Introduction to Motorsport Data Analysis: Delta-t

Motorsport Data Analysis: Delta-t

“I’ve got motorsports data but I’m not sure how to interpret it.”

Motorsport data acquisition systems promise to make you faster but in practice, it is easy to get overwhelmed. Even completely put off.

Data is “easy” and “intuitive” if you have an engineering degree, you wrote the software or take a motorsports data course, but a confusing mess otherwise. This article will introduce you to one of the fundamental methods for analysis and explain how it is derived.

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More than an Expensive Lap Timer

Despite all its promise, your high-tech data logging kit can rapidly become no-more than an (extremely) expensive lap timer. I see this happen ALL the time and it is a real shame.

The thing is, without data logging in motorsports, you can only judge your racecar driving performance on lap times. Whilst clearly what racecar driving is all about (!) sadly, lap times alone are too crude a metric to use when you are looking to improve.

There is lots of great help on data logging, in books, seminars, online and even people at the track who are happy to talk you through “all things with data.”

The thing is, when you are back on your own, working through your own data, for many people, data can still feel like an opaque topic. Perhaps that is you?

The Channel Of Data To Get You Going…

So how would you feel if you could get started with just ONE channel of data?

A single chart that would show you all your racing gains (and losses πŸ™‚ ) around the track.

Presented in a super simple, yet meaningful way.

Ignoring all the other the data for now, imagine how good it would feel to know that this one chart would enable you to finally get going with data. To finally start to understand. To finally start to get some payback on that logger! πŸ™‚

What if this one chart was able to truly help you evaluate different race driving approaches and provide solid guidance on where to look for improvements?

If you knew…

  • what to expect from this data channel,
  • how it was derived and,
  • how it can help you …

perhaps (nee I hope!) that would give you more confidence when approaching your racecar data next time.

Amazingly this single channel does actually exist.

In fact, this it is so important, everyone calls it something else! (as if this wasn’t complicated enough πŸ™‚ )


Delta-t = Lap delta = TDiff = Time Slip … (etc)

It does not really matter what it is called.

This one channel of data gives you the objective information you need to (start too) understand (and improve πŸ™‚ ) your racecar and your racecar driving.

Armed with delta-t, “paddock opinion”, feel and lap times alone, become obsolete.

It is not perfect (I can hear engineers screaming at me for such a bold statement!), however, in motorsports data analysis, delta-t is where I feel you should start.

Buckle up. Here is how it works…

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The Racing Car: Think Inputs & Outputs

The racing car considering inputs and outputs - Motorsport data analysis delta-t

Start by thinking of the racing car as a system that has things you can control (inputs) and things that happen as a result (outputs).

The question for the racing driver is how do I operate these inputs to give the fastest lap time.

The way the racing driver can judge their performance is by looking at the outputs.

Imagine this chart: Distance verses Elapsed Time

Distance v Elapsed time motorsport

Along the bottom axis is distance travelled.

Along the vertical axis is elapsed time.

Distance is the length the racing car has travelled from the start-finish line on this lap.

Elapsed time is the time that has past since the racing car last crossed the start-finish line.

The diagonal line represents what the racing car did.

This is not normally a chart you will see but to understand how delta-t is derived it is important to follow this through.

Looking at what the racing car did, you can then find the lap time and how far it travelled around the lap.

Finding the Distance and Lap time

By reading along the bottom you can find the distance travelled on the lap:

Find distance motorsport

By reading back across, you can find the time taken to complete the lap.

Find lap time motorsport

Hopefully this is fairly simple so far.

But we want to compare laps so I’ll add another one:

Adding Another Lap: Which was faster?

Which was faster

I’ve now added another lap and you can see that this lap took less time.

By comparing the second lap time to the first, you can see that Lap 2 was faster.

Still all straight forward.

But what if we compared the difference earlier in the lap?

Finding the time difference at an earlier distance

Which was faster earlier in the lap

The difference in the elapsed time shows that the time taken for the second lap to get to 1km distance was less than the first lap.

Put another way:

At 1km, Lap 2 was faster than Lap 1, although the gap is less than for the full lap.

Easy but important – for the first time you can see which was faster part way round the lap.

Taking that on a step further then.

What if you were to find the time differences at 1000s of distance points?

Finding the time difference at 1000s of distance points

Finding time delta for 1000s of distance points

By finding the time difference at every measured distance point, you can find out the time gap throughout the whole lap – not just at discrete distances like 1km or the end of the lap.

This is delta-t.

Technical Note:

Depending on your data logging system distance is measured more or less frequently. A system that uses a wheel speed sensor might measure the distance a few hundred times per second. Systems that use GPS data might measure data between 5 or 20 times per second.

The wheel speed systems have the advantage of higher fidelity but are often an additional sensor to fit, plus are susceptible to errors from wheel locking. GPS systems are not affected by wheel locking but can be slow and suffer issues depending on the number, position and visibility of their satellites.

It is also worth noting that the distance driven will be slightly different each lap – as the driver tries different lines or when they make a mistake. There are ways to work around this but it will affect the comparison to varying degrees so it is worth considering if you get strange values.

Whatever the technology used, Delta-t calculations gives 1000s of data points so you can put Delta-t into its own chart:

Motorsport Data Analysis Delta-T Chart

Delta t in its own chart 1

Note that the vertical axis is now Delta-t in seconds.

What the chart enables you to do is find the precise time difference between the two laps at any distance around the track.

Just to reinforce this:

  • Lap time alone enables you to compare the time difference once per lap
  • Delta-t enables you to compare the time difference practically continuously

Much more useful that lap times alone.

In reality…

In reality the lines for distance verses elapsed time are never straight like in my example.

This is because the racing car is constantly changing speed.

Therefore if you plotted distance verses elapsed time for the two laps it is more likely to look like this:

In reality the lines are not straight

In the example above, you can see that at times the lines are closer, and at other times further apart. They even cross a couple of times.

This means that sometimes Lap 1 was faster and sometimes Lap 2 was faster.

This information is exactly what we are after then – to know precisely where one lap gained in lap time and another lost out.

Plotting the delta-t chart you can see more clearly:

Delta-t shows gains and losses in detail

Delta t on its own

Walking through from the start of the lap on the left and looking just at the lower delta-t chart you can see that as the lap progresses:

  1. Lap 2 was initially ahead
  2. Then Lap 1 gained back a lot and became faster
  3. Then near the end, Lap 2 regained its advantage to finish the fastest overall

Hopefully that makes sense?

In practice, it can be a bit confusing sometimes which lap is faster going up and which is faster going down. Once you understand that though you instantly gain clarity on how they compare.

It is amazing really, as from this one channel, without any other data or video, you can quickly develop an understanding of a drivers confidence, consistency and progression during a session. You also know exactly where to look and how to quantify what you find.

It can be a revelation!

Next Steps

Armed with this new knowledge, I suggest you try doing this for yourself.

If you have your own data great, but even if do not have any data of your own yet that is not an issue. Most of the data logger manufacturers allow free downloads of their analysis software. These also typically come with demo data for you to experiment with.

I recommend trying out AIM Studio as this has a good range of data samples. You can get the software here.

I’ve put together a few slides taking you through an AIM Studio example in the handout slides to accompany this article. This includes more details on how to interpret the chart. The hand out is free for newsletter subscribers (who also get instant access to several other useful resources like spreadsheets, courses and videos) so if this is something you’re interested in having a look at, just sign up on the form below.

Delta t in AIM Studio worked

Levelling up

The next step is to try to determine why there is a difference.

In terms of interpreting this, I’d be interested to consider what happened on Lap 1 to make it faster and see whether that could be incorporated to go faster overall.

I’d start by considering what was different between the two laps (driver, setup, weather etc.)

Then I would start to look at the other data channels to try to develop an explanation. Whilst doing this, I typically keep referencing back to the delta-t channel to try to quantify the explanations, before coming to a conclusion and recommendations.

In motorsport data analysis, delta t is the one go-to channel so hopefully, you are now super clear on it. To recap.

Motorsport Data Analysis: Delta-t Recap


  • … is the first place to start when doing motorsports data analysis
  • … compares the time difference between two laps around the lap
  • shows gains and losses in a meaningful metric – lap time
  • … enables you to evaluate different race driving approaches and see which is better
  • … acts as your sign-post for investigating why there are differences

It is fascinating when you first see this data for real on your own race car. Truly!

Happy analysing!

Do not forget to grab the free 23-page handout to accompany this article by signing up below. This includes a real data worked example using the popular AIM Studio software. Subscribers automatically also get access to our Vault of free resources, including useful spreadsheets, courses and exclusive videos.

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