Introduction to Soccer Pass Network Analysis with Python
summary
Covers four concrete steps: fetching StatsBomb event data with statsbombpy, drawing a pitch with mplsoccer, building a pass network map (edges as arrows weighted by number of successful passes, nodes placed at players’ mean XY and labeled by jersey), and analyzing that directed graph with NetworkX. Uses a Real Madrid v Liverpool 2017/18 match, filters to starting XI (drops events after first substitution), finds Toni Kroos has the highest degree, compares clustering (Liverpool higher), computes indegree/outdegree, betweenness, eccentricity and a geodesic metric using inverse weights.
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chapters (10)
- 0:00Introduction
- 1:02Match Dataset
- 1:48Pitch Drawing
- 3:52Event Data
- 4:54Pass Maker and Pass Receiver
- 6:35Pass Makers
- 7:32Pitch Function
- 8:25Graph Function
- 9:24Geodetic Distance
- 9:46Average Eccentricity
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