Using Testosterone and Cortisol as Biomarker for Training Individualization in Elite Basketball

Schelling, Xavi; Calleja-González, Julio; Torres-Ronda, Lorena; Cepeda, Nicolás Terrados

doi:10.1519/jsc.0000000000000642

Cited by 32 publications

(28 citation statements)

References 34 publications

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“…However, the PL did not show the same tendency that the other variables did, possibly because its concept is more closely related to actions that are different from linear running or sprinting (i.e., changes of direction). Considering that the PL reflects changes in acceleration over time (a physical quantity known as "jerk") (Schelling et al, 2015) and not acceleration itself, it might also present some limitations as a descriptor of exercise intensity and, thus, does not share the tendency of other variables.…”

Section: Discussionmentioning

confidence: 99%

External Load Variations Between Medium- and Large-Sided Soccer Games: Ball Possession Games vs Regular Games with Small Goals

Clemente

Praça

Bredt

et al. 2019

Journal of Human Kinetics

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This study compared external load variations between 5 vs 5 and 10 vs 10 sided game formats played under two conditions: (i) a ball possession game with two floaters, and (ii) a regular game with goalkeepers and small goals. Twenty-two professional soccer players participated in this study: four central defenders, four wide defenders, nine central midfielders, three wide forwards, and three strikers. Total distance (TD), running distance (RD), sprinting distance (SD), number of sprints (NS), and player’s training load (PL) were recorded by GPS units. Within-format analyses revealed very likely large increases in TD (20.0%, [9.2; 31.9]; effect size (ES): 1.48, [0.71; 2.25]) and RD (130.9%, [20.2; 343.7]; ES: 1.32, [0.29; 2.35]) during the regular game when compared to the ball possession game in the 5 vs 5 format. In the 10 vs 10 format, large increases in TD (27.9%, [17.7; 39.1]; ES: 3.54, [2.34; 4.74]) and PL (27.4%, [12.6; 44.1]; ES: 2.46, [1.20; 3.72]) were observed in the regular condition when compared to the ball possession condition. Between-formats analyses revealed that, in the 10 vs 10 format, when compared to the 5 vs 5 format, RD was very likely larger (123.5%, [33.7; 273.7]), as was SD (195.8%, [20.5; 626.2]). However, very likely large decreases in PL were observed in the 10 vs 10 format (-19.6%; [-29.4; -8.3]) in the ball possession condition. Unclear differences were revealed based on variations in external load variables between formats in the regular condition. Smaller formats reduce the area available for running and sprinting and, thus, may be more adequate for increasing player’s training load (based on accelerometer data).

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Section: Discussionmentioning

confidence: 99%

External Load Variations Between Medium- and Large-Sided Soccer Games: Ball Possession Games vs Regular Games with Small Goals

Clemente

Praça

Bredt

et al. 2019

Journal of Human Kinetics

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“…Specifically in regards to athlete monitoring, a decrease of 30% has been attributed towards overtraining with a number of investigations reporting significant relationships between performance and the TC ratio [ 8 , 66 , 69 ]. A longitudinal study involving basketball athletes over four consecutive years concluded that an athlete’s hormonal status is linked with playing position, with power forwards and small forwards exhibiting the most catabolic state [ 70 ]. Overall, all players presented the most catabolic state in the final third of the regular season [ 70 ].…”

Section: Fatigue Monitoring Toolsmentioning

confidence: 99%

Monitoring and Managing Fatigue in Basketball

Edwards

Spiteri

Piggott

et al. 2018

Sports

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The sport of basketball exposes athletes to frequent high intensity movements including sprinting, jumping, accelerations, decelerations and changes of direction during training and competition which can lead to acute and accumulated chronic fatigue. Fatigue may affect the ability of the athlete to perform over the course of a lengthy season. The ability of practitioners to quantify the workload and subsequent fatigue in basketball athletes in order to monitor and manage fatigue levels may be beneficial in maintaining high levels of performance and preventing unfavorable physical and physiological training adaptations. There is currently limited research quantifying training or competition workload outside of time motion analysis in basketball. In addition, systematic research investigating methods to monitor and manage athlete fatigue in basketball throughout a season is scarce. To effectively optimize and maintain peak training and playing performance throughout a basketball season, potential workload and fatigue monitoring strategies need to be discussed.

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“…While each player responds individually to the stress of practice and competition [ 2 ], there remains a clear need to use updated sports performance models to inform starting points for player preparation. One of the most common methods of monitoring sports performance is using game-related statistics to evaluate technical and tactical behavior, as well as the efficiency of players and teams throughout the season.…”

Section: Introductionmentioning

confidence: 99%

Exploring Game Performance in the National Basketball Association Using Player Tracking Data

et al. 2015

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Recent player tracking technology provides new information about basketball game performance. The aim of this study was to (i) compare the game performances of all-star and non all-star basketball players from the National Basketball Association (NBA), and (ii) describe the different basketball game performance profiles based on the different game roles. Archival data were obtained from all 2013-2014 regular season games (n = 1230). The variables analyzed included the points per game, minutes played and the game actions recorded by the player tracking system. To accomplish the first aim, the performance per minute of play was analyzed using a descriptive discriminant analysis to identify which variables best predict the all-star and non all-star playing categories. The all-star players showed slower velocities in defense and performed better in elbow touches, defensive rebounds, close touches, close points and pull-up points, possibly due to optimized attention processes that are key for perceiving the required appropriate environmental information. The second aim was addressed using a k-means cluster analysis, with the aim of creating maximal different performance profile groupings. Afterwards, a descriptive discriminant analysis identified which variables best predict the different playing clusters. The results identified different playing profile of performers, particularly related to the game roles of scoring, passing, defensive and all-round game behavior. Coaching staffs may apply this information to different players, while accounting for individual differences and functional variability, to optimize practice planning and, consequently, the game performances of individuals and teams.

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Using Testosterone and Cortisol as Biomarker for Training Individualization in Elite Basketball

Cited by 32 publications

References 34 publications

External Load Variations Between Medium- and Large-Sided Soccer Games: Ball Possession Games vs Regular Games with Small Goals

External Load Variations Between Medium- and Large-Sided Soccer Games: Ball Possession Games vs Regular Games with Small Goals

Monitoring and Managing Fatigue in Basketball

Exploring Game Performance in the National Basketball Association Using Player Tracking Data

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