Passing the ball is one of the key skills of a football player yet the metrics commonly used to evaluate passing ability are crude and largely limited to various forms of a pass completion rate. These metrics can be misleading for two general reasons: they do not account for the difficulty of the attempted pass nor the various levels of uncertainty involved in empirical observations based on different numbers of passes per player. We address both these deficiencies by building a statistical model in which the success of a pass depends on the skill of the executing player as well as other factors including the origin and destination of the pass, the skill of his teammates and the opponents, and proxies for the defensive pressure put on the executing player as well as random chance. We fit the model by using data from the 2006-2007 season of the English Premier League provided by Opta, estimate each player's passing skill and make predictions for the next season. The model predictions considerably outperform a naive method of simply using the previous season's completion rate as a predictor of the following season's completion rate. In particular, we show how a change in the difficulty of passes attempted in both seasons explains a significant proportion of the shift in the observed performance of some players-a fact that is ignored if the raw completion rate is used to evaluate player skill.
Summary The paper presents a model that can be used to identify the goal scoring ability of footballers. By decomposing the scoring process into the generation of shots and the conversion of shots to goals, abilities can be estimated from two mixed effects models. We compare several versions of our model as a tool for predicting the number of goals that a player will score in the following season with that of a naive method whereby a player's goals‐per‐minute ratio is assumed to be constant from one season to the next. We find that our model outperforms the naive model and that this outperformance can be attributed, in some part, to the model's disaggregating a player's ability and chance that may have influenced his goal scoring statistic in the previous season.
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