via Forbes: Ever since the concept of Moneyball and the use of analytics crossed the Atlantic, football clubs have been trying to understand how they can gain an advantage over their competitors by maximising their use of data.
The appeal is simple. Crunching the numbers can provide insights that allow you to optimise your game plan and identify talent. Perhaps analytics will prove a player is undervalued or an ideal fit for a particular club, or it will show which players in the academy have the ability to make the step up.
In an industry where the prize money is huge and where recruiting a player can cost tens if not hundreds of millions of pounds, this insight can be significant.
GPS technology can track metrics like distance, acceleration and heart rate, and algorithms turn this into useful insight that can determine session intensity and minimise the risk of injury. After all, if you’re spending all that money on recruitment and wages, you need to get the most out of it.
“Think about how much Premier League football players earn, so them being out for nine months is a massive cost,” he says.
But its not just about protecting first team players, its about projecting which youth players can potentially make the grade in the first team. Combining training data with psychological and coaching data can create an accurate profile for a candidate.
“Prediction is the holy grail of sport science,” he says. “It could save us money by bringing a player through.”
But analytics in football is still at a relatively early stage and there are problems to iron out. The first is the need for a more centralised approach as predicting certain types of injury requires access to more data. A single football club is too small a sample as they might not suffer a certain type of injury or not enough of them.
“You’ve got to have the injuries to predict the models, so it might be up to governing bodies to encourage collaboration,” says Fitzpatrick. “You might get two or three friendly clubs who share data but small samples are an issue.”
Intriguingly, clubs do share information about players who might be transferred. This helps the selling team shift the player and the buying team to conduct due diligence. And of course, it will benefit both clubs in the future if this is standard practice.
“We try and share as much as we can because transfers are a two-way street,” he says. “But we might be selective and send the raw data without insights or algorithms.”
There are also conversations to be had about privacy. A lot of the data that clubs collect on players is sensitive and with the new EU General Data Protection Regulations (GDPR) giving citizens greater control over their personal information and the right for it to be deleted, it’s something football clubs will have to get to grips with.
Fitzpatrick recalls the example of a rugby player who wanted his former club to release the data it held on him so it could be used by his new team.
“[Privacy] is a growing area as we collect more data,” Fitzpatrick admits. “With underage players at the academy we have to work with parents to get consent. And with my PhD, it’s good to get ethical consent and make sure you’re not doing things that would cause harm.”
Under the stewardship of Rafa Benitez, Newcastle have avoided relegation in their first season back in the Premier League. Much of this can be attributed to his shrewd management given the lack of star names and top-flight experience in the side, but it will be interesting to learn how much of a role the club’s sport science team has been in the success.
Several years ago, Newcastle’s recruitment was spearheaded by chief scout Graham Carr, who won plaudits for his ability to identify talent like Yohan Cabaye and Hatem Ben Arfa for relatively low sums. However this golden touch dried up and Carr departed in 2017.
With Benitez already announcing his intention to add to his squad this summer, maybe he’ll give the sports science team a ring.
Source: Forbes | How Sports Science Helps Newcastle United Prevent Injury And Identify Academy Stars