I am attempting to conduct a logistic regression for a tennis analytics project, endeavoring to predict the probability of a player winning a point in which he is the server. My response variable (service points) is binary in the sense that it can have only two outcomes for each observation - a success (service point win) or a failure (service point loss).
I have an issue with my data: For a given player, I have the point by point data for hundreds of matches. So take my data for R. Nadal as an example:
250 matches, each with about 70 dependent variable observations (service points). So for each match I currently have the two variables: Total_Service_Points_Played and Total_Service_Points_Won.
Eg - Match 1: Total_Service_Points_Played: 70 ; Total_Service_Points_Won: 47
So my data isn't in 1's and 0's. Is there a way I can implement a logistic regression with my dependent variable observations in their current form? Is there any simple transformation that comes to mind?
What springs to mind for me is to flesh out my match data into 1's and 0's. So following on from Match 1 above I would have: 47 1's followed by 26 0's . My data doesn't provide information as to what sequence these 1's and 0's arrived in, but since the depdendent variable observations are i.i.d this won't cause an issue? Correct me if I'm wrong please. Another issue posed by this technique would be the massive increase in my data - from 250 observations as a ratio (service point wins/service points played) to 250*70=17500 observations or more.
As a side note, the last thing I'm wondering is about the dispersion of my dependent variable data. Specifically, in the ratio of serve wins to total serve points as above, there exists no values < 0.2 or 20% .... In addition, there exists no value > 0.9 ..... Does this fit the bill for the (link=logit) argument? I know this relates to an S shape curve which is undefined at 0 and 1, but approaches both values.... I might be going off track here but is this something to be concerned about?