- Thread starter luchins
- Start date
- Tags standard standardize

Depends on the purpose of your analysis. Feel free to provide more details. Standardizing isn't required in most models, beyond regularization models and centering interaction terms in regression.

Example I saw this discussion:

https://stats.stackexchange.com/que...players-service-point-win-percentage-which-of

This guy asked to stack exange:

(My personal question which I would ask to you, are

'' Hello, I am conducting a regression in order to predict a

Model 1 If my

If using logistic I would endeavor to build a model with serve win % as my Dependent Variable and my Indipendent Variable's as

..... Would this be a good model to use? Preliminary logistic regressions just involving serve win % regressed on surface + player ranking + opponent ranking ... are showing some strange results so im losing faith in logistic for this data.

An alternative I'm considering is to use raw variables in a linear regression type model with interactions.... Along the lines of Aiken & West 1991My dependent variable will be number of service points won in match, and my independent variables will be:

+ no. service points played in match + the surface the match played on

+ the player's ranking points +the opponents ranking points

+ an interaction between player and opponent ranking points

+ an interaction between surface and no. points played

+ average service points won in last n matches

+ average % of service points won in last m matches

Do either of these models stand out as smart or appropriate ways to model this data?

For context, for each player I have between 100-350 matches worth of data. I would love to hear what you guys think, or if you have any other suggestions on how to predict serve win % using the stated variables I would really appreciate it. I'm conducting this analysis in R so any code/package suggestions would also be great''