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Volume 21 (2022): Issue 1 (March 2022)

Volume 20 (2021): Issue 2 (December 2021)

Volume 20 (2021): Issue 1 (July 2021)

Volume 19 (2020): Issue 2 (December 2020)

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

Volume 18 (2019): Issue 3 (December 2019)

Volume 18 (2019): Issue 2 (September 2019)
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)

Volume 17 (2018): Issue 2 (December 2018)

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

Volume 16 (2017): Issue 3 (December 2017)

Volume 16 (2017): Issue 2 (November 2017)

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 20 (2021): Issue 1 (July 2021)

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

Search

7 Articles
Open Access

Sports Information Systems: A systematic review

Published Online: 08 May 2021
Page range: 1 - 22

Abstract

Abstract

Many professional sport organizations are currently in the process of finding or already using sports information systems (SIS) to integrate data from different information and measurement systems. The problem is that requirements are very heterogeneous. That is why no consistent definition of SIS and their categories exist, and it is often not clear which fields and functions SIS must cover. This work aims to provide a structured comparison of commercial SIS available on the market to provide an overview of the relevant features and characterize categories. Following PRISMA guidelines, a systematic search for relevant SIS providers was conducted. A catalog of 164 review items was created to define relevant features of SIS and to conduct semi-standardized interviews with product representatives. Overall 36 eligible SIS from 11 countries were identified and 21 of them were interviewed. The analysis of the interviews has shown that there are features that are present in all SIS, whereas others differ or are generally less represented. As a result, different SIS categories have been defined. The study suggests a more differentiated categorization of SIS is necessary and terms need to be defined more precisely. This review should be considered when companies designing SIS or sport organizations select SIS.

Keywords

  • INFORMATION SYSTEMS IN SPORTS
  • SPORTS INFORMATION SYSTEMS
  • ATHLETE MANAGEMENT SYSTEMS
  • REVIEW
  • SPORTS INFORMATICS
Open Access

Comparing bottom-up and top-down ratings for individual soccer players

Published Online: 08 May 2021
Page range: 23 - 42

Abstract

Abstract

Correctly assessing the contributions of an individual player in a team sport is challenging. However, an ability to better evaluate each player can translate into improved team performance, through better recruitment or team selection decisions. Two main ideas have emerged for using data to evaluate players: Top-down ratings observe the performance of the team as a whole and then distribute credit for this performance onto the players involved. Bottom-up ratings assign a value to each action performed, and then evaluate a player based on the sum of values for actions performed by that player. This paper compares a variant of plus-minus ratings, which is a top-down rating, and a bottom-up rating based on valuing actions by estimating probabilities. The reliability of ratings is measured by whether similar ratings are produced when using different data sets, while the validity of ratings is evaluated through the quality of match outcome forecasts generated when the ratings are used as predictor variables. The results indicate that the plus-minus ratings perform better than the bottom-up ratings with respect to the reliability and validity measures chosen and that plus-minus ratings have certain advantages that may be difficult to replicate in bottom-up ratings.

Keywords

  • ASSOCIATION FOOTBALL
  • RANKING
  • PREDICTION
  • PLUS-MINUS
  • VALIDITY
Open Access

Strictness vs. flexibility: Simulation-based recognition of strategies and its success in soccer

Published Online: 08 May 2021
Page range: 43 - 54

Abstract

Abstract

Introduction: Recognition and optimization of strategies in sport games is difficult in particular in case of team games, where a number of players are acting “independently” of each other. One way to improve the situation is to cluster the teams into a small number of tactical groups and to analyze the interaction of those groups. The aim of the study is the evaluation of the applicability of SOCCER© simulation in professional soccer by analyzing and simulation of the tactical group interaction.

Methods: The players’ positions of tactical groups in soccer can be mapped to formation-patterns and then reflect strategic behaviour and interaction. Based on this information, Monte Carlo-Simulation allows for generating strategies, which – at least from the mathematical point of view – are optimal. In practice, behaviour can be orientated in those optimal strategies but normally is changing depending on the opponent team’s activities. Analyzing the game under the aspect of such simulated strategies revealed how strictly resp. flexible a team follows resp. varies strategic patterns.

Approach: A Simulation- and Validation-Study on the basis of 40 position data sets of the 2014/15 German Bundesliga has been conducted to analyze and to optimize such strategic team behaviour in professional soccer.

Results: The Validation-Study demonstrated the applicability of our tactical model. The results of the Simulation-Study revealed that offensive player groups need less tactical strictness in order to gain successful ball possession whereas defensive player groups need tactical strictness to do so.

Conclusion: The strategic behaviour could be recognized and served as basis for optimization analysis: offensive players should play with a more flexible tactical orientation to stay in possession of the ball, whereas defensive players should play with a more planned orientation in order to be successful. The strategic behaviour of tactical groups can be recognized and optimized using Monte Carlo-based analysis, proposing a new and innovative approach to quantify tactical performance in soccer.

Keywords

  • STRATEGY
  • KPI
  • OFFENCE DYNAMICS
  • BALL CONTACT
  • SPACE CONTROL
  • CORRELATION
  • EFFICIENCY
Open Access

Can Elite Australian Football Player’s Game Performance Be Predicted?

Published Online: 10 Aug 2021
Page range: 55 - 78

Abstract

Abstract

In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.

Keywords

  • PLAYER RATING
  • AUSTRALIAN FOOTBALL LEAGUE
  • MACHINE LEARNING
Open Access

Comparison of the Evaluation of Performance Preconditions in Tennis with the Use of Equal and Expertly Judged Criteria Weights

Published Online: 10 Aug 2021
Page range: 79 - 91

Abstract

Abstract

Tennis performance is influenced by various factors, among which physical performance factors play an important role. The aim of the study was an analysis of possibilities of the use of Saaty’s method for assessing the level of performance prerequisites and comparing the results obtained using equal weights and various weights. The research on Czech female players (U12; n = 211) was based on the results of the TENDIAG1 test battery (9 items) and the results were processed by FuzzME software and relevant statistical methods (correlation coefficient r, Student´s t-test, effect size index d). The results of Saaty’s method show that the most important athletic performance criteria for tennis coaches are the leg reaction time and the running speed, while the least important are endurance and strength. The evaluation using various criteria weights offers a finer scale for assessing athletes’ performance prerequisites despite the proven high degree of association between the results obtained with equal and various weights and the insignificant difference of mean values. The results have shown possibilities for the use of a fuzzy approach in sports practice and motivate further research towards broadening the structure or the number of evaluation criteria.

Keywords

  • FUZZY LOGIC
  • SAATY´S METHOD
  • FUZZME SOFTWARE
  • TENNIS PLAYERS
Open Access

Optimizing Team Sport Training With Multi-Objective Evolutionary Computation

Published Online: 25 Sep 2021
Page range: 92 - 105

Abstract

Abstract

This research introduces a new novel method for mathematically optimizing team sport training models to enhance two measures of athletic performance using an evolutionary computation based approach. A common training load model, consisting of daily training load prescriptions, was optimized using an evolutionary multi-objective algorithm to produce improvements in the mean match-day running intensity across a competitive season. The optimized training model was then compared to real-world observed training and performance data to assess the potential improvements in performance that could be achieved. The results demonstrated that it is possible to increase and maintain a stable level of match-day running performance across a competitive season whilst adhering to model-based and real-world constraints, using an intelligently optimized training design compared a to standard human design, across multiple performance criteria (BF+0 = 5651, BF+0 = 11803). This work demonstrates the value of evolutionary algorithms to design and optimize team sport training models and provides support staff with an effective decision support system to plan and prescribe optimal strategies to enhance in-season athlete performance.

Keywords

  • TRAINING LOAD
  • GENETIC ALGORITHMS
  • EVOLUTIONARY COMPUTATIONS
  • ARTIFICIAL INTELLIGENCE
Open Access

Validation of Velocity Measuring Devices in Velocity Based Strength Training

Published Online: 25 Sep 2021
Page range: 106 - 118

Abstract

Abstract

To control and monitor strength training with a barbell various systems are on the consumer market. They provide the user with information regarding velocity, acceleration and trajectory of the barbell. Some systems additionally calculate the 1-repetition-maximum (1RM) of exercises and use it to suggest individual intensities for future training. Three systems were tested: GymAware, PUSH Band 2.0 and Vmaxpro. The GymAware system bases on linear position transducers, PUSH Band 2.0 and Vmaxpro base on inertial measurement units. The aim of this paper was to determine the accuracy of the three systems with regard to the determination of the average velocity of each repetition of three barbell strength exercises (squat, barbell rowing, deadlift). The velocity data of the three systems were compared to a Vicon system using linear regression analyses and Bland-Altman-diagrams.

In the linear regression analyses the smallest coefficient of determination (R2.) in each exercise can be observed for PUSH Band 2.0. In the Bland-Altman diagrams the mean value of the differences in the average velocities is near zero for all systems and all exercises. PUSH Band 2.0 has the largest differences between the Limits of Agreement. For GymAware and Vmaxpro these differences are comparable.

Keywords

  • VELOCITY BASED TOOLS
  • VICON
  • PUSH
  • GYMAWARE
  • VMAXPRO
7 Articles
Open Access

Sports Information Systems: A systematic review

Published Online: 08 May 2021
Page range: 1 - 22

Abstract

Abstract

Many professional sport organizations are currently in the process of finding or already using sports information systems (SIS) to integrate data from different information and measurement systems. The problem is that requirements are very heterogeneous. That is why no consistent definition of SIS and their categories exist, and it is often not clear which fields and functions SIS must cover. This work aims to provide a structured comparison of commercial SIS available on the market to provide an overview of the relevant features and characterize categories. Following PRISMA guidelines, a systematic search for relevant SIS providers was conducted. A catalog of 164 review items was created to define relevant features of SIS and to conduct semi-standardized interviews with product representatives. Overall 36 eligible SIS from 11 countries were identified and 21 of them were interviewed. The analysis of the interviews has shown that there are features that are present in all SIS, whereas others differ or are generally less represented. As a result, different SIS categories have been defined. The study suggests a more differentiated categorization of SIS is necessary and terms need to be defined more precisely. This review should be considered when companies designing SIS or sport organizations select SIS.

Keywords

  • INFORMATION SYSTEMS IN SPORTS
  • SPORTS INFORMATION SYSTEMS
  • ATHLETE MANAGEMENT SYSTEMS
  • REVIEW
  • SPORTS INFORMATICS
Open Access

Comparing bottom-up and top-down ratings for individual soccer players

Published Online: 08 May 2021
Page range: 23 - 42

Abstract

Abstract

Correctly assessing the contributions of an individual player in a team sport is challenging. However, an ability to better evaluate each player can translate into improved team performance, through better recruitment or team selection decisions. Two main ideas have emerged for using data to evaluate players: Top-down ratings observe the performance of the team as a whole and then distribute credit for this performance onto the players involved. Bottom-up ratings assign a value to each action performed, and then evaluate a player based on the sum of values for actions performed by that player. This paper compares a variant of plus-minus ratings, which is a top-down rating, and a bottom-up rating based on valuing actions by estimating probabilities. The reliability of ratings is measured by whether similar ratings are produced when using different data sets, while the validity of ratings is evaluated through the quality of match outcome forecasts generated when the ratings are used as predictor variables. The results indicate that the plus-minus ratings perform better than the bottom-up ratings with respect to the reliability and validity measures chosen and that plus-minus ratings have certain advantages that may be difficult to replicate in bottom-up ratings.

Keywords

  • ASSOCIATION FOOTBALL
  • RANKING
  • PREDICTION
  • PLUS-MINUS
  • VALIDITY
Open Access

Strictness vs. flexibility: Simulation-based recognition of strategies and its success in soccer

Published Online: 08 May 2021
Page range: 43 - 54

Abstract

Abstract

Introduction: Recognition and optimization of strategies in sport games is difficult in particular in case of team games, where a number of players are acting “independently” of each other. One way to improve the situation is to cluster the teams into a small number of tactical groups and to analyze the interaction of those groups. The aim of the study is the evaluation of the applicability of SOCCER© simulation in professional soccer by analyzing and simulation of the tactical group interaction.

Methods: The players’ positions of tactical groups in soccer can be mapped to formation-patterns and then reflect strategic behaviour and interaction. Based on this information, Monte Carlo-Simulation allows for generating strategies, which – at least from the mathematical point of view – are optimal. In practice, behaviour can be orientated in those optimal strategies but normally is changing depending on the opponent team’s activities. Analyzing the game under the aspect of such simulated strategies revealed how strictly resp. flexible a team follows resp. varies strategic patterns.

Approach: A Simulation- and Validation-Study on the basis of 40 position data sets of the 2014/15 German Bundesliga has been conducted to analyze and to optimize such strategic team behaviour in professional soccer.

Results: The Validation-Study demonstrated the applicability of our tactical model. The results of the Simulation-Study revealed that offensive player groups need less tactical strictness in order to gain successful ball possession whereas defensive player groups need tactical strictness to do so.

Conclusion: The strategic behaviour could be recognized and served as basis for optimization analysis: offensive players should play with a more flexible tactical orientation to stay in possession of the ball, whereas defensive players should play with a more planned orientation in order to be successful. The strategic behaviour of tactical groups can be recognized and optimized using Monte Carlo-based analysis, proposing a new and innovative approach to quantify tactical performance in soccer.

Keywords

  • STRATEGY
  • KPI
  • OFFENCE DYNAMICS
  • BALL CONTACT
  • SPACE CONTROL
  • CORRELATION
  • EFFICIENCY
Open Access

Can Elite Australian Football Player’s Game Performance Be Predicted?

Published Online: 10 Aug 2021
Page range: 55 - 78

Abstract

Abstract

In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.

Keywords

  • PLAYER RATING
  • AUSTRALIAN FOOTBALL LEAGUE
  • MACHINE LEARNING
Open Access

Comparison of the Evaluation of Performance Preconditions in Tennis with the Use of Equal and Expertly Judged Criteria Weights

Published Online: 10 Aug 2021
Page range: 79 - 91

Abstract

Abstract

Tennis performance is influenced by various factors, among which physical performance factors play an important role. The aim of the study was an analysis of possibilities of the use of Saaty’s method for assessing the level of performance prerequisites and comparing the results obtained using equal weights and various weights. The research on Czech female players (U12; n = 211) was based on the results of the TENDIAG1 test battery (9 items) and the results were processed by FuzzME software and relevant statistical methods (correlation coefficient r, Student´s t-test, effect size index d). The results of Saaty’s method show that the most important athletic performance criteria for tennis coaches are the leg reaction time and the running speed, while the least important are endurance and strength. The evaluation using various criteria weights offers a finer scale for assessing athletes’ performance prerequisites despite the proven high degree of association between the results obtained with equal and various weights and the insignificant difference of mean values. The results have shown possibilities for the use of a fuzzy approach in sports practice and motivate further research towards broadening the structure or the number of evaluation criteria.

Keywords

  • FUZZY LOGIC
  • SAATY´S METHOD
  • FUZZME SOFTWARE
  • TENNIS PLAYERS
Open Access

Optimizing Team Sport Training With Multi-Objective Evolutionary Computation

Published Online: 25 Sep 2021
Page range: 92 - 105

Abstract

Abstract

This research introduces a new novel method for mathematically optimizing team sport training models to enhance two measures of athletic performance using an evolutionary computation based approach. A common training load model, consisting of daily training load prescriptions, was optimized using an evolutionary multi-objective algorithm to produce improvements in the mean match-day running intensity across a competitive season. The optimized training model was then compared to real-world observed training and performance data to assess the potential improvements in performance that could be achieved. The results demonstrated that it is possible to increase and maintain a stable level of match-day running performance across a competitive season whilst adhering to model-based and real-world constraints, using an intelligently optimized training design compared a to standard human design, across multiple performance criteria (BF+0 = 5651, BF+0 = 11803). This work demonstrates the value of evolutionary algorithms to design and optimize team sport training models and provides support staff with an effective decision support system to plan and prescribe optimal strategies to enhance in-season athlete performance.

Keywords

  • TRAINING LOAD
  • GENETIC ALGORITHMS
  • EVOLUTIONARY COMPUTATIONS
  • ARTIFICIAL INTELLIGENCE
Open Access

Validation of Velocity Measuring Devices in Velocity Based Strength Training

Published Online: 25 Sep 2021
Page range: 106 - 118

Abstract

Abstract

To control and monitor strength training with a barbell various systems are on the consumer market. They provide the user with information regarding velocity, acceleration and trajectory of the barbell. Some systems additionally calculate the 1-repetition-maximum (1RM) of exercises and use it to suggest individual intensities for future training. Three systems were tested: GymAware, PUSH Band 2.0 and Vmaxpro. The GymAware system bases on linear position transducers, PUSH Band 2.0 and Vmaxpro base on inertial measurement units. The aim of this paper was to determine the accuracy of the three systems with regard to the determination of the average velocity of each repetition of three barbell strength exercises (squat, barbell rowing, deadlift). The velocity data of the three systems were compared to a Vicon system using linear regression analyses and Bland-Altman-diagrams.

In the linear regression analyses the smallest coefficient of determination (R2.) in each exercise can be observed for PUSH Band 2.0. In the Bland-Altman diagrams the mean value of the differences in the average velocities is near zero for all systems and all exercises. PUSH Band 2.0 has the largest differences between the Limits of Agreement. For GymAware and Vmaxpro these differences are comparable.

Keywords

  • VELOCITY BASED TOOLS
  • VICON
  • PUSH
  • GYMAWARE
  • VMAXPRO

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