March 2025
Intermediate to advanced
352 pages
9h 30m
Portuguese (Portugal, Brazil)
Content preview from APIs práticas para IA e ciência de dados
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Capítulo 9. Usando APIs para análise de dados
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Os teus olhos vêem o jogo muito melhor do que os números. Mas os números vêem todos os jogos.
Dean Oliver, estatístico desportivo
O mundo do desporto adora todas as formas de análise de dados - tabelas, gráficos e estatísticas que descrevem os resultados de eventos ou prevêem o que vai acontecer a seguir. Quando um fã de desporto vê essas análises de dados, provavelmente nunca pensa na fonte de dados utilizada para as criar. Em muitos casos, a fonte de dados é uma API. Neste capítulo, aprenderás as práticas recomendadas para consumir APIs e criar produtos de análise de dados usando Jupyter Notebooks, uma ferramenta popular usada por cientistas de dados.
Métricas personalizadas para análise desportiva
Uma das formas mais célebres de análise é a métrica personalizada, um cálculo que resume comportamentos, capacidades e resultados complicados sob a forma de um número. Todos os desportos têm métricas a que os jogadores, treinadores, diretores e adeptos prestam atenção. O basebol tem a mais longa história de métricas, desde a histórica média de corridas ganhas (ERA) até às modernas corridas ponderadas criadas mais (wRC+) e vitórias contra substituição (WAR). Tanto os adeptos como os profissionais do futebol concentram-se nos objetivos esperados (xG), um método para definir remates de qualidade que motivou uma variedade ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.