To anyone who's watched soccer, it's obvious that all passes are not created equal. Some are routine. Some are exceptional. The usual simple statistic that divides the completed ones by the attempted ones is missing quite a lot of context. Last year, to help solve that problem, ASA debuted a passing efficiency model designed to take into account the difficulty of the pass, similar to how expected goals is developed. Over 300,000 passes from 2015 were used to build three different models, and this year those models were calibrated to match 2017 performance. Ted Knutson over at Statsbomb just revealed a similar model build on 20,000,000 passes from Opta's dataset, which calls into question whether or not our 300,000 sample size is sufficient, but alas, all the MLS passes in the history of MLS wouldn't reach a third of that larger sample, so here we are.

This year we've broken out the model by individual player, which makes things pretty interesting because you can see how different players take different levels of risk depending on which part of the field they are on. For example, Philadelphia Union right back Keegan Rosenberry has an expected pass completion percentage of 57.9 percent in his own defensive third. His main competitor Ray Gaddis has 67.7 percent in the same area. They both have actual completion percentages near their expected level. Gaddis makes higher percentage passes when controlling the ball in a defensive position. That may not tell you which player is more effective but it does indicate that Rosenberry is more likely to send the ball up field, while Ray is going to look for a closer teammate.

Here's a link to the table with the latest results, but it also has it's own tab on our menu, titled "Player xPassing". Thanks to the work of Kevin Minkus (@KevinMinkus) and Drew Olsen (@DrewJOlsen) these stats will be updated regularly, along with all our other statistics.