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Replication Project-ish: Projecting MLS Performance based on MLS Next Pro Data
americansocceranalysis.com - Kieran Doyle
In 2022, Daniel Dinsdale and Joe Gallagher of Stats Perform (the artist formerly known as Opta) released an arXiv of a paper titled “Transfer Portal: Accurately Forecasting the Impact of a Player Transfer in Soccer”. The paper is a worthwhile read, but the idea can sort of be summed up as the three approaches.Â
- Identify how the player is currently doing on a per 90’ basis, for the metrics you care about.
- Identify how their team and league fits into the global hierarchy, in relation to other teams and leagues.
- For players with insufficient data, weight the average of their small data sample and some prior for the league/age/position etc.
Then take all those things, stick it in some neural networks, and try to predict the impact if that player moved from team A to team B. And it works! It’s a pretty good predictor of how players do when they move clubs, reducing mean squared error by 50% compared to assuming their performance translated over one-to-one. This is a cool result, and league translation/transfer projection is an extremely difficult task.Â