This month’s article is all about analysing your run data and determining if there are any bad runs entered into your weather station. Bad runs will affect your weather stations ability to accurately predict your ET.
So what exactly is a bad run?
As a rule of thumb, as altitude increases so should ET – the higher the altitude the slower you go. A bad run is any run that has a slower ET at a lower altitude than an existing run. Have a look at the following example runs entered into your weather station:
| Table 1 |
| Run |
Altitude |
ET |
| 1 |
1000 |
10.00 |
| 2 |
1500 |
10.025 |
| 3 |
2000 |
10.05 |
All of these runs ‘fit’ together – as the altitude goes up so does the ET. Now let’s suppose we enter another run (number 4) into your weather station:
| Table 2 |
| Run |
Altitude |
ET |
| 1 |
1000 |
10.00 |
| 2 |
1500 |
10.025 |
| 3 |
2000 |
10.05 |
| 4 |
1300 |
10.035 |
Now we have a problem. Run 4 does not make sense with the existing data as the ET is slower and the altitude higher than run 2. This is a classic example of a bad run entered into your weather station.
How do bad runs affect ET prediction?
As explained in a previous article, Weather Stations 101, a drag racing weather station predicts ET by internally graphing all your entered runs and using some statistical analysis to calculate what your ET will be given a specific altitude. For those of you so inclined, you can investigate this process in more detail by looking into regression analysis.
If we graph the runs from Table 1 we get the following:
From this example we can see there is a nice and simple correlation between altitude and ET as represented by the straight line in the graph. Note - this is purely an example and is unlikely to be this simple.
If you had this data entered into your weather station it would accurately predict your ET. For example, if the current altitude was 1200 ft, the predicted ET would be 10.01.
Now let’s add the bad run to your weather station:
That single bad run has now affected your weather stations ability to predict your ET. In this case it has had the effect of pulling the prediction line up which will in turn predict slower ETs, particularly as the altitude gets closer to 1000 ft. Have a look at the graph, see how the bad run acts like a magnet and pulls the prediction line up and to the left? This may be enough for you to lose the next round of racing.
How do you get rid of bad runs?
The first step to eliminating bad runs is to not put them into your weather station in the first place. If you keep track of your runs in a log book or use a spreadsheet it is fairly easy recognise bad runs before you enter them. If you realise it is a bad run try and work out why; did you deep stage, was the track going off did you turn the tyres?
If you do have bad runs in your weather station then make use of the “Show Bad Runs” function that is available in most quality weather stations (menu option 7 in the PerformAIRE Eclipse). This will give you a list of runs you should consider deleting from your weather station.
I say consider because I thoroughly recommend reviewing all your runs in a spreadsheet or on paper before you delete anything from your weather station. The more runs you enter, the more interconnected the data becomes and it may be difficult to determine which are bad runs.
Do you need some help?
As a service to BullsEye customers I am available to analyse your weather station data and provide you with recommendations on which runs to keep and which runs to delete. Simply send me your run data (run number, altitude, ET, wind factors etc.) to kym@bullseyeracing.com.au and I will graph the results so you can be sure you are getting the most out of your weather station.
Next month I will be looking into analysing time slips to help win more races. |
