Analyzing Golden State Warriors’ passing network using GraphFrames in Spark

By Yuki Katoh

Databricks recently announced GraphFrames, awesome Spark extension to implement graph processing using DataFrames.
I performed graph analysis and visualized beautiful ball movement network of Golden State Warriors using rich data provided by NBA.com’s stats

### Passes received & made

The league’s MVP Stephen Curry received the most passes and the team’s MVP Draymond Green provides the most passes.
We’ve seen most of the offense start with their pick & roll or Curry’s off-ball cuts with Green as a pass provider.

via GIPHY

inDegree

outDegree

Label Propagation

Label Propagation is an algorithm to find communities in a graph network.
The algorithm nicely classifies players into backcourt and frontcourt without providing label!

PageRank can detect important nodes (players in this case) in a network.
It’s no surprise that Stephen Curry, Draymond Green and Klay Thompson are the top three.
The algoritm detects Shaun Livingston and Andre Iguodala play key roles in the Warriors’ passing games.

library(networkD3)
setwd('/Users/yuki/Documents/code_for_blog/gsw_passing_network')

passes  read.csv("passes.csv")

groups  read.csv("groups.csv")

size  read.csv("size.csv")
passes$source  as.numeric(as.factor(passes$PLAYER))-1

passes$target  as.numeric(as.factor(passes$PASS_TO))-1

passes$PASS  passes$PASS/50
groups$nodeid  groups$name

groups$name  as.numeric(as.factor(groups$name))-1

groups$group  as.numeric(as.factor(groups$label))-1

nodes  merge(groups,size[-1],by="id")

nodes$pagerank  nodes$pagerank^2*100
forceNetwork(Links = passes,

             Nodes = nodes,

             Source = "source",

             fontFamily = "Arial",

             colourScale = JS("d3.scale.category10()"),

             Target = "target",

             Value = "PASS",

        ...read more
Source:: r-bloggers.com