
• He was just riding on a Klopp’s wave and team.
• Mo Salah was dragging the team.
Then it fell apart when he got his signings and started playing his football.
This season with Michael Carrick, data showed that;
• Carrick ball isn’t sustainable
• He is riding on Amorim wave and team.
• Bruno is dragging the team.
Next season when Carrick gets his signings and play his football, it’ll all fall apart.
Bookmark it.
📺 Visualization of UEFA prize money distribution across 108 clubs in 🔵🟠🟢 league stages (as of 7 May):
🔵 UCL:
🔺 €143.2m 🏴 Arsenal
🔻 €21.2m 🇰🇿 Kairat Almaty
🟠 UEL:
🔺 €35.0m 🏴 Aston Villa
🔻 €6.6m 🇸🇪 Malmö
🟢 UECL:
🔺 €17.8m 🇪🇸 Rayo Vallecano
🔻 €4.4m 🇮🇪 Shelbourne
💶 Detailed analysis of UEFA prize money for ALL 108 TEAMS in 🔵 UCL 🟠 UEL 🟢 UECL available on our page (in bio)
🏴 Hearts v Rangers 🏴
Statistically the most consequential match in 🏆 races across all European leagues this upcoming round (2-4 May).
Difference for 🇱🇻 Hearts' 🏆 chances between winning and losing this game is a gigantic 52%!
🟢⚪️ Celtic should hope Rangers win this as it would boost their title chances by +5.7%!
But, ironically, it would simultaneously hurt 🟢⚪️ Celtic's Top 2 chances by -7.5%!
For 🔵 Rangers, anything other than a win decreases both their 🏆 and Top 2 chances (not knowing the outcome of Celtic's game)!
👉 Check our "What is at Stake?" feature to learn what is the optimal combination of results this weekend for each team in 60+ domestic leagues in our 🕹️ Simulator
🇪🇸 Pedri
ℹ️ Calculé via ses stats par 90 minutes club + sélection cette saison, par rapport aux joueurs évoluant au même poste dans un club des 5 premiers championnats.
📊 Elite correspond au top 10 % à son poste, critique le pire 10 %. Le reste par tranche de 20%.