Can ai accurately analyse your motorsport data? Could it really examine your performance, make sense of the numbers, and offer concrete tips to improve your driving skills? This idea might seem a bit like science fiction, but I tried it and this is what I discovered.
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Motorsport Data Analysis Has Long Been A Pain…
If you’ve ever tried to make sense of motorsports data, you’ll know it’s not straightforward.
Despite all the potential benefits, it can be quite a hassle to get valuable insights from it.
Whether you use it regularly or just now and then, the whole process can feel outdated and complicated.
It’s all too common to invest in a data logger full of enthusiasm, only to leave it gathering dust after a few attempts to understand the data – know anyone with “another expensive lap timer?”
Can Ai Analyse Your Data? Today?
Now, AI is a buzzword with lots of big promises for the future. But is there a way it can be helpful to you in motorsports right now?
Surprisingly, the answer might be yes, especially if you’ve got the knack for interacting with AI.
I’ve been working on what I call ‘AI-fu’, or ‘prompt engineering’, to make AI tools work more effectively for me. And things just got more interesting with AI being able to process images.
Think about it: could you take a picture of your performance data, run it through ChatGPT with a few smart prompts (more below…), and potentially get some insightful feedback?
Could an AI really look at a photo and spot areas to improve, estimate how much lap time you could save, and offer advice tailored to your driving style?
How You Can Use ChatGPT to Analyse YOUR Motorsports Data
There’s already plenty to digest here, so I’ll keep this introduction brief. Below is a screenshot I’ve taken of some AiM data from Brands Hatch in the UK.
First, I’d like you to examine the data in the screenshot closely. Consider where the driver might improve. What are your thoughts on this data?
Your reaction might range from “No clue!” to an in-depth analysis, or perhaps somewhere in between.
Whatever your initial thoughts are, make a note of them.
Okay, now I’m going to share verbatim the conversation I had. Take a look. Where there is a logo it is me writing, and the reply is titled “ChatGPT” each time.
If you’re not familiar with this AI technology, pay close attention to how I phrase each prompt. There are numerous subtle details here designed to ensure I get the analysis I’m seeking. Remember, this isn’t an intellectual contest; my aim is to help the AI in providing a fast, accurate analysis of the data.
See you on the other side….
ChatGPT Motorsports Data Analysis Chat – Nov 2023
You are a professional motorsports race engineer. Your driver is out at Brands Hatch, and you have gathered data on their performance using an AiM Solo data logger. This uses GPS data to derive speed, lateral, and longitudinal acceleration.
In the attachment, you can see a screen dump of the driver’s two best laps overlaid from the latest session. You can observe the GPS speed trace, the lateral acceleration – which is analogous to steering input, and the longitudinal acceleration – which is analogous to braking and accelerating.
Also shown is a gSum or vector-g channel. This is a combination of lateral and longitudinal acceleration as a vector and is the equivalent of the friction circle laid out in distance. The channel has been manipulated slightly. Given the hilly track, the longitudinal acceleration has been “corrected” for the slope of the track. This means that the peak values of longitudinal acceleration should now be comparable.
There is a theory for the gSum that the peaks should be the same height, and where they are not, there is an opportunity to drive a corner faster. The ramp angles up and down from both sides of the gSum channel should also be fairly steep. If they are not or if there is a dip, then there are opportunities to improve the entry and/or exit phases of the corner. By looking at the associated lateral or longitudinal traces, you can determine whether the opportunity is from braking, accelerating, or taking a different line.
The challenge with this data is that it is quite noisy. The gSum channel has, therefore, been “filtered,” but really, this just means a moving average has been applied, and it is still not a great representation with a lot of noise, sadly.
Finally, there is a variance channel at the bottom showing where the driver gained or lost time from one lap to another. Can you give me your assessment of the top three opportunities this drive has to improve? You can use the map in the bottom left to reference corner names.
From the data displayed, the top three opportunities for the driver to improve at Brands Hatch, referencing the gSum channel, the lateral and longitudinal acceleration, and the variance channel, could be:
- Druid’s Hairpin (Turn 2):
- The gSum trace shows that there’s a less steep ramp-up around the Druid’s Hairpin, which indicates that the entry speed could be higher or that the driver could be on the brakes less aggressively, possibly trail-braking into the corner.
- The GPS speed trace dips significantly here, but the longitudinal acceleration doesn’t show a corresponding sharp increase in braking g-force that would justify such a slow entry speed.
- Improving here could involve carrying more speed into the corner and a smoother transition from braking to cornering, which might result in a higher exit speed as well.
- Graham Hill Bend (Turn 3) and Cooper Straight:
- The variance channel shows time lost after Graham Hill Bend leading onto Cooper Straight.
- The GPS speed trace indicates that the speed carried through Graham Hill Bend could be higher. The lateral g-force does not peak as high as in other corners, suggesting that there’s additional grip available that isn’t being used.
- Enhancing the line through Graham Hill Bend, focusing on carrying more speed and a smoother line, could lead to higher speed onto the straight, thereby reducing the time variance.
- Surtees (Turn 4) into McLaren and Clearways (Turns 5 and 6):
- There is a noticeable difference in the peaks of the gSum around the Surtees to Clearways complex. This section also shows up in the variance channel as an area of time loss.
- The lateral g-forces show fluctuations rather than smooth transitions, suggesting that the driver is not maintaining a steady steering input, which is essential for this complex series of corners.
- Working on a smoother steering input and possibly taking a different line that allows for maintaining higher lateral g’s without unsettling the car might lead to a more consistent and faster cornering speed through this section.
For all these corners, a combination of video analysis, further sensor data (if available), and driver feedback would help to refine these assessments and implement the most effective improvements.
Yes I tend to agree. Can you give me some questions to go back with to the driver in order to try and get their perspective. There are several reasons why they could be driving in this way and it would be good to get their input before sharing this analysis.
Absolutely, it’s crucial to get the driver’s perspective on their performance. Here are some questions you could ask:
- For Druid’s Hairpin (Turn 2):
- “Can you talk me through your braking approach into Druid’s? Are you feeling any issues with the car’s stability when you’re trail-braking?”
- “Is there a particular reason you’re braking early for the Druid’s Hairpin? Are you encountering any understeer or oversteer that’s affecting your entry speed?”
- For Graham Hill Bend (Turn 3) and Cooper Straight:
- “On exiting Graham Hill Bend, how confident are you in the grip levels? Is there a reason you’re not carrying more speed through the bend?”
- “Are there any handling characteristics of the car on exit that make you hesitant to apply the throttle earlier?”
- For Surtees (Turn 4) into McLaren and Clearways (Turns 5 and 6):
- “Could you describe your steering inputs through the Surtees to Clearways complex? Are there any mid-corner corrections that are affecting your speed?”
- “What’s your level of comfort with the car’s balance in the final sector? Is the car behaving predictably through the series of corners?”
- “How does the car feel in terms of balance and grip throughout the lap? Are there specific areas where you feel the car could be optimised?”
- “Are there any distractions or visibility concerns on the track that might be affecting your cornering or braking?”
- “Could you share any strategies you’re applying that might explain the conservative approach in certain corners?”
- “Do you feel that the car’s setup is allowing you to exploit its full potential, or are there areas where you believe adjustments could help?”
Understanding the driver’s experience and comfort level in the car can provide insights that the data alone may not reveal. Their feedback will be essential in forming a complete picture of what’s happening on the track.
From those opportunities do you think you can estimate how much lap time could be gained if the driver put your suggestions into action? I appreciate this will be an estimate. Give me one for each opportunity
Estimating the potential lap time improvement requires interpreting the variance channel along with the magnitude of the suboptimal areas identified. Here are estimates for each opportunity:
- Druid’s Hairpin (Turn 2):
- Improvement could be in the region of 0.1 to 0.2 seconds, assuming the driver can brake later, carry more speed through the apex, and get on the power earlier for the downhill section that follows.
- Graham Hill Bend (Turn 3) and Cooper Straight:
- If the driver carries more speed through Turn 3 and onto the straight, there might be an estimated 0.2 to 0.3 seconds to gain, as any speed advantage here is carried all the way down the straight.
- Surtees (Turn 4) into McLaren and Clearways (Turns 5 and 6):
- Smoothing out the steering input and maintaining a higher minimum corner speed could potentially yield a gain of around 0.3 to 0.5 seconds, due to the compound effect of increased speed through multiple corners leading onto one of the track’s main straights.
The cumulative gain across all these opportunities could be significant, potentially up to a second or more, but it’s important to note that these improvements are not additive. Gains in one corner can affect the approach and exit of subsequent corners. The actual time saved could also vary based on how well the suggestions are implemented and how they complement the driver’s style and the car’s setup.
What else can you interpret from the data in the image? Anything else useful for the driver?
Beyond the specific opportunities identified, here’s what else we can interpret from the data and how it might be useful for the driver:
- Consistency Across Laps: The variance channel at the bottom provides a clear visual representation of the consistency between the two laps. The driver appears to be quite consistent in some sections, but there are spikes where improvements can be made. Focusing on consistency, especially in the more complex sections of the track, can lead to overall lap time improvement.
- Braking Zones: The longitudinal acceleration data indicates how the driver is braking. By closely examining the shape and peaks of these traces, you might advise the driver on braking technique, such as brake pressure application and release (trail braking), which could result in smoother and faster corner entries.
- Accelerating Points: The points where the longitudinal acceleration trace dips back to zero or becomes positive (indicating acceleration) are key. These points indicate when the driver is getting back on the throttle. Finding the optimal point to begin acceleration is critical for carrying speed onto straights.
- Turn Entry and Exit: By analysing the lateral acceleration in conjunction with the speed trace, you can assess how well the driver is entering and exiting the turns. The ideal is to see a smooth, rounded peak in lateral acceleration, indicating a smooth arc through the corner.
- Use of Track Width: While not directly shown in the data, the lateral acceleration profile might give hints about whether the driver is using the full width of the track, which can affect cornering speed. Discussing with the driver about their line choice and track utilisation can provide insights.
- Sector Analysis: The data seems to be broken down into sectors (as indicated by the coloured bar above the speed trace). Analysing sector times can often reveal where a driver is strong or weak. It may be beneficial to focus on sectors where the most time is lost compared to their best.
- Equipment Check: If there’s any discrepancy in data that cannot be explained by driving alone, it may be worth checking the vehicle’s systems and calibration of the data logger to ensure accuracy.
- Physical and Mental State: Finally, discussing the physical and mental state of the driver during the laps can be revealing. Fatigue, focus, and confidence levels can all affect lap times significantly.
Using the data in conjunction with the driver’s feedback will help to create a more comprehensive improvement plan. Remember, the ultimate goal is to support the driver in merging their style with the car’s capabilities to maximise performance.
Anything interesting you can interpret from the sessions lap times on the left? As well as the specifics for finding more lap time on one single lap, I’m also keep to give the driver some more general feedback on their overall performance.
The session’s lap times on the left indicate several aspects that could be useful for giving general feedback on overall performance:
- Lap Time Consistency: The lap times show some variability, suggesting that the driver’s consistency could be improved. Focusing on producing consistent lap times is often more beneficial than achieving a single fast lap, as it indicates better control and predictability, crucial for race conditions.
- Progression Throughout the Session: There is a progression seen in the lap times, with the fastest laps appearing towards the middle of the session. This could suggest the driver is learning and adapting to the track as the session goes on, or it may reflect changes in track conditions, fuel load, or tyre performance.
- Potential Fatigue or Concentration Drop-off: Towards the end of the session, the lap times seem to drop off slightly. This might indicate physical or mental fatigue, or perhaps the driver is pushing too hard and making small errors. It’s worth discussing with the driver what they were experiencing during these times.
- Optimal Tyre Performance Window: The fastest laps are often an indication of the optimal performance window for the tyres. It’s useful to determine when this occurs during a session to plan qualifying and race stints accordingly.
- Best Lap vs Average Lap: Comparing the fastest lap to the average lap time can provide insights into the driver’s overall pace. If the fastest lap is significantly quicker than the average, there may be untapped potential in achieving more consistently fast lap times.
General feedback to the driver could focus on the need for consistency, adaptation to changing track or car conditions, maintaining concentration throughout the session, managing physical and mental demands, and the strategic use of tyres. It’s also important to celebrate the positives, such as the progression in lap times, which shows a learning curve and adaptability.
If they ask me, what kind of % or lap time delta would be a good reference for them to aim at in order to improve their consistency? Clearly they are not going to be able to do every lap in the same time – unless this was a sim – so what would be a good figure for them to be aiming at. If it is easier you can try to split this into novice, experienced, front runner and pro targets because I recognise that are likely to be different.
Achieving lap time consistency varies greatly depending on the level of the driver, the car, and the track conditions. However, here are some general targets each type of driver might aim for in terms of percentage or lap time delta from their best lap:
- Novice: A novice driver should initially aim for lap times within 5-7% of their personal best. As they improve, they can tighten this goal to within 3-5%. In terms of lap times, if their best lap at Brands Hatch Indy circuit is around 55 seconds, they should be aiming to keep most laps within about 2.75 to 3.85 seconds of that time.
- Experienced: An experienced driver should be aiming for more consistency, with lap times within 2-3% of their best. On a 55-second lap, that means staying within 1.1 to 1.65 seconds of their best time.
- Front Runner: A front runner in a competitive series should be seeking consistency within 1-2% of their best lap time, equating to staying within 0.55 to 1.1 seconds of their best on a 55-second lap.
- Professional: A professional driver should consistently be within 1% of their best lap, often even closer. At a 55-second lap time, this means most laps should be within 0.55 seconds of the best time.
These targets are ideal ranges, and the driver should aim to reduce the delta as they gain experience. It’s also important to consider that factors like traffic, tyre degradation, fuel load, and changing weather conditions can affect lap time consistency. The driver should focus on consistent execution of their driving technique and learning how to adapt to the evolving conditions of the track and car throughout a session.
Given what me drive has done then, and assuming any outlier laps were not his fault – traffic or such like – how well do you think he’s done?
Based on the lap times displayed, if we disregard any outlier laps that could be attributed to traffic or other external factors, the driver seems to be performing quite well, especially considering that the two best laps are very close in terms of performance. Here’s what stands out:
- Peak Performance: The driver has managed to put in competitive lap times, with the two best laps being quite close to each other, which is indicative of the ability to reach peak performance more than once.
- Learning and Adaptation: There is evidence of improvement and adaptation over the session. This learning curve is crucial for race conditions where adaptation to the evolving track and car dynamics is key.
- Potential for Consistency: While there is variability in the lap times, the driver has shown the potential for consistency. With focused training and experience, there’s a good chance these lap times could cluster even closer together.
- Room for Improvement: There are laps that are notably slower than the best, which suggests that while the driver can achieve strong lap times, there is room for improvement in consistency.
In summary, the driver is showing promise with strong individual lap times and the potential to learn and adapt quickly. The main area for improvement appears to be consistency across the session. With coaching and experience, especially on maintaining focus and driving technique under varying conditions, the driver could likely reduce the variability in lap times and improve overall performance.
On my scale therefore , where would you rate him ?
Based on the lap times and assuming the best laps are close to the driver’s optimal performance, the driver appears to be beyond the novice stage but not yet consistently at the front runner level. Here’s a rough estimate:
- They are not a Novice, as they have demonstrated the ability to put in competitive lap times and show some consistency.
- They seem more advanced than just Experienced because they’ve set closely matched best laps, suggesting a good understanding of the track and car.
- They might not yet be a consistent Front Runner, as there are laps that show greater variability, indicating potential lapses in concentration or execution, possibly due to inexperience or adapting to changing conditions.
- They are not at the Professional level of consistency, where every lap would be very close to the best time, except when mitigating factors like traffic come into play.
So, on your scale, this driver would likely fit into the upper end of the Experienced category, with moments that show the potential for being a Front Runner. They are showing the capacity for quick laps but would need to work on consistency to be a regular front runner or professional level competitor.
…. tbc 🤣
Summary – Ai Motorsports Data Analysis Example
So, what did you make of that? Clearly, you can continue chatting with it, but for me, that was impressive.
Regarding the observations, when I looked at the data, I had similar general thoughts. Perhaps with the most emphasis on the Graham Hill suggestion. The potential lap time gains for each, I also agreed with – of course, these are essentially impossible to determine with certainty, but the order of magnitude was about where I was thinking.
The driver is a season three racing driver, and benefiting from excellent coaching, achieved his first podium at this event, leading much of the race and narrowly missing a first win due to his tyres wearing out in the last two laps. He consistently clocked 54s (as per AI suggestion…), with the potential for 53s in my humble opinion. He’s on the brink of regularly joining the front runners, so I was impressed with the AI analysis here too.
Your Next Steps
Brands Hatch is, of course, quite an easy track. How would this compare with your data on your tracks? Why don’t you give it a try?
I encourage you, as always, to provide me with feedback and thoughts if you try my advice.
This time I encourage you to:
- fire up your laptop,
- pull up some recent data,
- get the data channels I’ve detailed on screen (for the GPS-corrected version, read this, but its non essential)
- take a picture, and
- submit it to ChatGPT 4 with my prompts.
- See what comes back…. 🤯
Give it a try and be sure to sign up to ahead of the curve to never miss the latest articles.
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