I am currently carrying out an investigation to find if certain factors such as playing home or away or position of a footballer affects overall pass completion using logistic regression. I am using R to compute my data. In my current section in which I am trying to analyse uses the data of every player to convey a general conclusion to whether or not the position of a player affects the successfulness of pass completion.
so far I have computed:
test.logit <- glm( cbind(Total.Successful.Passes.All,Total.Unsuccessful.Passes.All) ~ as.factor(Position.Id), data=passes.data, family = "binomial") summary(test.logit)
and my output was:
Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.28482 0.01256 22.67 <2e-16 as.factor(Position.Id)2 0.99768 0.01438 69.38 <2e-16 as.factor(Position.Id)4 1.06679 0.01398 76.29 <2e-16 as.factor(Position.Id)6 0.68090 0.01652 41.23 <2e-16 Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 32638 on 10269 degrees of freedom Residual deviance: 26499 on 10266 degrees of freedom AIC: 60422 Number of Fisher Scoring iterations: 4
the intercept is goalkeepers,position.Id 2 is for a defender, 4 = midfielder and 6 = striker
Is this a good set of results to come to a conclusion? and with the large deviances?