Professional Motorsport Data Analysis: Throttle Histogram
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Throttle Histogram
03.46
| 00:00 | - It may seem strange to be looking at the throttle histogram in the cornering performance section but the connection between cornering performance and throttle use is strong. |
| 00:08 | Afterall, a lot of the cornering we do throughout a lap happens with at least some throttle applied. |
| 00:14 | Thinking back to the supporting concepts section of this course, we discussed the throttle histogram and how it can be utilised to break up the throttle usage into some discreet sections to characterise its application. |
| 00:27 | A histogram is useful because the statistics it provides summarises a lot of information in one display which allows us to interpret a lot of information very quickly. |
| 00:38 | The power of using statistical tools like a histogram comes from its ability to help point out which parts of the logged data we should be concentrating on. |
| 00:46 | These kinds of tools tell us about the trend of what's happening and we let this guide us to what should be dug into further in the actual raw log data. |
| 00:54 | The first thing to consider is the setup of the histogram. |
| 00:58 | Obviously the channel we're selecting to calculate the statistics of is the throttle position channel. |
| 01:04 | Regardless of the data analysis sofware we're using, we'll have options to vary the number of bins as well as whether the units are shown as a percentage or time. |
| 01:14 | The number of bins defines how coarsely we discretise the data and my preference is 10 bins which breaks the throttle percentage down into 10% increments. |
| 01:24 | In my experience, bins any smaller than this tend to be unnecessary. |
| 01:28 | Choosing display units of time shows us how many seconds are spent in each of the bins. |
| 01:34 | Whereas choosing units of percentage show us the percentage of the lap spent in each of the bins. |
| 01:40 | I prefer using percentage because it normalises the values to the lap time. |
| 01:43 | It's also worth noting that in most data analysis software the histogram will be zoom linked, meaning that the time window we're using the view the data determines the period over which the histogram statistics are being calculated. |
| 01:58 | In this example, you can see that I have a full lap shown in this time/distance plot. |
| 02:02 | Notice how if I zoom into a particular section, the histogram updates. |
| 02:08 | Most of the time we want to use the throttle histogram over a full lap. |
| 02:12 | Just be aware of the scope of data you've got selected. |
| 02:15 | Like most things in data analysis, the histogram is most meaningful when we use it for comparison between two different data sets. |
| 02:23 | Here in this example, we're looking at the throttle histogram of two drivers using the same car. |
| 02:29 | Looking at the 90-100% range, we can see that the green driver is spending significantly longer at full throttle over a full lap. |
| 02:37 | Looking through this middle area of the histogram, we can see that in most bins, the white driver is spending more time at partial throttle. |
| 02:45 | Looking at the 0-10% area, we can see that the white driver is spending a lot more time with very low or 0 throttle percentage. |
| 02:54 | Using these trends that the histogram has given us, let's understand what's happening by looking at the logged data. |
| 03:00 | Looking at the throttle trace throughout the lap, it's clear that the green driver is in general getting to full throttle earlier at the exit of each corner, which accounts for both the difference in the full and the part throttle. |
| 03:12 | We can also see that the white driver is lifting off the throttle on corner entry a lot earlier. |
| 03:17 | Which is what we saw in the low end of the histogram. |
| 03:20 | In general, the data with the higher proportion of the lap spent at full throttle is going to be the fastest. |
| 03:26 | This could be from having more grip, better corner rotation, better setup or just more driver confidence. |
| 03:33 | Regardless of why, using a histogram is a useful tool to quickly see and assess this metric and help point us towards what to dig deeper in in the logged data. |
