I have a dataset containing a hobby project of mine: http://pastebin.com/y540VP1U It is a table (space separated)
id track driver vehicle km_h standard_vehicle_km_h L25 LosSantosGP StarlaLeyba Verlierer 134.40 142.01 L26 LosSantosGP YolandeApodaca Verlierer 134.24 142.01 L27 LosSantosGP TeenaCuenca Omnis 126.36 137.93 L17 NODOGrapeseed StarlaLeyba Fusilade 157.55 135.81 L18 NODOGrapeseed JoellenSeneca 9F 166.07 145.40 ...
containing these fields:
id- Unique identifier for the row
track- The name of the racetrack driven on
driver- The name of the person who drove on the
vehicle- The name of the vehicle the
km_h- The average speed (kilometers/hour) of this
standard_vehicle_km_h- The average speed of one specific person (let's call him Broughy) on a reference track (namely CuttingCoronersGP) in the
Broughy drove every vehicle available on the reference track CuttingCoronersGP, and recorded the average speed for each vehicle. I am certain, that he is a) a very good driver, and b) his recorded speeds are consistent. In the dataset you'll also see (e.g. lines 65–91) that some people drove on the reference track "CuttingCoronersGP" as well.
What I am trying to find out is:
Predict the average speed of a person when he announces to drive vehicle X on track Y with reasonable confidence.
My thoughts until now (I'm pretty much a statistics layman, but an ok software developer):
I need a multiplier
t for the different tracks, since they can vary greatly in speed (lots of tight corners versus an oval "Indianapolis"-like track). I would need a couple of recordings of drivers to determine how much slower or faster the track is in comparison to the reference track CuttingCoronersGP.
I also need a multiplier
d for each driver, since some drivers are really fast, and some just began driving. I might need about 10 recordings of a person to be able to predict his multiplier. I may use the median of his recorded speeds (after discarding outliers).
Finally to predict the speed any driver would take on any given track in any vehicle would be
standard_vehicle_km_h * t * d
t seem pretty dependent on each other, I'm stuck.
Maybe someone can have a look at the dataset, play around a bit with it and has an idea how I am able to do a meaningful prediction for any driver on any track?