Paper Info
Title
Analysis of sports statistics via graph-signal smoothness prior.
Abstract
Since teams in a sporting league compete head-tohead according to a structured schedule, it is natural to interpret statistics emanating from competitions as signals on a graph modeling similarities among competing entities. In this paper, we analyse available sports statistics to predict game outcomes from a graph signal processing (GSP) perspective: GSP tools are used to remove (denoise) unwanted variability to reveal underlying predictable trends, and to interpolate missing data-predicted game outcomes in terms of point differential. First, we construct a graph for the desired graph-signal (point differential for every team pair): for an N-team league, we construct N subgraphs G(j), each containing N - 1 nodes representing teams competing against opponent j. We next assign weight to each intra-subgraph edge based on similarity in observed statistics (e.g., total points scored, assists, etc) of the two connecting nodes (teams). We then connect nodes in different subgraphs representing the same teams, where the weight of an inter-subgraph edge connecting nodes in subgraphs G(k) and G(l) now reflects the similarity between opponents k and l. Finally, assuming a graph-signal smoothness prior, we compute the desired graph-signal on the constructed graph via an alternating convex programming procedure. Experimental results show that our graph-based scheme achieves better prediction than a competing k-nearest neighbor (kNN) scheme.
Year
Venue
Field
2015
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Strength of a graph,Line graph,Simplex graph,Directed graph,Null graph,Random geometric graph,Statistics,Mathematics,Graph (abstract data type),Complement graph
DocType
ISSN
Citations 
Conference
2309-9402
0
PageRank 
References 
Authors
0.34
10
3
Name
Order
Citations
PageRank
Haitian Zheng1224.44
Gene Cheung Connie Chan21387121.82
Lu Fang334355.27