Applied Sports Science newsletter – February 7, 2020

… Hegerberg had struggled with injuries as well as Bernd Schröder’s unforgiving methods in Potsdam and despite not getting onto the scoresheet in the 135(ish) minutes she featured against Lyon, the teenage Norwegian impressed. So much so in fact, that Lyon snapped up her signature at the end of the season. And it’s with Hegerberg in the side that Lyon have ascended to the unforgiving all-conquering team they are today, the Norwegian the missing piece to a puzzle that was barely incomplete.

In what can only be described as surprising news, the New York Yankees announced Wednesday afternoon that starting pitcher James Paxton would miss the next three-to-four months after undergoing a microscopic discectomy procedure to remove a cyst in his low back. According to the New York Post, Paxton had been dealing with back pain since the tail end of the 2019 season, though up until this point his symptoms were able to be controlled; after a flare up of his symptoms occurred within the last month and failure of corticosteroid injections to provide adequate pain relief, it was ultimately decided that Paxton would undergo surgery.

The job of a coach is to make decisions. It is choosing the best players at all times, preparing them well, and being able to manage the best way to play, to be successful. In order to complete this task, coaches are receiving more and more help and the field of science has started to take part in the decisions that are taken in the game. When finishing a training session or a match, players provide to the technical staff with data of all kinds. If they have used a GPS device, they will know what distance each player has covered during the session, at what speed, how frequently the technical-practical actions have taken place, the accelerations and decelerations, etc. If the club a sophisticated software, it will be able to collect the number of passes, throwings, the distance between players, each player’s heat map, etc. It is even possible to talk with the player about his perception of the effort exerted (fatigue) or his mood. All this data can help us make better decisions and that is why we are so interested to have this information.

However, in order for these reports to be really useful for the coaches and for the sports data scientist to fully bring in his expertise to the team, there are several conditions that should be fulfilled.

Wisconsin basketball strength and conditioning coach Erik Helland resigned Thursday after being placed on leave for allegedly using a racial epithet in front of Badgers players as he recounted a story from his days in the NBA.

The school said it had no evidence Helland had directed racially insensitive language toward any player amid speculation over the reason the Badgers’ second-leading scorer, Kobe King, recently announced plans to transfer.

… “Ryland looks after all the performance science for us,” said Foxall.

“Before the Leyton Orient game it was the first time for many years that we’d seen all the players training. There was an 11 v 11 with players on the side as well.

“A lot of that is down to the work that Ryland has done and the proposal that he and the manager brought to the board at the start of the season is now coming to fruition.

“We think it’s a good investment and it shows how we’re trying to progress the club.”

… Though popular belief holds that running on trails or softer surfaces is easier on the joints, well-established scientific evidence says otherwise. It turns out that the brain has its own version of a car’s road sensing suspension—something termed “muscle tuning.” While running, the brain constantly anticipates the stiffness of the surface—using data from past experience and information from the previous stride—and “tunes” how strongly the leg muscles contract before the foot hits the ground.

So when the trail gets softer, the leg becomes stiffer, leaving the net impact to the leg roughly the same. It’s how the body maintains the overall stiffness of the surface/shoe/leg combination and it’s the reason why running on softer surfaces doesn’t necessarily result in a lower rate of injury. The overall impact to the leg remains virtually the same whether running on trails, a beach or concrete.

Purpose: Endurance runners training with high-intensity intermittent exercise might experience damage to cardiac muscle. We have therefore compared changes of cardiac biomarkers after workload-matched intermittent and continuous exercise.

Methods: Twelve endurance runners [11 males, 1 female; means ± SD V.
O2max, 62.4 ± 5.4 ml kg–1 min–1; velocity of V.O2max (v V.O2max), 17.1 ± 1.4 km h–1] completed an intermittent and continuous exercise trial in random order. Intermittent exercise consisted of running at 90% vV.O2max for 2 min followed by 50% vV.O2max for 2 min, repeated for 92 min. Continuous exercise was performed at 70% vV.

O2max for 92 min. Blood samples were drawn before and 0, 2, 4, 24, and 48 h after exercise for assay of various cardiac biomarkers. Changes in concentration of biomarkers were averaged for the comparison of intermittent with continuous exercise after adjustment for baseline concentration and exercise intensity expressed as percent of heart-rate reserve (%HRR); magnitudes were assessed by standardization.

Results: There were moderate and large increases in high-sensitivity cardiac troponin-I and -T respectively following exercise. The differences between the increases adjusted to the mean intensity of 78 %HRR were trivial, but at 85 %HRR the increases for cardiac troponin-I and -T were moderately higher for intermittent compared with continuous exercise (factor difference, ×/÷90% confidence limits: 3.4, ×/÷1.9 and 2.1, ×/÷1.8 respectively). Differences in the changes in other cardiac biomarkers were trivial.

Conclusion: Prolonged intermittent exercise is potentially more damaging to cardiac muscle than continuous exercise of the same average running speed at higher average heart rates in endurance runners. [full text]

PURPOSE:

To quantify the sleep/wake behaviors of adolescent, female basketball players and to examine the impact of daily training load on sleep/wake behaviors during a 14-day training camp.
METHODS:

Elite, adolescent, female basketball players (N = 11) had their sleep/wake behaviors monitored using self-report sleep diaries and wrist-worn activity monitors during a 14-day training camp. Each day, players completed 1 to 5 training sessions (session duration: 114 [54] min). Training load was determined using the session rating of perceived exertion model in arbitrary units. Daily training loads were summated across sessions on each day and split into tertiles corresponding to low, moderate, and high training load categories, with rest days included as a separate category. Separate linear mixed models and effect size analyses were conducted to assess differences in sleep/wake behaviors among daily training load categories.
RESULTS:

Sleep onset and offset times were delayed (P < .05) on rest days compared with training days. Time in bed and total sleep time were longer (P < .05) on rest days compared with training days. Players did not obtain the recommended 8 to 10 hours of sleep per night on training days. A moderate increase in sleep efficiency was evident during days with high training loads compared with low. CONCLUSIONS:

Elite, adolescent, female basketball players did not consistently meet the sleep duration recommendations of 8 to 10 hours per night during a 14-day training camp. Rest days delayed sleep onset and offset times, resulting in longer sleep durations compared with training days. Sleep/wake behaviors were not impacted by variations in the training load administered to players.

German soccer’s Bundesliga has firmly established itself as one of the world’s top sports brands. By fully embracing the digital revolution the league has, in recent years, also emerged as soccer’s innovation leader. This is the result of a multi-faceted strategy incorporating on and off-field technology, ongoing research into consumer trends and investments in startups through its ‘DFL for Equity’ initiative.

These combined initiatives have led to the Bundesliga becoming the most engaged of all top European leagues across digital channels, as the league increased its fanbase by over 100 per cent over the last three seasons. Fuelling this growth is the league’s long-term strategy aimed at reaching more fans, more often, in more ways.

In the not-too-distant future, you’ll be able to walk into a Niketown store, buy a Tiger Woods-branded golf shirt and, when lining up your shot on the fairway, hear Woods’s voice gently suggesting a change in technique—say, a longer pause at the top of a long-iron shot.

That’s the vision of Steven Webster, the 46-year-old cofounder and CEO of Asensei, a San Francisco-based startup that wants to turn your clothes into a personal coach. Forget smartwatches such as Fitbit, which merely track your steps or monitor your heart rate. Thanks to a new generation of sensors that can be woven into fabric, smart apparel can precisely analyze the way you run—or critique your golf swing—using the same motion capture technology that is used in Hollywood.

… The most common kinds of musculoskeletal issues are overuse injuries. That’s where an individual does the same action repetitively, whether it’s shooting a basketball or sprinting toward the goal line.

“It creates a sort of micro-trauma on the body,” explains Ram Shalev, CEO of Physimax.

Since 2013, this Israeli startup has been using video capture and artificial intelligence to assess athletes’ physical abilities and endurance in real time. Israel’s Start-Up Nation cycling team is considering testing the system ahead of its participation in this June’s Tour de France.

Jason Kreis is well aware of the challenge he faces in getting his United States under-23 mean’s national team to this summer’s Olympics in Tokyo.

After all, the U.S. has not taken part in the Summer Games since way back in 2008, where a team led by future World Cup players such as Michael Bradley and Stuart Holden failed to make it past the group stage in Beijing.

“The way I view it is that it makes my job easier,” Kreis told Yahoo Sports in a phone interview, amid three-plus weeks of working with many of his players during the senior team’s annual January training camp. “We know that this isn’t easy. We know that there’s been failure before. So we should have a little extra energy, a little extra something to prove. I like that situation for us.”

The coach arrived in Kansas City grieving the death of a son, tasked with saving a franchise in unfathomable crisis. The loyal GM followed him and found the quarterback who would change everything. And on Sunday, with yet another come-from-behind victory, Andy Reid finally finished what he needed to do—lift the Lombardi Trophy—for everyone but himself.

… For years, the NFL was reluctant to embrace the more diverse and progressive offensive ideas that define the college game. But players such as Mahomes—who has a knack for defying the conventional wisdom as to how a quarterback should play football—have forced veteran professional coaches to incorporate those schemes in order to maximize talents. He makes throws across his body and on the run that have traditionally ended in disaster; he’s capable of jaw-dropping touchdown scrambles that culminate with almost balletic precision. His skill set is broader than that of any other quarterback in NFL history, and he proved that on Sunday night, when he emerged as the most powerful player. The Chiefs twice decided to run a play instead of punting or kicking field goals on fourth downs, which would have been unheard of a decade ago. They also harkened back to the 1940s on another play: a run near the goal line in which four players in the backfield spun around before the snap of the ball, as if launching into a coordinated dance routine. It was football’s past and future, melded into one entertaining package.

The NFL’s second Big Data Bowl asked participants to predict the outcome of running plays based on player tracking data at the time of a handoff. The winning team of Dmitry Gordeev and Philipp Singer—both data scientists from Austrian insurance company Uniqa—acknowledged they had “very little contact with American football” before this contest and turned to prior data modeling competitions for inspiration.

Gordeev and Singer, both 31, had won a previous challenge on the Google-owned Kaggle data science platform related to natural language processing that proved helpful, as did their review of a contest they didn’t enter on . . . um . . . [checks notes] . . . scalar coupling in molecular physics.

“Another one which we didn’t participate in but we kind of followed was predicting quantum bonds between atoms in complicated molecules,” says Gordeev, who studied applied math and data mining at Moscow State University, earning a specialist degree that’s akin to a master’s. “That was probably by far the closest one to the NFL—and I’m not kidding. I’m absolutely serious.”