Guest Blog: What can dementia research learn from Formula 1?
When we think of high-performance motorsport, we might not think that there is any overlap with academia. But Formula 1 and science have many more parallels than you may realise. Here, Dr Donald Lyall from the University of Glasgow explores what dementia research can learn from F1’s pursuit of excellence and what this could look like for the future. You can find out more about Dr Lyall here.
“I don’t understand anything about motorsport”
– Charles Leclerc, immediately after setting pole for the 2025 Hungarian Grand Prix1
High-performance sport, like Formula 1 racing (F1), overlaps with scientific achievement in many ways. This is perhaps because both consist of groups of people working in the pursuit of a (sometimes literal) highly ambitious goal. My name is Donald Lyall, and I’m a senior lecturer in Population Brain Health at the University of Glasgow, as well as an F1 and McLaren fan (long-suffering until recently). Here, I’ll discuss a few things that I think are, for better or worse, common between F1 and science – and what the academic community can perhaps reflect on.
Success requires investment, the right people – and time
Universities are graded every few years on the quality of their science (The Research Excellence Framework), including ‘Research Power’2. This is defined, broadly, as the rating (out of five stars) of their research multiplied by the number of staff. Building a high-quality research lab requires support – including financial – at both institutional and national levels. When Toto Wollf, team principal, CEO and co-owner of Mercedes F1 team, first joined in 2013, he observed:
“[the board’s] expectations were to win championships – on resource equal to what I had at Williams, where our expectation was to come fifth”3
Universities are currently in varying degrees (if you’ll pardon the pun) of financial precarity, and at a time when researchers are sometimes being told to prioritize teaching4, universities and stakeholders will need to be conscious that to stay competitive, there is no way around it: world-leading science isn’t cheap. Dementia is chronically underfunded relative to other conditions, and ‘an ounce of prevention is worth a pound of cure’ (Benjamin Franklin)5. Funding research towards even a 6-month delay in the average age of onset of dementia, would have significant benefit for public health6.
Scientific research is slow and laborious, and defined (to some extent) by its peaks. A lot of science turns on short-term grants, short-term staff contracts, precarious positions, and short-term performance indicators7 – which can harm long-term success for the group in general. Zak Brown, CEO of McLaren, noted that the process of building organisational momentum is gradual – he only accepted the role in 2017, on the promise of ‘runway’ – several years of job security, come what may, to enact his vision and show progress8. McLaren were sixth in 2019 with 62 points. Five years later, they won the 2024 constructors championship with 666 points.
Stay lean, and prioritize what’s important
Universities can be large and bureaucratic. While investment and facilities are a correlate of success, they are not a guarantee9,10. To quote a recent editorial in the journal Brain11:
“We are losing sight of the academic mission: to think, to enquire, to design and perform new research, to innovate, to teach and communicate our findings for the purpose of societal improvement. There are many reasons why this has occurred over just a quarter of century but a key contributor has been the corporatization of academic institutions”
A 2021 UK government report highlighted the need for less bureaucracy, and streamlining of processes including research and innovation12, and this theme of efficiency runs through F1. The Aston Martin team has undergone a period of significant investment in recent years, but as their technical director Adrian Newey observed:
“[Red Bull Racing] had the most successful season of all time…out of an industrial estate in Milton Keynes, with a wind tunnel from World War Two.”13
Part of this was their famously reactive, low red-tape ‘racing team’ rather than business-like structure10,14,15, coupled with appropriate support. By contrast, technical director Paddy Lowe moved from McLaren to Mercedes in 2013. McLaren endured a subsequent lean period while Mercedes won the majority of constructors/drivers championships from 2014 to 2021. He observed that a key distinction between the two was the total prioritisation of performance – not emailing between departments to ‘argue and check every pound’16. This focus on the ultimate rather than short-term outcome is a common theme in recently successful F1 teams:
“In Jaguar times [before success] it was all about the process. There had to be a number to define everything, and a spreadsheet went with it. Follow the process and the performance will come…but it didn’t. Whereas now, we put a lot of process in, but are also creative and have freedom to diverge when required. It doesn’t matter how you achieve the outcome – just that you do’ – Ben Waterhouse, Head of Performance Engineering at Red Bull Racing (constructors champions 2010-2013; 2022-2023)15
Academics will appreciate this phenomenon, and the need to focus on the bottom line: scientific progress.
‘Rubbish in; rubbish out’ – the importance of high-quality data, and common sense
Scientists and F1 thrive on ingenuity and technological development. Being highly data-driven, there is the risk that ‘the tail can wag the dog’, where data directs us towards findings which are erroneous, confounded, or not especially helpful.
“The most important nut is the one behind the steering wheel” – Martin Brundle17
In epidemiology, there are many examples of associations which, while statistically significant correlations, don’t pass ‘common sense’ critical muster. Are yellow teeth a risk factor for lung cancer?18 No, Michael, that is so not right19 – smoking is. This speaks to the role of human expertise – and/or common sense, in data-driven industries.
Sometimes in dementia research, studies use cutting-edge approaches to data interrogation. These often reconfirm what we already knew (e.g. risk factors include higher age; smoking), and/or identify risk factors which are part of ongoing decline rather than being causal. This is because the data itself was not perfect: perhaps having a relatively small (and therefore less reliable) sample size, the data did not follow up participants for very long, or the study didn’t account for potential confounding factors like genetics20. One example is underweight: people with dementia can often present with relative frailty, and for a period underweight was considered as a specific risk factor for dementia. Later, with sufficiently high-quality, long-term data, it became clear this phenomenon was ‘reverse causality’: people decline as part of the dementia process, lose weight, and present as underweight21.
Peter Prodromou, technical director of McLaren, remarked at a lecture at the University of Glasgow (which I attended), that an overlooked ingredient of success in a data-driven industry, is listening to the driver. An F1 car has an incredible number of sensors and data in minute aspects of performance – but only the driver can tell you the symptoms, and appraise what’s truly important. Analogously, poor quality data in dementia research will give you a poor-quality answer, and that’s where human expertise is hard to beat. Machine learning/artificial intelligence can generate a high-performing model, but due to its complexity you may have no idea how what variables are contributing, and how: just like how a human brain and its unknown computations can sometimes trump empirical data20. There are many examples of critical appraisal and common-sense overriding data in Formula 1 to yield success. F1 teams have access to highly advanced, data-driven weather prediction algorithms22. The Stewart Grand Prix team, however, won the 1999 European Grand Prix partly through the driver overriding this:
“I went on radio and asked for wets. They asked if I was sure as they had slicks ready – and I said yes. As I pulled away, the rain started tipping it down.” – Johnny Herbert (winner)23.
A fundamental overlap is therefore the need for good quality data – large sample sizes; detailed data; long follow-up from healthy baseline – married to human expertise. And listening to the people at the heart of dementia research – those with lived experience of dementia, including carers.
What can dementia research learn from F1?
We have seen that F1 and science overlap in several ways, including the need for institutional investment, patience, lean structures, and human expertise married to high-quality data. What can science learn from F1, in sum?
- Achievement isn’t cheap. Finance and support are necessary for breakthroughs – but must be married to clear leadership10.
- The best minds need space and time to think. Scientists need dedicated time which is not accounted for by administration, supervision or other professional tasks24.
- The right people in the right place with the right idea…still need time. Scientific breakthroughs require commitment and time: not short-term performance targets which slow down the overall goal 8.
References
1 Drivers React After Qualifying | 2025 Hungarian Grand Prix – YouTube. (https://www.youtube.com/watch?v=bOwL3RozqEs).
2 Results and submissions : REF 2021. (https://results2021.ref.ac.uk/).
3 Toto Wolff says his first job at Mercedes was to ask for more money! (https://www.gpblog.com/en/news/toto-wolff-says-his-first-job-at-mercedes-was-to-ask-for-more-money-.html).
4 Cutting unfunded research time ‘hits Newcastle University’s Russell Group standing’. (https://www.timeshighereducation.com/news/cutting-unfunded-research-hits-newcastle-russell-group-standing).
5 Spee RF, Kemps HM, Vromen T. ‘An ounce of prevention is worth a pound of cure’. Netherlands Heart Journal 2023; 32: 2.
6 Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM. Forecasting the global burden of Alzheimer’s disease. Alzheimer’s and Dementia 2007; 3: 186–91.
7 I’m striking because insecure academic contracts are ruining my mental health | Sarah Darley | The Guardian. (https://www.theguardian.com/education/2019/nov/29/im-striking-because-insecure-academic-contracts-are-ruining-my-mental-health).
8 Zak Brown On McLaren, Classic F1 Cars And More | Beyond The Grid | Official F1 Podcast – YouTube. (https://www.youtube.com/watch?v=ukzK1scbctc).
9 Andrew Green Interview | Beyond The Grid | Official F1 Podcast – YouTube. (https://www.youtube.com/watch?v=ruSFAuhl5r4).
10 Mark Hughes: This is the beginning of the end of Red Bull domination – The Race. (https://www.the-race.com/formula-1/horner-verstappen-red-bull-f1-upheaval-changes/).
11 Husain M. On the responsibilities of intellectuals and the rise of bullshit jobs in universities. Brain 2025; 148: 687–8.
12 Review of research bureaucracy – GOV.UK. (https://www.gov.uk/government/publications/review-of-research-bureaucracy).
13 34: How To “Provoke” An F1 Team Into Ultra-High Performance – James Allen On F1 | Podcast on Spotify. (https://open.spotify.com/episode/6TlSwS0DJbN2hmuvpunCq4).
14 Christian Horner Interview | Beyond The Grid | Official F1 Podcast – YouTube. (https://www.youtube.com/watch?v=b_OIvylHEz0).
15 How To Design a Championship Winning Formula 1 Car ✍️ | Talking Bull Podcast – YouTube. (https://www.youtube.com/watch?v=awJ6gi4Q4kg).
16 Paddy Lowe On Leaving Williams And Winning With Mercedes | Beyond The Grid | Official F1 Podcast – YouTube. (https://www.youtube.com/watch?v=j65L14gt19c).
17 The Martin Brundle Quotes Page. (https://vidcad.tripod.com/quotes97.htm).
18 Davies NM, Holmes M V., Davey Smith G. Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians. BMJ (Online) 2018. doi:10.1136/bmj.k601.
19 EXPLAINED: Understanding one of the most chaotic, controversial title showdowns in F1 history | Formula 1®. (https://www.formula1.com/en/latest/article/explained-understanding-one-of-the-most-chaotic-controversial-title.4B98awxwP7JPgBWxIt5KnL).
20 Lyall DM, Kormilitzin A, Lancaster C, Sousa J, Petermann-Rocha F, Buckley C, et al. Artificial intelligence for dementia—Applied models and digital health. Alzheimer’s & Dementia 2023; 19: 5872–84.
21 Kivimäki M, Luukkonen R, Batty GD, Ferrie JE, Pentti J, Nyberg ST, et al. Body mass index and risk of dementia: Analysis of individual-level data from 1.3 million individuals. Alzheimers Dement 2018; 14: 601–9.
22 Watching the skies – how weather forecasting works in F1. (https://www.formula1.com/en/latest/article/how-weather-forecasting-works-in-f1.33RB53BtCdZkcuEpvUU9iz).
23 ORAL HISTORY: The inside story of Stewart GP’s fairytale 1999 European Grand Prix victory | Formula 1®. (https://www.formula1.com/en/latest/article/oral-history-the-inside-story-of-stewart-gps-fairytale-99-victory.2lCDkxrUBfEdyFprakzrJy).
24 ‘Adrian is an amazing individual’ – Andy Cowell on Adrian Newey’s Aston Martin impact and gearing up for 2026. (https://www.formula1.com/en/latest/article/adrian-is-an-amazing-individual-cowell-on-neweys-aston-martin-impact-and.2AkD2XolFa87GwAzyMhWq2).