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Success in the MLS SuperDraft: evaluating player characteristics and performance using mixed effects models

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  • Sean Hellingman

    (Thompson Rivers University)

  • Zilin Wang

    (Wilfrid Laurier University)

  • Mary Thompson

    (University of Waterloo)

Abstract

Drafting is a common way for many North American professional sports teams to obtain new players. The Major League Soccer (MLS) SuperDraft takes place prior to the start of each season to select valuable players. Being able to make well informed decisions surrounding draft selections is an important aspect of managing a team. This paper seeks to identify desirable characteristics of players drafted by MLS teams. Modelling the number of MLS games played and the probability of playing at least 30 MLS games, Cox proportional hazards models and mixed effects Logistic regression models were used to identify desirable characteristics and attempt to predict the success of future drafted players in MLS. The performances of the techniques have been evaluated and compared through 10-fold cross-validation. Results reveal significant player characteristics and multiple significant sources of variability during drafting. Furthermore, predictions were made for players who were selected in the 2018 and 2019 MLS SuperDrafts.

Suggested Citation

  • Sean Hellingman & Zilin Wang & Mary Thompson, 2025. "Success in the MLS SuperDraft: evaluating player characteristics and performance using mixed effects models," Computational Statistics, Springer, vol. 40(9), pages 5135-5161, December.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:9:d:10.1007_s00180-025-01601-w
    DOI: 10.1007/s00180-025-01601-w
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