Science of Cycling: How to be an elite cyclist

welcome to the physiological society it's lovely to see so many people who have responded to the advertising campaign we've been running on all BBC channels for the last two weeks my name is Michael Hutchinson I am here to introduce tonight's talk which is the science of cycling how to be an elite cyclist I'm speaking as someone who was an elite cyclist for 10 years I've never quite felt I was getting it right I am genuinely very curious to see what we're going to hear tonight if there's anyone who can tell us what it takes to be an elite psych that's how you make an elite cyclist it is a professor Louie pass field from the University of Kent Louie is an enormous ly experienced sports science scientist and exercise physiologist and as well as his academic interest he has been lead physiologist with the British Olympic cycling team in 1992 Olympics 1996 Olympics and for the all-conquering breakthrough 2008 Olympics in Beijing so if it wasn't for Louie I think I would be confident saying none of us would ever have heard of Bradley Wiggins that fair living I'm not at all but I'm quite happy that you said this as well as his academic and his sort of practical team work Louie was also a very distinguished racing cyclist himself in the the relatively recent past I think would be fair to say and was in 2006 at the Melbourne Commonwealth Games for four-and-a-half memorable but catastrophically unsuccessful minutes my pursuit coach the catastrophic ly unsuccessful but it was all my fault and the memorable bit of it was all Louie's fault so I'll introduce you to Louie and I can't wait to hear what he has to say thank you very much Michael um it's a real pleasure to be here so thank you all very much for coming this evening and also to the physiological society for inviting me to I'm one of those fortunate people that gets to play for his work and so being a sports scientist means that it's always easy for me to get up in the morning and carry on with carry on with wherever I've left my work from the night before and so I have a real passion in sharing something of what I do one of the things that I'm always very proud of is the fact that I've been able to combine both academic and practical work so Michaels talked a lot about the applied work that I've done with British Cycling but I've also worked with in academia on the research side as well and what I'm going to do this evening is just give you a few snippets from personal a sort of personal story or journey if you like on different aspects as much to prompt questions from you so the brief that I've been given is to talk for about 30 minutes or so and leave plenty of time for questions so I'm hoping that that I'll be able to do that if not by at least the fact that I won't answer any useful questions in the in the process my talk so the useful questions will have to come from you at the end so that's a that's a warning for you if you like I often them just give a brief introduction to myself to people because I've been in sport science now for way longer than I can remember so I put it on the slide and my motivation for going into sport science was that I wanted to be the first Briton to win the Tour de France and so my plan was that I'd study sport science and then this would have happened instead and that but unfortunately by studying sport science I learned to rather more painful lessons than I expected the first one is that sport science doesn't have all the answers that I was looking for so if you've paid this evening and you're expecting some scientific basis to any answers that had been elite cyclists then you might want to ask for a refund now and save any more time and the other lesson I learnt was that I was born at the wrong time in the year like the talent consequently to actually achieve my dream and but what I did find was that shortly after graduating in sport science I was an Olympic training camp but as a scientist rather than as an athlete and this was a much better way of enjoying the benefits of high performance in the and the privileges of going to Olympic Games Commonwealth Games World Championships and so on and so I've kind of been stuck in this rut for the last 25 odd years so that that's really kind of just a little bit about me and my background but the key thing there is my passion has always been about trying to enhance performance and particular my own performance and as I get a little bit older I become increasingly interested in trying to extend the benefit of my experience and the privileged but opportunities that I've had to a much wider audience so tonight just very briefly what I'll do is touch on three very broad areas to try and tell a story firstly just to look a little bit of work I've done around understanding the demands of elite cycling then a little bit looking at elite riders and their performances and what I've tried to do is stray from some of the most obvious and stories and things that you would hear from standard physiology textbooks or standard physiologist if you like and just pick out some things that might be a little more a little more quirky or interesting and then my real passion is training and the study of training and this is an area which is surprisingly unscientific so there's definitely no answers here but I'll kind of try and give you a sense of what I'm looking at at the moment and where we may be headed in the future as I said with a view to you asking whatever questions that occur to you as we go through so to start off with I just thought my very first piece of research was based around using power meters which is a device you can touch the bicycle and many of you in here may already have them where you can measure exactly how hard someone is working on their own bicycle as they're riding so it makes cycling a wonderful sport for scientists because we can analyze things sorry we can gather a huge amounts of data and analyze that in great detail to try and better understand what a performance takes but it was back in 1992 that I first got my hands on a device like this and so it was only from about that time on words that we were able to understand really what the demands of elite cycling were prior to the invention of these devices and they were actually developed by really smart East German back in the 80s but they didn't sort of make it their way into Europe until much later on but prior to that we had to make any estimations about what it took to be an elite athlete from our observations of elite athletes in the lab and and so there was always a question about whether we're measuring the right things in the lab and how whether what we measured in the lab actually correspond with what happened when someone actually started racing and we really had no idea about that and then it was also coupled with the fact that at that time cycling was a rather amateur sporting in the UK and we had very few British role models that we could look at to try and understand what early performance was all about so that this was a huge step forward for us and then the lottery funding when it came online in 96 changed the shape of British Cycling at a time when actually I stepped back and and so what happened was then British Cycling involved rapidly to the kind of force that it we see today in international competition and then I came back in in around 2006 to see this completely different sport which was then hugely exciting to working and but my first piece of work that I was involved in doing was trying to understand the demands of track cycling because up until that point no one had been able to measure how hard it was to race on the track and an obvious starting point was to look at the our record and because one of the most famous athletes in the world of all time Eddy Merckx had set one of the our records this was something that was really quite prestigious within cycling and then a few years later Francesco Moser in 1984 broke Max's world record and this sort of resurrected interest in the our record not least because he did it by using some substantial technological innovations and so the question was was murk's a better athlete than Moser or did Moser beat Marx's world record because of the technological infant innovations and then because that had stimulated this interest that there was then a sudden rush of new world records fueled particularly by the rivalry of Graeme Obree the scart and Chris Boardman and we saw the evolution of World Records starting from 1876 here with the first established world record of around about 26 kilometers an hour 25 and a half and you can see the steady increase in world record performances up until this time here where Moser then triggered this interest and then this rapid rise up until the record that Boardman set in 1996 this paper was published in 98 which is why it stops then but also the UCI changed the rules at that time too so we were really focused on looking at this period here and trying to understand what had happened to performances was it better and fit a cyclist that drove these increases or was it changes in the riding position so to give you some prompting in your memory of how this might have looked this would have been the sort of traditional racing position that mercs would have used when he said his world record and then Moser came along and introduced an aerodynamic helmet and an aerodynamic bicycle so quite radically different set up and particularly solid wheels as well Aubry in 93 set a world record where they kind of saw your arms off cycling position where he bent over like this and rode in this sort of position which was incredibly aerodynamic and then was that position was outlawed and but further refine to a Superman position which looks something along the lines of what we see in our elite cyclists today and so we were just curious to say well okay was it the changes in position that drove this or was it actually the riders own level of performance um I'm going to jump forward a few years to just bring in another aspect of this but I'll come back to the answer in a second in essence what we were able to do now is to take a a real bicycle and then put it into a computer so we build a cycling model so this particular model that we've got here and this is one I worked on with a colleague at the University of Brighton Patrick gangly who who has essentially taken every aspect of this bicycle and created it within a computer within a mathematical model means that we can now change any aspect of that rider and the bike send it round a track and estimate how fast that bike will go or how much power is required to go in any different speed and what we did was with the the our record study was a precursor to this kind of a now where we we made estimations about how hard it would be with a different equipment to ride at those different speeds I'll skip that slide there we go so this is the last few world records that we see on that on that previous graph of the evolution of the our record and you can see the change in this in the distance that was achieved for all the riders from 50 1.15 kilometers up to 56 point three seven five kilometers and then here you can see how hard we calculated that the riders had to work in order to achieve these speeds and essentially what happens is a nice pattern falls out of this so the colors here are not simply to give contrast between the successive world records but actually the black lines indicate where the new world record was established by a higher performance than the preceding one in other words the rider had to be fitter the red lines showed that the performance was achieved by a lower performance a lower power output in other words the rider was actually taking advantage of a technological innovation that enabled him to cut through the air more easily and actually wasn't as fit in order to do that performance so what we can see here quite nicely is how the our record is first broken by more of an innovation so the power output is lower here to do the set to go for this greatest distance or for higher speed then the power output increases again it decreases it increases it decreases it increases decreases so actually we see a combination as you scan down that that line there of an evolution in terms of technological design and people going faster because of the changes in equipment and then also an increase in performance because these riders are starting to be able to produce more power and our crude estimations of the our record today as it stands would have us in broadly this same sort of category as we are now so it's likely that the current our record is still produced by a power output in this kind of range of around four hundred thirty 440 maybe 450 watts that seems to be very close to the kind of human limits that and the best and most elite cyclists can achieve and it's hard to appreciate exactly all much of a superlative performance that is but I just thought I'd put a little note here is that this performance here is around about two and a half times as much power as a normal person could achieve it with them with a modest amount of training so the guys that are setting these World Records are two and a half times or 250 percent fitter than we are and that theme of developing power in changing our performance is something that I'll come back to a couple of times during the talk but that's just why I wanted to bring that out if I pause for a second is there anyone got a question on something I need to clarify or yes please do yes absolutely that's a really good question yeah so one of the things that Moser did is well and and mercs before him was to use altitude in order to try and benefit that there enhance their performance because at altitude the air pressure is lower and in cycling about 90 percent of the work we're doing is overcoming air resistance so if the air pressure is lower it's easier to cut through that air and therefore we can go faster the trade-off is that from a physical perspective we actually compromise our own body's performance - so there's actually a fairly delicate balancing act in terms of what the benefits are of going to altitude versus how much your your performance is compromised because you can't work as hard so in short events for example Sprint's you would definitely expect to see better performances and for example Chris Hoy went for the kilo world record at high altitude because that would represent an ideal environment there where you're not so worried about being able get oxygen from the air and it's your performance in theory is less compromised with something like the Aero record there's a debate about whether that's going to be really effective or not so it is a variable and actually our original study we made some estimations about that but one of the things that we found is that riders are hugely variable in their ability to cope with altitude so some riders can go to altitude and find they don't experience a huge decrements other riders and see that vary quite markedly and interestingly in the lab at Kent recently we had some guys from the gcn Network when the little video blog and they two of them went into simulated altitude in the lab and one of them commented that his best international performance this is Matt Stevens was when the world championships were in Columbia at altitude whereas the other presenter Simon Richardson said he'd never performed well altitude which is unfortunate cause he's a mounted by writer as well and in the lab that day that's exactly how it worked out to Matt's performances what did change a little bit but not usually when we put him into the altitude chamber and simulated altitude but he wasn't too badly affected Simon almost sort of appear to run into a wall and grind to a halt as a consequence so it you know appear to be those kind of differences too so it's hard to say you know sort of there's a specific altitude that everyone should aim for rather if you could you might have an adjustable velodrome that you would move to different heights for different riders we've obviously not so practical but it's a it's a good question thank you yes I forget because there's so many relative to the second Salafis before them suitable for an hour yes so if you have that person juice before hundreds inky watts on Williams Bobby yes then okay and why diem stabbing and it's just for just for clarity although we think these numbers are probably fairly relevant today these actually still date back to the 90s so this isn't the most up-to-date ones which is perhaps partly why you don't recognize the numbers exactly so this so this number here isn't bradley wiggins but that probably is a fairly this range here is probably a reasonable estimation of his power output today as well this record here is actually Tony Romo Inger back in 94 I think it was beating MIG well in Jurong yes this is injuring a world record here so yeah this was an astonishing ride absolutely yeah but thank you okay so and what I'll do now is move on a little bit so that what it wanted to do was just sort of give some idea of what elite performance is look like and how demanding they might be and then start think about actually analyzing riders performance I'm going to take a completely different tack from what you might expect in from a physiological perspective but just to throw up some a couple of quirky or interesting questions and the data I'm using here is that with a study that I did with a colleague of mine from the University Kent James Hopkirk which basically means he did all the work and I'm taking the credit and borrowing his slides and but we did talk about that this data and the study quite a lot and what we actually did was think there's an awful lot of information now available lot Kirsti of the internet on ryders performances historically and we wondered whether we could actually if you like crawl over the Internet gather a lot of that information together and ask some fundamental questions about riders performances and the evolution of riders performances so what we were able to do is to get focus on 25 major races things like the Tour de France some of the Belgium's Orient's are in the early season classic races and look at the the race performances of the riders in these 25 races that we remarked to catalogue them all so we ended up cataloging results for 400 around four hundred ten thousand different rides if you like or a race results and that compromised that comprised of rather nearly six thousand riders so we've got results for 25 races for 6,000 riders which is about 400 10,000 results and we took the period dating from 1980 to 2014 because that's where we found we could get what we thought was a reasonably comprehensive and reasonably reliable data set courtesy of some helpful websites so that was where we kind of started from and then we we we asked a couple of interesting or what we thought were interesting questions the first thing we thought of was to say well what age the most riders seem to perform at their best now obviously as you appreciate particularly road racing actually measuring performances is a little different difficult here you could just pick the winners but sometimes riders will do a great ride in for a team member or there are all sorts of other things that might happen bad luck and so on so we rather than just pick winners we thought we'd look at top ten placings and then relate it to the age of the riders so what you see here is the number of top ten places from our big data set and then the age of the riders here and effectively what this curve is telling us then is what's the optimal age for a rider to get into the top ten of any given race and essentially in this instance is those 25 races that we've catalogued and you can see that quite clearly this there's this curve shows that we get a little spike at 18 years old and that's really an artifact of the fact that junior rate riders move to the senior ranks at 18 so the more the older riders tend to do better at junior level so the 18 year olds tend to outperform the 16 and 70 year olds so that's why you get this little spike here and then their performance is dropped quite markedly so it's very unusual to see 19 and 20 year olds getting into the top 10 but then as they start to get older you can see this their the number of top 10 placing starts to increase markedly and it peaks at around 27 years old so it appears to be that on the basis of the performances that we've been able to analyze from 1980 to 2014 the optimal age for a rider to get into the top ten is twenty seven twenty twenty eight years old and then as you can see after that their performances or the number of top ten places starts to drop away and then it's unusual to have anyone going much beyond thirty-five or thirty-six but we did have a few cyclists right out to forty five years old so it again one of those interesting things it's like what's the optimal age for a rider and it appears to be the answers around 27 we did the same thing exclusively for stage races as well so we looked at Tour de France Giro d'Italia and welter in case that gave us a different result because there's some discussion that for those races Europe perhaps a little bit older but actually we found the curves were almost identical that the peak happened in almost exactly the same place if we were looking just at stage races alone so anyone in the room that's around 27 years old then you're about your prime for a top 10 place in a top European race the other thing another thing that we were also interested in is well what proportion of riders actually can get into the top 10 and so this was an interesting one because what we were asking here is do the majority of professional riders actually get a top 10 placing at some point in their career or is getting a top 10 placing actually an unusual occurrence and again this was something that previously as far as we're aware hadn't been looked at and actually the answer was so dramatic that we've had to doctor the slide or the graph here big because we can't illustrate it very clearly or easily so what we're looking at here is this is the percentage of riders that achieve top 10 places and then along here is the number of top 10 places that a rider may achieve over their whole career so the zero here indicates the number of riders that have not achieved a top 10 placing at all and actually this bar goes straight off the scale right up to the top because the answer is it's 78% of all the riders that we looked at did not achieve a top 10 place in their career at all so when we're looking at the professional peloton and we're watching all of those riders going through the toilet France or whatever it is 78% of those riders never achieve a top 10 performance and then you will see that what happens is that then this these performances actually fall off a cliff pretty sharply so 8% of the riders get one top 10 performance and bear in mind we've picked out some of the most prestigious races here so there may be smaller races that some of those riders do achieve performances in but we will pick it we were looking at the really high-profile races so only 8% of riders get one top ten winning their whole career and only just over three percent get two wins and then you can see that we've got this thing ever increasing decreasing tale of riders that gain 4 5 6 7 8 10 places so what we learn from this is that on average and this is probably a slightly misleading statistic riders achieve one top 10 place in their whole career in a very prestigious race but actually probably more relevant relevant here is the median figure because this is sued by the huge number of zeros in the very large number here so most riders don't achieve a top 10 place ever in their career in a prestigious race but what we do see then is this small town of riders that are incredibly successful so actually it appears that even in an elite group of riders there is a another super elite group which is a very small clique of riders that achieve multiple top 10 places there are only a very small echelon of riders that are capable of placing in the top 10 on a regular basis and these are presumably those highest earners in the peloton the vast majority of riders are there to make up the race and we'll probably never see the front at the finish and then the last thing and the reason that I mentioned my birth date and actually went to trouble looking at Michael's before I came here as well and he he bucked the trend that or he's not an elite rider is that there's a well-established pattern in in elite sport not just cycling but in other sports and particularly team sports football and and so on where they type the time of year that you're born impacts on your likelihood of being successful in sport now it's tempting to be cheesy and call it astrology but but in actual fact it does appear that the aquarium's and the the capricorns and aries athletes outperform other athletes when you look at what happens in an elite sport this hadn't really been looked at in cycling so we went back through all those results again and we ask the question of those people that are placing in the top 10 when in the year were they born and what you can see is a clear pattern emerging from this data where you can see that the overall trend is quite clearly that there are more people winning races at who were born at the beginning of the year then there are at the end of the year and this line here there's the stats behind this are a little bit complicated but this line here indicates this is what you would expect if the year if the time of year made no difference at all everyone will be clustered around this black line what we actually see is January February March are all quite clearly above that line and then as the year progresses that the trend comes down to this average and then drops below it for people in October Michael's birthdays in November I believe and then and December I'm a December so that's my main excuse for why I didn't win the Tour de France coming back to right at the start you see is that I was born at the wrong time of year perhaps if I could have just held on for a little bit longer and been born in January instead and then everything would have been fine in terms of my aspirations but it's a it's an interesting observation this one and it raises some intriguing questions about the nature and nurture of elite elite cyclists you look like you have a question yes so this particular analysis that I'm showing you here is just for cycling but the same pattern has been in seen in many sports so and and I've discovered that Google is quite good at giving you the birthday of famous athletes so you can say what's the birthdate of so-and-so and Google will tend to throw that person straight back et with a birthdate so it's quite interesting to just do a kind of a little dipstick audit in different sports of famous people and see when their birth dates are but you do tend to find a pattern where quite a you know that this this emerges where people born at the beginning of the year pops out yes sir region is say in UK my limit years start in September yes excuse tine January so and this is one of the concerns within academia in schools in particular is that you see academic performance following a similar pattern but based around school years instead so so that the older pupils tend to do tend to do better they're in an academic year so it's not based around the same it doesn't flex around the same date and I can't remember which sport it is now but they're the the time that they move between junior senior is is bounded in a different way and the date the birth date pattern follows in exactly the same way I should have done a little bit more reading before I came in this evening to anticipate that question but absolutely but I think it's also really interesting and it really challenges us on what the nature what the the map the kind of the nub of nature and nurture is in in all of this that we see this clear pattern and some rationale for why that should be emerging but depending upon what time of year you're born can have an effect on on this and then it's curious to think well what would we do if we were trying to scoop up some wasted talent here are there people that that have the capacity to outperform but because of the time of year they're born they just lose out on some of those key and developmental opportunities because they're slightly less mature slightly underperforming and escaped the coach's attention for example and don't get moved on or given the same competitive opportunities I think that the main reason is is simply that for example in cycling if you're born on the 1st of January you'll be competing with people who are then you're in youngest of that cohort the cutoff date is the 31st so anyone born on the 31st of December will be the youngest person in that in that in that racing year and then when you move to the next year you've got a whole group of people some of whom are almost a year older than you so that that's that that's the difficulty so at 7 16 17 years those extra 11 months that some people have seems to make a difference to their ability to perform gets them noticed and therefore they appear to have an advantage from that which then persists throughout their their whole athletic career it's a it's a curious gets the sign is a correlation between wins somebody you can get and their actual function within who's on I'm at obviously yes in a protein how do you like I'm never going to get a wig but that is a decent Cyprus but mock English or Chris actually won't get experience will tell us that that is that would be the case and we had hoped to be able to look at something like that but actually that the the challenges are just of pulling out this information from that extended data meant that we couldn't break it down in the way that you've suggested one of the other things that we really wanted to look at and our main motivation was to try and look at young riders and how successful they were as young riders and whether they then went on to be the most successful riders but again even that was very very difficult to pull out we couldn't get a confident answer from our analysis in that book you know can you identify a world champion at 16 and see that pattern continued throughout the whole career and actually we found all we couldn't do is just profile individual riders and say this is the period of time they were racing for this is when they started winning and then just look at individual riders we couldn't say there's a clear pattern here the analysis was too complex at least for us in the first instance on that one so break it down into different roles within team we just haven't been able to look at that but logically you would expect that to be the case exactly yes yes but we do look at individual stage results too so we counted every single stage as a potential race okay well I'm glad I've prompted some questions already and I hope that gives me an excuse to run a little longer in terms of what I promised you in terms of keeping to 30 minutes but what I thought I'd do now is just look then it well we've seen a little bit about what Ally performance takes and we've seen some factors that can influence that performance or dictate the level of success or annotate the level of success of individual riders and I thought it'd be interesting to to focus towards the end of my presentation on the training side and again returning to this this aspect of nature and nurture and what I've done here is shown um some data from a classic study much heavily publicized series of studies that was performed by Professor Bouchard and published back in 2000 which looks at the genetics of training and it essentially asks is there a genetic link between how well people respond to training or not and this was a huge study conducted in in the United States across the 90s and they conducted this study in several different universities because they recruited almost 500 people to their study and then they then trained them for 20 weeks and they looked at their change in fitness as a consequence of that training but they were they were very clever in that what they also did was when they recruited their nearly 500 people they recruited them in family groups so you have 500 people but with little family clusters so people from the same household with sharing some of the same genetic material so that they could then look to see whether there was a kind of closer agreement between those people from similar family groups compared with people from different family groups and in this way start to disentangle whether genetics plays an important part in training um and and in terms of the genetic role of training the answer is is very very mixed and what I do is just kind of pull a few little bits out of this which won't give you any specific guidance but just hopefully serve to confuse you further and make me sound a little what this graph shows is this is the number of people in in this in the study and and how much they actually improved as a consequence of 20 weeks of training and one of the first things we note that actually there is a very small group of people around about ten who didn't improve at all as a consequence of training so they did 20 weeks of training and at the end of that study period they hadn't improved at all now initially scientists were a little skeptical I thought maybe that you know this is just a measurement error or whatever but these kinds of observations are fairly regularly repeated so we do seem to find that there are people that don't respond very well to particular types of training so that's not entirely so it's not entirely surprising then what we see is a spread of increases in fitness and crudely this is around about a 10% increase in Fitness somewhere in this region here so the vast majority of people over 20 weeks are improving their performance by around about 10 to 15 percent now when we when I showed you that first slide we looked at the power output of the world record performances for one hour and I made the comment that those people were performing around about two hundred and fifty percent above a normal person's level of performance so here we've got a study where people have trained for twenty weeks and they've increased by around ten to fifteen percent so you can see if you're going to make up a gap of two hundred and fifty percent you've got a fair amount of work to go and it's interesting that actually within sport science that kind of rate of progression isn't something that is very heavily focused on so although this study is very well known the implications of well how much can people improve by and what would you expect if you were starting from here how long is it going to take you to get there is a question that most athletes are very interested in whenever I've been testing people they've always said ok that's my result today but where can I be in a week's time in a month's time in a year's time and we kind of tend to shrug and say yeah I'm not really sure but this data gives us a kind of a first insight purely from looking at across a population at what might be possible but notice that that in this particular graph here the very top level seems to be to gain a thousand units of of increase in Fitness here and they appear to be around about five people that make that kind of progress as we've got a cohort of around 500 participants that's 1% of that group of people that have increased by around about this this thousand unit I'm going to come back to that particular amount of improvement in a moment which is why I'm pulling it out now but so you can see that broadly there's this a kind of normal distribution of improvement in Fitness that's based around 10 to 15 percent increase in fitness as a consequence of 20 weeks of training you'd look like you had a question they did look at that factor so they said it is the amount you increase related to where you started from so someone with a huge increase is it just because they weren't very fit to start with that would be an obvious one and they found that no that that wasn't the case so they were able to rule that out as the starting level of fitness as being a major kind of confounding factor if you like that wasn't the factory case at all what they did find because as I said genetics is part of this it was part of the basis of the stories there is a genetic link here but it's not very strong so it accounts for around 20 to 30% of the of the improvement that you see so there was a definite kind of clustering around family groups so you could identify some families where they did respond much more markedly to training than other families but by and large the genetics didn't explain the whole picture which was what they hadn't they had anticipated that genetics would play a significant role and so they were surprised at how little or how small a role it played and since that time we've seen this pursuit of what are the genes that make up elite performance and and today we're still struggling to find anything that we can identify with any any conviction that hasn't stopped some companies from sort of selling you genetic tests to say you know send us a sample of your saliva or or any cellular material and we'll make a prediction about what sports you're good at but from what we can tell so far actually it's very very difficult to make any meaningful prediction at all from simply a genetic profile and that includes training as well I hope I haven't contradicted what Yana said when he was here two weeks ago um so I wanted to just look a little bit more in a little bit more detail than you know what does it take to increase performance how do we get to that two hundred and fifty percent or two and a half times the normal performance and I asked one of my colleagues Chris Fulton who works with me just to have a quick look at some studies that have looked at training in recent times and then what kind of in performance in fact they've reported and so here we've picked out three studies we've got one from alfred numerator where he he studied riders for four weeks and then improved their performance in four weeks by 2.6 to four percent so that gives us a little bit of feel for what we can expect Bengt run a stat over in Norway he did a study where they trained for 12 weeks and they saw a four point six to six percent increase in performance so a longer study a little bit more improvement in performance but still very consistent with the kind of improvements that we've seen on the previous slide and and and then again the same with this silt'e study here and these are just three studies that we've picked out as being reasonably well controlled recent studies that show a typical before a performance effect and actually I had one of my previous research students do a similar sort of exercise and she condensed a whole load of studies looking specifically at cycle cycling related training and we had which she found around about 15 or 20 studies that we catalogued and overall with all of these studies they were conducted for nine weeks so nine weeks of training and on average over nine weeks of training they found an effect of about eleven percent increase in performance over that time so it kind of gives you a feel for what sort of increase in improvement can be seen but in under these circumstances it's perhaps worth also thinking about what's the minimum and the maximum that you might be seen as well and although I haven't recorded the actual minimum maximum here a surrogate for that is say - what's the variability or spread in that data are they widely spread or are they very narrowly tightly bunched and we use the standard deviation to measure that and so in terms of the length of the studies they varied by around plus or minus five weeks so some studies were up around fourteen fifteen weeks or maybe a little bit or somewhere down at only four or five weeks in this tabulated data here but the variability in the changing performance was huge nine percent so some studies were seeing the effects of over 20 percent others were seeing maybe only one or two percent change across that whole period so what we're seeing here is a pattern where training studies are actually delivering highly variable responses in terms of the benefit to their fitness which is kind of complicating the story a little bit already so we're seeing this is massive discrepancy between where normal people are and what we know elite performance is but we're also seeing that actually the training studies that conducted some people seem to be quite successful others less so we know that there's a small but not huge genetic component to that but the yeah these these changes that we're seeing appear to be highly variable this is the slide that I showed you before and I've said that I wanted to pick out this group of those that increase by more than a thousand units here that last slide and the reason I wanted to do that was because there was a very early study that caught my attention there was published back in 1977 as American physiologist called bill Hickson and he did a study where they trained I think it was eight participants as hard as they could for ten weeks in their study and the astonishing thing about this particular study was it encapsulated in their title and they said they got linear increases in Fitness across the training period and what this line shows you here is the group of participants it's how their foot is increased on average across the training study so this line here shows you the ten weeks of training so this is week one week two week three and so forth and then these are the average Fitness scores for all of their participants grouped together so they started off here down at around three or using these little units over here it's 3,000 and then they increased said that by the end of the study on average these participants had gone above four liters or 4,000 units so that's an increase of 1018 now what's extraordinary that is they had about eight people they increased by about 1018 units in ten weeks and then you look at this study this massive study in America that was done and here they've got maybe 1% of their whole study group have increased by that same amount in this study that's the average result and they were headlining their study by saying we get linear increases in fitness which is what caught my attention when I was a student reading this paper for the first time so what it tells or suggests to me is that what we're actually seeing is it really does depend upon what training you do to this fact that these people can get all of their participants into that little group right on the end there suggest that they've done something fundamentally different in their study to what the standard physiological studies are done this is a well-respected you know highly regarded study with good scientists doing good science you know there's nothing wrong with this at all but they're getting nothing like the kind of improvement in fitness and of course most people are doing exercise because they want to increase their fitness and here's something giving us a bit of a clue that well maybe there are some obvious things where we're overlooking here now I don't know what the answers are as I said I put these I kind of throw out these little hooks for you to come back later on but having read this study quite carefully on a number of occasions a couple of things that were interesting to note on this the first one was the the scientist involved in the study were so curious about their results that after 10 weeks they went to their participants they said we know you only signed up for 10 weeks but would you mind carrying on so we can see whether we can keep this line going and to a person their participant said no your program is too tough there's no way I'm continuing any further and they actually wrote this into the paper so although they've got these fantastic improvements in fitness it was so brutal a regime that their participants weren't prepared to continue any longer it was well beyond what they'd signed up for so that kind of maybe suggests one what one thing that that's um that's key to us and then the the other thing was that these people when you look at how often they trained in the study and compare it with all the other studies that I've mentioned previously this was at the higher end so they were doing six days a week two sessions a day on occasions it was a really it really was a punishing schedule so there may be again you know some hints to look at there in terms of this question so as I said I'm interested in this area and I don't have all the answers but there's very much something I'm focused upon in my own research so as I kind of move to my final few slides just to give you a kind of insight into where I'm headed not the answers but where I'm headed I just thought we'd look at the kind of constants of the question of how we might improve that training response and sorts of things we might look at just before I do that any questions on the on the previous slide I really skimmed over a lot although Michael I'm kind of doing your job for you on tape because we're going to do Q&A okay fine yes yes it's a really really good question and a really important question as well and I suspect part of your motivation rasulina so you may be aware that there's a huge gender bias in the recruitment of participants and so in terms of what I've what I'm presenting here are the the Heritage study which is the big genetic study has a reasonable gender mix the the previous one that I'm looking at was mostly males as far as I can tell it's an older study so it wasn't even reported so much there from working with elite athletes you would say that you would expect women to respond just as well to men through training but that the ceiling will be a little bit lower and typically I mean I kind of go on a rule of thumb of around eight to ten percent difference in in performance so where you see a male performance record performance for example with a different where there's a difference of more than eight to ten percent that suggests at some point you're going to see some kind of balancing out either the women are going to jump up because they've been underperforming or you've had an exceptional women woman that set that record and it will you know so the analysis that we did on the cycling was the men's European peloton so we haven't done a similar exercise for women so it would be it would be very interesting to do the same thing the difficulty there is one of the ways we were able to do that analysis is because we could get so much data for women's racing because the participation numbers are smaller it's going to take us longer to gather enough information to get a really clear picture emerging in the same way so it's a it's not a reason for not doing it at all well it's still an important issue and an interesting one too but that's why we didn't try it in the first instance it were and it was quite a painful process we had to hand clean a lot of that data yeah yeah that's why I say it's you know I like to yes James and I did it together but actually he led in doing a lot of the hard work on that and actually one of our colleagues Christina deeds that did a lot of computing side of it too um so one of the really interesting things and this is perhaps where you can bring it back to home a little bit closer it is to think about an individual training session itself just one training session on its own and so one of my colleagues I referred to earlier Sara Coakley as part of a study that she did brought a group of subject participants into the lab and asked them to ride all of them to ride at the same intensity so we measured their maximum and then we said right at now what we're going to do is ask you to write at 70% of your maximum for as long as you possibly can now the reason that that that's important is because typically that's the way we suggest people standardize their training so if you've got a group of different athletes who have been coached by an individual the coach may well set it training intensity is based on percentages of someone's maximum so in the same way as we're looking at here with 70% and the coach may well suggest training intensity is based around percentages of maximum they may be they may use different units so they might use power output they might use heartrate they might use lactate or whatever it is but essentially they're working from percentages of maximum so it's kind of similar a similar scenario to what we see here and now the key thing here is though we should then look at how long these people can go for so what we've done is say at a the same percentage of your maximum each of you go for as long as you possibly can and what you can see here is these are the different participants here so this is participant number one and that's how long he goes for nearly 50 minutes participant number 15 because we've ranked them on how long they went for manages around about ten minutes now this is supposed to be the same intensity so we would expect I did normally that they should all fall in a pretty much a straight line somewhere in the middle but in actual fact I think what you'll agree is what we see is a huge diversity in people's responses to what's supposed to be the same exercise intensity so in other words what it's telling us is that when you since you specify somebody's training and you give everybody what you think is the trait same session a fixed percentage of your maximum they actually find it very different in terms of the challenge that's presented some people will find it really tough like number 15 here other people will find it really quite straightforward and they can keep going for an awfully long period of time like this person here and although we did this study fairly recently it's not a new finding if you like there's a classic study actually done on well-trained endurance cyclists from back in the 80s that has shown exactly the same pattern emerging and try to explain the underpinning physiology as to why that that should happen but it this has potentially quite profound implications in for our training because if this is your standard training session that your coach has set and he's got two different athletes and one is athlete fifteen and one is athlete number one that training session is going to feel very very different to them and that might explain some of the difference in responses that we've seen from those previous studies they're all specifying their training as percentages at maximum but actually one person is finding it so easy that they could have done fifty minutes but they weren't given the chance another one is going I'm struggling to finish this session at all because this is this is really really tough you had a question yes we ya know that that's right when we tend to standardize these maximums based around laboratory test protocols and what what we often ask the people to do is to is to ride or run increasingly fast until they hit a peak but the test typically takes around about ten minutes to complete so someone like Marcel Kittel although he would have an incredibly powerful sprint because it's a ten minute test he's not able to use very much of that sprint at the end he's kind of running out of gas rather than being able to use that sprint to keep going for a long period of time if we were to say just do a six second sprint then you would see that peak power coming in in a different way and a completely different distribution on this but it would probably be equally biased but in a completely different way the point here really is that the way that we specify training at the moment doesn't really seem to work and actually all those training studies are showing we're getting these very divergent results but part of it is is probably because of the way that we're actually setting out the training programs in the first instance so we're kind of learning the obvious lesson that we need to tailor sessions to the individual we can't make generic prescriptions but we're quite some way from actually nailing what that answer is and you look like you're about to ask me a question related to that for a long time he actually just cracked mentally yes that so one of the slides that I wanted to put into this but I was trying to keep to 30 minutes honest was a recent study that a colleague of mine has done a professor Sam McCrory at the University of Kent where he's compared mental the mental fatigue that occurs during exercise and what Easton is kind of use a reverse model where he's given people a mentally challenging task and so one of the things that you were sadly spared tonight was I was going to give you a mentally challenging challenging task I could put up on the screen if you like a series of colors and then ask you to put your hand up when a certain sequence appeared so you would have to kind of concentrate on each color being flashed up and then go what was the sequence that Lois said he wanted to see was it red green yellow and then I put my hand up or you know and then I'd go red green and then another green so oh no I don't put my hand up and and then I'd say you know you do that for a period of time and they say okay how do you now feel having had to kind of concentrate on that for just a couple of minutes how would you feel if I asked you do that for an hour or or more and the answer is that what you can show is that people that cognitive load becomes significantly fatiguing over a period of time now the fascinating thing that that term Sam McCrory then showed was that if you ask people then to exercise after they've done that kind of task their exercise is compromised so perhaps not entirely surprisingly what happens is people perceived that that training session to be tougher than normal and they fatigue much earlier now the muscles haven't changed at all all they've done is use their brain but the cost of working hard mentally has increased their perception of effort and means they they fatigue much earlier this most recent study what he did was he compared a group of elite cyclists with a group of recreational cyclists and he showed that the elite cyclists were more resistant to fatigue so given that cognitive challenge beforehand they then went out and reduced exactly the same performances before whereas the recreations like I said know I'm really tired now I can't I can't repeat the same thing so the mental side of these things is also very important and and it can have quite a profound effect and there's a suggestion that actually under these circumstances what what happens in physical training is that part of it is we're building a mental resistance to fatigue too so it's important that we don't ignore them the mental component component of that and actually Sam's done a little bit of work where he's superimposed mental training on top of physical training and suggests that people get more benefit from doing that than just doing the physical training alone so there's there's all sorts of things going on in this which is why the whole training conundrum is not simple to unpick sorry it's a very long answer to your question but I hope it's kind of given that did I did I hit the bit that you were really interested in okay well interestingly the actual cognitive test he gave them they did back they did perform better in but but but I'm not sure that means elite cyclists are smarter I think they just it might be context specific but it's a nice thought Michael I'm sure it's one you'd appreciate at the back so so what is it did you say the endpoint so so what what notionally what we do is we ask people to give us a rating there are two different scales but for simplicity we'll say it's nought to ten and when you get to ten that's the maximum effort that you can sustain yeah absolutely to to get people to exercise at percentages of their lactate threshold yes yes yeah no you're you're absolutely right is probably getting a little bit technical for some people in terms of that but actually I have one of my colleagues here in the audience Kieran is looking at something quite similar to that for for his research looking at different ways of trying to structure training sessions to see whether we can find it a way that individualizes them but in a way that we can still generalize across lots of people so for example measuring people's lactate threshold and then setting exercise intensities off that would be an interesting way of doing it yes yes and you're right so the classic study I referred to earlier was done by Eddie Coyle in 1988 and what he did was he measured people's lactate threshold and as well as their maximum and he found those people that went longer had higher lactate thresholds so so there's a suggest there are some hints as to the sorts of things that are important but at the moment it still doesn't tell us what the optimal training effect is it just helps us explain why and it specifies training session might be highly variable for different people so so what - as I try and draw and draw to a conclusion one of the things that struck me is that it kind of comes back to a point I made early on we're now getting an awful lot of data and information on on athletes and their which athletes are routinely collecting this data themselves and it struck me that actually if we could mine this data the training data in the sort of similar way to the way we've looked at the performance data at the start perhaps we could understand something more about before our training and what come what comprises successful training so one of the studies that I did recently was to get a group of 14 runners and they recorded all of their training for a year with their GPS devices so every single running session they did was recorded on a GPS device and now of course we can do exactly the same thing with cyclists - and not only the GPS but we can measure their power output their heart rate their speed and so on so all of a sudden we're looking at large bodies of data which describe the training that someone's been done and then the possibility might then become apparent that we can mine that data set to find out which bits of that training were more effective than others so completely reversing them all the model where we're setting the training and then seeing the effect here we're just measuring the training and then determining the effect based on whatever happens in those particular athletes and I kind of put this slide up to show you this would be an example of one runner and each of these kind of squiggly lines that you can't really see properly is one training session and so for one year this is what one runners training looks like so that's an awful lot of an awful lot of data and then if we do it for lots of athletes too then we're looking at a situation that looks like that slide multiplied by several and if we're then starting to try and analyze all of that data then perhaps we can start to find a way in which we can pick out which aspects of training are more successful than others in a way that we can then generalize and help guide athletes with and so when when sorry and of course so one other point here as well is that if you were a coach working with athletes there's no way that you'd be able to keep track of all this data make sense of it and interpret it incorporate that back into the the way that you guide an athlete's training but by analyzing that data with on a computer of course then it may well be possible so one of the things that I did recently was working with young East Coast Metis who's a statistician at UCL just a few streets away over here and we took all of that data and try to find a way in which we could simplify it because you can imagine looking at all the data on that previous slide there that's pretty tricky but how can we simplify it in a way that we could then start to do the number crunching and the stats on it and this is an example now of actually one athletes training divided into different quarters for the year so we were able to look at the changing performance for the first quarter of the year the second quarter the year the third quarter of the year and the fourth quarter of the year but these blue lines here represent two rep giving you two representations of all of the training for that quarter so these lines here or those lines there they're two they're two versions of the same thing describe all of the training that athlete did in that period of time and what we then said was well which bits of this training what speeds contributed to the change in performance we saw here what speeds contributed to the change in performance we saw here what sorry what training contributed to the change in forms here and so on through that and to my surprise we were able to actually pick out in a group of runners significant speeds that dictated or related to the changes in performance in other words there were certain speeds we could say for that group of runners the amount of time they spent training at that speed related to their changing performance so it's the first if you like hint of what may become possible where in the future our training devices gather data on us but they don't just sit there passively recording actually what they'll do is after the first ride or run they will gather that data and then they'll be analyzed and then the device will switch its function and actually start prescribing instead so it will say speed up slow down go faster change your route whatever it is because it's actually tailoring or optimizing your training to maximise your own performance based on your own data as opposed to necessarily being set by a coach now that doesn't mean say we won't need coaches because it will be much far more complicated than any computer algorithm can do but at least it may help us to get a little bit more information out of the data that we have here and then one little extra bit that I couldn't resist putting in here as well is it might be an interesting tool also to help us detect cheating cyclists too because if we get to know what we would expect to be a normal response from ideal training then we could spot somebody potentially who is in some way defrauding that system by manipulating some aspects of their performance for example taking drugs so we they may then start to stand out as an unusual response from all of their data and when you're dealing with that large amount of data it may be very difficult to manipulate that to hide what you're doing so if you are taking drugs and you're responding unusually which is the whole point you're trying to accelerate your responses then that would be an awful lot of data to try and manipulate to hide that fact so one of the things that we've also done is talked to that the cycling anti-doping foundation part of the UCI about the possibility of using these methods or developing these methods in that way so my very last couple of slides now I thought I'd just show you the evolution of an actual elite Rider this is based on someone who like michael has now moved into the media so it was but the benefit of this was at the time we gathered this data there was no real longitudinal observations on elite riders so what we have here is them is the laboratory tests for this rider from 1991 when I first started working at him with him right the way through to 2008 the Beijing Olympics and a little bit beyond which was when he then retired shortly after that and he doesn't mind me saying who it is it's Rob Hayles who you may may hear joining the commentary teams on various occasions um so what I've done really said that it is his first performance in the lab we'll call it a hundred percent that was his maximum and then we're looking at how his performance has evolved over time now those of you that are vaguely familiar with Rob will know and Michael can probably give us rhyme or verse on his Palmeiras but but he's a multiple Olympic medalist he's um he actually won the British Road Race Championship one year so he sir he's a hugely successful rider albeit largely on the track what we're seeing here is that is his development over a period of 17 years his first test being as I said 91 up to about 2008 2009 and and if we just pick out his first performance and then his highest performance in the lab what we can see is that over a period of around 17 years he's achieved a 17% improvement in performance over that period of time so this is the kind of data that we're only just starting to get our hands on now for the obvious reason it takes around about 17 years to track an elite Rider over the evolution of their of their career and so whilst we're now able to monitor riders quite routinely at for example at British Cycling that kind of pattern of scientific quantification has only happened in the last few years so we'll we'll understand this process much better in a few more years time but this kind of data is still fairly unique in terms of looking at the evolution of riders over that longer period of time which is why I wanted to share it with you and obviously to finish off in that context of saying that's how much an elite performance is ahead of the normal person this is the kind of improvements we can expect and presumably this is somebody who's got his training right over those 17 years to be as successful as he has but he's achieved largely a 17% improvement over that time as a consequence and then my final Authority is that once you've got all of those things right I couldn't resist a couple of pictures that I found of Rob the first one indicating that he doesn't always necessarily make the right choice when it comes to bikes so well this is Rob here on his chopper and so clearly getting the right equipment and refining your but you're cycling position is an important part of the performance as well as we saw in the our record and then the final thing is of course you've got to learn how to ride it - and Rob famously managed to fall off in the Olympic final in the Madison when he was riding with Mark Cavendish I think when they did actually manage to he got up back up and they managed to get the bronze medal but I thought that would be an appropriate point to finish and thank you very much for your attention Thank You Louie a skew faster I got a couple of we've got a little bit of time for a few questions should we so shall we sit down does that does that work I'm rhyme happy - yeah she said knowing them I have I've got a couple of questions but why don't we sit only any questions from Lee from the floor kind of the follow-through at home because yes we had your water shader through our wood but modeling in computer Monday have you ever tried to model things like any web design record or don't buy the backlight material to see if you can work out what you can I personally haven't done that but because of the way that the UCI changed the rules after the the our record that of Boardman's there that there was some attempts because they essentially what they tried to do was to force people to ride in a merc style equipment including even the hairnet and things and for a for a few years so so there was a bit of work that was done particular by the professional team supporting riders that were thinking about going through our record and showing the added cost of of that but I haven't gone back and done that myself to if you like extend the power of figures back if performance tends he did I think he was in 2000 just before he retired he popped up in and this is probably an area that Michael would be able to comment on what be more detailed a knife yeah but he went back to doing the marks position set up in 2000 and I think the estimation I think the reckoning was that that record was about 420 Watts when he went back to attacking the Mercs record in erode he just broke Marx's record off that by 10 10 meters I think so that's kind of kind of fits in with the with the model can I can I ask I'm going to ask a question I am going to ask given you talked a lot about training and how you model training and how you work for now I interviewed Allen dr. Allen Williams from Manchester Metropolitan University a couple of years ago who has done a lot of work in genetics and I asked him the question everybody wants to the answer to I said how much of it is genetics how much of it is hard work and he said well actually it's almost exactly he thought fifty-fifty he said that's not a number he pulled out of the air he he did the work he did the maths he said it was 5050 if you improve training does that change that balance if training gets better does it make it easier for someone with perhaps not exactly the right genetics to make it as an elite cyclist so which bit is how much bit of that because that was a there were multiple steps yes question was the bit your camera chap no the question is if training gets better yeah does that make it easier not easy but easier for someone who might not have the perfect genetics to make it to the top of elite cycling I think if I were to give you a really short concise answer it would be no right so now I ask a really short concise question well well just why if you want to qualify that yes it would which is that my suspicion is that we will find that there are some individuals that are super talented and actually I was struck by reading your own book oh thank you the description of your early experiences of cycling and how fast you cycled at a very early stage in your cycling career and feeling quite jealous and envious of the fact that you could get on a bike and go like a rocket when I'd spent ten years struggling to perform at about 80 percent of the performance that you were able to achieve very early on if it's any comfort I never really got any better I started fairly good and I stayed fairly good and so I but I think what we're looking for is that we recognize from that spread of those top ten placings that actually there's a few riders even in the pro peloton that get a top ten placing regularly or more than once and they really are the creme de la creme or of an already elite group and I think that those people were probably born head and shoulders above everybody else and then with the hard work were able to get that extra little bit that then pushes them beyond their peers so that the optimal training moves those people or continues to hold them further ahead so I think for us mere mortals we can't necessarily expect to achieve elite status but I do think that with optimal training what we can do is surprise ourselves with what we can achieve so I've several times been working with writers who've been told by their coaches that that rider has achieved his or her maximum potential and I always think that's a very dangerous statement to make and at least on three occasions that I can think of in my own career as a scientist working with cyclists I've seen cyclists go on to surpass that performance they've been told was that their very best performance and not just through technological innovations so not just cuz they're riding a faster bike or more aerodynamic dynamic position but they've improved their their training and their fitness too so I'm always very wary of any attempt to set what the limits are I prefer to kind of live in this world of opportunity and possibilities set the context and try and allow people to achieve the very best they can and and looking at ways of modifying training I think is one of those and then just one further observation very very long-winded answer this but there's a long question as you pointed out one of the things that I'm struck by and this is also very personal reflection too is that many people are very reluctant to experiment with their training and try radically different methods or strategies and that kind of thing and I think that will constrain us too because what we'll find is that different people will probably need very different types of training session to move forward and if you don't experiment with different types of training and try things out different balances mixes length of time intersections sessions and that sort of thing you probably run the risk of not finding those sessions that will really enable you to achieve your full potential so I think some of this is also those riders that follow the tradition for example will be the ones that perhaps don't get the chance to achieve their full potential unless they happen to be the ones that it the traditional approach works for there may be other people out there that would benefit much more from a completely different approach and trying to find out what that is and how we get a handle on it is a real challenge can I ask a topical follow-up question which I was good was going to throw it over the floor but I want to know the answer to this which is if you look at the Team GB at the minute in Rio being very successful and having their performances questioned by several other teams and one of the answers that they have given is well we just focus on the Olympics once every four years that's what we're about I know some of the coaches and the staff at Team GB who talk about the fact that they train for a four year cycle is this one way to it does this work as you think as a means of optimizing performance instead of most athletes train on an annual basis they start in early in the year they train to the Tour de France they train the World Championships they take a break and they repeat the cycle every year yeah is it better can you get better performance do you think by training on a longer cycle so that you maybe spend a year doing endurance work and then a year doing gym work and then a year or something like that I suppose the short answer is I don't know but that there's some interesting observations that I would make so some of you may well have heard of Steve Peters there's the psychologist that worked with British Cycling Liverpool Football Club and was it Ronnie O'Sullivan erroneous olive only rugby team and I remember one day at a team meeting when I was working at British Cycling him trying trying to offer the helpful perspective that that's cyclists generally are committed to working as hard as they possibly can at any given moment so in other words he was kind of suggesting that it might be a four-year cycle but actually at any moment they give their giving their their best and it was so it was a very well-intentioned comment and I completely agreed with the sentiment he was expressing but the sienten is sorry the scientist in me also can't help but kind of challenge these these kind of ideas so I was sort of flipping this one around in my head and actually I thought I'm not sure that's true I think actually there are times when we will go the extra mile and find extra resources and put the extra effort in and Olympic year being a very obvious example of when that might happen and so I think actually by working in a four year cycle it's quite possible that you construct your training in a way that you don't end up scraping out all of your resources every single year but actually leave yourself a little bit of breathing room so that you can push even harder for those very special events and and so there's an element with which I think that that might be an important fact but it's more complicated but it's purely physiologic much more complicated than that and the other kind of one of the things I remind you out from the talk today was that study I showed you where they got this tremendous increase in performance with the eight eight participants where they improved by the same amount is only one percent of the bigger study those people said they wouldn't carry on now I'm sure they were quite happy to get fitter if it was just a question doing training go they were saying this is such a tough regime we don't want to do sustaining the work that's required to move to the next level and as you get into elite performance or Michael you're probably a better commented a better position than anyone here to talk about kind of how much you have to sacrifice everything else in your life just to squeeze out that extra little bit of a percentage but Rob Hayles profile of 17 years and 17 percent you know telling you you're looking for one percent each year you know again you know I think it's hard to imagine exactly the kind of effort and focus and commitment that comes from this but that's why I think working in bigger cycles might well be helpful questions from the questions from the floor I think we've probably got time if Louie can keep it short which the evidence large suggests that might not happen but we probably got time for another couple of questions of who you can keep it concise we've got sir here Thank You T on the two sides so good we've looked at - - studies that talk to us about training response but in fairness but we raise more questions than they gave answers first two years old okay I'll try and keep this concise fabulous ask me so two things what one is I've kind of I've taken a particular perspective in order to showcase my point so there are a lot more studies available but they say the same things and they're guilty of the same kinds of issues that I've raised here so so it's not that there isn't more there aren't more studies more contemporary studies but they're done in the same vein and they suffer the same limitations so that there isn't anyone that's addressed the kind of issues that I pulled out yet so I think that that's kind of the perhaps that's the quick answer to your your thing yeah and one last question do we have a last question oh no ah sir yes yes very clearly enough you use to stop doing all this long yes during training there you go I think if your time limited it's an excellent strategy I think if you're seeking elite performance then you probably need to mix your training up a lot more than is implied by just going out doing high-intensity work so if you talk to elite coaches and athletes that they haven't missed that trick they know the importance of high intensity training and they do include it in their work but but it's carefully measured in timed and so I think that I say if you're if you're looking if you're time pop or and you're looking for effective sessions and the message is a very helpful one but four elite performances it's more complicated than that I think at twenty past seven I think we are we get chucked out at half past so I think we probably ought to wind that up at this point I'd like to say thank you very very much and professionally we pasteurize energy to it's great to be at is how to speak and pause and have people ask questions along the way so you've been a real pleasure to work with today as well but thank you very very much though I think we've all enjoyed it very much indeed thank you you

Channel: The Physiological Society

Views: 472,789

Keywords:

Id: iUuwBfXDlTs

Length: 78min 20sec (4700 seconds)

Published: Thu Sep 22 2016

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[u/howheels]

December birthday -- My excuse for not getting those KOMs