Sport

This notebook shows how to use scikit-network to analyse sport data.

We here consider the results of tennis matches of ATP Tour in the period 2001–2016.

[1]:

from IPython.display import SVG

[2]:

import numpy as np
import pandas as pd
from scipy import sparse

[3]:

from sknetwork.data import from_edge_list
from sknetwork.ranking import PageRank, top_k
from sknetwork.topology import get_core_decomposition
from sknetwork.utils import directed2undirected
from sknetwork.embedding import Spectral
from sknetwork.visualization import visualize_graph

Load data

[4]:

filename = 'atp.csv'

[5]:

df = pd.read_csv(filename, sep=';')

[6]:

df.head()

[6]:

	ATP	Location	Tournament	Date	Series	Court	Surface	Round	Best of	Winner	...	L4	W5	L5	Wsets	Lsets	Comment	MaxW	MaxL	AvgW	AvgL
0	25	Houston	U.S. Men's Clay Court Championships	2005-04-21	International	Outdoor	Clay	2nd Round	3	Haas T.	...	NaN	NaN	NaN	2.0	0.0	Completed	NaN	NaN	NaN	NaN
1	26	Estoril	Estoril Open	2005-04-27	International Series	Outdoor	Clay	2nd Round	3	Gaudio G.	...	NaN	NaN	NaN	2.0	0.0	Completed	NaN	NaN	NaN	NaN
2	28	Rome	Telecom Italia Masters Roma	2005-05-03	Masters	Outdoor	Clay	1st Round	3	Sanguinetti D.	...	NaN	NaN	NaN	2.0	1.0	Completed	NaN	NaN	NaN	NaN
3	28	Rome	Telecom Italia Masters Roma	2005-05-04	Masters	Outdoor	Clay	2nd Round	3	Almagro N.	...	NaN	NaN	NaN	2.0	0.0	Completed	NaN	NaN	NaN	NaN
4	29	Hamburg	Hamburg TMS	2005-05-11	Masters	Outdoor	Clay	2nd Round	3	Hrbaty D.	...	NaN	NaN	NaN	2.0	0.0	Completed	NaN	NaN	NaN	NaN

5 rows × 32 columns

[7]:

df = df[df['Comment']=='Completed']

[8]:

len(df)

[8]:

Build graph

[9]:

edge_list = list(df[['Winner', 'Loser']].itertuples(index=False, name=None))

[10]:

len(edge_list)

[10]:

[11]:

graph = from_edge_list(edge_list, directed=True)

[12]:

adjacency = graph.adjacency
names = graph.names

[13]:

adjacency

[13]:

<1255x1255 sparse matrix of type '<class 'numpy.int64'>'
        with 28212 stored elements in Compressed Sparse Row format>

[14]:

len(names)

[14]:

Ranking

[15]:

# top-10 players in number of wins
out_weights = adjacency.dot(np.ones(len(names)))
print(names[top_k(out_weights, 10)])

['Federer R.' 'Nadal R.' 'Djokovic N.' 'Ferrer D.' 'Murray A.'
 'Roddick A.' 'Berdych T.' 'Robredo T.' 'Davydenko N.' 'Hewitt L.']

[16]:

# top-10 players in terms of PageRank
pagerank = PageRank()
adjacency_transpose = sparse.csr_matrix(adjacency.T)
scores = pagerank.fit_predict(adjacency_transpose)
print(names[top_k(scores, 10)])

['Federer R.' 'Nadal R.' 'Djokovic N.' 'Murray A.' 'Ferrer D.'
 'Roddick A.' 'Berdych T.' 'Hewitt L.' 'Davydenko N.' 'Wawrinka S.']

[17]:

index = top_k(scores, 10)
sub_adjacency = adjacency[index][:, index]

[18]:

SVG(visualize_graph(sub_adjacency, names=names[index], scores=scores[index]))

[18]:

Core decomposition

[19]:

adjacency_sym = directed2undirected(adjacency)

[20]:

values = get_core_decomposition(adjacency_sym)

[21]:

print(names[values==np.max(values)])

['Acasuso J.' 'Almagro N.' 'Ancic M.' 'Anderson K.' 'Andreev I.'
 'Andujar P.' 'Baghdatis M.' 'Beck K.' 'Becker B.' 'Bellucci T.'
 'Benneteau J.' 'Berdych T.' 'Berlocq C.' 'Berrer M.' 'Bjorkman J.'
 'Blake J.' 'Bolelli S.' 'Calleri A.' 'Canas G.' 'Chardy J.' 'Chela J.I.'
 'Cilic M.' 'Clement A.' 'Coria G.' 'Cuevas P.' 'Darcis S.' 'Davydenko N.'
 'Del Potro J.M.' 'Dent T.' 'Dimitrov G.' 'Djokovic N.' 'Dodig I.'
 'Dolgopolov O.' 'Falla A.' 'Federer R.' 'Ferrer D.' 'Ferrero J.C.'
 'Fish M.' 'Fognini F.' 'Gabashvili T.' 'Garcia-Lopez G.' 'Gasquet R.'
 'Gaudio G.' 'Gicquel M.' 'Gimeno-Traver D.' 'Ginepri R.' 'Giraldo S.'
 'Golubev A.' 'Gonzalez F.' 'Granollers M.' 'Grosjean S.' 'Gulbis E.'
 'Haas T.' 'Haase R.' 'Hanescu V.' 'Harrison R.' 'Henman T.' 'Hernych J.'
 'Hewitt L.' 'Horna L.' 'Hrbaty D.' 'Isner J.' 'Istomin D.' 'Johansson T.'
 'Karlovic I.' 'Kiefer N.' 'Kohlschreiber P.' 'Korolev E.' 'Koubek S.'
 'Kubot L.' 'Kunitsyn I.' 'Lapentti N.' 'Lee H.T.' 'Ljubicic I.'
 'Llodra M.' 'Lopez F.' 'Lu Y.H.' 'Mahut N.' 'Malisse X.' 'Mannarino A.'
 'Martin A.' 'Massu N.' 'Mathieu P.H.' 'Mayer F.' 'Mayer L.' 'Melzer J.'
 'Mirnyi M.' 'Monaco J.' 'Monfils G.' 'Montanes A.' 'Moya C.' 'Muller G.'
 'Murray A.' 'Nadal R.' 'Nalbandian D.' 'Nieminen J.' 'Nishikori K.'
 'Novak J.' 'Paire B.' 'Pavel A.' 'Petzschner P.' 'Phau B.' 'Querrey S.'
 'Ramirez-Hidalgo R.' 'Raonic M.' 'Robredo T.' 'Rochus C.' 'Rochus O.'
 'Roddick A.' 'Roger-Vasselin E.' 'Rosol L.' 'Russell M.' 'Safin M.'
 'Santoro F.' 'Schuettler R.' 'Sela D.' 'Seppi A.' 'Serra F.' 'Simon G.'
 'Soderling R.' 'Spadea V.' 'Srichaphan P.' 'Stakhovsky S.' 'Starace P.'
 'Stepanek R.' 'Tipsarevic J.' 'Tomic B.' 'Troicki V.' 'Tsonga J.W.'
 'Tursunov D.' 'Verdasco F.' 'Vliegen K.' 'Volandri F.' 'Wawrinka S.'
 'Young D.' 'Youzhny M.' 'Zverev M.']

Embedding

[22]:

spectral = Spectral(2, normalized=False)

[23]:

embedding = spectral.fit_transform(adjacency)

[24]:

mask = values==np.max(values)

[25]:

SVG(visualize_graph(position=embedding[mask], names=names[mask], scores=scores[mask], node_size=5, width=400, height=1000))

[25]: