Introducing Squad Score
Every football squad is made up of 5 categories of players First-team regulars who fit the system Squad players who offer reasonable cover Young players who may develop
Going beyond data scouting
One of the key questions about data scouting is how a player’s individual output is affected by the team they play in. It is unfair to expect a potentially highly skilled pas
About Us
Who are we? Tim Keech trained as an economist and worked in consultancy before founding MRKT Insights. He works with clubs and ownership groups looking to find competitive advantag
Finding the next Trent Alexander-Arnold with event-level data scouting.
Trent Alexander-Arnold has had a remarkable start to his career. His record of assisting goals is exceptional, yes it helps to be playing passes to Salah, Mané and Firmino,
The best development environment you have never considered
Every season football clubs need to consider the best way of developing the talent on their books. Do they keep the players at the club and get them games against players their own
Can an algorithm find you the perfect right back?
Imagine a league with 20 teams, each requires a right back and a pool of the best 20 right backs are gathered together, with the clubs, in a room. Each club submits it’s list
Player recruitment
MRKT Insights LLP are a football consultancy partnership specialising in technical scouting, recruitment, and analytics. We are actively recruiting for clients at several different
Scouting with old data
MRKT Insights is a new company that has drawn together data scientists, scouts, and analysts. We offer bespoke services to each client depending on their needs. The work can be ext
Tampa Bay Rowdies Announcement
MRKT Insights LLP are delighted to announce Tampa Bay Rowdies as our latest partners. We will be providing player recruitment services, using our in-house analytics and scouting te
Moneyball was never just about statistics
Billy Beane didn’t choose to use the sabermetric and data-driven processes we know as “Moneyball” because he loved statistics. He did it because sensible use of d
