Professor Chris Gore, head of Physiology at the Australian Institute of Sport, has had over 20 years experience in the science of sport at altitude, including the study of the physiological effects of altitude on the body and designing altitude training regimes for athletes.
The effects of altitude have been known for some time, however their effects on sport became prominent during the 1968 Mexico Olympics, which were held at over 2000 metres. At these games, endurance sports suffered whilst records were set in sprint events. Many games in the current 2010 FIFA World Cup are being held at altitude, and all of the highly professional teams have had some form of altitude training before the competition.
I spoke to Chris about the science of sport at altitude, including the physiological effects on the body, the different physics that apply to sports played at altitude, how altitude training works and the ethics of artificial altitude training. Listen to this show here, or press play below - and read on for some more info. This question came in from the guys at Green and Gold Rugby as part of our call for science stories for science week. With the World Cup currently being played at altitude, I thought it best to bring this particular question forward - thanks for the question guys! I will be writing up a more comprehensive story on the topic soon.
Much of the effect of altitude on sport concerns our aerobic performance. As the atmospheric pressure is less at altitude than at sea level, less oxygen is taken into the blood. The maximum capacity of the body to utilise oxygen is known as VO2 max. At altitude, VO2 max decreases, meaning that for every 1000 metres climbed, our aerobic performance (VO2 max) decreases by 7%. For endurance and aerobic events, if you are not acclimatised, your performance will decrease.
The body reacts to the decreased atmospheric pressure by making more red blood cells to help the uptake of oxygen. This is where the benefit of altitude training comes in. If you spend a number of weeks at altitude and then return to sea level, these extra red blood cells could help your aerobic ability on your return. The Live High, Train Low concept has become reasonably well established. What this means is that for most of the day, you live at altitude, but you conduct your training at low altitude. As you are less able to work at high altitude because of your decreased aerobic ability, and hence less able to build strength or work on skills, training is conducted at low altitude. However, you live most of the time at high altitude in order to increase your VO2 Max.
The second part of the problem is the different physical dynamics that are present at altitude. For ball sports, as the air is thinner, there is less drag and hence balls tend to travel further. There is also less curve on the ball (or swing in cricket) as there is less friction caused by the air. This can mean that skills learnt at sea level are not necessarily attuned to the higher altitudes.
This mix of physiology and physics means that some sports are better performed at high altitudes and others at low altitudes. Foot races up to about 400 metres are faster at high altitudes as these are sprint events where aerobic capacity is less important (that is, they are anaerobic sports that don't require much oxygen) and the decreased drag improves the time. Chris Hoy famously attempted to break the World 1 kilometre Cycling sprint time at altitude in Bolivia and Bob Beamon smashed the long jump world record in Mexico, both largely due to decreased drag.
However, sport at altitude is not simply a matter of VO2 max and air friction. More than 200 genes are turned on during hypoxia, which is the condition where the body is deprived of oxygen. The AIS is studying many aspects of the problem including how lactic acid builds up in muscles differently at altitude than at sea level. Studies are also being conducted into the effect of altitude on skills. Chris considers that for a mid altitude of 2500 metres, it takes at least 2 or 3 weeks of acclimatising to be ready to compete, and possibly up to 8 months to be completely adjusted.
High altitude football teams have been found to have an advantage at home, but interestingly, low altitude teams have a similar advantage as it has been found (but not yet explained) that people coming down from altitude can struggle to adjust at sea level, becoming sluggish and lethargic. However, this is only for very high altitudes of 4 - 5 kilometres.
Chris considers that altitude training will only improve your performance by 1 or 2% and only for elite athletes. The effects may last up to 3 weeks but those who have spent a lifetime at altitude may see positive effects for much longer at sea level.
The use of "artificial altitude" - that is, hyperbaric chambers - has been ruled OK by the World Anti-Doping Agency. To hear more on this topic, all the topics mentioned above, and plenty more, tune in here, or press play below:
Here are some videos of the two altitude feats mentioned in the podcast, the Long Jump World Record of Bob Beamon in Mexico, and the attempted 1km Cycling World Record of Chris Hoy in Bolivia:
Bob Beamon:
Chris Hoy:
References:
McSharry, P. (2007). Effect of altitude on physiological performance: a statistical analysis using results of international football games BMJ, 335 (7633), 1278-1281 DOI: 10.1136/bmj.39393.451516.AD
Loland S, & Caplan A (2008). Ethics of technologically constructed hypoxic environments in sport. Scandinavian journal of medicine & science in sports, 18 Suppl 1, 70-5 PMID: 18665954
Friedmann-Bette B (2008). Classical altitude training. Scandinavian journal of medicine & science in sports, 18 Suppl 1, 11-20 PMID: 18665948
Levine BD, & Stray-Gundersen J (1997). "Living high-training low": effect of moderate-altitude acclimatization with low-altitude training on performance. Journal of applied physiology (Bethesda, Md. : 1985), 83 (1), 102-12 PMID: 9216951
Bärtsch P, Saltin B, Dvorak J, & Federation Internationale de Football Association (2008). Consensus statement on playing football at different altitude. Scandinavian journal of medicine & science in sports, 18 Suppl 1, 96-9 PMID: 18665957
Saturday, 26 June 2010
Friday, 18 June 2010
How do you spell goal?
It's not often I get the chance to pursue three of my passions - sport, mathematics and online social media - at the same time. The 2010 Football World Cup combines these facets of life in a way we probably haven't seen before, providing numerous opportunities for data mining, funky visualisations and general nerd-indulgence, as well as knocking out twitter for a time.
One creative exploration I saw recently was by @neilkod, who collected data from 30 GB of tweets on how people spelt the word "goal". Data mining Twitter is an evolving field - see our recent story on how by using Twitter data you can predict the success of a film. You can find the full goal data table here, and I have listed the top and bottom few below:
As expected, on top is the word "goal" (71%) followed by "Goal" (17%) and "GOAL" (6%). Then there are various misspellings, before the excited tweets come in, including the 140 character "Goooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooal".
Interestingly, the caps lock induced "gOAL" is only used once.
You can also visualise this in a chart - note that in the chart below, the x-axis is a log scale due to the fact that the leading terms are so far ahead:
Now it's time to get our nerd on....
Zipf's law is a curious law that arose out of an analysis of language by linguist George Kingsley Zipf, who theorised that given a large body of language (that is, a long book), the frequency of each word is close to inversely proportional to its rank in the frequency table. That is:
where a is close to 1. This is known as a "power law" and suggests that the most frequent word will occur approximately twice as often as the second most frequent word, which occurs twice as often as the fourth most frequent word, etc. There is an excellent take on this over at Plus Magazine. As you can see in the log/log chart below, after the 5th version of "goal" a Zipf curve fits remarkably well.
There has never been a real explanation of why Zipf's law should apply to languages and there is controversy surrounding whether it gives any meaningful insight. Power laws relating rank to frequency have been demonstrated to occur naturally in many places - the size of cities, the number of hits on websites, the magnitude of earthquakes and the diameters of moon craters have all been shown to follow power laws.
Wentian Li demonstrated in his paper Random Texts Exhibit Zipf's-Law-Like Word Frequency Distribution, published in IEEE Transactions on Information Theory, that words generated by simply randomly combining letters fit the Zipf distribution. Li showed mathematically that the power law distribution of frequency against rank is a natural consequence of the word length distribution, with words of length 1 occurring more frequently than words of length 2 and so forth. His underlying theory is that the rank distribution arises naturally out of the fact that word length plays a part - long words tend not to be very common, whilst shorter words are. Li argues that as Zipf distributions arise in randomly-generated texts with no linguistic structure, the law may be a statistical artifact rather than a meaningful linguistic property.
Our results mirror Li's quite closely. It is clear that the most used versions of the word "goal" - where goal is spelt correctly with various capitalisations - should not fit the Zipf distribution as these words are not random - they are the actual correct spellings people are looking to write in their tweets. However, after the initial few words, random spelling errors, and then the simple randomness of how long people hold their fingers on the keys in the excitement of a goal, take hold. From this point, we see exactly the same as Li - that the Zipf distribution arises from random words, with longer words less common than shorter words.
If you would like to see some truly insightful twitter world cup visualisations, check out The Guardian's World Cup 2010 Twitter replay. I haven't been replaying the Australia vs. Germany game very often....
One creative exploration I saw recently was by @neilkod, who collected data from 30 GB of tweets on how people spelt the word "goal". Data mining Twitter is an evolving field - see our recent story on how by using Twitter data you can predict the success of a film. You can find the full goal data table here, and I have listed the top and bottom few below:
Rank | Word | Count |
1 | goal | 50225 |
2 | Goal | 11727 |
3 | GOAL | 4202 |
4 | goAl | 798 |
5 | goall | 340 |
6 | GOAAAL | 92 |
7 | goaL | 88 |
8 | Goall | 75 |
9 | GoaL | 69 |
10 | GOAl | 66 |
11 | goaaaal | 61 |
12 | GOal | 50 |
.... | .... | .... |
1249 | GGGGGGGGGGGGGGGGOOOOO OOOOOOOOOOOOOOOOOOOA AAAAAAAAAAAALLLLLLLLLLLLLLLL | 1 |
1250 | GGGGGGGGGGGGGGGGGGGOO OOOOOOOOOOOOOOOOAAAA AAAAAAAAAALLLLLLLLLLLLLLLLL | 1 |
1251 | GGGGGGGGGGGGGGGGGGGOO OOOOOOOOOOOOOALLLLLLLL | 1 |
1252 | GGGGGGGGGGGGGGGGGGGGG GGGGGGGGGOOOOOOOOOOO OOOOOOOOOOOOOOOOOOOO AAAAAAAAAAAAAAAAAAAAAAA AAALLLLLLLLLLLLLLLLLLLLLLLLL | 1 |
As expected, on top is the word "goal" (71%) followed by "Goal" (17%) and "GOAL" (6%). Then there are various misspellings, before the excited tweets come in, including the 140 character "Goooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooal".
Interestingly, the caps lock induced "gOAL" is only used once.
You can also visualise this in a chart - note that in the chart below, the x-axis is a log scale due to the fact that the leading terms are so far ahead:
Now it's time to get our nerd on....
Zipf's law is a curious law that arose out of an analysis of language by linguist George Kingsley Zipf, who theorised that given a large body of language (that is, a long book), the frequency of each word is close to inversely proportional to its rank in the frequency table. That is:
where a is close to 1. This is known as a "power law" and suggests that the most frequent word will occur approximately twice as often as the second most frequent word, which occurs twice as often as the fourth most frequent word, etc. There is an excellent take on this over at Plus Magazine. As you can see in the log/log chart below, after the 5th version of "goal" a Zipf curve fits remarkably well.
There has never been a real explanation of why Zipf's law should apply to languages and there is controversy surrounding whether it gives any meaningful insight. Power laws relating rank to frequency have been demonstrated to occur naturally in many places - the size of cities, the number of hits on websites, the magnitude of earthquakes and the diameters of moon craters have all been shown to follow power laws.
Wentian Li demonstrated in his paper Random Texts Exhibit Zipf's-Law-Like Word Frequency Distribution, published in IEEE Transactions on Information Theory, that words generated by simply randomly combining letters fit the Zipf distribution. Li showed mathematically that the power law distribution of frequency against rank is a natural consequence of the word length distribution, with words of length 1 occurring more frequently than words of length 2 and so forth. His underlying theory is that the rank distribution arises naturally out of the fact that word length plays a part - long words tend not to be very common, whilst shorter words are. Li argues that as Zipf distributions arise in randomly-generated texts with no linguistic structure, the law may be a statistical artifact rather than a meaningful linguistic property.
Our results mirror Li's quite closely. It is clear that the most used versions of the word "goal" - where goal is spelt correctly with various capitalisations - should not fit the Zipf distribution as these words are not random - they are the actual correct spellings people are looking to write in their tweets. However, after the initial few words, random spelling errors, and then the simple randomness of how long people hold their fingers on the keys in the excitement of a goal, take hold. From this point, we see exactly the same as Li - that the Zipf distribution arises from random words, with longer words less common than shorter words.
If you would like to see some truly insightful twitter world cup visualisations, check out The Guardian's World Cup 2010 Twitter replay. I haven't been replaying the Australia vs. Germany game very often....
Labels:
Maths and Stats,
Sport,
Technology,
Visualisation
Tuesday, 8 June 2010
Visualising the Music Universe
Every now and then I like to post data visualisations, and this one comes from one of my favourite web applications, Last.fm. Last.fm remembers what songs you play on your iPod (or whatever music player you use). Given that we now have an electronic device ban in our workplace, I'm giving their online streaming radio a go. (Ask me what I think about this ban and I'll give you a forthright answer, offline...) In any case, last.fm is a data lovers dream. The following visualisation shows my listening habits in 2009. The planets represent the top tags given to songs I listened to throughout the year - generally, these tags are song genres. Planet size is proportional to how much of that tag I listened to. The moons represent my top artists of 2009, with the light side showing how much I listened to the artist this year and the dark side showing last year's listening. Moons that are clustered together suggest that those artists are similarly tagged. The orbits show the correlation between the artist and the tag - artists with a closer orbit are more strongly associated with that tag than artists on the outer orbits.
Very funky, even if sometimes the moon is larger than the planet, and the small planet "Old People" appears in my solar system - you'll need to check out the high resolution picture to make out the smaller planets and moons.
Very funky, even if sometimes the moon is larger than the planet, and the small planet "Old People" appears in my solar system - you'll need to check out the high resolution picture to make out the smaller planets and moons.
Labels:
Astronomy and Space,
Music,
Visualisation
Saturday, 5 June 2010
Bring us your burning science questions
Do you have a burning science question that you need answered?
For National Science Week 2010, along with Diffusion Science Radio, we are asking you for your science questions, which we'll then pose to
relevant experts. Chemists, mathematicians, physicists, politicians, sociologists - we'll track down whoever we need to to respond to your queries.
A selection of the best questions and answers will be played over the airways on Diffusion on 2SER during Science Week, August 14 to 22. You can listen to Diffusion on Monday nights on 2SER 107.3FM in Sydney, across Australia at various times on the Community Radio Network, streaming online, or via podcast. The expert interviews will be prerecorded so we can answer as many of your questions as possible during the show. The Science Week show will also be podcast here on Mr Science and many of the questions we don't get to on air will also be published here.
Send in your questions via email, twitter (@westius), on the Facebook page or in the comments section below. Feel free to ask any question you like, no matter how broad or specific, and be creative. Want to know more about climate change or the LHC? Interested in recent Nobel Prize winners? Intrigued by how you might go about solving the Reimann Hypothesis? Why do you smell?
Whatever your question, we'll have a shot at finding someone who can answer it. We're looking for science questions, but don't limit your queries to hard science - feel free to ask about how science influences our lives. For instance, what exactly is a Carbon Trading Scheme and how does it combat climate change?
So start sending in your questions - the earlier, the better, especially if your question is obscure and the relevant expert in Gross–Pitaevskii physics lives in country Urumqi.
For National Science Week 2010, along with Diffusion Science Radio, we are asking you for your science questions, which we'll then pose to
relevant experts. Chemists, mathematicians, physicists, politicians, sociologists - we'll track down whoever we need to to respond to your queries.
A selection of the best questions and answers will be played over the airways on Diffusion on 2SER during Science Week, August 14 to 22. You can listen to Diffusion on Monday nights on 2SER 107.3FM in Sydney, across Australia at various times on the Community Radio Network, streaming online, or via podcast. The expert interviews will be prerecorded so we can answer as many of your questions as possible during the show. The Science Week show will also be podcast here on Mr Science and many of the questions we don't get to on air will also be published here.
Send in your questions via email, twitter (@westius), on the Facebook page or in the comments section below. Feel free to ask any question you like, no matter how broad or specific, and be creative. Want to know more about climate change or the LHC? Interested in recent Nobel Prize winners? Intrigued by how you might go about solving the Reimann Hypothesis? Why do you smell?
Whatever your question, we'll have a shot at finding someone who can answer it. We're looking for science questions, but don't limit your queries to hard science - feel free to ask about how science influences our lives. For instance, what exactly is a Carbon Trading Scheme and how does it combat climate change?
So start sending in your questions - the earlier, the better, especially if your question is obscure and the relevant expert in Gross–Pitaevskii physics lives in country Urumqi.
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