April 2025
Intermediate to advanced
352 pages
9h 18m
Portuguese (Portugal, Brazil)
Content preview from Analítica de futebol com Python e R
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Capítulo 5. Modelos lineares generalizados: Porcentagem de conclusão em relação ao esperado
Este trabalho foi traduzido com recurso a IA. Agradecemos o teu feedback e comentários: translation-feedback@oreilly.com
Nos Capítulos 3 e 4, utilizaste a regressão simples e múltipla para ajustar os dados de jogo a jogo ao contexto da jogada. No caso dos portadores de bola, ajustaste a situação (como o down, a distância, as jardas a percorrer) para calibrar as estatísticas individuais dos jogadores ao nível da jogada e, mais tarde, ao nível da época. Esta abordagem pode claramente ser aplicada ao jogo de passes e, mais especificamente, aos quarterbacks. Como discutido no Capítulo 3, o quarterback do Minnesota Sam Bradford estabeleceu o recorde da NFL para a percentagem de conclusão sazonal em 2016, completando uns impressionantes 71,6% dos seus passes.
Bradford, no entanto, era apenas um quarterback mediano em termos de eficiência - medido em jardas por tentativa de passe, pontos esperados por tentativa de passe ou passes para touchdown. Os Vikings venceram apenas 7 dos seus 15 jogos naquele ano. A razão pela qual a percentagem de conclusão de Bradford era tão elevada era que ele tinha uma média de apenas 6,6 jardas em profundidade por alvo (37º na NFL, segundo a PFF). Em geral, os passes que são lançados a distâncias maiores são completados a uma taxa menor.
Para veres isto, vais criar a Figura 5-1 em Python ou a Figura 5-2 em R. Primeiro, carrega os dados. Depois, filtra as jogadas de ...
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