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Graph-based Approaches for Analysing Team Interaction on the Example of Soccer Markus Brandt and Ulf Brefeld Knowledge Mining & Assessment TU Darmstadt / DIPF brefeld@cs.tu-darmstadt.de Motivation Team interaction important for


  1. Graph-based Approaches for Analysing Team Interaction on the Example of Soccer Markus Brandt and Ulf Brefeld Knowledge Mining & Assessment TU Darmstadt / DIPF brefeld@cs.tu-darmstadt.de

  2. Motivation ๏ Team interaction important for playmaking, team strategy and thus also for winning ๏ BUT: interaction not covered well by counting- based approaches and descriptive statistics ๏ Here: use of Page rank to capture team interaction Ulf Brefeld brefeld@cs.tu-darmstadt.de 2

  3. Representation pass-based receiver-based interaction-based ๏ Captures ๏ Importance of ๏ Importance of natural flow successful pass team interaction ๏ Receiver gets ๏ Credits for ๏ Credits for both the credits player who involved players passes the ball Ulf Brefeld brefeld@cs.tu-darmstadt.de 3

  4. Representation pass-based receiver-based interaction-based ๏ Captures ๏ Importance of ๏ Importance of natural flow successful pass team interaction ๏ Receiver gets ๏ Credits for ๏ Credits for both the credits player who involved players passes the ball Ulf Brefeld brefeld@cs.tu-darmstadt.de 4

  5. Representation pass-based receiver-based interaction-based ๏ Captures ๏ Importance of ๏ Importance of natural flow successful pass team interaction ๏ Receiver gets ๏ Credits for ๏ Credits for both the credits player who involved players passes the ball Ulf Brefeld brefeld@cs.tu-darmstadt.de 5

  6. Chain Score ๏ Average length of chains a player p is involved in: Page Rank ๏ Normalised sum of page ranks of nodes pointing to p given by Ulf Brefeld brefeld@cs.tu-darmstadt.de 6

  7. Empirical Results ๏ Bundesliga seasons 2009/10 - 2013/14 ๏ 1530 games, >250000 pass chains ๏ Player ranking ๏ Prediction of winning team distribution of chain lengths Ulf Brefeld brefeld@cs.tu-darmstadt.de 7

  8. Top-ranked Players chain score page rank (pass) page rank (receiver) page rank (interaction) ๏ Chain score ranks players according to #chains ๏ Page rank captures team interaction + playmaking Ulf Brefeld brefeld@cs.tu-darmstadt.de 8

  9. Preliminary Results train: 2009/10 - 2012/13, test 2013/14 #completed passes chain score page rank pass receive interact pass receive interact pass receive interact ๏ Baselines: home team: 47.39%, better team 52.94% ๏ 2 features (home and away team score) ๏ Chain score worst, completed passes and page rank perform similarly Ulf Brefeld brefeld@cs.tu-darmstadt.de 9

  10. Conclusion ๏ Page rank captures team interaction and playmaking better than simple frequency-based approaches ๏ Representation depends on application at-hand (e.g., pass, receiver, interaction-based) ๏ Preliminary results for predicting winning teams show that counting-based baselines (#completed passes) perform similarly Ulf Brefeld brefeld@cs.tu-darmstadt.de 10

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