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Volume 19 (2020): Issue 1 (July 2020)

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Special Issue: Selected papers presented at the 12th Symposium of the Section Computer Science in Sport of the German Association of Sport Science (September 4.-7., 2018)

Volume 18 (2019): Issue 1 (July 2019)

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Volume 17 (2018): Issue 1 (July 2018)

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Volume 16 (2017): Issue 1 (July 2017)

Volume 15 (2016): Issue 2 (December 2016)

Volume 15 (2016): Issue 1 (July 2016)

Journal Details
Format
Journal
eISSN
1684-4769
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English

Search

Volume 16 (2017): Issue 1 (July 2017)

Journal Details
Format
Journal
eISSN
1684-4769
First Published
16 Apr 2016
Publication timeframe
2 times per year
Languages
English

Search

5 Articles
Open Access

Issues in Using Self-Organizing Maps in Human Movement and Sport Science

Published Online: 22 Jul 2017
Page range: 1 - 17

Abstract

Abstract

Self-Organizing Maps (SOMs) are steadily more integrated as data-analysis tools in human movement and sport science. One of the issues limiting researchers’ confidence in their applications and conclusions concerns the (arbitrary) selection of training parameters, their effect on the quality of the SOM and the sensitivity of any subsequent analyses. In this paper, we demonstrate how quality and sensitivity may be examined to increase the validity of SOM-based data-analysis. For this purpose, we use two related data sets where the research question concerns coordination variability in a volleyball spike. SOMs are an attractive tool for analysing this problem because of their ability to reduce the highdimensional time series to a two-dimensional problem while preserving the topological, non-linear relations in the original data. In a first step, we systematically search the SOM parameter space for a set of options that produces significantly lower continuity, accuracy and combined map errors and we discuss the sensitivity of SOM-based analyses of coordination variability to changes in training parameters. In a second step, we further investigate the effect of using different numbers of trials and variables on the SOM quality and sensitivity. These sensitivity analyses are able to validate the conclusions from statistical tests. Using this type of analysis can guide researchers to select SOM parameters that optimally represent their data and to examine how they affect the subsequent analyses. This may also enforce confidence in any conclusions that are drawn from studies using SOMs and enhance their integration in human movement and sport science.

Keywords

  • Self-Organizing Map
  • artificial neural network
  • coordination
  • variability
  • volleyball
Open Access

Computational Estimation of Football Player Wages

Published Online: 22 Jul 2017
Page range: 18 - 38

Abstract

Abstract

The wage of a football player is a function of numerous aspects such as the player’s skills, performance in the previous seasons, age, trajectory of improvement, personality, and more. Based on these aspects, salaries of football players are determined through negotiation between the team management and the agents. In this study we propose an objective quantitative method to determine football players’ wages based on their skills. The method is based on the application of pattern recognition algorithms to performance (e.g., scoring), behavior (e.g., aggression), and abilities (e.g., acceleration) data of football players. Experimental results using data from 6,082 players show that the Pearson correlation between the predicted and actual salary of the players is ~0.77 (p < .001). The proposed method can be used as an assistive technology when negotiating players salaries, as well as for performing quantitative analysis of links between the salary and the performance of football players. The method is based on the performance and skills of the players, but does not take into account aspects that are not related directly to the game such as the popularity of the player among fans, predicted merchandise sales, etc, which are also factors of high impact on the salary, especially in the case of the team lead players and superstars. Analysis of player salaries in eight European football leagues show that the skills that mostly affect the salary are largely consistent across leagues, but some differences exist. Analysis of underpaid and overpaid players shows that overpaid players tend to be stronger, but are inferior in their reactions, vision, acceleration, agility, and balance compared to underpaid football players.

Keywords

  • football
  • soccer
  • sports economy
  • machine learning
Open Access

Network structure of UEFA Champions League teams: association with classical notational variables and variance between different levels of success

Published Online: 22 Jul 2017
Page range: 39 - 50

Abstract

Abstract

The aim of this study was to analyse the general properties of the network of elite football teams that participated in UEFA Champions League 2015–2016. Analysis of variance of the general network measures between performances in competition was made. Moreover, the association between performance variables (goals, shots, and percentage of ball possession) and general network measures also was tested. The best sixteen teams that participated in UEFA Champions League 2015–2016 were analysed in a total of 109 official matches. Statistically significant differences between maximum stages in competition were found in total links (p = 0.003; ES = 0.087), network density (p = 0.003; ES = 0.088), and clustering coefficient (p = 0.007; ES = 0.078). Total links (r = 0.439; p = 0.001), network density (r = 0.433; p = 0.001) and clustering coefficient (r = 0.367; p = 0.001) had a moderate positive correlations with percentage of ball possession. This study revealed that teams that achieved the quarterfinals and finals had greater values of general network measures than the remaining teams, thus suggesting that higher values of homogeneity in network process may improve the success of the teams. Moderate correlations were found between ball possession and the general network measures suggesting that teams with more capacity to perform longer passing sequences may involve more players in a more homogeneity manner.

Keywords

  • Applied Mathematics
  • graph theory
  • soccer
  • football
  • match analysis
Open Access

Ordinal versus nominal regression models and the problem of correctly predicting draws in soccer

Published Online: 22 Jul 2017
Page range: 50 - 64

Abstract

Abstract

Ordinal regression models are frequently used in academic literature to model outcomes of soccer matches, and seem to be preferred over nominal models. One reason is that, obviously, there is a natural hierarchy of outcomes, with victory being preferred to a draw and a draw being preferred to a loss. However, the often used ordinal models have an assumption of proportional odds: the influence of an independent variable on the log odds is the same for each outcome. This paper illustrates how ordinal regression models therefore fail to fully utilize independent variables that contain information about the likelihood of matches ending in a draw. However, in practice, this flaw does not seem to have a substantial effect on the predictive accuracy of an ordered logit regression model when compared to a multinomial logistic regression model.

Keywords

  • association football
  • forecasting
  • ordered regression
Open Access

A Pilot Study on Offensive Success in Soccer Based on Space and Ball Control – Key Performance Indicators and Key to Understand Game Dynamics

Published Online: 22 Jul 2017
Page range: 65 - 75

Abstract

Abstract

The intention of Key Performance Indicators (KPI) is to map complex system-behaviour to single values for scaling, rating and ranking systems or system components. Very often, however, this mapping only reduces important information about tactical behaviour or playing dynamics without replacing it by useful ones. The presented approach tries to bridge the gap between complex dynamics and numerical indicators in the case of offensive effectiveness in soccer in two steps. First, a model is developed which visualises offensive actions in a process-oriented way by using information units to represent offensive performance – i.e. Key Performance Indicators. Second, this model is organised in relation to time intervals, which enables to measure the effectiveness for a whole half-time as well as for arbitrary intervals of any desired lengths.

This contribution is meant as an introduction to a new modelling idea, where examples are calculated as case studies to demonstrate how it works. Therefore, only two games have been exemplarily analysed yet: The first one, which is used to demonstrate the method, is an example for similar quantitative indicators but different dynamic behaviour. The last one is used to demonstrate the results in the case of teams with extreme different strengths.

Keywords

  • KPI
  • offensive dynamics
  • ball control
  • space control
  • correlation
  • efficiency
5 Articles
Open Access

Issues in Using Self-Organizing Maps in Human Movement and Sport Science

Published Online: 22 Jul 2017
Page range: 1 - 17

Abstract

Abstract

Self-Organizing Maps (SOMs) are steadily more integrated as data-analysis tools in human movement and sport science. One of the issues limiting researchers’ confidence in their applications and conclusions concerns the (arbitrary) selection of training parameters, their effect on the quality of the SOM and the sensitivity of any subsequent analyses. In this paper, we demonstrate how quality and sensitivity may be examined to increase the validity of SOM-based data-analysis. For this purpose, we use two related data sets where the research question concerns coordination variability in a volleyball spike. SOMs are an attractive tool for analysing this problem because of their ability to reduce the highdimensional time series to a two-dimensional problem while preserving the topological, non-linear relations in the original data. In a first step, we systematically search the SOM parameter space for a set of options that produces significantly lower continuity, accuracy and combined map errors and we discuss the sensitivity of SOM-based analyses of coordination variability to changes in training parameters. In a second step, we further investigate the effect of using different numbers of trials and variables on the SOM quality and sensitivity. These sensitivity analyses are able to validate the conclusions from statistical tests. Using this type of analysis can guide researchers to select SOM parameters that optimally represent their data and to examine how they affect the subsequent analyses. This may also enforce confidence in any conclusions that are drawn from studies using SOMs and enhance their integration in human movement and sport science.

Keywords

  • Self-Organizing Map
  • artificial neural network
  • coordination
  • variability
  • volleyball
Open Access

Computational Estimation of Football Player Wages

Published Online: 22 Jul 2017
Page range: 18 - 38

Abstract

Abstract

The wage of a football player is a function of numerous aspects such as the player’s skills, performance in the previous seasons, age, trajectory of improvement, personality, and more. Based on these aspects, salaries of football players are determined through negotiation between the team management and the agents. In this study we propose an objective quantitative method to determine football players’ wages based on their skills. The method is based on the application of pattern recognition algorithms to performance (e.g., scoring), behavior (e.g., aggression), and abilities (e.g., acceleration) data of football players. Experimental results using data from 6,082 players show that the Pearson correlation between the predicted and actual salary of the players is ~0.77 (p < .001). The proposed method can be used as an assistive technology when negotiating players salaries, as well as for performing quantitative analysis of links between the salary and the performance of football players. The method is based on the performance and skills of the players, but does not take into account aspects that are not related directly to the game such as the popularity of the player among fans, predicted merchandise sales, etc, which are also factors of high impact on the salary, especially in the case of the team lead players and superstars. Analysis of player salaries in eight European football leagues show that the skills that mostly affect the salary are largely consistent across leagues, but some differences exist. Analysis of underpaid and overpaid players shows that overpaid players tend to be stronger, but are inferior in their reactions, vision, acceleration, agility, and balance compared to underpaid football players.

Keywords

  • football
  • soccer
  • sports economy
  • machine learning
Open Access

Network structure of UEFA Champions League teams: association with classical notational variables and variance between different levels of success

Published Online: 22 Jul 2017
Page range: 39 - 50

Abstract

Abstract

The aim of this study was to analyse the general properties of the network of elite football teams that participated in UEFA Champions League 2015–2016. Analysis of variance of the general network measures between performances in competition was made. Moreover, the association between performance variables (goals, shots, and percentage of ball possession) and general network measures also was tested. The best sixteen teams that participated in UEFA Champions League 2015–2016 were analysed in a total of 109 official matches. Statistically significant differences between maximum stages in competition were found in total links (p = 0.003; ES = 0.087), network density (p = 0.003; ES = 0.088), and clustering coefficient (p = 0.007; ES = 0.078). Total links (r = 0.439; p = 0.001), network density (r = 0.433; p = 0.001) and clustering coefficient (r = 0.367; p = 0.001) had a moderate positive correlations with percentage of ball possession. This study revealed that teams that achieved the quarterfinals and finals had greater values of general network measures than the remaining teams, thus suggesting that higher values of homogeneity in network process may improve the success of the teams. Moderate correlations were found between ball possession and the general network measures suggesting that teams with more capacity to perform longer passing sequences may involve more players in a more homogeneity manner.

Keywords

  • Applied Mathematics
  • graph theory
  • soccer
  • football
  • match analysis
Open Access

Ordinal versus nominal regression models and the problem of correctly predicting draws in soccer

Published Online: 22 Jul 2017
Page range: 50 - 64

Abstract

Abstract

Ordinal regression models are frequently used in academic literature to model outcomes of soccer matches, and seem to be preferred over nominal models. One reason is that, obviously, there is a natural hierarchy of outcomes, with victory being preferred to a draw and a draw being preferred to a loss. However, the often used ordinal models have an assumption of proportional odds: the influence of an independent variable on the log odds is the same for each outcome. This paper illustrates how ordinal regression models therefore fail to fully utilize independent variables that contain information about the likelihood of matches ending in a draw. However, in practice, this flaw does not seem to have a substantial effect on the predictive accuracy of an ordered logit regression model when compared to a multinomial logistic regression model.

Keywords

  • association football
  • forecasting
  • ordered regression
Open Access

A Pilot Study on Offensive Success in Soccer Based on Space and Ball Control – Key Performance Indicators and Key to Understand Game Dynamics

Published Online: 22 Jul 2017
Page range: 65 - 75

Abstract

Abstract

The intention of Key Performance Indicators (KPI) is to map complex system-behaviour to single values for scaling, rating and ranking systems or system components. Very often, however, this mapping only reduces important information about tactical behaviour or playing dynamics without replacing it by useful ones. The presented approach tries to bridge the gap between complex dynamics and numerical indicators in the case of offensive effectiveness in soccer in two steps. First, a model is developed which visualises offensive actions in a process-oriented way by using information units to represent offensive performance – i.e. Key Performance Indicators. Second, this model is organised in relation to time intervals, which enables to measure the effectiveness for a whole half-time as well as for arbitrary intervals of any desired lengths.

This contribution is meant as an introduction to a new modelling idea, where examples are calculated as case studies to demonstrate how it works. Therefore, only two games have been exemplarily analysed yet: The first one, which is used to demonstrate the method, is an example for similar quantitative indicators but different dynamic behaviour. The last one is used to demonstrate the results in the case of teams with extreme different strengths.

Keywords

  • KPI
  • offensive dynamics
  • ball control
  • space control
  • correlation
  • efficiency

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