Some Probability And Statistics On The Individual Time Trial

The Tour de France (TDF) is the marquee cycling event on the calender for any top international pro cyclist as well as their squads. Everyone wants to do well here because its arguably the biggest and most glamorous stage for displaying athletic talent. The competition is tough, the fans are many, the stages are epic and the prize money is fat.

In this post, I'm trying to figure out what kind of a statistical distribution is seen in the finishing times from this year's prologue TT (Tour de France). I will also try to quantify the probability of getting close to the fastest time trialist in the world. Alberto Contador tried pretty darn well. How well?

Only one way to find out these things.

So here's what I did.

Step 1 : I obtained Cyclingnews.com data for the TDF Prologue TT on July 4, 2009. I obtained 180 data points corresponding to all the competing cyclists.

Step 2 : To make sense of this data clutter, I put them into Microsoft Excel 2007 and ran a descriptive statistics analysis on it. Here's what I obtained. What you're about to see is powerful.

Fig 1 : Descriptive statistical figures for the finishing times of a sample set of 180 cyclists from the Tour de France 2009.

So is my sample set taken from a normal distribution or something different? Let's try to answer that reasonably with the table above.

The mean, median and mode are very close to each other which MAY indicate its normally distributed. The average of the average deviation of each cyclist from the mean was 0.63 min or 37.8 seconds. The minimum time belonged to Fabian Cancellara, with a blitzy 19.53 mins whereas the maximum time belonged to Yauheni Hutarovich. I also have a Kurtosis and Skewness of 0.558 and -0.068 respectively.

Positive Kurtosis indicates a relatively appreciable peak which makes me suspect the distribution is leptokurtic (too tall instead of normally high). The book Using Multivariate Statistics (Tabachnick & Fidell, 1996) explains that if my Kurtosis statistic is more than 2 times [sqrt(24/180)] = 0.73, the data is not normally distributed. Since 0.558 is less than 0.73, we're ok.

Negative Skewness indicates that my data is left skewed. The same book mentioned above explains that if my Skewness statistic is more than 2 times [sqrt(6/180)] = 0.365, the distribution is not normal. Since -0.068 is less than 0.365, we're ok here as well.

Step 3 : The above only gives rough indications of the type of distribution. Nothing beats setting up a visual of the spread. So I made a histogram, with a chosen bin width of 0.20 min.

Fig 2 : The histogram for the data set. Please see source of data on CyclingNews.

The graph agrees with the skewness and kurtosis statistics. The data has central tendency but is ever so slightly skewed towards the left. This is the data for the best cyclists in the world. Not really a Gaussian, but not too far away from it either. What kind of distribution it is will take more analysis and tests for goodness of fit, which I'm going to tackle some other time.


So What Does All This Mean?

Looking at the data and Fig 2, we can say that the course conditions in Monaco on that July day were such that nearly 48% of all 180 cyclists managed to get times below the average, which might mean they were pretty fit and came well prepared (or something else worked in their favor which I can't quantify). Thus, the 48th percentile is the average time, i.e 21 min and 30 seconds.

To put it in another fashion, the probability of a world class cyclist racing on this course in a time less than the average time is 0.48.

52% of the 180 performed under par, with about 8% of those 52 giving exactly average times. The probability is 0.52 that a cyclist is at average time or above it on this course.

We can also say that 72% of the 180 cyclists lie between one standard deviation on both sides of the average, 93% lie between two standard deviations about the average and 99% lie between 3 standard deviations. Pretty close to the 68-95-99 rule obeyed by normal distributions eh?


Alberto Contador Vs Fabian Cancellara As Time Trialists

Our last question is the most interesting. So if you're a top pro at the peak of your abilities, what are you chances of ever getting close to Fabian Cancellara's blitzkrieg results? Then the next question would be, how close do you want to get to 'Spartacus'? Within 2%? 3%?

Let's do 2% as a start. Within 2% is 23 seconds difference. Now that's probably the limit of what a time trialist can accept to cap the gap, so to speak!!

Let's look at what Contador obtained that day from the data. Bert raced the course 18 seconds slower than Cancellara for an amazing second place. In other words, there was a mere 1.54% time difference between the best all round cyclist in the world and the fastest time trialist in the world. Just 4 cyclists managed to come within 2% of Cancellara's time - Contador, Wiggins, Kloden and Evans. 4/180 = 0.02 = 2%.

In other words, just 2% of the 180 cyclists got a time less than or equal to 19 minutes and 55 seconds (this 2% window we're talking about).

Put in another way, this is the 2nd percentile. This is where the glory is at. And the money. And the kisses from the long legged European girls.

The probability that you're in this 23 second window from the best man on the bike is low. Just 0.022 or 1 in 45 chance. Keep in mind this is for the best in the world.

Now you know why you and I are not racing in the Tour de France. Let's just scratch our butts and cheer these beasts on.

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E-Hub Marketing : A Important Lesson In Statistics

Like the past 100 years, even today cycling products come and go. And with them, so do their marketing sound bytes...

Any intelligent cyclist must carefully inspect marketing data handed to him, and question what is missing and why its missing. Weak data can lead to weak correlations, spurious percentage differences and other logical fallacies. Until the missing numbers are accounted for, I don't advise anyone to take faith in where they put their money.

When James posted a small article yesterday on the E-Hub at the Bicycle Design blog, I got very amused and decided to take a peek at the product website. I spent a little time looking at the interesting item proudly displayed but then had an itching desire to see the numbers behind the invention. Not just plain numbers. I wanted to see if they're meaningful numbers.

This page has a statement from Dr. Alen Orbanić (a University mathematician from Slovenia) telling us that the designers behind the innovation carried out a surefire experiment to prove without doubt that using the E-hub for cycling showed the following things :

1) Increased average power output when compared to cycling with a conventional rear hub.

2) 4% reduction in average and maximal heart rates in cyclists using this product, when compared to the same figure for cycling with conventional hub.

3) 10-15% of blood lactate reduction using the E-hub versus using a conventional hub.


So What Was The Experiment?

Well, I'll tell you the part of it they conducted outdoors. They brought together a population of cyclists from 20-60 years of age. How many? Not specified. Then they categorized them as "Professionals", "Recreational" and "Amateurs". How did they define who belonged where? No indication. What were their weights, fitness levels etc? No indication.

So this population of cyclists were asked to fit themselves with a Polar heart rate measuring system who then mounted Ergomo powermeter fitted MTBs to ride a 2km track (1.24 miles) with 14 degrees of average inclination. Apparently, they did this twice, one with the E-hub and one with a classic hub after 24 hours of rest between the two. Levels of lactic acid were measured twice, immediately after each run with a hub.

Fig 1 : A snippet showing how things were measured by the authors. Typos abound. Click to zoom.

I'm surprised a tad bit by two things. 14 degrees of average inclination? Wow. That is an average of 25% grade. Second, I'm surprised recreational cyclists could manage this effort. Either Slovenian humans are exceptional, or the drive train was really dumbed down for spinning, or something is just plain wrong with this number presented to us. I have written in the past about the W/lb required to maintain a certain speed on a given grade.


So What Does The Data Look Like?

The authors go on to claim they gathered a "vast quantity of data" but for the sake of the reader's reading convenience, they picked 3 'random' data points corresponding to 3 cyclists, for each class of cyclist. I guess this is a solid example of where you can't really thank people for their kindness :).

Here are the numbers :

Fig 2 : 3 randomly selected cyclists in each class showed the above numbers with and without an e-hub. And how were they randomly chosen? No indication so could we not say this is an example of data mining?

Fig 3 : % differences in heart rate and power between the two hubs.

Fig 4 : % differences in average blood lactate between the two hubs.

Right off the bat, I see this is poorly presented data, at least for a professional level. From the surface, I can come up with 3 weaknesses :

1. Sample Points & Averages : There's a rule of thumb in good statistics. You need a minimum of 30 sample points before you do descriptive analysis on it to explain trends.

Take a look at the amount of power these cyclists are producing on this so-called 25% grade, 1.2 mile track. Professionals are producing puny average power outputs while recreational and amateurs are easily rivaling them, not only in power but also in speed.

This leads me to question firstly how the authors classified and defined these cyclists. It seems to me from this meager amount of data that all three classes were almost equal in their cycling abilities?

I also have to say that averages can fool you if data jumps all around the place wildly. For the meager sample points presented above, you can see that the average power is pretty sensitive to outliers.

Infact, if we had been handed 30 sample points or more for each class of cyclist, it is likely the data could have shown a decreased average power, which could have reduced the resultant power differences between the E-Hub and the classic hub. Any guarantee that's not the case? The authors haven't proven it here but go on to artificially bump up the averages using just 3 data points mined from here and there. Furthermore, their conclusions about the apparent efficiency increase with the E-Hub is only relevant for these 3 sample points.

2. Spread : Closely following the absence of more samples is the question, what's the spread and deviation of this "vast amount" of data? I don't have any idea of it as there's no indication of standard deviation. The data is meaningless. How can I tell if a majority of data points in this experiment are close to the average power output or not? What if outliers are pushing the average up?

3. Range : Because only one sample data point (for power, HR and lactic acid) have been presented to us going across for each cyclist, we have no idea of the true range, or the true maximum and minimum values that would be observed. The data point presented to us is just one of what could be many and they are all bound to vary, because that's how all processes are... they vary! Hence, the range could vary pretty significantly if we had more tests on the same individual.

4. Instrument & Measurement Error : Lastly, what about the instruments used? Were they calibrated properly and accurate to other power measurement systems? What's the bias in the system, if any? Are these numbers from just random variability or regression to the mean? It is often taken for granted by some that measurement systems (instrument+human operator) that produce such outstanding numbers are always pin-point accurate.

I simply have to conclude that this data, so far, to me is just meaningless. The rest of the data that follows on the webpage, done on an indoor ergometer, suffers from exactly the same types of weaknesses I have mentioned. These are basic rules to follow in statistics and I'm surprised they weren't in this case.

The product itself may be great. I cannot disagree for certain there. But the numbers don't show me much so far. Thus, I think the declaration that this hub system really improves the efficiency of a cyclist compared to what we usually use must be taken with a handful of salt.

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Statisculation & Sporting Prejudice In Anti-Helmet Propaganda

This post is In Memory Of Andrew Callighan, who died 21 April (Saturday), two days after he was struck by a pickup truck while riding his bicycle in Michigan. Andrew, who was not wearing a helmet, was thrown several feet from his bike by the impact of the crash and was found on the side of the road when police and other rescue workers arrived. He sustained severe and multiple skull fractures and was pronounced dead Saturday at Helen Devos Children's Hospital in Grand Rapids. Michigan has no state law regarding the use of bicycle helmets.

Get your coffee ready. We're going to explore 2 topics that I feel must be addressed. Each of those 2 topics involves a detailed case study (highlighted in blue font), and my comments are given below a table, or graph. My primary audience are the folks who use mis-information to suggest that helmets cause not only a decrease in cycling, but a host of other problems, some of which we will cover. I do not necessarily support mandatory helmet legislation, and don't give a damn about what is policy and what isn't. But I don't understand the logic of people who will use everything in their arsenal to fight law, if they are given the chance. I reckon at least 90% of these people would shut up if they had no 'mis-information', flawed or half baked research articles and spurious surveys to feed from in the first place. Helmets have their limitations, and I have no assumptions about them having super powers. While I do call for safety, what I'm calling for more is good research, accounting for alternative explanations, and applying caution and critical reading skills before believing in or spreading the material in research papers around.

Example : Mr. Burton, a transport planner, takes it upon himself to inform others that he read 'something' , somewhere that suggests helmets do zilch for safety.

* * *

INTRODUCTION

There's that saying that when your number comes by, its time to go. But the more stubborn of us are bound to challenge that and ask : "Really?"

Leaving out any religious connotations, I'm convinced by the fact that if you hold yours or someone else's life in your hands, and if you choose to rule out common sense and smart thinking in favor of stupidity or negligence, then its more likely that you or the other party is at higher priority on death's list. In this case, the 18 year old driver who handled the pickup probably made some poor driving judgments that took the life of the 12 year old boy, who also decided (or maybe even taught to do) that day that he would avoid wearing a helmet and take some risks.

On a third level are the noble folks who build structurally unsound products that don't function when they are called to do so. I have nothing to say to these people if they don't realize the damage they're doing, but hey...that's for another topic another day.

Over the years, the above 3 elements have finger pointed at each other whenever a traffic related issue came up. Drivers complain about stupid cyclists, cyclists complain about stupid drivers, and both of them complain about stupid cars, stupid bikes, stupid helmets and why, even stupid transportation laws and politicians. I am sorry to say that I'm ashamed of all three parties! Human tendency is to always hold self righteousness high and pass the soup bowl of blame to someone else, or provide an excuse for stupidity. If only one of the three could have done the right thing themselves, and followed the rules, or made life easier for the rest and lived and let lived, or did something like they said they would do, then we could be a more safe and constructive society.

Accidents can be avoided. Injuries can be prevented. Even wearing a helmet may not prevent the accident, but just like entering a lottery increases your odds of winning by a huge margin than your neighbor who didn't (probability of winning by not entering is a big zero), wearing a helmet increases the odds of preventing critical head injuries that could otherwise rob the quality of your life pretty quickly. [See Brain Injury Library, TBI Consulting]

1. STATISCULATION

The cabal of cyclists who don't support the wearing of helmets always have some excuse to make. Fine. If you don't like them, that's your choice. But by choosing to do so, you're agreeing to taking a huge risk with your critical 3 pound brain, your skin, your bones, the value of your life and that of your family's.

But there's one interesting thing here. To make their case loud and clear, they do some sketchy things. Among them is pulling up statistics from heaven knows where that show the growth of some negative events for cycling and attribute those events to some form of helmet law. The event can be anything from the decrease of cyclist numbers on the road to the increase in head injuries.

How they come up with this definitive correlation is not explained to the rest of us. What matters to them is that it correlated, somehow. Finally they come to the wonderful conclusion that helmet laws are indeed responsible for decreasing cycling. Or that helmets actually increase the rate of head injuries.

But for every explanation, there are alternative ways to think. For instance, in the first scenario, did wearing helmets really decrease cycling or did cyclists just stop riding, having found something better to do with their time and money? In the second scenario, did helmets increase the rate of head injury or did cyclists ride more faster (due to the false sense of security) and push the helmet they were wearing beyond design conditions for which it was made?

Oops. Didn't think of those, did you?

Sadly, in the age of the internet, we don't have too much of information anymore. We have too much of mis-information. Mis-information is spread by people who have more time to waste than the person willing to read and agree to them.

Be careful with statistics and spurious looking "graphs". At best, they can help understand a trend and simplify this complex world we live in. At worst, they can be employed by the person creating and using them to deliberately tie in two unrelated events and LIE. When they are presented to the rest of the world, the flu is passed around. Misleading people with the use of statistics is called Statisculation. The people most likely to be misled/awestruck are the ignorant who don't give a damn how statistics work or how the figures presented to them were arrived at. If they have a thought process to begin with that they absolutely stand by, and if they find any 'statistic' that will support that thought process, they will welcome it by all means and pass it onto others. [See How Statistics Can Lie, United States Golf Association]

CASE-STUDY : DID THE HELMET LEGISLATION REALLY DECREASE CYCLING IN AUSTRALIA AND LEAD TO OBESITY?

JACOBSEN'S FIRST FORMAL ANALYSIS OF THE SAFETY PRINCIPLE : In 2003, in a paper written for Injury Prevention, P L Jacobsen famously validated the "Safety In Numbers" principle, a well known concept in transport circles. The paper proved this principle by using census data to show that the likelihood of a collision between motorists and cyclists in many Californian cities decreased as the numbers of cyclists and pedestrians increased (inverse proportionality). This principle was represented by his exponential growth equation relating "relative risk of cycling" with the "amount of cycling". If cycling doubled, he said, the risk per km falls by 34% according to his exponential relationship. You can read the original paper here [Free, PDF].

D. ROBINSON'S RESEARCH TO VALIDATE JACOBSEN : On the other end of the globe in Australia, D. Robinson, a researcher from University of New England in New South Wales, tried to replicate and validate Jacobsen's safety principle. In 1990, a mandatory helmet law was passed in Australia, making it the first in the world to do so. Robinson sought to also find out if there was any correlation between the injury rates as reported by hospitals prior to and after 1990 (year of helmet law), compared with the number of cyclists on the road in the same time periods. She chose localized areas in Australia for this reporting, as opposed to several cities and communities that would make up the continent. You can read the original paper here [Free, PDF]

BEFORE THE HELMET LAW WAS PASSED : Robinson reported that cycling dramatically gained popularity in WA in the 1980's and as a result, cycling became more safer because the number of cyclists being admitted to hospitals decreased as per information from WA Health Department! Between 1982 and 1989, number of regular cyclists on the roads doubled. Take note that Robinson defines a "regular" cyclist as anyone who cycled at least once every week. The number of injuries and deaths per 10,000 cyclists decreased from 5.6 in 1982 to 3.8 in 1989, a 32.14% decrease. She rounded that to 33%, and finally concluded that it is consistent with Jacobsen's growth rule which states that if cycling ever doubles, the risk per km falls by 34%.

ESTIMATIONS WERE USED : Australia did not have data on bicycle use for these years. So Robinson used "estimates" that she borrowed from the Australian Bureau of Statistics for 1982, 1986 and 1989. The amount of error in these estimations are unknown and how they were estimated is also largely upto the guessing of the reader of the paper. If the estimations had error in them, we might as well find that the number of cyclists didn't increase, but they fluctuated or stayed somewhat constant in that time frame. In that case, the data wouldn't really validate the Jacobsen's growth principle for Australia or the rest of her theory that is to follow.

ROAD VEHICLE TREND? What gets cloudy here is that she does not show how the trend of motor vehicles on the road varied in this time frame. Suppose the number of motor vehicles decreased (due to population migration, motorists choosing biking instead of driving, or other reasons) then logically, that reason could also be attributed to the lesser number of fatal injuries in cyclists. No data of number of vehicles and the trends in their use during these years in WA has been provided to us.

AFTER THE HELMET LAW WAS PASSED : Robinson reported that certain "surveys" showed cycling had decreased in the years following the legislation year of 1990. The graph on the right side is extremely unreadable, as obtained from the original paper, so I've obtained a better one from a primary source that Robinson cited in her references. This primary source happens to be a 1995 paper from Monash University by Carr et. al which I will discuss a little below. You can access the paper here here [Free, PDF].

WHAT SURVEYS? : What the source "surveys" were, we not told explicitly. It is often assumed by many people that somehow surveys provide accurate information. But are they really scientific? How can you remove biases and the tendency to report wrong beliefs like we often see in surveys? What is the amount of error in these surveys? What is its confidence rate in %? Afterall, robots aren't behind surveys. Humans are. Also, if census data was used to distill the details of how much people were using their bicycle, the amount and frequency of data collection always has limitations. A census may take place infrequently (every 5 or 10 years) and it also may not yield comprehensive data about bicycle use by the population.

SMALL DATA SAMPLES : Even if the surveys were well documented and have 90% confidence , they reported on a small sample set for Melbourne, Victoria for the years 1987 to 1992. The number of years in that sample after the legislation was passed is a mere 2. It is not for the whole of Australia either. It is for Melbourne, Victoria. More importantly, the counting of cyclists was done in the same month (May) between 1990-92. What about the rest of months? Did the count decrease or increase? People may have different agendas from the May of one year compared to the May of the other year. Some may just be late to get on the bike due to being busy with other engagements. Robinson's data does not explore the cycling trend in those other months.

THE CYCLING TRENDS BETWEEN 1990-92: Now for years 1990-92, for the month of May, the decrease is not so dramatic as shown by Table 2. More cyclists were wearing helmets and the number of cyclists counted decreased in the first year and then rose again in the next if you check the numbers. Going by Robinson's numbers for adult cyclists, there was a 29% decrease change in cycling counts in 1991 from 1990, after the helmet legislation. However, in 1992, there was a 34% increase change in adult cycling counts from 1991. The levels had almost returned back to 1990 levels. Robinson doesn't delve into this too much, but still diverts the reader's attention to decrease in child cyclists and injuries.

DEDICATION OF CHILD CYCLISTS VS ADULT CYCLISTS : We all know that children are fickle minded. As they grow up, or due to some form or another of peer or parental pressure, their interests and hobbies and life goals change. Pretty darn quick. Children also could have been discouraged of cycling not because of helmet laws, but due to the fact that the helmets they were now required to wear by law were DORKY LOOKING, uncool, user unfriendly, or plain ugly to show around in public. I'm very much interested to see a sample helmet from 1990 in Australia and what kids thought about it THEN. Was there a survey of that?? Robinson does not go deep into this very important issue at all. But she's quick to take the naked numbers of decrease in child cyclists and point fingers at helmet laws. It is adult cyclists who are the dedicated ones. They have to go to work, and if that is to be done by riding a bike, they'll do it because they're the ones to put food on the table, not their kids. In the year following helmet law in 1990, according to table 2, the decrease in adult cyclists was lesser than the decrease in number of child cyclists compared to 1990 (-461 adult to -649 child, 1991) . In 1992, the increase in number of adult cyclists from 1991 was more than those of child cyclists (+378 adult to +89 child, 1992). As one can see, child cycling never recovered properly in that year compared to adult cycling. Robinson really didn't question this and find out WHY? She just quickly moves on to prove her big theory.

ROBINSON'S LOGIC : From the data in the table above, Robinson's logic is that the increases in numbers wearing helmets were "generally" less than decreases in numbers counted...which led her to write that this proves non-helmeted cyclists are more likely to be discouraged to wear helmets and continue cycling.

Wow. Wait a minute.

How can she relate 'discouragement' in Australian cycling with numbers for a small sample set of 30 days for 2 years for Melbourne, Victoria?? I can't understand that logic. Also, the more dedicated of cyclists are in the adult population, not in children. The decrease in child cyclists was more than that of adults.

SEASONAL VARIATIONS UNACCOUNTED FOR : The data also doesn't account for seasonal variations in cycling precisely because it investigated only the month of May. We all know that cycling is a seasonal activity. Only few are brave to venture out in winter in the elements. Victoria has a winter season. People ski there on its slopes, among other activities. Melbourne is colder than other mainland Australian state capital cities in the winter. More commonly, Melbourne experiences frost and fog in winter. Also looking at a climate chart from the Bureau of Meteorology, the month of May is one of the coldest in Melbourne, with temperatures ranging between a low of 9 deg C to a high of 17 deg C.

PRE-MODIFIED 8 YEAR HOSPITAL ADMISSIONS DATA (June, 1986 - June, 1994) : This comes from the Monash University citation that Robinson provided in her paper. The original data from hospitals for cyclist head injuries showed a decrease after 1990 but a sudden increase in 1993 and it was determined by "examiners" that this apparent increase was due to some "Casemix" anomaly in the Victorian Hospital System (increased admissions from hospitals because of the promise of more hospital funding from the government that year). So anyway, the original Hospital Admissions data for head injuries for cyclists was then modified through some sophisticated "multi-variate time-series modeling techniques" that even I have a hard time researching what they exactly did to the data. Anyone who didn't complete a sophisticated course in statistics can really bite the dust here.

MODIFIED 8 YEAR HOSPITAL ADMISSIONS DATA WITH MULTI-VARIATE TIME SERIES ANALYSIS : The modified graph after they applied their time-model to it just surprised me. Observe the lessened curve in helmeted cyclist head injuries after 1990 as shown by dotted line, compared to the solid lines that show the same before the model was applied. Again, the original data was modified through sophisticated statistical methods that only the researchers know exactly. Anyone has to seriously question the validity of these 'intervention analysis' techniques employed by different groups of researchers and understand it thoroughly before taking it, misplacing it and chanting slogans with it. At this point, I challenge all the people who decry the use of helmets : Do you fully understand these type of sophisticated statistical tools that researchers use to modify and play around with data? Do you understand the complex decisions that are behind these actions? Can you blindly say yes, before you've done your research and link to these articles to support your cause? A course in Time Series Analysis to fully understand what the Monash researchers have done in this paper requires atleast a semester or two of university-level study. This isn't the introductory level statistics that you do in your biology class.

The modified hospital data looked encouraging for cycling than the original. It was estimated from these modified data that in the first four years of helmet legislation, a 39.5% reduction in the number of head injuries was observed in Victoria(level shift). I presume that is what this graph shows. However, in comes at group of Australian researchers - Cameron et al, Mead et. al etc - who suggests that hey, the decrease in head injuries is dramatic compared to pre-law levels and then declare that this decrease MAY have been due an overall decrease in bicycle use, and not helmet use at all. Infact this has been suggested in Page 1 of the Monash report.

CONFLICTING RESEARCH CONCLUSIONS/SUGGESTIONS : Interestingly, in page 21 of the Monash report, there's evidence of some conflicting statements. They say their analysis is insufficient in distinguishing between reductions due to helmet wearing and reductions solely due to the possible reductions in exposure, and then boldly go on to say a little later that they think "its fair to assume" from their analysis that helmet legislation and the subsequent discouragement in cycling caused the decrease in head injuries that they "modeled" all this time. So what's the correct and final story on this one?

If you also didn't read between the lines of the report, page 15 says that the researchers didn't even investigate other measures of road safety in their models which may explain what happened to the decrease in head injuries to cyclists. How safe is this ignorance?

The rest of the paper from Robinson, which referenced the Monash paper for food, delves into injury rates for Victoria and finally concludes that : "Thus, as predicted by the growth rule, the risk of injury per cyclist increased when cycling decreased because of helmet laws in Australia."

Robinson's paper provides an inadequate picture of what really happened to cycling in the months after the helmet legislation in Australia. More so, one of the prominent references from Monash University she's given in her citation (Carr et al) use murky modeling techniques to modify original data, (which needs deep and further study). They also seem to be inadequate in their research as reported by themselves and make conflicting statements in several pages of their report, yet they say its "fair to assume" that helmet legislation decreased cycling numbers and hence cycling head injuries.

Robinson uses all this to do too much generalizing. She uses small sample sets, and data for localized regions in Australia (possibly from other researchers) to arrive at the grand conclusion that because the helmet law was passed in 1990, cycling collectively decreased in Australia in years thereafter! I may agree with the fact that there is safety in numbers but I cannot validate this paper to make a conclusion that helmet laws decreased cycling in Australia and dramatically increased the risk of injury just due to it. The trends for cycling that Robinson has reported is episodic and localized, and I would encourage her to investigate the effects of cycling over a long period of time and in many different places, at the same time, also delving into some of the other causes, apart from helmet law, that affected the numbers. I highlighted some of these possible causes in the writeup. It doesn't hurt to sometimes ask "WHY", even more than once.

Without giving a picture of all the factors mentioned above and their relative contributions to Australian cycling, no intelligent person reading Robinson's paper can accurately put faith in the fact that helmet law was the prime motivator for a long term, permanent, and nation wide cycling decrease in Australia. I urge her and the umpteen groups of researchers who have all fed on each other's research material to give these old papers a good second look. Continue to explore alternative explanations for a decrease in cycling levels. See if they are accurate, and still really relevant for 2009.

These are the papers that are being used to bring down safety laws in several countries of the world. I'm not even sure that the people who reference this material fully understand the use of your sophisticated statistical analysis techniques, and data manipulation tools and the implications of these actions.

PASS IT ON, BUDDY!

Many different websites, bloggers, forum participators and "medical experts" link directly to cycle-helmets.com, a website which makes it own interpretations based on Robinson and other Australian papers. Then they twist it to their liking and start throwing the bombs. Like this misinformation, portraying helmet laws as fighting with public health :

Most people who read this would not have read the original research papers but will assimilate other people's wrong interpretations of it and finally, what they'll receive, believe in and spread out to others are false assertions such as "helmets decrease cycling", or "helmets cause more injuries" or "helmets decrease public health" and so on and so forth.

If Australia has an obesity problem, does it really have to do a lot with "punishing" helmet laws or more to do with laziness of people (an age old problem, even before helmet laws), personality and psychological issues, and the human desire to put in more calories into the body than what is burnt. There is a definite science behind obesity and understanding it will help solve problems. Does the author of cycle-helmets really believe that there is some sort of major underworld partnership going on between obesity in Australia and bicycle helmet legislation?

Many other sections of cycle-helmets.com contain mis-information through videos. My favorite one was the following below, a link to a video showing a car running over a helmet, as if suggesting to the reader that a helmet should be somehow designed with super powers to withstand the weight and force of a car over your head.

Oh, and if it breaks, it must be a worthless piece of junk right?

Websites like cycle-helmets.com are easily visited by people because of its suggestive URL. Any one trying to do an internet search for cycle helmets will be caught unwary and visit the link. Then they'll be pulled into reading some fantastic BS on the drawbacks of helmets, helmet laws and how it brought down the entire continent of Australia and continues to do so.

Choose wisely what you read. Scrutinize everything, especially research papers. Case in point : After multiple peer reviews, Ed Coyle's research study on Lance Armstrong (Improved muscular efficiency displayed as Tour de France champion matures, 2005) which tried to show how Armstrong's body became more efficient between 1993 and 1999, was found to have some glaring calculation errors in the delta efficiency. [See Coyle Study on Armstrong : A Minor Error Or Scientific Hoax?] Ofcourse, Lance will not talk about this on his Twitter page. He may not even understand how the numbers were arrived at.

2. SPORTING PREJUDICE

Taking the example above of the "helmet laws & decreasing cyclists" correlation, it could very well be that the number of people cycling decreased because some of them discovered another sport and chose to commit to that over biking. I'm not saying that's exactly true, but what if it were? The question to ask then is : Is cycling the only way to keep fit?

If a person wants to be healthy and lead a better life, cycling is not the only avenue. If he got discouraged in cycling because of the need to wear helmets, he may not necessarily have gone back to drinking and smoking and sitting on the couch watching football all day. Unless you can prove that, this argument has no weight in it.

As cyclists, we all love to support our cause and make ridership grow. No harm in that. But healthy living comes in many forms. Bicycling is a solution. But its not THE solution. You can walk to work, golf, or play tennis on weekends, or even chill out in the swimming pool. Why the heck do you have to ride a bike to remain fit? Is it the only sport around? Please stop the desire to homogenize sporting and let people be themselves. Embrace your hobby and talk about it, but learn to shut up and let people do their own thing. Simple. Lately, pushing has come to shoving to make people ride more. I do not approve of this behavior, either from cyclists, advocates, or public health politicians. Promote all healthy ways of living, don't bias yourself to one.

CASE-STUDY : DOES LACK OF CYCLING SUDDENLY CAUSE DISEASE?

In a letter to the editor of the Canadian Family Physician, Thomas DeMarco MD argues that helmet legislation could decrease cycling. In his writeup, he references an Australian experiment with helmet law and connects it to the falling in ridership. We already covered the Australian Helmet Law crisis above and the flaws in a prominent research paper. But the citation given here is some 'C. Komanoff' who read 'some data' from a so-called 'Monash University' at some 'pro bike conference' in Ore, 1994. Great. What the heck is that 'data'? We won't know.

NO CYCLING = DISEASE ARGUMENT : In the following lines, he emphasizes that "most importantly, less cycling means less physical activity which translates to more atherosclerosis, obesity, non-insulin dependent diabetes mellitus and osteoporosis." Wow! Thats a big bunch of disease. How did Mr. DeMarco arrive exactly at the definitive correlation? No cycling = disease? And did he begin with the false thinking that cycling is the only sport, past time or recreation around, or that people who don't commit to cycling suddenly commit to physical inactivity in an instant, which inturn gets them sick and about to die? Can you please prove that?

IS CYCLING THE ONLY WAY TO STAY HEALTHY? What gives Thomas DeMarco the monopoly to generalize the human mind and the complex wants and desires of people? Or ignore other sports that can help one stay healthy? Most importantly, what gives cycling the monopoly to make people healthy? If this was the only way to be fit, we'd imagine a health fanatic world full of cyclists. That's not the case.

I, for one, am thankful that cycling is not the only sport/recreation around. I'd go beserk, otherwise.

CONCLUSION

The more I think about people complaining over helmets or helmet laws, the more I feel that they just care about the advance of their dogmas to others. They don't really care for their safety first and foremost. They don't think about the situation and environment they're cycling in (Europe is very different from America, where the status quo is motorship), and for them, the sport of cycling is the only salvation to a better life. If helmet laws are passed, they say that it will quickly bring down the numbers of cyclists on the road by quoting and data mining from questionable research articles and surveys done in other countries in different time periods. Then they argue that if ridership decreases, the number of people with AIDS, malaria, obesity, osteopororis, diabetes, blood pressure, cancer and any other ailment that you can think of will INCREASE. And we are to believe that.

False interpretations of statistics, or statisculation, and making absolutely baseless correlations between two unrelated events have been the defacto tools for these groups of people to fight helmet laws.

Yet, given all this, you oddballs may still opt to rule out helmets and decry the need for safety to ride the way you feel is best. Which is absolutely fine, as long as you don't mis-inform others, while forgetting the personal risks of going riding without a helmet.

But there could be a point when you arrive at the cross-roads. Say it was a Monday morning and you see your 12 year old kid, your own life and blood, walk out of the house with a bicycle. He's going to ride beside the road to get to school, which is about a mile away. He's not wearing a helmet. (At least that's what he learnt from the family growing up).

In a chilling moment a few hours later, the telephone rings at your house and the local cops have some life changing news for you and your wife. Its very distressing and there are no words to describe that sinking feeling. They happened to bring some really bad news about your son. He was riding his bike to school but apparently... he never made it there. They are requesting you at the scene immediately. Suddenly when your world was going all smoothly, someone in your family has become a statistic.

What will your line of thinking be then? I'm just curious.

ADDITIONAL/RELATED RESOURCES :

How A Bicycle Helmet Works

How Bicycle Helmets Are Made

Helmets : How They Work And What They Do

The Effectiveness Of Bicycle Helmets : A Review by Dr. Michael Henderson, who is a physician who has spent most of his professional life in highway safety research and administration. He established and ran Australia's first government crash research group and test lab, and he chaired the Standards Australia committee that wrote the first standard covering bicycle helmets.

How Helmets Are Tested In Snell Labs

Damned Lies And Statistics : Untangling Numbers From The Media, Politicians And Activists

Brain Injury Resource Center

What's Your Excuse, Washington City Paper (March 11, 2009)

Bicycle Accident Victim, 12, loved the outdoors

Current U.S Bicycle Helmet Laws

Continuing Misinformation About Declining Hunter Numbers

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