In basketball, each player’s skill level is the key to a team’s success or failure, the skill level is affected by many personal and environmental factors. A physics-informed AI statistics has become extremely impor...In basketball, each player’s skill level is the key to a team’s success or failure, the skill level is affected by many personal and environmental factors. A physics-informed AI statistics has become extremely important. In this article, a complex non-linear process is considered by taking into account the average points per game of each player, playing time, shooting percentage, and others. This physics-informed statistics is to construct a multiple linear regression model with physics-informed neural networks. Based on the official data provided by the American Basketball League, and combined with specific methods of R program analysis, the regression model affecting the player’s average points per game is verified, and the key factors affecting the player’s average points per game are finally elucidated. The paper provides a novel window for coaches to make meaningful in-game adjustments to team members.展开更多
目的探索广义线性模型(generalized linear model,GLM)在Python软件中的实现方法,并比较其与其他常用统计软件在算法过程和结果方面的异同。方法分别利用Python软件statsmodles库中的GLM函数、Logit和Poisson函数,R软件GLM函数,SAS的PRO...目的探索广义线性模型(generalized linear model,GLM)在Python软件中的实现方法,并比较其与其他常用统计软件在算法过程和结果方面的异同。方法分别利用Python软件statsmodles库中的GLM函数、Logit和Poisson函数,R软件GLM函数,SAS的PROC GENMOD过程步,对二项分布和泊松分布的数据集进行分析,比较三种软件的算法过程和分析结果。结果三种软件构建GLM的逻辑相似,但在代码实现和模型拟合方法等方面稍有区别,各软件的结果基本相同。结论Python软件可采用不同的算法构建广义线性模型,并且能提供与其他主流统计软件相同的统计分析结论。展开更多
文摘In basketball, each player’s skill level is the key to a team’s success or failure, the skill level is affected by many personal and environmental factors. A physics-informed AI statistics has become extremely important. In this article, a complex non-linear process is considered by taking into account the average points per game of each player, playing time, shooting percentage, and others. This physics-informed statistics is to construct a multiple linear regression model with physics-informed neural networks. Based on the official data provided by the American Basketball League, and combined with specific methods of R program analysis, the regression model affecting the player’s average points per game is verified, and the key factors affecting the player’s average points per game are finally elucidated. The paper provides a novel window for coaches to make meaningful in-game adjustments to team members.
文摘目的探索广义线性模型(generalized linear model,GLM)在Python软件中的实现方法,并比较其与其他常用统计软件在算法过程和结果方面的异同。方法分别利用Python软件statsmodles库中的GLM函数、Logit和Poisson函数,R软件GLM函数,SAS的PROC GENMOD过程步,对二项分布和泊松分布的数据集进行分析,比较三种软件的算法过程和分析结果。结果三种软件构建GLM的逻辑相似,但在代码实现和模型拟合方法等方面稍有区别,各软件的结果基本相同。结论Python软件可采用不同的算法构建广义线性模型,并且能提供与其他主流统计软件相同的统计分析结论。
