Revisiting the ‘Whys’ and ‘Hows’ of the Warm-Up: Are We Asking the Right Questions?

The warm-up is considered benefcial for increasing body temperature, stimulating the neuromuscular system and overall preparing the athletes for the demands of training sessions and competitions. Even when warm-up–derived benefts are slight and transient, they may still beneft preparedness for subsequent eforts. However, sports training and competition performance are highly afected by contextual factors (e.g., how is the opponent acting?), and it is not always clear what should be the preferred warm-up modalities, structure and load for each athlete and context. Further, we propose that the warm-up can also be used as a pedagogical and training moment. The warm-up may serve several different (albeit complementary) goals (e.g., rising body temperature, neuromuscular activation, attentional focus) and be performed under a plethora of different structures, modalities, and loads. The current commentary highlights the warm-up period as an opportunity to teach or improve certain skills or physical capacities, and not only as a preparation for the subsequent efforts. Moreover, the (justifed) call for individualized warm-ups would beneft from educating athletes about exploring different warm-up tasks and loads, providing a broad foundation for future individualization of the warm-up and for more active, engaged, and well-informed participation of the athletes in deciding their own warm-up practices

Predicting peek readiness-to-train of soccer players using long short-term memory recurrent neural networks

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern machine-learning methods has the potential to further fuel this process by deriving useful insights that are not easily observable in the raw data streams. This paper tackles the problem of deriving peaks in soccer players' ability to perform from subjective self-reported wellness data collected using the PMSys system. For this, we train a long short-term memory recurrent neural network model using data from two professional Norwegian soccer teams. We show that our model can predict performance peaks in most scenarios with a precision and recall of at least 90%. Equipped with such insight, coaches and trainers can better plan individual and team training sessions, and perhaps avoid over training and injuries

The variability of physical match demands in elite women's football

Peak locomotor demands are considered as key metrics for conditioning drills prescription and training monitoring. However, research in female football has focused on absolute values when reporting match demands, leading to sparse information being provided regarding the degrees of variability of such metrics. Thus, the aims of this study were to investigate the sources of variability of match physical performance parameters in female football players and to provide a framework for the interpretation of meaningful changes between matches. 54 female players from four top-level clubs were monitored during one season. GPS APEX (STATSports, Northern Ireland), with a sampling frequency of 10 Hz, were used in 60 official matches (n = 393) to determine the full-match and 1-min peak locomotor demands of total distance (TD), high-speed running distance (HSRD), sprint distance (SpD), accelerations and decelerations (Acc/Dec) and peak speed (Pspeed). For each variable, the between-team, between-match, between-position, between-player, and within-player variability was estimated using linear mixed-effect modelling. With exception to SpD (29.4 vs. 31.9%), all other metrics presented a higher observed match-to-match variability in the 1-min peaks than in the full-match (6.5 vs. 4.6%; 18.7% vs. 15.9%; 12.9 vs. 11.7%; for TD, HSRD and Acc/Dec, respectively). With the exception of SpD, higher changes in 1-min peaks than in full-match values are required to identify meaningful changes in each variable. Different sources of variability seem to impact differently the match physical performance of female football players. Furthermore, to identify meaningful changes, higher changes in 1-min peaks than in full-match values are required

Real-time Analysis of Physical Performance Parameters in Elite Soccer

Technology is having vast impact on the sports industry, and in particular soccer. All over the world, soccer teams are adapting digital information systems to quantify performance metrics. The goal is to assess strengths and weaknesses of individual players, training regimes, and play strategies; to improve performance and win games. However, most existing methods rely on post-game analytic. This limits coaches to review games in retrospect without any means to do changes during sessions. In collaboration with an elite soccer club, we have developed Metrix which is a computerized toolkit for coaches to perform realtime monitoring and analysis of the players' performance. Using sensor technology to track movement, performance parameters are instantly available to coaches through a mobile phone client. Metrix provides coaches with a toolkit to individualize training load to different playing positions on the field, or to the player himself. Our results show that Metrix is able to quantify player performance and propagate it to coaches in real-time during a match or practice, i.e., latency is below 100 ms on the field. In our initial user evaluation, the coaches express that this is a valuable asset in day-to-day work

Position specific physical performance and running intensity fluctuations in elite women’s football

The purpose of the present study was to investigate the physical performance of elite female football players during match play along with transient alterations in running performance following 1- and 5-min univariate peak periods. 54 elite female players from four top-level Norwegian teams were monitored for one season (n = 393 match observations), and physical performance data collected using STATSport GPS APEX. Results revealed significant differences in physical performance between the positions during full match play, particularly between wide and central players. Both full backs (FBs) and wide midfielders (WMs) covered more total distance (TD), high-speed running distance (HSRD), and sprint distance (SpD) than center backs (CBs) (p < 0.05–0.001), while WMs also covered more HSRD than both central midfielders (CMs) (p < 0.01) and forwards (FWs) (p < 0.05), and more acceleration -and deceleration distance (Accdist and Decdist) than both CBs and CMs (p < 0.01–0.001). A similar pattern was observed for the peak period analysis, with FBs and WMs covering more SpD in peak 1 min than CBs and CM (p < 0.001) and more SpD in peak 5-min than CBs, CMs, and FWs (p < 0.001). Irrespective of the variable analyzed, greater distances were covered during the peak 5-min period than in the next-5 and mean 5-min periods (p z: 0.07–0.20), decreases in distance covered were also observed for each variable following each univariate peak 5-min period. In conclusion, practitioners should account for differences in physical performance when developing training programs for female football players and be aware of transient reductions in physical performance following univariate peak 1- and 5-min periods. Specifically, the very high intensity in 1-min peak periods adds support to the principal of executing speed endurance activities during training to mirror and be prepared for the physical demands of match play

Position specific physical performance and running intensity fluctuations in elite women's football

The purpose of the present study was to investigate the physical performance of elite female football players during match play along with transient alterations in running performance following 1- and 5-min univariate peak periods. 54 elite female players from four top-level Norwegian teams were monitored for one season (n = 393 match observations), and physical performance data collected using STATSport GPS APEX. Results revealed significant differences in physical performance between the positions during full match play, particularly between wide and central players. Both full backs (FBs) and wide midfielders (WMs) covered more total distance (TD), high-speed running distance (HSRD), and sprint distance (SpD) than center backs (CBs) (p dist and Decdist ) than both CBs and CMs (p z : 0.07-0.20), decreases in distance covered were also observed for each variable following each univariate peak 5-min period. In conclusion, practitioners should account for differences in physical performance when developing training programs for female football players and be aware of transient reductions in physical performance following univariate peak 1- and 5-min periods. Specifically, the very high intensity in 1-min peak periods adds support to the principal of executing speed endurance activities during training to mirror and be prepared for the physical demands of match play. Keywords: global positioning system; peak periods; physical performance; women's football

Predicting peek readiness-to-train of soccer players using long short-term memory recurrent neural networks

We are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern machine-learning methods has the potential to further fuel this process by deriving useful insights that are not easily observable in the raw data streams. This paper tackles the problem of deriving peaks in soccer players' ability to perform from subjective self-reported wellness data collected using the PMSys system. For this, we train a long short-term memory recurrent neural network model using data from two professional Norwegian soccer teams. We show that our model can predict performance peaks in most scenarios with a precision and recall of at least 90%. Equipped with such insight, coaches and trainers can better plan individual and team training sessions, and perhaps avoid over training and injuries