%0 Generic %1 pappalardo2017quantifying %A Pappalardo, Luca %A Cintia, Paolo %D 2017 %K complexsystems datascience scienceofsuccess sportsanalytics %T Quantifying the relation between performance and success in soccer %U http://arxiv.org/abs/1705.00885 %X The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this work, we analyze more than 6,000 games and 10 million events in the six major European leagues and investigate the relation between team performance and success in soccer competitions. We discover that a team's success in the national tournament is significantly related to its typical performance. Moreover, we observe that while victory and defeats can be explained by the team's performance during a game, draws are difficult to describe with a machine learning approach. We then perform a simulation of an entire season of the six leagues where the outcome of every game is replaced by a synthetic outcome (victory, defeat, or draw) based on a machine learning model trained on the previous seasons. We find that the final rankings in the simulated tournaments are close to the actual rankings in the real tournaments, suggesting that a complex systems' view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.

@misc{pappalardo2017quantifying, abstract = {The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this work, we analyze more than 6,000 games and 10 million events in the six major European leagues and investigate the relation between team performance and success in soccer competitions. We discover that a team's success in the national tournament is significantly related to its typical performance. Moreover, we observe that while victory and defeats can be explained by the team's performance during a game, draws are difficult to describe with a machine learning approach. We then perform a simulation of an entire season of the six leagues where the outcome of every game is replaced by a synthetic outcome (victory, defeat, or draw) based on a machine learning model trained on the previous seasons. We find that the final rankings in the simulated tournaments are close to the actual rankings in the real tournaments, suggesting that a complex systems' view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.}, added-at = {2017-05-03T16:06:16.000+0200}, author = {Pappalardo, Luca and Cintia, Paolo}, biburl = {https://www.bibsonomy.org/bibtex/2545e9ba84b3a0c6870c4269f87afbbf7/lucapappalardo}, description = {Quantifying the relation between performance and success in soccer}, interhash = {3fb9eac6382cb25fd184f25a9a915187}, intrahash = {545e9ba84b3a0c6870c4269f87afbbf7}, keywords = {complexsystems datascience scienceofsuccess sportsanalytics}, note = {cite arxiv:1705.00885}, timestamp = {2017-05-03T16:06:16.000+0200}, title = {Quantifying the relation between performance and success in soccer}, url = {http://arxiv.org/abs/1705.00885}, year = 2017 }