Data-Driven Recruitment Transforms Liverpool's Competitive Edge

Key Quotes

"his preference was julian brandt who was an attacking midfielder at leverkusen and hindsight bias is a terrible thing so people have said to me oh you compared brandt and mo today there's miles between the two but at the time they were among the best young forwards in europe not so much between them mo just fit our style much better we did rate him higher and he played in the right role so that sort of wide forward role brandt could play there he's more of an attacking midfielder and jürgen being german knew that market really well had his own network in germany giving really positive reports about brandt and we kind of agreed it's like yeah he's a great player but if you look at the price point it's higher the performance is lower and the ceiling for mo was also higher"

Dr. Ian Graham explains that while Julian Brandt was a highly-rated player, Mohamed Salah was a better fit for Liverpool's style and had a higher potential ceiling. This highlights how data analysis can identify players who align with a team's specific needs, even if they are not the most widely recognized talents. Graham emphasizes that the data suggested Salah's potential was greater than Brandt's, despite Brandt having strong personal recommendations.

"players go through an aging curve so -- start playing in your late teens early 20s performances typically aren't the highest level those performances improve with experience of playing against top level opposition typically till you're about 27 center backs skew older wingers skew a bit younger and then the performance -- sort of slowly declines and mo was just on a trajectory that said okay it was a a good kid scenario he could be one of the best players in the world so i think we rated him above premier league average at the age of 24 when he was playing in italy and he's just sort of list who are the under 24 wide forwards who were above average and not already playing for a champions league club and it was it was mo that was that was it it was quite unusual for that to happen and then the price point was low for two reasons roma were in financial distress so that market knowledge was really important and that's -- you know that's my colleagues at the club that's our analysis didn't say roma in financial distress and the other factor was he failed at chelsea"

Dr. Ian Graham details how data analysis of player aging curves indicated Mohamed Salah's trajectory suggested he could become one of the world's best players. Graham points out that Salah was rated above average for his age and position, and his low price was influenced by Roma's financial issues and his previous unsuccessful spell at Chelsea. This demonstrates how data can identify undervalued assets by looking beyond immediate perceptions and past failures.

"it's like okay he's failed at chelsea but this is the list of players that had similar performances to him at a similar age and a similar amount of experience they're all hits or they're 90 hits so you really it gives you something to weigh that chelsea failure against and also yeah it's just even digging into the chelsea failure is something i think i mean i don't know i think other people didn't do -- i think that the sort of i think not just in football but in life doing something different sets you up to fail and so the way to not fail and the way to sort of get your next job in football is kind of to do the same thing as everyone else is doing nobody gets sacked for signing an inaudible and signing mo was something you could get sacked for if it didn't work out"

Dr. Ian Graham explains that data provides an objective historical comparison for players who have experienced setbacks, such as Mohamed Salah's failure at Chelsea. Graham highlights that by comparing Salah's performance metrics to those of other players with similar profiles who became successful, the club could mitigate the perceived risk of his past failure. This illustrates how data can challenge conventional wisdom and provide a rational basis for making bold recruitment decisions.

"the question is you know the most important thing they're both world class at it both a huge upgrade on our current goalkeepers so the next question is well what else do they do and playing out from the from the back with jurgen with pep as well that becomes a more and more important thing and alisson has proven he could do it a similar sort of thing with minyole signing him from sunderland he'd been a brilliant shot stopper at sunderland started off really good at liverpool for his sort of first season or so but similarly with sunderland he'd never been sort of tested with his -- with the ball at his feet in the way that he was at liverpool so yeah his -- his footwork was the reason it was alisson the other reason of course is like when you look at analyzing performance it's not just about performance it's about how much that costs so you always want to get the biggest bang for your buck and that's not to say liverpool were cheap but if you're going to spend a pound i want to spend that pound in the most effective way possible and again poor old roma -- in summer 2017 we'd signed -- mo because they were in financial difficulties atletico weren't in financial difficulties roma were guess what the price is a lot lower from roma than it is from atletico"

Dr. Ian Graham discusses the selection of Alisson Becker over another top goalkeeper, highlighting that while both were world-class shot-stoppers, Alisson's ability to play out from the back was a crucial factor. Graham explains that this skill, along with the cost-effectiveness derived from Roma's financial situation, made Alisson the more advantageous signing. This demonstrates how data analysis extends beyond primary performance metrics to consider a player's suitability for a team's evolving tactical approach and financial considerations.

"there's this concept in football as in many areas of life of diminishing returns and so for every extra attacker you put on who wants to take a shot you know if you say coutinho takes five shots a game mo takes three shots a game you put them on the pitch together you're not going to get six you're going to get like five or five and a half because they take shots from each other so because our attack was so loaded there was a diminishing returns it was hard for us to put well i didn't have to do it thankfully it was hard for jurgen to put all four players -- out on the pitch at the same time whereas you know it was the opposite of diminishing returns like great defender it's even that is so interesting because you see teams struggling to win a game in the 93rd minute and they put two more strikers on they suddenly got seven attacking players on the pitch the pundits and the commentators go well they might score now they've got seven what an attacking team it is but of course it's not like that is it it's not like four times more attacking than it was 20 minutes ago"

Dr. Ian Graham explains the concept of diminishing returns in football, particularly with an overloaded attack, where adding more attackers does not proportionally increase scoring potential because