Nick Watney didn’t feel sick, and that was the problem.
He’d risen early and played the first round of the PGA Tour’s RBC Heritage on June 18 and was a bit worn out. But he went to bed that night feeling OK, and it wasn’t until he got up on Friday that he sensed something was very wrong. The device he was wearing on his wrist to monitor his breathing showed an alarming spike in the breaths per minute he was taking.
He’d been warned that might indicate he was suffering from COVID-19.
He’s special. Throughout his childhood in East Los Angeles, that was obvious to everyone, from his Mexican immigrant parents laboring at a meatpacking plant to the former players guiding his soccer career.
Efraín Álvarez started playing for youth national teams at 12 and signed his first professional contract at 15. Last year, at 16, he made his Major League Soccer debut as a late-game substitute and created two goals in a season-opening comeback victory for his hometown team, the Galaxy.
“He’s the best talent in MLS by far,” declared Zlatan Ibrahimovic, the world-class striker who was a teammate at the time.
Playmaker; Eran Amit, Shai Rosenblit, Amir Zviran, Moran Gad, Steve Barrett from
Practitioners and scientists have been investigating the use of different methods of training and analysis in order to improve their athletes performance, or reduce their risk of injury. Within a lab environment, alterations within biomechanical variables (gait analysis) have been associated with the predisposition of increased injury incidence.
One of these alterations is an increase in an individual’s ground contact time during the gait cycle, at the same velocity. Using Playermaker and the development of such metrics, it becomes possible to take the lab to the field and measure gait metrics in both environments. However, in order to assess changes within these variables, specific activities have been recommended in order to ascertain if players have experienced a change[1][2].
The purpose of this investigation was to examine the importance of strength and power in relation to key performance indicators (KPIs) within competitive soccer match play. This was achieved through using an experimental approach in which 15 subjects were recruited from a professional soccer club’s scholarship squad during the 2013/14 season. Following anthropometric measures, power and strength were assessed across a range of tests which included the squat jump (SJ), countermovement jump (CMJ), 20-m sprint, and arrowhead change of direction test. A predicted 1 repetition maximum (1RM) was also obtained for strength by performing a 3-RM test for both the back squat and bench press, and a total score of athleticism (TSA) was provided by summing Z-scores for all fitness tests together, providing 1 complete score for athleticism. Performance analysis data were collected during 16 matches for the following KPIs: passing, shooting, dribbling, tackling, and heading. Alongside this, data concerning player ball involvements (touches) were recorded. Results showed that there was a significant correlation (p ≤ 0.05) between CMJ (r = 0.80), SJ (r = 0.79), and TSA (r = 0.64) in relation to heading success. Similarly, a significant correlation (p ≤ 0.05) between predicted 1RM squat strength and tackle success (r = 0.61). These data support the notion that strength and power training are important to soccer performance, particularly when players are required to win duels of a physical nature. There were no other relationships found between the fitness data and the KPIs recorded during match play, which may indicate that other aspects of a player’s development such as technical skill, cognitive function, and sensory awareness are more important for soccer-specific performance.
Purpose: To examine differences in weekly load between the first (FT) and the under 19 team (U19) within a professional football setting. Methods: Data were collected in 11 FT and 9 U19 players during one season (2016-2017). FT data was divided into two week types, with (FT-M1) or without (FT-M0) a mid-week match. Indicators were total distance (TD) and distances covered at 12-15, 15-20, 20-25 and >25 km‧h−1. All indicators were analysed as weekly external load (m), intensity (m‧min−1) and load monotony (a.u.). Results: TD-based external load was higher for U19 compared to FT-M0 (very likely moderate; mean difference = 4180m; 90% compatibility limits ±1508m) and FT-M1 (likely large; 4684m; ±1320m). However, the difference in the higher velocity zones was substantially less (trivial to possibly small), with TD >25 km‧h−1 being lower than FT-M0 (very likely moderate; -118.3m; ±56.4m) and FT-M1 (likely small; -78.7m; ±61.6m). All external intensity indicators were lower for U19 (likely small to almost certainly large). External load monotony was higher compared to FT-M1 (possibly small to almost certainly very large). Compared to FT-M0, monotony was higher for TD (possibly very large; 0.25 a.u.; ±0.08 a.u.) and TD >25 km‧h−1 (possibly moderate; 0.19 a.u.; ±0.16 a.u.) but lower for TD 12-15 (possibly small; -0.06 a.u.; ±0.07 a.u.) and 15-20 km‧h−1 (likely moderate; -0.11 a.u.; ±0.06 a.u.). Conclusions: Despite higher weekly external loads at low velocity for elite youth players, external intensity and within-week load variation increase substantially when these players may transition to professional football.
The U.S. Soccer Development Academy has been shut down, with MLS’s new youth league one of several rising to fill its place on the player-development landscape. Several of the youth national teams have been shuttered for the remainder of the year as the coronavirus pandemic hits the federation’s budget and imposes lingering uncertainty on the entire sport.
None of that, though, will derail U.S. Soccer’s approach to scouting, the fed’s directors of player identification, Tony Lepore and Mirelle van Rijbroek, said in a recent discussion with MLSsoccer.com.
“We have to be adaptable and flexible, more than ever before,” said Lepore, who leads scouting on the boys’ side. “We’ve seen a lot of transition recently; there’s the pandemic, but also we have new national-team coaches, new president, new CEO, a lot of new leadership. And I think what’s important about the philosophy – we have a really sound mission as well – especially during these times of transition, it really is like an anchor that helps guide all of our work, our approach to scouting and talent ID.”
This study aimed to investigate the relationship between the acute to chronic workload ratio (ACWR), based upon participant session rating of perceived exertion (sRPE), using two models [(1) rolling averages (ACWRRA); and (2) exponentially weighted moving averages (ACWREWMA)] and the injury rate in young male team soccer players aged 17.1 ± 0.7 years during a competitive mesocycle. Twenty-two players were enrolled in this study and performed four training sessions per week with 2 days of recovery and 1 match day per week. During each training session and each weekly match, training time and sRPE were recorded. In addition, training impulse (TRIMP), monotony, and strain were subsequently calculated. The rate of injury was recorded for each soccer player over a period of 4 weeks (i.e., 28 days) using a daily questionnaire. The results showed that over the course of the study, the number of non-contact injuries was significantly higher than that for contact injuries (2.5 vs. 0.5, p = 0.01). There were also significant positive correlations between sRPE and training time (r = 0.411, p = 0.039), ACWRRA (r = 0.47, p = 0.049), and ACWREWMA (r = 0.51, p = 0.038). In addition, small-to-medium correlations were detected between ACWR and non-contact injury occurrence (ACWRRA, r = 0.31, p = 0.05; ACWREWMA, r = 0.53, p = 0.03). Explained variance (r 2) for non-contact injury was significantly greater using the ACWREWMA model (ranging between 21 and 52%) compared with ACWRRA (ranging between 17 and 39%). In conclusion, the results of this study showed that the ACWREWMA model is more sensitive than ACWRRA to identify non-contact injury occurrence in male team soccer players during a short period in the competitive season. [full text]
The hidden talents approach investigates social and cognitive abilities that are enhanced through adversity; this approach has led to new findings; however, it also comes with theoretical and methodological challenges.
Hidden talents may include the ability to detect and memorize threats, find creative solutions, understand other people, and deal with changing environments.
To date, some results support the hidden talents approach, others contradict it, and still others provide mixed evidence; thus, there is much scope for future research to advance knowledge.
The Sport Journal; imitrije Cabarkapa, Andrew C. Fry and Eric M. Mosier from
Basketball is one of the most popular international sports, but the current sport science literature does not directly address on-court performance such as force and power during a game. This case study examined the accuracy of a three-dimensional markerless motion capture system (3-D MCS) for determining the biomechanical characteristics of the basketball dunk. A former collegiate (NCAA Division-I) basketball player (age=26 yrs, height=2.08 m, weight=111.4 kg) performed 30 maximum effort dunks utilizing a two-hands, no-step, two-leg jumping approach. A uni-axial force plate (FP) positioned under a regulation basket sampled data at 1000 Hz. Additionally, a 3-D MCS composed of eight cameras placed 3.7 m high surrounding the recording area collected data at 50 Hz, from which ground reaction forces were derived using inverse dynamics. The dunks were analyzed by both systems for peak force and peak power. Peak force (X±SD) was similar (p<0.05) for both systems (FP= 2963.9±92.1 N, 3-D MCS= 3353.2±255.9 N), as was peak power (FP= 5943±323, 3-D MCS= 5931±700 W). Bland-Altman plots with 95% confidence intervals for both force and power indicated all measurements made with the 3-D MCS accurately assessed peak force and peak power during a basketball dunk as performed in the current study. These data provide strength and conditioning professionals with a better understanding of the magnitude of forces and powers that athletes experience during a basketball game, as well as validate use of a novel technology to monitor athletes’ progress and optimize overall athletic performance. [full text]
Instead of a pricey multi-camera markered motion capture lab with a full-time biomechanist to run it—we’ve been hard at work with our friends @DrMikeSonne
@proplayai
to bring that same lab to your iPhone and TRAQ.
Privacy has rarely been a luxury afforded to some of the best professional athletes in the world.
Many have to fight through swarms of press and paparazzi before sitting down to have a meal at a restaurant. Athletes are constantly under a microscope and deal with violations of their privacy repeatedly.
But what happens when these invasions of privacy might violate federal laws? For example, when an athlete’s independent medical history under the Health Insurance Portability and Accountability Act of 1996, better known as HIPAA, becomes public?
Chief Executive Peter Moore perhaps summed it up best when he said Liverpool had “merged analogue and digital” to become “the essence of a modern football club”.
In his words, they had combined science, structure and human qualities to become the best team in the country.
While covering the club for the last few years on TGG, we’ve also seen trends that have put the club ahead of the rest, helping Jurgen Klopp to deliver a first league title in 30 years.
In short, there are things they do better than anyone else which, when put together, have made them unassailable.
Collective behavior is a hallmark of every living system and utilizing methods from statistical physics (such as correlation functions) could aid in our understanding of their underlying rules. We analyzed five football (soccer) matches as this game provides a unique but yet mostly unexplored example to study a system of collective cooperation and competition. The aim of our study was to analyze the collective motion patterns exhibited by football players to unfold the underlying coordination among them in order to understand collective strategies associated with team performance. By analyzing pairwise relationships among all the players using spatio-temporal correlation functions we reveal that there exist identifiable collective dynamics that characterize winning and losing teams. Using our metric we find clear and robust differences between the players, indicating a difference in their behavior and their interactions. And this enables us to assign a unique behavioral pattern – a ‘fingerprint’ – for each individual and for each team. Furthermore, we reveal there exists a relationship between the market value of the players and the metrics introduced here, suggesting that these metrics could potentially serve as valuable performance indicators in the future, with applications ranging from talent identification to player scouting. In a broader context team sports could open up new directions for quantitative analyses of human collective behavior.