Analyzing football matches using relational performance data

Transcription

1 Analyzing football matches using relational performance data Jan Van Haaren 7 June 2012

2 Analyzing football matches using relational performance data Recent evolutions in football Restrictions of the traditional learning setting Statistical relational learning offers a solution

3 Analyzing football matches using relational performance data Recent evolutions in football Restrictions of the traditional learning setting Statistical relational learning offers a solution

4 Increasing gap between rich and poor clubs Broadcasting revenues (since early 1980s) Commercialisation of leagues (since early 1990s) Bosman ruling (in 1995)

5 Introduction of financial fair play regulations Introduce discipline and rationality Reduce salaries and transfer fees Stimulate investment in youth and facilities Force clubs to spend less than they earn

6 Increasing interest in analytics Adopt a well-thought-out transfer policy Exploit their player potential the best they can Analyze individual and collective performances

7 Technological tools acquire lots of data Football is unstructured and low-scoring Sharp contrast with popular American sports Camera-based tracking systems

8 Take-away message I Football clubs have an increasing interest in football analytics. Recent technological evolutions allow to derive meaningful statistics from football matches.

9 Analyzing football matches using relational performance data Recent evolutions in football Restrictions of the traditional learning setting Statistical relational learning offers a solution

10 Background on machine learning Machine learning designs algorithms whose performance on a task improves over time Experience is typically defined as the amount of available data The focus is to automatically learn patterns from the data and use them to make intelligent decisions Algorithms must generalize from training examples to produce a useful output for unseen test examples

11 Classification # Number of passes (H) Number of passes (A)... Number of tackles (H) Number of tackles (A) Outcome Home win Away win Draw Away win Home win Home win Home win Draw Home win Away win ?

12 Regression # Number of passes (H) Number of passes (A)... Number of tackles (H) Number of tackles (A) Goal difference ?

13 Collective classification # Player Position 1 Stekelenburg, M.? 2 van der Wiel, G.? 3 Heitinga, J.? 4 Mathijsen, J.? 5 Bouma, W.? 6 van Bommel, M.? 7 Kuyt, D.? 8 de Jong, N.? 9 Huntelaar, K.-J.? 10 Sneijder, W.? 11 Robben, A.? 12 Vorm, M.? # Player Position 13 Vlaar, R.? 14 Schaars, S.? 15 Willems, J.? 16 van Persie, R.? 17 Strootman, K.? 18 de Jong, L.? 19 Narsingh, L.? 20 Afellay, I.? 21 Boulahrouz, K.? 22 Krul, T.? 23 van der Vaart, R.? Goalkeeper? Defender? Midfielder? Attacker? Substitute? Reserve?

14 Collective regression # Number of passes (H) Number of passes (A)... Number of tackles (A) Goal difference Number of tackles (H) ??

15 Link prediction Player 2 Player 1 Opponent Opponent Player 3 Player 4

16 Take-away message II The traditional machine learning setting is restricted to simple tasks such as regression and classification. More challenging learning tasks require a more expressive approach to handle structured data.

17 Analyzing football matches using relational performance data Recent evolutions in football Restrictions of the traditional learning setting Statistical relational learning offers a solution

18 Motivation for statistical relational learning Offers a lot of flexibility i.e., not tied to a fixed set of parameters Reasons about time-dependent information e.g., player and team forms Reasons about positional information e.g., passes and tackles performed

19 Background on statistical relational learning Models of domains that exhibit both uncertainty and a complex, relational structure Multi-relational domains contain entities of potentially different types that engage in a variety of relations Player Team League plays for plays in Age Formation Reputation

20 Performing prediction tasks in klog 1. Relational representation 2. Graphical representation 3. Propositional representation 4. Learn a model in the propositional domain

21 Relational representation in klog team(teamid, side) team(t1, home) team(t2, away) player(playerid, teamid, position) player(p1, t1, midfielder) player(p2, t1, striker) player(p3, t1, midfielder) player(p4, t2, midfielder) pass(playerid, playerid) pass(p1, p3) pass(p2, p4) cross(playerid, playerid) cross(p3, p2)

22 Graphical representation in klog t1 t2 p1 p3 p2 p4 pass cross pass

23 Propositional representation in klog klog constructs feature vectors by calculating a graph kernel between pairs of subgraphs klog uses the notion of topological distance to generate features relevant to each predicate Source: Frasconi, P., Costa, F., De Raedt, L., and De Grave, K. Technical report, 2011.

24 Learning models in klog klog learns a model in the resulting feature space Support Vector Machines klog supports a variety of learning tasks Regression Multi-class classification Collective regression Link prediction (not implemented yet though)

25 Take-away message III Relational learning offers a lot of flexibility. Relational learning allows to perform many challenging learning tasks.

26 Analyzing football matches using relational performance data Recent technological advances allow football clubs to perform extensive match analysis. Many interesting learning and analysis tasks stumble upon the limitations of traditional machine learning. Relational learning provides an interesting framework for football-related predictions and analyses.

27 Prediction for the European Championship 1. Germany (27) 2. The Netherlands (24) 3. Spain (22) 4. France (19) 5. Poland (16) 6. Italy (15) 7. Czech (10) 8. Portugal (9) 9. Sweden (5) 10. Denmark (2) 11. Ireland (1) 12. Croatia (1) 13. Ukraine (1) 14. Russia (1) 15. England (0) 16. Greece (0)

28 Analyzing football matches using relational performance data Jan Van Haaren 7 June 2012