Soccer analytics often follow one of two approaches: 1) regression models on number of shots taken or goals scored to predict match winners, or 2) spatial and/or temporal analysis of plays for evaluation of strategy. We propose a new model to evaluate skill importance in soccer. Play by play data were collected on 22 NCAA Division I Women’s Soccer matches with a new skill notation system. Using a Bayesian approach, we model play sequences as discrete absorbing Markov chains. Using posterior distributions, we estimate the probability of 35 distinct offensive skills leading to a shot during a single possession.
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