This month’s article is all about analyzing your run data and removing any bad runs in your weather station so you can improve your ET predictions. Bad runs will affect your weather station’s ability to predict your ET accurately.
So what exactly is a bad run?
As a rule of thumb, as the density altitude (DA) increases, so should your 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:
| RUN | DENSITY ALTITUDE | ET |
| 1 | 1000 | 10.0 |
| 2 | 1500 | 10.025 |
| 3 | 200 | 10.05 |
All of these runs ‘fit’ together – as the altitude increases, so does the ET. Now, let’s suppose you enter another run (number 4) into your weather station:
| RUN | DENSITY ALTITUDE | ET |
| 1 | 1000 | 10.0 |
| 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 an example of a bad run.
How do bad runs affect ET prediction?
As explained in a previous article, Weather Stations 101, a drag racing weather station predicts ET by graphing all your entered runs and using statistical analysis to calculate what your ET should be at a particular density altitude. If you’re interested, you can investigate this process in more detail by looking at 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 density 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 were 1200 ft, the predicted ET would be 10.01.
Now let’s add the bad run into your weather station:
That single bad run has now affected your weather station’s 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 eliminate 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 to recognize bad runs before you enter them. When you realize it is a bad run, try and work out why; did you deep stage, was the track going off, or did you turn the tires?
If you do have bad runs in your weather station, use the “Show Bad Runs” function that is available in most quality weather stations. 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.
