Player values vs salaries earned for each EPL club (2018/19)






































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Using Data to Analyse Team Formations

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I recently presented a research paper at the FC Barcelona Sports Analytics Summit on detecting and analysing team formations using tracking data (click here and scroll down to find the paper). I thought it would be nice to publish a shorter version on my blog. The work, which was awarded the "best research paper" prize at the conference, was done in collaboration with Mark Glickman at the Harvard Sports Analytics Lab. Too long? You can take a look at the poster version here , or the cartoon version here . Introduction A vital aspect of a football manager’s job is to select team formations – the spatial configuration of the players on the field. The choice of formation determines player roles, how they interact, and influences the playing style of both teams during a match. Despite their central role in team strategy, descriptions of formations are largely reliant on classifications based on the number of defenders, midfielders and forwards: crude summaries of pla
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Structure in football: putting formations into context

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This article was published as a chapter in the FC Barcelona Innovation Hub Football Analytics Guide, 2020. The Guide is freely available for download by filling out the online form here . Links to other work cited in this chapter are given at the end. Formations are the foundations of tactics in football. They provide structure to a team that helps the players to position themselves and broadly defines their specific roles in attack and defence. They are the means through which managers attempt to maintain control of high-value territory while denying their opponents the same.  The debate around formation tactics is as old as the game itself and is a central theme in the history of how the game is played. Innovations in formations have mirrored the evolving balance between defensive solidarity and attacking flair, discipline and freedom of expression and outcomes versus entertainment. From the early 2-3-5 ‘pyramid’ in the late 1800s to the more balanced W-M formation of Herbert Chapman
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From blogs to the BBC, the concept of expected goals  (or xG) has entered the mainstream media's lexicon. It has caught on because it's a useful concept; it's a useful concept because football is a low-scoring game. Chance (or luck) can be the difference between victory and defeat, a good day or an off-day. Expected goals, however, measures what would have happened on an average day. It's a simple quantity to measure. Shots are assigned a number between the 0 or 1: the proportion of similar shots (from the same position, for example) that have resulted in a goal. This number, sometimes referred to as  chance quality , is then added up over every shot taken by a team during a match (or a season) to calculate the number of goals that you would have 'expected' them to score. Conventional xG models do not take into account the identity of the player taking the shot. You need a dataset comprising many thousands of shots to properly measure chance qual
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