Andrew H. Hunter

Does individual quality mask the detection of performance trade-offs? A test using analyses of human physical performance

Why are performance trade-offs so rarely detected in animals when their underlying physiological basis seems so intuitive? One possibility is that individual variation in health, fitness, nutrition, development or genetics, or ‘individual quality’, makes some individuals better or worse performers across all motor tasks. If this is the case, then correcting for individual quality should reveal functional trade-offs that might otherwise be overlooked. We tested this idea by exploring trade-offs in maximum physical performance and motor skill function in semi-professional soccer players. We assessed individual performance across five maximum ‘athletic’ tasks providing independent measures of power, stamina and speed, as well as five soccer-specific ‘motor skill’ tasks providing independent measures of foot control. We expected to find functional trade-offs between pairs of traits (e.g. endurance versus power/speed tasks or specialist–generalist performance) – but only after correcting for individual quality. Analyses of standardised raw data found positive associations among several pairs of traits, but no evidence of performance trade-offs. Indeed, peak performance across a single athletic task (degree of specialisation) was positively associated with performance averaged across all other athletic tasks (generalist). However, after accounting for an individuals overall quality, several functional trade-offs became evident. Within our quality-corrected data, 1500 m-speed (endurance) was negatively associated with squat time (power), jump distance (power) and agility speed – reflecting the expected speed–endurance trade-off; and degree of specialisation was negatively associated with average performance across tasks. Taken together, our data support the idea that individual variation in general quality can mask the detection of performance trade-offs at the whole-animal level. These results highlight the possibility that studies may spuriously conclude certain functional trade-offs are unimportant or non-existent when analyses that account for variation in general quality may reveal their cryptic presence.

Multivariate analyses of individual variation in soccer skill as a tool for talent identification and development: utilising evolutionary theory in sports science

ABSTRACT The development of a comprehensive protocol for quantifying soccer-specific skill could markedly improve both talent identification and development. Surprisingly, most protocols for talent identification in soccer still focus on the more generic athletic attributes of team sports, such as speed, strength, agility and endurance, rather than on a player’s technical skills. We used a multivariate methodology borrowed from evolutionary analyses of adaptation to develop our quantitative assessment of individual soccer-specific skill. We tested the performance of 40 individual academy-level players in eight different soccer-specific tasks across an age range of 13–18 years old. We first quantified the repeatability of each skill performance then explored the effects of age on soccer-specific skill, correlations between each of the pairs of skill tasks independent of age, and finally developed an individual metric of overall skill performance that could be easily used by coaches. All of our measured traits were highly repeatable when assessed over a short period and we found that an individual’s overall skill – as well as their performance in their best task – was strongly positively correlated with age. Most importantly, our study established a simple but comprehensive methodology for assessing skill performance in soccer players, thus allowing coaches to rapidly assess the relative abilities of their players, identify promising youths and work on eliminating skill deficits in players.

Skill not athleticism predicts individual variation in match performance of soccer players

Just as evolutionary biologists endeavour to link phenotypes to fitness, sport scientists try to identify traits that determine athlete success. Both disciplines would benefit from collaboration, and to illustrate this, we used an analytical approach common to evolutionary biology to isolate the phenotypes that promote success in soccer, a complex activity of humans played in nearly every modern society. Using path analysis, we quantified the relationships among morphology, balance, skill, athleticism and performance of soccer players. We focused on performance in two complex motor activities: a simple game of soccer tennis (1 on 1), and a standard soccer match (11 on 11). In both contests, players with greater skill and balance were more likely to perform better. However, maximal athletic ability was not associated with success in a game. A social network analysis revealed that skill also predicted movement. The relationships between phenotypes and success during individual and team sports have potential implications for how selection acts on these phenotypes, in humans and other species, and thus should ultimately interest evolutionary biologists. Hence, we propose a field of evolutionary sports science that lies at the nexus of evolutionary biology and sports science. This would allow biologists to take advantage of the staggering quantity of data on performance in sporting events to answer evolutionary questions that are more difficult to answer for other species. In return, sports scientists could benefit from the theoretical framework developed to study natural selection in non-human species.

Modeling the two-dimensional accuracy of soccer kicks

In many sports, athletes perform motor tasks that simultaneously require both speed and accuracy for success, such as kicking a ball. Because of the biomechanical trade-off between speed and accuracy, athletes must balance these competing demands. Modelling the optimal compromise between speed and accuracy requires one to quantifyhow task speed affects the dispersion around a target, a level of experimental detail not previously addressed. Using soccer penalties as a system, we measured two-dimensional kicking error over a range of speeds, target heights, and kicking techniques. Twenty experienced soccer players executed a total of 8466 kicks at two targets (high and low). Players kicked with the side of their foot or the instep at ball speeds ranging from 40% to 100% of their maximum. The inaccuracy of kicks was measured in horizontal and vertical dimensions. For both horizontal and vertical inaccuracy, variance increased as a power function of speed, whose parameter values depended on the combination of kicking technique and target height. Kicking precision was greater when aiming at a low target compared to a high target. Side-foot kicks were more accurate than instep kicks. The centre of the dispersion of shots shifted as a function of speed. An analysis of the covariance between horizontal and vertical error revealed right-footed kickers tended to miss below and to the left of the target or above and to the right, while left-footed kickers tended along the reflected axis. Our analysis provides relationships needed to model the optimal strategy for penalty kickers.

Anticipating the Direction of Soccer Penalty Shots Depends on the Speed and Technique of the Kick

To succeed at a sport, athletes must manage the biomechanical trade-offs that constrain their performance. Here, we investigate a previously unknown trade-off in soccer: how the speed of a kick makes the outcome more predictable to an opponent. For this analysis, we focused on penalty kicks to build on previous models of factors that influence scoring. More than 700 participants completed an online survey, watching videos of penalty shots from the perspective of a goalkeeper. Participants (ranging in soccer playing experience from never played to professional) watched 60 penalty kicks, each of which was occluded at a particular moment (−0.4 s to 0.0 s) before the kicker contacted the ball. For each kick, participants had to predict shot direction toward the goal (left or right). As expected, predictions became more accurate as time of occlusion approached ball contact. However, the effect of occlusion was more pronounced when players kicked with the side of the foot than when they kicked with the top of the foot (instep). For side-foot kicks, the direction of shots was predicted more accurately for faster kicks, especially when a large portion of the kicker’s approach was presented. Given the trade-off between kicking speed and directional predictability, a penalty kicker might benefit from kicking below their maximal speed.

Behaviors of shooter and goalkeeper interact to determine the outcome of soccer penalties

During a soccer penalty, the shooters strategy and the goalkeepers strategy interact to determine the outcome. However, most models of penalty success overlook its interactive nature. Here, we quantified aspects of shooter and goalkeeper strategies that interact to influence the outcome of soccer penalties—namely, how the speed of the shot affects the goalkeepers leave time or shot‐blocking success, and the effectiveness of deceptive strategies. We competed 7 goalkeepers and 17 shooters in a series of penalty shoot‐out competitions with a total of 1278 shots taken. Each player was free to use any strategy within the rules of a penalty shot, and game‐like pressure was created via monetary incentive for goal‐scoring (or blocking). We found that faster shots lead to earlier leave times and were less likely blocked by goalkeepers, and—unlike most previous studies—that deceptive shooting strategies did not decrease the likelihood goalkeepers moved in the correct direction. To help identify optimal strategies for shooters and goalkeepers, we generated distributions and mathematical functions sport scientists can use to develop more comprehensive models of penalty success.

Supplementary material from "Skill not athleticism predicts individual variation in match performance of soccer players"

Just as evolutionary biologists endeavour to link phenotypes to fitness, sport scientists try to identify traits that determine athlete success. Both disciplines would benefit from collaboration, and to illustrate this, we used an analytical approach common to evolutionary biology to isolate the phenotypes that promote success in soccer, a complex activity of humans played in nearly every modern society. Using path analysis, we quantified the relationships among morphology, balance, skill, athleticism and performance of soccer players. We focused on performance in two complex motor activities: a simple game of soccer tennis (1 on 1), and a standard soccer match (11 on 11). In both contests, players with greater skill and balance were more likely to perform better. However, maximal athletic ability was not associated with success in a game. A social network analysis revealed that skill also predicted ball movement, as determined using social network analyses. The relationships between phenotypes and success during individual and team sports have potential implications for how selection acts on these phenotypes, in humans and other species, and thus should ultimately interest evolutionary biologists. Hence, we propose a field of evolutionary sports science that lies at the nexus of evolutionary biology and sports science. This would allow biologists to take advantage of the staggering quantity of data on performance in sporting events to answer evolutionary questions that are more difficult to answer for other species. In return, sports scientists could benefit from the theoretical framework developed to study natural selection in non-human species.