An analytical advantage - using data to reduce lap times… (2025)

Back in 2014 we wrote an article advising how we use data to help us lap faster. Technology has moved on and some of the challenges that we previously faced can now be resolved using “out of the box” functionality in the datalogging software.

Satellite overviews are now an in-built feature of Race Studio 3

​The problem hasn’t gone away though… We are always wondering “how can we lap as quickly as possible?“
 
With the advancements in technology since 2014 – along with the reduction in cost meaning that the relevant equipment can now be acquired by amateur racers (such as ourselves) – we thought that it was time to revisit how we can use today’s available data streams to monitor and improve our performance.
 
 
Opening Notes
A key ingredient of fast lap times is consistency. Unlike four-wheeled motorsport, where the machine makes more difference than the man (or woman!) – with two-wheeled motorsport the rider makes the majority of the difference in overall performance. Making the rider comfortable on the machine can help them to be more consistent, which will eventually help them to be faster.
 
But there are other variables – such as changing track conditions, weather, temperature, etc – which may make data comparison and/or analysis between different days (or even different sessions within a day) more difficult.
 
 
Where to begin
It is necessary to have a clear understanding of “where you are now” so that you can progress closer to “where you want to be”.

• A machine set-up sheet such as that used by Two Daves Racing can help you to capture all applicable machine settings. This will help with consistency when visiting multiple tracks – as you can always refer back to what the settings were last time you headed out on track.
• A dyno sheet will document where the strengths of your machine exist, by plotting torque and bhp against engine rpm – so that you can attempt to utilize the ideal rpm range. Below is an example dyno sheet for our Aprilia RS660 Trofeo. You may notice that the torque and power values on any dyno sheet will cross at exactly 5252rpm – this is because power (bhp) is a mathematical equation of… ((torque multiplied by RPM) divided by 5252). Due to how a dynamometer works, it can only measure the torque applied to the machine via the tyre – so a conversion is necessary to obtain the power (bhp) values.

Ideal rev range highlighted (~7700rpm to ~11500rpm)

• Data capture hardware: On our Aprilia RS660 Trofeo we are using an AiM Solo2 DL GPS lap timer and datalogger, which includes a connection to the ECU data in addition to the “where am I in the world” and “how fast am I going” information that the GPS data provides. If you are interested in the installation of the hardware and a complete list of all of the available data channels then click here.
• Data analysis software: AiM provide their own data analysis software (Race Studio 3) which will be used for the examples in this article.

 
 
Using the collected data
With the above data available to you (and following a series of consistent laps by the rider), you are now in a position to analyse the data and perform some checks.
 
It could be argued that the analysis can be split into two sections – factors that are influenced by the machine configuration and those that are influenced by the rider. Let’s take a look at some examples.
 
Machine Behaviour
As indicated on the dyno sheet, our machine has strong torque between ~7700rpm and ~11500rpm, so ideally, this should be the range that is used during the lap. By using a histogram we can view the percentages of where the engine speed was during the lap. Two similar laps are shown below, with both having the majority of time spent within our desired range.

A comparison of the engine rev range used during two similar laps

​This provides a high-level indication that the machine’s gear ratios may be close to correct – but by overlaying the gear selection over the track map (then for even closer inspection the engine rpm over the track map), we can confirm whether the gear ratios are appropriate – or whether we are curious that a change could be beneficial. A discussion with the rider to validate why certain gears were used in certain corners may also be beneficial here.
 
Note: Following a change to machine setup we can revisit the data and perform a before/after comparison of the setups to validate whether or not they had the intended consequences.

Gear selection (for a single racing lap) overlayed onto the track map

Engine rpm (for a single racing lap) overlayed onto the track map

​​Riding Behaviour
As you may expect, the majority of the improvement is likely to come via monitoring and changes to rider behaviour.
 
A good starting point to check for consistency is to overlay multiple laps to check for discrepancies. Although discrepancies may indicate a lack of consistency, they also provide an opportunity for analysis – as taking a different approach to a corner may actually yield an unexpected improvement. In the screenshot below we display a comparison of two similar laps using the GPS speed, with the time comparison displayed at the bottom.

Comparing the GPS Speed of multiple laps

​A quick view shows that the laps are very similar, but the red lap has a few areas where additional speed is carried through the corners – except for the final corner where the blue lap had the advantage. We can then dig down further to see the racing lines taken on track (and can even view the racing lines over a satellite image) to see where the time was saved.

A comparison of two different racing laps (using the GPS location data)

Viewing the (GPS location data) racing line over a satellite image of the circuit

We can also use this view of the GPS Speed data to answer questions such as:

• Are the braking positions consistent?
• Are the deceleration rates (per corner) consistent?

 
Once we are happy that we have identified all useable aspects from the GPS data alone, then we can start to add in elements of the ECU data channels. I have decided to add in the Throttle Position sensor to answer questions such as:

• Is the throttle being fully opened to 100%?
• Is the “power on” position consistent over multiple laps?
• Are there any instances of coasting? (i.e. neither being “hard on the power” nor “hard on the brakes”)

Overlaying ECU Throttle % data against GPS Speed data

​The data looks consistent, which is a good sign, but there is a series of unexplained throttle closings at the start of section 9. Further data channels will need to be added to investigate and explain this.
 
If we add in the ECU data channels for Pitch Rate and Front Wheel Speed, we can see that following the application of 100% throttle the machine pitches and then the front wheel starts to slow (which is an indication that it is airborne and no longer being driven by contact with the track surface). Following a brief reduction of the throttle to ~80% the front wheel lands and then the throttle is increased to 100% again.

Overlaying Pitch Rate, ECU Throttle %, Front Wheel Speed and GPS Speed data

​The rate of GPS Speed increase appears unaffected by this short reduction in throttle opening, and the closing of the throttle appears to be consistent, so this is nothing to worry about!
 
A final couple of checks that we can make are:

• Is the rider consistently upshifting at the same RPM?
• Is the rider hitting the rev limiter?
• Is there an ideal RPM to upshift at various points of the circuit?

To answer these questions we can compare the RPM and Gear selection ECU data streams.

Comparing the RPM and Gear selection data

A curiosity of the comparison of these laps above is that the Gear selection data value for the red lap briefly displays as “1st gear” within section 4. Looking at the RPM values, it appears that the gear selection was never made (as the RPM would have spiked). The elapsed time is too short for the rider to have selected from 2nd–1st–2nd so an assumption will need to be made that either the sensor configuration is not quite right or that the rider was heavy-footed. Irregardless, we can ignore the data point at this time unless it becomes a recurring issue that requires resolution.
 
The differences detailed above are minute, but close analysis does advise that there is no benefit in exceeding 11,000rpm. This knowledge is useful to feedback to the rider, as less stress on the engine may help to improve reliability.
 
We have discussed several areas that checks can be made. Further checks can also be made against:

• Traction Control intervention – to analyse the lost time and help to refine your setup.
• Lateral and Longitudinal acceleration – to check that consistent forces are being applied through the tyres. This is also good to check in the event of a crash to understand what may have gone wrong.
• Suspension movement (via potentiometers, not available within this setup) – to help improve the chassis setup of your machine.

 
 
Closing Summary
In practical terms, here is a procedure to quickly get up to speed on a new track:

1. Do a couple of sessions and use the data-logger to find the most important corners that make the difference to lap times, using the GPS Speed channel to identify those corners on the quickest laps.
2. Concentrate on one corner at a time and experiment with methods for that corner, so you can find a formula to achieve the fastest way through that corner.
3. More often than not, the most important corners are long fast sweepers which require careful speed control, or corners that lead onto a straight. However, this isn't always the case and your datalogger can reveal hidden secrets to quicker times around your track, which could be information that your competition may not have picked up on!

 
Working in this way with the GPS Speed channel is going to give you a great feel for using your datalogger. Eventually, you will then want more information to work with – which will help you to refine your approach, start to look at more data channels, and as a result lap faster and faster…