Hello, I'm
Moritz Philipp Haaf
Data Analyst
Football & Digital Analytics
Get To Know More
About Me
Experience
5+ years
in Data & Analytics
Education
B.Sc. Business Administration
M.A. Digital Media Management
I am a certified Data Analyst with a passion for transforming complex datasets into actionable insights that drive business growth and operational efficiency. With a strong background in sports, media, and technology, I excel at leveraging data-driven strategies to enhance decision-making and optimize performance. My technical toolkit includes Python, SQL, R, Tableau, Looker Studio, Datorama, and Power BI, which I utilize to analyze data, automate workflows, and deliver results that make a difference.
Explore My
Professional Journey
Publicis Media Austria
Senior Digital Data & Dashboard Manager
Self-Employed | Remote
Data Analyst – Freelance
Regionalmedien Austria AG
Analytics & Ad Tech Development Manager
Red Bull Media House
Digital Competence & Ad Tech Specialist
Sportradar Media Services GmbH
Manager Digital Advertising
Hawk-Eye Innovations Ltd
Football Systems Operator
E2 Communications GmbH
Oddsserve & Ad Operations Manager
TorAlarm GmbH
Internship
Explore My
Skill Set
Core Competencies
Data Analytics
Experienced
Data Science
Intermediate
Business Intelligence
Experienced
Data Visualization
Experienced
Dashboard Development
Experienced
Ad Tech
Experienced
Project Management
Intermediate
AI & Machine Learning
Basic
Football Analytics
Intermediate
Sports Technology
Intermediate
Tools & Technologies
Check Out My Recent
Projects
Football Analytics Portfolio – Expected Goals (xG) & Passing Networks
Project Overview
This project explores advanced football analytics by combining machine learning and data visualization techniques to evaluate expected goals (xG) and analyze team passing networks.
It features an XGBoost-based xG prediction model and uses NetworkX for analyzing passing structures.
The findings are presented through an interactive Streamlit dashboard.
View the full implementation on
GitHub.
Technologies Used
- Programming Language:
Python - Machine Learning:
XGBoost,scikit-learn - Data Processing:
pandas,NumPy - Data Visualization:
mplsoccer,Matplotlib,Plotly - Web App:
Streamlit - Data Source:
StatsBombevent data - Version Control:
Git,GitHub
Bundesliga Performance & Valuation: Bayer Leverkusen Case Study
Project Overview
An analytical deep dive into Bayer 04 Leverkusen’s historic unbeaten Bundesliga 2023/24 season:
- Explored player performance, valuation trends, and match dominance using
Pandas&DuckDB - Visualized cumulative goals/assists, W-D-L results, and standout match moments
(e.g. 3–0 vs Bayern) - Used
Ridge RegressionandRandom Forestto evaluate what influences player market value - Featured storytelling and feature importance with
Seaborn,Matplotlib, andadjustText
Technologies Used:
- Python:
pandas,NumPy,matplotlib,seaborn,plotly - SQL:
DuckDB - Machine Learning:
scikit-learn(Ridge,RandomForestRegressor)
More to come
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