F1 Data Analysis: Vettel vs Verstappen — SQL & Power BI A SQL + Power BI comparison of Sebastian ...

F1 Data Analysis: Vettel vs Verstappen — SQL & Power BI A SQL + Power BI comparison of Sebastian ...F1 Data Analysis: Vettel vs Verstappen — SQL & Power BI A SQL + Power BI comparison of Sebastian ...

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Maruffuzzman Tanvir

• Jul 18

F1 Data Analysis: Vettel vs Verstappen — SQL & Power BI A SQL + Power BI comparison of Sebastian Vettel's (2010–2013) and Max Verstappen's (2021–2024) four-title runs at Red Bull Racing — same team, different eras, very different paths to the top.

Built on the Ergast F1 database: 14 SQL queries across 5 categories (dominance, race execution, consistency, teammate benchmarking), visualized in a 5-page Power BI dashboard.

Key finding: Vettel converted a higher share of available points on average (69.05% vs Verstappen's 63.28%), despite winning fewer races overall.

Tech Stack SQL Server · DBeaver · Power BI · Excel

Full project: github.com/MZT49/four-time-champions-vettel-vs-verstappen

Data Analysis Data Visualization SQL

Orbix Studio

max

• Jul 15

Live football data has always existed. A dashboard that makes it feel this immediate hasn't.

Clutch Lens brings the entire match into one screen: live streaming, real-time scoreboard, possession and shot stats, dual team heatmaps, player leaderboard, and a tactic map that shows exactly how the game is being played, not just who's winning it.

France 0 VS Spain 2. Semi-finals. World Cup 2026. Every number that matters, right where it should be.

Dark. Live. Built for the football obsessed.

Designing a sports analytics platform that needs to feel this considered? Let's build it together.

Tools: Figma

#SportsUI #DashboardDesign #FIFA2026 #WorldCup2026 #UIDesign #DarkUI #ContraFreelance

worldcup2026 dashboard Figma

Anjuman Sharear

• Jul 15

Amazing design

Yurii Funkendorf

pro

• Jul 14

Two leaderboard designs. One clear winner.Guess which one

48 voted

84%

9 voted

16%

57 votes

Closed

Maty Sandoval

max

• Jul 14

Option 1 for me — the glassy dark panel makes the top ranks pop way more. Was the flatter option 2 your original client-approved direction, or is this an internal redesign push?

Advik Rathee

• Jul 14

Built this one for an e-commerce customer behavior project - 18,000+ customers, segmented using RFM (Recency, Frequency, Monetary) analysis.

The interesting part wasn't the segmentation itself, it was what it revealed: nearly 22% of customers were "at risk" of churning, and most businesses wouldn't know that until those customers had already stopped buying.

This is the kind of insight that's usually sitting in your order data already, it just needs the right lens. Happy to take a look at your numbers if you're curious what's hiding in yours.

Tools used: Power BI, SQL, Python (Pandas, Scikit-learn for clustering)