Less money mo problems
When Juventus signed Cristiano Ronaldo, there probably wasn’t much for their directors to deliberate over. There are only a handful of strikers in world football that could improve their team, and even fewer who could expand their commercial potential. As one of the wealthiest clubs in the world, the search needn’t have been far and wide.
For the vast majority of clubs though, there are a huge number of players out there that could improve their team. Stade Rennais, for example, are the world’s 107th best team according to our World Super League. We estimate that are about 2500 players globally that they could realistically target that could improve their squad – many playing for better teams, but many of the best players from worse teams, too.
Stade Rennais, however, have a budget that is about eight times smaller than Juventus’. In other words, not only only do they have a much bigger pool to sift through, they have a much smaller budget with which to do it.
This is the reality for most clubs, and the challenge becomes bigger and bigger as you fall down football’s food chain. While Juventus had a clearly defined and small pool they can and should scout, Stade Rennais have an enormous and amorphous pool, only a fraction of which they might have good knowledge of from traditional scouting.
This is where data is incredibly useful, because it allows us to get an overview of thousands of players in an instant. Data should be treated with caution – it’s critical to look at the right data (our research on successful transfers drives our search and discover functionality in Acquisition) in order to increase our odds of ruling in suitable targets, and ruling out unsuitable targets. The wrong data – metrics that we think are important but are actually misleading – can only deepen our problems. With the right tools we can create much more manageable and appropriate shortlists that can be tackled with traditional scouting.
The onus is therefore on those with less money to innovate, to cut through the increased noise they have to deal with in a transfer window. Leveraging the power of data is one way to do this, to get to a position where you can begin to define the right pools to scout, and the right talent to acquire.