Newcastle United’s 2016/17 season has been a wildly successful one. The Magpies secured promotion to the Premier League - where most would agree a club of their stature belongs - after just one season in the Championship, and despite dips in form have never really Second only to high-flying Brighton, Rafa Benitez’s side have given fans reason to be jubilant after a torrid few years lingering near the bottom of England’s top flight.
As senior teams become data-driven entities, so too are youth teams, as clubs realize the value of homegrown talent in a hugely inflated transfer market. At all levels of a top club, player performance will be measured, and data-centric feedback will be given to athletes to further their professional development. John Fitzpatrick is a Sports Scientist at Newcastle United, and focuses on the dose-response relationship in training programs. At the Sports Analytics Innovation Summit in London this year, John presented on the dose-response relationship his team use to monitor training load in elite youth football players.
Ultimately, the dose-response is between fitness and fatigue. ‘We have here in the gold the training dose. From that, we get a large spike in fatigue, we get a small gain in fitness, and while that fatigue is larger than the fitness we get a drop in performance. As that rate of decay of them [decreases], performance increases. This forms the basis of all our periodization and tapering strategies.’ The important question, John says, is picking out which data points to use to measure if this theory actually applies, and how the team can assure that athletes reach peak performance on match day.
The first thing John and his team looked at is establishing which are the reliable measures of fatigue, and which are sensitive. ‘So we used a three-stage approach to this. First off we assessed test/re-test reliability. So, we conducted all tests on a Monday, after a weekend off, and then the following Monday. And we’re hoping to see very similar measures on all the tests.
‘After this, we then assess the fatigue response. So, how we did that is we tested every day throughout the week, and we monitored the response after our tough Tuesday training session. We saw the drop off in scores, and then the recovery later in the week. We then replicated that week, conducted exactly the same tests - we had a standardized training session… and hopefully the response we saw the previous week, we’d see again.’ This thoroughness returned three measurements the club could actually use to measure fatigue - Drop Jump RSI, the vertical data from the accelerometers, and mediolateral movements.
They then took these measures to their dose-response projection to see if those measures showed different responses under different training loads. What they found was that certain external training load measures [showed] large associations with measures of fatigue, that there are trivial differences between these associations when comparing arbitrary versus individualised speed thresholds, and that subjective wellness (fatigue, soreness & total wellness) and Drop Jump RSI Performance showed the most consistent dose-response relationship with measures of training load.
Finally, the team looked at the relationship between training load and the increase in fitness that they hope to achieve. They found that - with a very strong correlation - time spent above maximum aerobic speed had by far the biggest affect on fitness, both subjective and objective. ‘We can now, hopefully, because this relationship is a bit stronger, try and use this to predict that if we use target x amount of minutes above maximum aerobic speed in training we might see this improvement in fitness.’
It’s only because of the precision and thoroughness used by John and his team that they were able to reach a point where predictions could be made. From these predictions, training loads could be altered with a fair idea of the outcomes across the squad. The first study allowed them to put together a reliable weekly fatigue monitoring schedule, using only the measurements that proved correlative. It also allowed them to create a recovery report which detailed whether training loads were benefiting players or damaging them. If the latter was the case, the report gave an easy way for the club to open up the conversation with a player about their workload. Finally, and ultimately most importantly, the information and insight gathered allowed the team to predict the affect of particular training drills on the squads fatigue and fitness from past data. This means that coaches can alter a training session in advance based on projected fatigue statistics and proximity of training session to match day.