Frame by frame completion probability of an American football pass
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da USP |
Texto Completo: | https://www.teses.usp.br/teses/disponiveis/11/11134/tde-07042022-155700/ |
Resumo: | American football is an increasingly popular sport, with a growing audience in many countries in the world. The most watched American football league in the world is the United States’ National Football League (NFL), where every offensive play can be either a run or a pass, and in this dissertation, it is the pass that matters. Not all pass plays in the NFL are created equal, many factors can affect the probability of a pass completion, such as receiver separation from the nearest defender, distance from receiver to passer, offensive formation, game score, among many others. When predicting the completion probability 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 to calculate empirical probabilities and predict very accurately who the target will be. The big question is: how likely is it for a pass to be completed in an NFL match while the ball is in the air? We develop a machine learning algorithm to answer this based on the aforementioned predictors. Using data from the 2018 NFL season, we obtained conditional and marginal predictions for pass completion probability based on a random forest model. This is based on a two-stage procedure: firstly, we calculate the probability of each offensive player being the pass target, then conditional on the target, we predict completion probability based on the random forest model. Finally, the general completion probability can be calculated using the law of total probability. We present animations for selected plays and show the pass completion probability frame by frame. |
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Frame by frame completion probability of an American football passProbabilidade de completar um passe no futebol americano quadro por quadroAprendizado de máquinasMachine learningNational Football LeagueNational Football LeagueR SoftwareRandom forestRandom forestSoftware RAmerican football is an increasingly popular sport, with a growing audience in many countries in the world. The most watched American football league in the world is the United States’ National Football League (NFL), where every offensive play can be either a run or a pass, and in this dissertation, it is the pass that matters. Not all pass plays in the NFL are created equal, many factors can affect the probability of a pass completion, such as receiver separation from the nearest defender, distance from receiver to passer, offensive formation, game score, among many others. When predicting the completion probability 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 to calculate empirical probabilities and predict very accurately who the target will be. The big question is: how likely is it for a pass to be completed in an NFL match while the ball is in the air? We develop a machine learning algorithm to answer this based on the aforementioned predictors. Using data from the 2018 NFL season, we obtained conditional and marginal predictions for pass completion probability based on a random forest model. This is based on a two-stage procedure: firstly, we calculate the probability of each offensive player being the pass target, then conditional on the target, we predict completion probability based on the random forest model. Finally, the general completion probability can be calculated using the law of total probability. We present animations for selected plays and show the pass completion probability frame by frame.Futebol americano é um esporte cada vez mais popular, com uma audiência crescente em muitos países do mundo. Nos Estados Unidos existe a National Football League (NFL), onde toda jogada ofensiva pode ser uma corrida ou um passe, e nessa dissertação o interesse está nos passes. Nem todas as jogadas de passe são iguais, vários fatores podem influenciar a probabilidade de completar um passe, como separação do recebedor para o defensor mais próximo, distância entre o passador e o recebedor, formação ofensiva, placar do jogo e vários outros. Quando se tenta prever a probabilidade de completar um passe, é essencial saber quem é o alvo do passe. Usando medidas de distância entre os jogadores e a bola, é possível calcular probabilidades empíricas e prever com alta acurácia quem será o alvo. A grande questão é: quão provável é um passe ser completado em uma partida da NFL enquanto a bola está no ar? Foi desenvolvido um algoritmo de aprendizado de máquinas para responder a essa pergunta baseado nos fatores mencionados. Usando dados da temporada de 2018 da NFL, foram obtidas probabilidades condicionais e marginais de completar passes, baseadas em um modelo de floresta aleatoória. Foi feito um procedimento em dois estágios: primeiro, calcularam-se as probabilidades de cada jogador ofensivo ser o alvo do passe, depois, dado que o jogador é o alvo, é prevista a probabilidade do passe ser completado baseado no modelo de floresta aleatória. Por último, a probabilidade geral do passe ser completado pode ser calculada usando a lei da probabilidade total.Biblioteca Digitais de Teses e Dissertações da USPMoral, Rafael de AndradeSilva, Gustavo Pompeu da2022-03-08info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/11/11134/tde-07042022-155700/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2022-04-08T20:35:02Zoai:teses.usp.br:tde-07042022-155700Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212022-04-08T20:35:02Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false |
dc.title.none.fl_str_mv |
Frame by frame completion probability of an American football pass Probabilidade de completar um passe no futebol americano quadro por quadro |
title |
Frame by frame completion probability of an American football pass |
spellingShingle |
Frame by frame completion probability of an American football pass Silva, Gustavo Pompeu da Aprendizado de máquinas Machine learning National Football League National Football League R Software Random forest Random forest Software R |
title_short |
Frame by frame completion probability of an American football pass |
title_full |
Frame by frame completion probability of an American football pass |
title_fullStr |
Frame by frame completion probability of an American football pass |
title_full_unstemmed |
Frame by frame completion probability of an American football pass |
title_sort |
Frame by frame completion probability of an American football pass |
author |
Silva, Gustavo Pompeu da |
author_facet |
Silva, Gustavo Pompeu da |
author_role |
author |
dc.contributor.none.fl_str_mv |
Moral, Rafael de Andrade |
dc.contributor.author.fl_str_mv |
Silva, Gustavo Pompeu da |
dc.subject.por.fl_str_mv |
Aprendizado de máquinas Machine learning National Football League National Football League R Software Random forest Random forest Software R |
topic |
Aprendizado de máquinas Machine learning National Football League National Football League R Software Random forest Random forest Software R |
description |
American football is an increasingly popular sport, with a growing audience in many countries in the world. The most watched American football league in the world is the United States’ National Football League (NFL), where every offensive play can be either a run or a pass, and in this dissertation, it is the pass that matters. Not all pass plays in the NFL are created equal, many factors can affect the probability of a pass completion, such as receiver separation from the nearest defender, distance from receiver to passer, offensive formation, game score, among many others. When predicting the completion probability 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 to calculate empirical probabilities and predict very accurately who the target will be. The big question is: how likely is it for a pass to be completed in an NFL match while the ball is in the air? We develop a machine learning algorithm to answer this based on the aforementioned predictors. Using data from the 2018 NFL season, we obtained conditional and marginal predictions for pass completion probability based on a random forest model. This is based on a two-stage procedure: firstly, we calculate the probability of each offensive player being the pass target, then conditional on the target, we predict completion probability based on the random forest model. Finally, the general completion probability can be calculated using the law of total probability. We present animations for selected plays and show the pass completion probability frame by frame. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-03-08 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://www.teses.usp.br/teses/disponiveis/11/11134/tde-07042022-155700/ |
url |
https://www.teses.usp.br/teses/disponiveis/11/11134/tde-07042022-155700/ |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
|
dc.rights.driver.fl_str_mv |
Liberar o conteúdo para acesso público. info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Liberar o conteúdo para acesso público. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.coverage.none.fl_str_mv |
|
dc.publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
publisher.none.fl_str_mv |
Biblioteca Digitais de Teses e Dissertações da USP |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da USP instname:Universidade de São Paulo (USP) instacron:USP |
instname_str |
Universidade de São Paulo (USP) |
instacron_str |
USP |
institution |
USP |
reponame_str |
Biblioteca Digital de Teses e Dissertações da USP |
collection |
Biblioteca Digital de Teses e Dissertações da USP |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP) |
repository.mail.fl_str_mv |
virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br |
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1815256781485506560 |