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American football is an increasingly popular sport, with a growing audiencein many countries in the world. The most watched American football league inthe world is the United States' National Football League (NFL), where everyoffensive play can be either a run or a pass, and in this work we focus onpasses. Many factors can affect the probability of pass completion, such asreceiver separation from the nearest defender, distance from receiver topasser, offense formation, among many others. When predicting the completionprobability of a pass, it is essential to know who the target of the pass is.By using distance measures between players and the ball, it is possible tocalculate empirical probabilities and predict very accurately who the targetwill be. The big question is: how likely is it for a pass to be completed in anNFL match while the ball is in the air? We developed a machine learningalgorithm to answer this based on several predictors. Using data from the 2018NFL season, we obtained conditional and marginal predictions for passcompletion probability based on a random forest model. This is based on atwo-stage procedure: first, we calculate the probability of each offensiveplayer being the pass target, then, conditional on the target, we predictcompletion probability based on the random forest model. Finally, the generalcompletion probability can be calculated using the law of total probability. Wepresent animations for selected plays and show the pass completion probabilityevolution.

Frame by frame completion probability of an NFL pass