this post was submitted on 09 Aug 2024
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Football (Soccer fútbol fußball 足球 )
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We're over a decade into the era of advanced statistics in football. While we haven't cracked it completely yet the way baseball has, great strides have been made in data analytics. You're absolutely right: simple measurements of goals/assists are mostly meaningless and should not be seen as indicative of performances or abilities.
I wouldn't go as far as calling it "mostly meaningless", but it definitely carries an enhancement bias, in that the better players will tend to be placed on the same team, where they mutually enhance each others goal/assist stats.
From a recruiting perspective for instance, I would assume that it would be interesting to devise a statistic to indicate how much a player improves the team they're on, while somehow factoring out the effects of the other players.
At the same time of course, a lot (most?) of what makes a good team is not just the skills of the individual players, but how the different players utilise each other's strengths and cover each other's weaknesses.
Well, to start off goals and assists only even begin to measure one type of player - attackers. They do not represent the impact someone like Rodri or Toni Kroos has.
Even then, goals scored and assists created have proved a poor statistic in terms of predictive power - hence why so called "advanced statistics" began being developed. Expected Goals (or xG) was an early development attempting to measure the quality of chances created, for example. The field is much more advanced at this point however, with xG expanded into concepts like Expected Threat as early as 2018^[1] and Possession Value models ^[2]
Attempts to translate statistics such as "X Above Replacement" has been made, see this article by Dan Altman for instance ^[3]