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© 2019 by SVEXA

Team Handball

Advising National Swedish Women’s Handball Team

Swimming Pool

Challenge: All decisions are made based on manual analysis of the athlete and there is a lack of tools to assist the coach in objectively analyzing the situation and give decision support.

Solution: Development of individual athlete profiles with associated algorithms for improved training decision support. Knowing the athlete's response patterns enable simulation of many different training plans and tapering models picking the most appropriate to execute.

Result: Correct individual profiles result in individually optimized training and predict performance metrics, such as chance of achieving personal best. Our first performance prediction algorithm was tested at the 2019 FINA World Championships and missed the exact times by on average only 0.30 %! Upcoming work includes the Swedish National team using these algorithms to optimize training and performance, initially for the winter season and thereafter for the 2020 Tokyo Olympics

Training Optimization for Elite Swimmers



Challenge: Injuries cause players to miss games and training, which is costly in terms of money, resources, and the team losing games. Prediction algorithms are needed to detect risks early and allow for adjustment in the training programs

Solution: Most teams collect large amounts of data about each player from several sources, such as GPS-tracking, heart rate variability, physiological and mobility screenings, subjective ratings, blood markers, and injury and medical records. These data are readily available and can be used to annotate injuries even at subclinical levels

Result: A more complete player profile, better training data for injury prediction and an algorithm that can be deployed across different teams and sports

Algorithm to Predict Injury from Retrospective Data in English Premier League


Chart & Stethoscope

Challenge: Illness is costly and categorizing them for better AI prediction algorithms is needed. Moreover, understanding the training context which could have contributed to the Illness e.g. high load, poor sleep, is needed

Solution: Multiple health and training data streams were used to predict illness at subclinical levels

Result: A more complete player profile with illness prediction that can be deployed across different teams and sports

Algorithm to Predict Illness from Retrospective Data in English Premier League



Challenge: The team lacked integrated physiological measurements 

Solution: Load and recovery estimation associated with actionable training recommendations

Result: World Junior Champions 2010, 2012

Strategic Consulting for the Swedish Women’s Handball Team


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Challenge: General heart rate zones that reflect intensity are not accurate across individuals. Training at a correct exercise intensity is crucial for appropriate training adaptations, avoiding "junk training" and minimizing the risk of overtraining and is core to physical activity guidelines

Solution: Measurable physiological and demographic parameters were used to build a model that accurately estimates individual heart rate zones that are associated with different physiological adaptations.

Result: From 2011 the individualized method of estimating heart rate zones was used to more precisely pinpoint the training effort needed to induce the appropriate training adaptations

New Algorithm For Deciding Heart Rate Zones and Thresholds


Sprint Runner

Challenge: Different running surfaces impact physiological parameters and maintaining a good running economy regardless of surface is a key performance indicator in successful trail- cross country runners and orienteers

Solution: Readily available data from chest straps was used to train an algorithm to detect road surface (tarmac, trail, gravel, off-trail)

Result: The algorithm is used to make more accurate energy expenditure and load estimates and can detect stride patterns associated with good running economy in different terrain types and at specific surfaces

Detection of Running Surface Using Accelerometry


Stock Market Down

Challenge: The conventional load algorithm is based on proportional increase in intensity which impacts accuracy

Solution: Previous research on individualized intensity zones was applied to the estimation of load with results validated against gold standard orthogonal methods

Result: From 2013 the more accurate and easy to deploy algorithm was applied to SVEXA Projects. 

New Data Driven Physiological Load Algorithm Based on Intensity Zones


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Challenge: Conventional endurance athlete training does not take advantage of individual integrated training and biological data  

Solution: Multiple non-conventional data streams were integrated to better understand physiology  

Result: From 2013 these insights were used to identify how different athletes tolerate tailored training intensities to maximize performance. The specific insight is that athletes with the same potential for performance can have dramatically variable response to different training regimens

Precision Profiles for Endurance Athletes Generate Training Insight


Marathon Runners

Challenge: Overtraining in endurance athletes prevents optimal performance and increases the chance of injuries

Solution: Integrated data analysis including objective, relative, subjective and metabolomic variables were used to predict gold standard estimates of overtraining

Result: A predictive algorithm that can identify the early signs of overtraining

Prediction of Overtraining in Endurance Athletes



Challenge: Physiological and mental stress are key elements in performance for esport athletes and there is a lack of training solutions that address this need

Solution: Deployment of Athlete Advisor with associated sensor monitoring, analysis and algorithms

Result: Tools to monitor and predict high stress periods and subsequently recommend adjusted training

Stress Management System For Esport Players


Running Athletic Women

Challenge: Measuring one variable, for example, VO2 max, does not give the full picture of a runner's performance level. This is standard practice

Solution: Integration of number of proprietary parameters into a model shows marked accuracy improvement

Result: Accurate prediction of running performance and subsequent training advice for future improvements

Predicting the Performance in Runners from Novice to Elite


Runner From Above

Challenge: There is limited actionable information on stride dynamics parameters as ground contact time, stride length and vertical oscillation as related to performance

Solution: More than 200 runners, ranging in ability from world-class runners to beginners, were recruited. Running economy was determined with indirect calorimetry together with accelerometry-based assessment of stride dynamics

Result: Running economy and stride dynamics needs to be adjusted for each runners body size, performance level and physiological profile. An algorithm based on these parameters was developed

Estimation of Stride Dynamics Parameters for Optimization of Running Economy