Professional Motorsport Data Analysis: Overlaying & Aligning Data
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Overlaying & Aligning Data
05.58
| 00:00 | - The last concept we need to explore before moving onto the bulk of this course is the overlay. |
| 00:06 | Overlays are used to directly compare two data sets by super imposing one over top the other. |
| 00:13 | This gives us an intuitive way to use an objective comparison and is one of the most useful features data analysis gives us. |
| 00:20 | We'll make a lot of use of overlays throughout this course and while there are dozens of great ways to utilise them, some of the most common comparisons found on a race engineer's screen are comparing one lap to another from the same logged data file, comparing two different drivers with the same setup or comparing two different setups from the same car. |
| 00:42 | The overlay can be made on any logged or math channel you have available and in most display types. |
| 00:48 | Here we have an example of an overlay of the speed trace of 2 different drivers. |
| 00:53 | Using an overlay of the speed trace is one of the primary tools we'll use as it's fundamental to what we're trying to optimise. |
| 01:00 | Covering the most distance in the minimum amount of time. |
| 01:04 | The differences in performance are easy to see but digging into the reasons for those differences is something we're going to be spending plenty of time on later in this course. |
| 01:14 | By placing the cursor in different areas of interest, we can quickly see the difference between two datasets. |
| 01:20 | Something that's extremely useful. |
| 01:22 | Here you can see the difference between the 2 data sets shown in the channel legend. |
| 01:27 | When using the time/distance plot with the horizontal axis set to distance, as seen here, this allows us to make a direct comparison between 2 data sets at the same position on track. |
| 01:38 | The distance is being calculated in the background of the data analysis software automatically and while there are a number of different algorithms that can be used for this, the basic calculation uses the velocity and the time to calculate the distance. |
| 01:54 | With that said, I do encourage you to dig into the options and help file of your chosen software to understand the exact method being used. |
| 02:03 | The velocity we're talking about in this case is the velocity of the vehicle. |
| 02:08 | This is an approximation regardless of the method being used. |
| 02:12 | Be it wheel speed, GPS or a combination of the 2. |
| 02:16 | We define this as an approximation because there are a number of sources of error like changes to the rolling radius of the tyre, different driving lines, wheel lock or slip, GPS measurement error or calibration error. |
| 02:29 | The end result of these errors means that the calculation of distance always has some degree of inconsistency. |
| 02:35 | The data analysis package and the settings you use will change how these errors are compensated for and displayed to the user. |
| 02:43 | But the good news is that this isn't a deal breaker. |
| 02:47 | It's just something we have to be aware of when comparing 2 sets of data against each other. |
| 02:53 | Over time as you become more familiar with the data of the car and the logging system, you'll get a feel for when and where to look out for this. |
| 03:02 | Let's use an example to gain a better understanding of the problem. |
| 03:05 | A common use case for a time/distance plot is looking at the difference in braking markers between 2 drivers. |
| 03:12 | When we use a distance plot without thinking about potential data alignment issues, we're making the assumption that we're comparing each car at the same point on track so if we use the delta function as we are in this example, we can say that driver A is braking around 10 metres later than driver B. |
| 03:31 | But what if there is some stretching in the distance data due to any of the areas we mentioned earlier. |
| 03:36 | There are 2 methods we can use to cross reference the distance channel and make sure we're comparing the cars at the same position on track. |
| 03:44 | Both of which rely on using the bumps and undulations in the circuit to our advantage. |
| 03:49 | First, we can use the logged vertical acceleration channel. |
| 03:53 | As the car moves along the track, the vertical acceleration channel produces a sort of signature or fingerprint of the track surface. |
| 04:01 | This allows us to see the common features in the data and use these to ensure the alignment is correct. |
| 04:06 | Alternatively we can also use one of the damper position channels, in a similar manner to using the vertical acceleration, we'll be able to pick up the features of the track. |
| 04:15 | Let's go back to our example and check if the braking points are really 10 metres apart. |
| 04:20 | As this car has both vertical acceleration and damper position logged, we can use both of these channels to make this point. |
| 04:29 | But in practice, you'll only need to use one of them. |
| 04:31 | Looking in the vicinity of the brake application in both the vertical acceleration and the damper position channels, you can see there are some common features that aren't lining up between the two data sets. |
| 04:43 | And to correct this, the analysis software generally has a function that allows the user to apply a manual offset between the 2 sets of data. |
| 04:51 | If we use this function here, you can see that as we manually offset the distance scale, we can get the features of the track to align. |
| 04:59 | Now that we've aligned the data manually, we can see that our braking point is essentially identical between the 2 drivers. |
| 05:06 | As we discussed in the data analysis fundamentals course, offsets in the beacon location, whether the beacon is being triggered by GPS or a physical lap beacon, will also give you offsets between your data sets that you can manage in a similar way to lap stretching. |
| 05:22 | In the real world, with most of the analysis we're doing, a lot of the time we don't need to worry about offsetting the data manually. |
| 05:30 | The built in methods of the data analysis software will generally handle everything just fine but it is worth keeping in mind and keeping an eye on as you can see how this can wreak havoc if it goes unnoticed. |
| 05:43 | It's also worth mentioning that these variances in stretching tend to become more of an issue at higher levels of the sport where the differences between 2 drivers, cars or setups, are generally much smaller. |
