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With the increase in availability of so-called positional data, describing the positioning of players and ball at every moment of the game, our work aims to determine the difficulty of every pass by calculating its success probability based on its surrounding circumstances. As most experts will agree, not all passes are of equal difficulty, however, most traditional metrics count them as such. With our work we can quantify how well players can execute passes, assess their risk profile, and even compute completion probabilities for hypothetical passes by combining physical and machine learning models. Our model uses the first 0.4 seconds of a ball trajectory and the movement vectors of all players to predict the intended target of a pass with an accuracy of <jats:inline-formula><jats:alternatives><jats:tex-math>$$93.0\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>93.0<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> for successful and <jats:inline-formula><jats:alternatives><jats:tex-math>$$72.0\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>72.0<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> for unsuccessful passes much higher than any previously published work. Our extreme gradient boosting model can then quantify the likelihood of a successful pass completion towards the identified target with an area under the curve (AUC) of <jats:inline-formula><jats:alternatives><jats:tex-math>$$93.4\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>93.4<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Finally, we discuss several potential applications, like player scouting or evaluating pass decisions.<\/jats:p>","DOI":"10.1007\/s10618-021-00810-3","type":"journal-article","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T06:03:29Z","timestamp":1641276209000},"page":"295-317","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Expected passes"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3129-8359","authenticated-orcid":false,"given":"Gabriel","family":"Anzer","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8613-6635","authenticated-orcid":false,"given":"Pascal","family":"Bauer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"810_CR1","doi-asserted-by":"publisher","unstructured":"Andrienko G et al. (2017) Visual analysis of pressure in football. 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An ethics approval for wider research program using the respective data is authorized by the ethics committee of the Faculty of Economics and Social Sciences at the University of T\u00fcbingen. The data are property of the DFL e.V. \/ DFB e.V. and cannot be shared public. However, interested researchers can request samples of data under non-disclosure agreement constraints at the respective institutions. With the description of the respective tracking vendors and systems, peers working in the football industry can reproduce the results by using any kind of professional football data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics and Reproducability"}}]}}