Á. Vales-Vázquez, C. Casal-López, P. Gómez-Rodríguez, H. Blanco-Pita
Abstract The purpose of this study was to compare the competitive profiles across the Spanish football leagues at the present time. The final standings (n=32) and results of the matches played (n=11,122) in the 2015/2016 season were analysed. Four categories of analysis were selected: Level of competitive balance of matches, Level of compactability of team standings, Magnitude of home-field advantage effect, and Degree of openness of the matches. Using statistical procedures for the comparison of means by analysis of variance (ANOVA) and the Chi-Squared test, it was concluded that in the panorama of Spanish football, the men's 2nd division stands out as the Championship that corresponds to a competitive profile with greater equality and that the women's 1st division presents the most unbalanced competitive profile (p < .05). A trend was also observed that indicated that the more professionalized Championships present a higher level of competitive balance of the matches, a higher level of compactability of the team standings, and a lower degree of openness of the matches with respect to the less professionalized Championships, due to the presence of statistically significant differences (p < .05) in the set of categories analysed.
{"title":"A comparison of competitive profiles across the Spanish football leagues","authors":"Á. Vales-Vázquez, C. Casal-López, P. Gómez-Rodríguez, H. Blanco-Pita","doi":"10.1515/ijcss-2017-0016","DOIUrl":"https://doi.org/10.1515/ijcss-2017-0016","url":null,"abstract":"Abstract The purpose of this study was to compare the competitive profiles across the Spanish football leagues at the present time. The final standings (n=32) and results of the matches played (n=11,122) in the 2015/2016 season were analysed. Four categories of analysis were selected: Level of competitive balance of matches, Level of compactability of team standings, Magnitude of home-field advantage effect, and Degree of openness of the matches. Using statistical procedures for the comparison of means by analysis of variance (ANOVA) and the Chi-Squared test, it was concluded that in the panorama of Spanish football, the men's 2nd division stands out as the Championship that corresponds to a competitive profile with greater equality and that the women's 1st division presents the most unbalanced competitive profile (p < .05). A trend was also observed that indicated that the more professionalized Championships present a higher level of competitive balance of the matches, a higher level of compactability of the team standings, and a lower degree of openness of the matches with respect to the less professionalized Championships, due to the presence of statistically significant differences (p < .05) in the set of categories analysed.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42236305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. Oh, L. E. Johnson, J. R. Olson, K. R. Shea, S. Braun
Abstract The purpose of this study was twofold: 1) to discover the differences in degree of energy expenditure (EE) during Just Dance 2015 using Xbox 360 Kinect, Wii-U, PS3 Move, and Control YouTube video; and 2) to uncover whether or not exergaming could elicit moderate to vigorous levels of intensity (≥ 40% Heart Rate Reserve (HRR)) based on heart rate average (HRavg) measurements. Twenty-five healthy college-aged students participated in this study. Data collection was comprised of baseline testing, a 30 second familiarization period with each gaming console, and a gaming session. Participants danced to the song “Love Me Again” on a Just Dance 2015 program on Xbox 360 Kinect, Wii-U, PS3 Move, and a control YouTube. EE and HRR were calculated using FT4 Polar Heart Rate Monitor. One-way repeated measures ANOVA indicated no significant differences in energy expenditure across the consoles, F(2.74, 65.86)=0.65, p=.570. The paired samples t-test indicated the HRavg for the Xbox 360 Kinect (117±18 bpm) was significantly greater than the HRavg for the Control (112±16 bpm), t(24)=3.03, p=.006. About a third (28%-36%) of participants met moderate levels of intensity while exergaming. Dancing on all three major gaming consoles and YouTube video increase energy expenditures and can be used as an alternative form of exercise with the ability to achieve moderate levels of intensity.
{"title":"Comparisons of Heart Rate and Energy Expenditure During Exergaming in College-age Adults","authors":"Y. Oh, L. E. Johnson, J. R. Olson, K. R. Shea, S. Braun","doi":"10.1515/ijcss-2017-0015","DOIUrl":"https://doi.org/10.1515/ijcss-2017-0015","url":null,"abstract":"Abstract The purpose of this study was twofold: 1) to discover the differences in degree of energy expenditure (EE) during Just Dance 2015 using Xbox 360 Kinect, Wii-U, PS3 Move, and Control YouTube video; and 2) to uncover whether or not exergaming could elicit moderate to vigorous levels of intensity (≥ 40% Heart Rate Reserve (HRR)) based on heart rate average (HRavg) measurements. Twenty-five healthy college-aged students participated in this study. Data collection was comprised of baseline testing, a 30 second familiarization period with each gaming console, and a gaming session. Participants danced to the song “Love Me Again” on a Just Dance 2015 program on Xbox 360 Kinect, Wii-U, PS3 Move, and a control YouTube. EE and HRR were calculated using FT4 Polar Heart Rate Monitor. One-way repeated measures ANOVA indicated no significant differences in energy expenditure across the consoles, F(2.74, 65.86)=0.65, p=.570. The paired samples t-test indicated the HRavg for the Xbox 360 Kinect (117±18 bpm) was significantly greater than the HRavg for the Control (112±16 bpm), t(24)=3.03, p=.006. About a third (28%-36%) of participants met moderate levels of intensity while exergaming. Dancing on all three major gaming consoles and YouTube video increase energy expenditures and can be used as an alternative form of exercise with the ability to achieve moderate levels of intensity.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42759775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Gerig, P. Wolf, R. Sigrist, R. Riener, G. Rauter
Abstract Robot-assisted training can be enhanced by using augmented feedback to support trainees during learning. Efficacy of augmented feedback is assumed to be dependent on the trainee's skill level and task characteristics. Thus, selecting the most efficient augmented feedback for individual subjects over the course of training is challenging. We present a general concept to automate feedback selection based on predicted performance improvement. As proof of concept, we applied our concept to trunkarm rowing. Using existing data, the assumption that improvement is skill level dependent was verified and a predictive linear mixed model was obtained. We used this model to automatically select feedback for new trainees. The observed improvements were used to adapt the prediction model to the individual subject. The prediction model did not over-fit and generalized to new subjects with this adaptation. Mainly, feedback was selected that showed the highest baseline to retention learning in previous studies. By this replication of our former best results we demonstrate that a simple decision rule based on improvement prediction has the potential to reasonably select feedback, or to provide a comprehensible suggestion to a human supervisor. To our knowledge, this is the first time an automated feedback selection has been realized in motor learning.
{"title":"Automated Feedback Selection for Robot-Assisted Training","authors":"N. Gerig, P. Wolf, R. Sigrist, R. Riener, G. Rauter","doi":"10.1515/ijcss-2017-0012","DOIUrl":"https://doi.org/10.1515/ijcss-2017-0012","url":null,"abstract":"Abstract Robot-assisted training can be enhanced by using augmented feedback to support trainees during learning. Efficacy of augmented feedback is assumed to be dependent on the trainee's skill level and task characteristics. Thus, selecting the most efficient augmented feedback for individual subjects over the course of training is challenging. We present a general concept to automate feedback selection based on predicted performance improvement. As proof of concept, we applied our concept to trunkarm rowing. Using existing data, the assumption that improvement is skill level dependent was verified and a predictive linear mixed model was obtained. We used this model to automatically select feedback for new trainees. The observed improvements were used to adapt the prediction model to the individual subject. The prediction model did not over-fit and generalized to new subjects with this adaptation. Mainly, feedback was selected that showed the highest baseline to retention learning in previous studies. By this replication of our former best results we demonstrate that a simple decision rule based on improvement prediction has the potential to reasonably select feedback, or to provide a comprehensible suggestion to a human supervisor. To our knowledge, this is the first time an automated feedback selection has been realized in motor learning.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijcss-2017-0012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44234650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Kvam and Sokol developed a successful logistic regression/Markov chain (LRMC) model for ranking college basketball teams part of Division I of the National Colligate Athletic Association (NCAA). In their 2006 publication, they illustrated that the LRMC model is one of the most successful ranking systems in predicting the outcome of the NCAA Division I Basketball Tournament. However, it cannot directly be extended to college football because of the lack of home-and-home matchups that LRMC exploits in performing its Logistic Regression. We present a common-opponents-based approach that allows us to perform a Logistic Regression and thus create a football LRMC (F-LRMC) model. This approach compares the margin of victory of home teams to their winning percentage in games played against common-opponents with the away team. Computational results show that F-LRMC is among the best of the many ranking systems tracked by Massey's College Football Ranking Composite.
{"title":"A Logistic Regression/Markov Chain Model for American College Football","authors":"Jason Kolbush, J. Sokol","doi":"10.1515/ijcss-2017-0014","DOIUrl":"https://doi.org/10.1515/ijcss-2017-0014","url":null,"abstract":"Abstract Kvam and Sokol developed a successful logistic regression/Markov chain (LRMC) model for ranking college basketball teams part of Division I of the National Colligate Athletic Association (NCAA). In their 2006 publication, they illustrated that the LRMC model is one of the most successful ranking systems in predicting the outcome of the NCAA Division I Basketball Tournament. However, it cannot directly be extended to college football because of the lack of home-and-home matchups that LRMC exploits in performing its Logistic Regression. We present a common-opponents-based approach that allows us to perform a Logistic Regression and thus create a football LRMC (F-LRMC) model. This approach compares the margin of victory of home teams to their winning percentage in games played against common-opponents with the away team. Computational results show that F-LRMC is among the best of the many ranking systems tracked by Massey's College Football Ranking Composite.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijcss-2017-0014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42081195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Aim of this study was to test whether a monoexponential formula is appropriate to analyze and predict individual responses to the change of load bouts online during training. Therefore, 234 heart rate (HR) data sets obtained from extensive interval protocols of four participants during a twelve-week training intervention on a bike ergometer were analyzed. First, HR for each interval was approximated using a monoexponential formula. HR at onset of exercise (HRstart), HR induced by load (HRsteady) and the slope of HR (c) were analyzed. Furthermore, a calculation routine incrementally predicted HRsteady using measured HR data after onset of exercise. Validity of original and approximated data sets were very high (r² =0.962, SD =0.025; Max =0.991, Min =0.702). HRstart was significantly different between all participants (one exception). HRsteady was similar in all participants. Parameter c was independent of the duration of intervention and intervals regarding one training session but was significantly different in all participants (one exception). Final HR was correctly predicted on average after 58.8 s (SD = 34.77, Max =150 s, Min =30 s) based on a difference criteria of less than 5 bpm. In 3 participants, HRsteady was predicted correctly in 142 out of 175 courses (81.1%).
{"title":"Predicting Short-Term HR Response to Varying Training Loads Using Exponential Equations","authors":"Katrin Hoffmann, J. Wiemeyer","doi":"10.1515/ijcss-2017-0011","DOIUrl":"https://doi.org/10.1515/ijcss-2017-0011","url":null,"abstract":"Abstract Aim of this study was to test whether a monoexponential formula is appropriate to analyze and predict individual responses to the change of load bouts online during training. Therefore, 234 heart rate (HR) data sets obtained from extensive interval protocols of four participants during a twelve-week training intervention on a bike ergometer were analyzed. First, HR for each interval was approximated using a monoexponential formula. HR at onset of exercise (HRstart), HR induced by load (HRsteady) and the slope of HR (c) were analyzed. Furthermore, a calculation routine incrementally predicted HRsteady using measured HR data after onset of exercise. Validity of original and approximated data sets were very high (r² =0.962, SD =0.025; Max =0.991, Min =0.702). HRstart was significantly different between all participants (one exception). HRsteady was similar in all participants. Parameter c was independent of the duration of intervention and intervals regarding one training session but was significantly different in all participants (one exception). Final HR was correctly predicted on average after 58.8 s (SD = 34.77, Max =150 s, Min =30 s) based on a difference criteria of less than 5 bpm. In 3 participants, HRsteady was predicted correctly in 142 out of 175 courses (81.1%).","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41939577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Kolossa, M. A. Azhar, C. Rasche, S. Endler, F. Hanakam, A. Ferrauti, M. Pfeiffer
Abstract Tracking and predicting the performance of athletes is of great interest, not only in training science but also, increasingly, for serious hobbyists. The increasing availability and use of smart watches and fitness trackers means that abundant data is becoming available, and the interest to optimally use this data for performance tracking and training optimization is great. One competitive model in this domain is the 3-time-constant fitness-fatigue model by Busso based on the model by Banister and colleagues. In the following, we will show that this model can be written equivalently as a linear, time-variant state-space model. With this understanding, it becomes clear that all methods for optimum tracking in statespace models are also directly applicable here. As an example, we show how a Kalman filter can be combined with the fitness-fatigue model in a mathematically consistent fashion. This gives us the opportunity to optimally consider measurements of performance to adapt the fitness and fatigue estimates in a datadriven manner. Results show that this approach is capable of clearly improving performance tracking and prediction over a range of different scenarios.
{"title":"Performance Estimation using the Fitness-Fatigue Model with Kalman Filter Feedback","authors":"D. Kolossa, M. A. Azhar, C. Rasche, S. Endler, F. Hanakam, A. Ferrauti, M. Pfeiffer","doi":"10.1515/ijcss-2017-0010","DOIUrl":"https://doi.org/10.1515/ijcss-2017-0010","url":null,"abstract":"Abstract Tracking and predicting the performance of athletes is of great interest, not only in training science but also, increasingly, for serious hobbyists. The increasing availability and use of smart watches and fitness trackers means that abundant data is becoming available, and the interest to optimally use this data for performance tracking and training optimization is great. One competitive model in this domain is the 3-time-constant fitness-fatigue model by Busso based on the model by Banister and colleagues. In the following, we will show that this model can be written equivalently as a linear, time-variant state-space model. With this understanding, it becomes clear that all methods for optimum tracking in statespace models are also directly applicable here. As an example, we show how a Kalman filter can be combined with the fitness-fatigue model in a mathematically consistent fashion. This gives us the opportunity to optimally consider measurements of performance to adapt the fitness and fatigue estimates in a datadriven manner. Results show that this approach is capable of clearly improving performance tracking and prediction over a range of different scenarios.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijcss-2017-0010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46320384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Two different computational approaches were used to predict Olympic distance triathlon race time of German male elite triathletes. Anthropometric measurements and two treadmill running tests to collect physiological variables were repeatedly conducted on eleven male elite triathletes between 2008 and 2012. After race time normalization, exploratory factor analysis (EFA), as a mathematical preselection method, followed by multiple linear regression (MLR) and dominance paired comparison (DPC), as a preselection method considering professional expertise, followed by nonlinear artificial neural network (ANN) were conducted to predict overall race time. Both computational approaches yielded two prediction models. MLR provided R² = 0.41 in case of anthropometric variables (predictive: pelvis width and shoulder width) and R² = 0.67 in case of physiological variables (predictive: maximum respiratory rate, running pace at 3-mmol·L-1 blood lactate and maximum blood lactate). ANNs using the five most important variables after DPC yielded R² = 0.43 in case of anthropometric variables and R² = 0.86 in case of physiological variables. The advantage of ANNs over MLRs was the possibility to take non-linear relationships into account. Overall, race time of male elite triathletes could be well predicted without interfering with individual training programs and season calendars.
{"title":"Predicting Elite Triathlon Performance: A Comparison of Multiple Regressions and Artificial Neural Networks","authors":"M. Hoffmann, T. Moeller, I. Seidel, T. Stein","doi":"10.1515/ijcss-2017-0009","DOIUrl":"https://doi.org/10.1515/ijcss-2017-0009","url":null,"abstract":"Abstract Two different computational approaches were used to predict Olympic distance triathlon race time of German male elite triathletes. Anthropometric measurements and two treadmill running tests to collect physiological variables were repeatedly conducted on eleven male elite triathletes between 2008 and 2012. After race time normalization, exploratory factor analysis (EFA), as a mathematical preselection method, followed by multiple linear regression (MLR) and dominance paired comparison (DPC), as a preselection method considering professional expertise, followed by nonlinear artificial neural network (ANN) were conducted to predict overall race time. Both computational approaches yielded two prediction models. MLR provided R² = 0.41 in case of anthropometric variables (predictive: pelvis width and shoulder width) and R² = 0.67 in case of physiological variables (predictive: maximum respiratory rate, running pace at 3-mmol·L-1 blood lactate and maximum blood lactate). ANNs using the five most important variables after DPC yielded R² = 0.43 in case of anthropometric variables and R² = 0.86 in case of physiological variables. The advantage of ANNs over MLRs was the possibility to take non-linear relationships into account. Overall, race time of male elite triathletes could be well predicted without interfering with individual training programs and season calendars.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijcss-2017-0009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47507801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Maximal oxygen uptake (VO2max) is one of the most distinguished parameters in endurance sports and plays an important role, for instance, in predicting endurance performance. Different models have been used to estimate VO2max or performance based on VO2max. These models can use linear or nonlinear approaches for modeling endurance performance. The aim of this study was to estimate VO2max in healthy adults based on the Queens College Step Test (QCST) as well as the Shuttle Run Test (SRT) and to use these values for linear and nonlinear models in order to predict the performance in a maximal 1000 m run (i.e. the speed in an incremental 4×1000 m Field Test (FT)). 53 female subjects participated in these three tests (QCST, SRT, FT). Maximal oxygen uptake values from QCST and SRT were used as (a) predictor variables in a multiple linear regression (MLR) model and as (b) input variables in a multilayer perceptron (MLP) after scaling in preprocessing. Model output was speed [km·h−1] in a maximal 1000 m run. Maximal oxygen uptake values estimated from QCST (40.8 ± 3.5 ml·kg−1·min−1) and SRT (46.7 ± 4.5 ml·kg−1·min−1) were significantly correlated (r = 0.38, p < 0.01) and maximal mean speed in the FT was 12.8 ± 1.6 km·h−1. Root mean squared error (RMSE) of the cross validated MLR model was 0.89 km·h−1 while it was 0.95 km·h−1 for MLP. Results showed that the accuracy of the applied MLP was comparable to the MLR, but did not outperform the linear approach.
{"title":"Linear and Nonlinear Prediction Models Show Comparable Precision for Maximal Mean Speed in a 4x1000 m Field Test","authors":"J. M. Jäger, J. Kurz, Hermann Müller","doi":"10.1515/ijcss-2017-0007","DOIUrl":"https://doi.org/10.1515/ijcss-2017-0007","url":null,"abstract":"Abstract Maximal oxygen uptake (VO2max) is one of the most distinguished parameters in endurance sports and plays an important role, for instance, in predicting endurance performance. Different models have been used to estimate VO2max or performance based on VO2max. These models can use linear or nonlinear approaches for modeling endurance performance. The aim of this study was to estimate VO2max in healthy adults based on the Queens College Step Test (QCST) as well as the Shuttle Run Test (SRT) and to use these values for linear and nonlinear models in order to predict the performance in a maximal 1000 m run (i.e. the speed in an incremental 4×1000 m Field Test (FT)). 53 female subjects participated in these three tests (QCST, SRT, FT). Maximal oxygen uptake values from QCST and SRT were used as (a) predictor variables in a multiple linear regression (MLR) model and as (b) input variables in a multilayer perceptron (MLP) after scaling in preprocessing. Model output was speed [km·h−1] in a maximal 1000 m run. Maximal oxygen uptake values estimated from QCST (40.8 ± 3.5 ml·kg−1·min−1) and SRT (46.7 ± 4.5 ml·kg−1·min−1) were significantly correlated (r = 0.38, p < 0.01) and maximal mean speed in the FT was 12.8 ± 1.6 km·h−1. Root mean squared error (RMSE) of the cross validated MLR model was 0.89 km·h−1 while it was 0.95 km·h−1 for MLP. Results showed that the accuracy of the applied MLP was comparable to the MLR, but did not outperform the linear approach.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49625382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Within road-cycling, the optimization of performance using mathematical models has primarily been performed in the individual time trial. Nevertheless, most races are 'mass-start' events in which many riders compete at the same time. In some special situations, e.g. breakaways from the peloton, the riders are forced to team up. To simulate those cooperative rides of two athletes, an extension of models and optimization approaches for individual time trials is presented. A slipstream model based on experimental data is provided to simulate the physical interaction between the two riders. In order to simulate real world behavior, a penalty for the difference in the exertion levels of the two riders is introduced. This means, that even though both riders aim to be as fast as possible as a group, neither of them should have an advantage over the other because of significantly different levels of fatigue during the ride. In our simulations, the advantage of cooperation of two equally trained athletes adds up to a time gain of about 10% compared to an individual ride.
{"title":"How to Stay Ahead of the Pack: Optimal Road Cycling Strategies for two Cooperating Riders","authors":"Stefan Wolf, D. Saupe","doi":"10.1515/ijcss-2017-0008","DOIUrl":"https://doi.org/10.1515/ijcss-2017-0008","url":null,"abstract":"Abstract Within road-cycling, the optimization of performance using mathematical models has primarily been performed in the individual time trial. Nevertheless, most races are 'mass-start' events in which many riders compete at the same time. In some special situations, e.g. breakaways from the peloton, the riders are forced to team up. To simulate those cooperative rides of two athletes, an extension of models and optimization approaches for individual time trials is presented. A slipstream model based on experimental data is provided to simulate the physical interaction between the two riders. In order to simulate real world behavior, a penalty for the difference in the exertion levels of the two riders is introduced. This means, that even though both riders aim to be as fast as possible as a group, neither of them should have an advantage over the other because of significantly different levels of fatigue during the ride. In our simulations, the advantage of cooperation of two equally trained athletes adds up to a time gain of about 10% compared to an individual ride.","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijcss-2017-0008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42673383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Analysing and predicting sports performance to optimise training and competition is a wide and complex field. To date, most methods heavily rely on the subjective experience of trainers and athletes. Nevertheless, objective mathematical methods and computer-based solutions have become increasingly popular over recent years and offer a wide range of research topics. This research is by nature interdisciplinary, involving sport and exercise science together with computational science and engineering. One major challenge of this research is handling the non-linear processes occurring in real-world settings. Additionally, most models are abstract and parameters cannot be measured directly. For instance, the capacity of individual energy stores within whole body physiological models can only be determined implicitly by external measurements. In designing training programs the major difficulty is the appropriate application of load and recovery phases to obtain an optimal adaptation process and reach peak performance. Unfortunately, to date there is limited research which has directly aimed to solve this problem using mathematical methods. In September 2016, a workshop, entitled Modeling in Endurance Sports, was held at the University of Konstanz, Germany. It aimed at mathematical, physiological, and computer science related approaches to analyse performance and physiological processes in endurance sports, such as running, cycling, rowing, skiing, and swimming. The topics addressed included data acquisition and visualisation, analysis and optimization of endurance training, modeling and simulation of performance, optimization of performance parameters, and modeling of physiological processes, including V ̇ O 2 kinetics, fatigue, and critical power. The workshop brought together experts, student researchers, and practitioners in sport science, exercise physiology, applied mathematics, and computer science. It was supported by the German Society of Sport Science (DVS) Section Sport Informatics, by the German National Science Foundation (DFG), and by the University of Konstanz. workshop topics,
{"title":"Editorial: Special Issue on Modeling in Endurance Sports","authors":"C. Abbiss, D. Saupe","doi":"10.1515/ijcss-2017-0006","DOIUrl":"https://doi.org/10.1515/ijcss-2017-0006","url":null,"abstract":"Analysing and predicting sports performance to optimise training and competition is a wide and complex field. To date, most methods heavily rely on the subjective experience of trainers and athletes. Nevertheless, objective mathematical methods and computer-based solutions have become increasingly popular over recent years and offer a wide range of research topics. This research is by nature interdisciplinary, involving sport and exercise science together with computational science and engineering. One major challenge of this research is handling the non-linear processes occurring in real-world settings. Additionally, most models are abstract and parameters cannot be measured directly. For instance, the capacity of individual energy stores within whole body physiological models can only be determined implicitly by external measurements. In designing training programs the major difficulty is the appropriate application of load and recovery phases to obtain an optimal adaptation process and reach peak performance. Unfortunately, to date there is limited research which has directly aimed to solve this problem using mathematical methods. In September 2016, a workshop, entitled Modeling in Endurance Sports, was held at the University of Konstanz, Germany. It aimed at mathematical, physiological, and computer science related approaches to analyse performance and physiological processes in endurance sports, such as running, cycling, rowing, skiing, and swimming. The topics addressed included data acquisition and visualisation, analysis and optimization of endurance training, modeling and simulation of performance, optimization of performance parameters, and modeling of physiological processes, including V ̇ O 2 kinetics, fatigue, and critical power. The workshop brought together experts, student researchers, and practitioners in sport science, exercise physiology, applied mathematics, and computer science. It was supported by the German Society of Sport Science (DVS) Section Sport Informatics, by the German National Science Foundation (DFG), and by the University of Konstanz. workshop topics,","PeriodicalId":38466,"journal":{"name":"International Journal of Computer Science in Sport","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43868175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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