|How long does it take to learn riding?|
Every unicyclist probably gets asked many times "How long does it take to learn riding?" The shortest correct answer is "It depends". Age is often being thought of as a prime discriminating factor. But now we know more...
In March 2003 someone posting in rec.sport.unicycling by the handle of "wobbling bear" suggested that we should study how the learning time depended on age. I took up this suggestion and posted a poll. Initially I asked people to supply:
There was a massive response: in about a week I collected data from 66 people. Mostly via the newsgroup, some by e-mail, private message or IRL communication. Many people volunteered additional data, notably:
I processed in this first batch all of the data received before 6 April 2003 09:00 am CET. I plan to process data that may be coming in at a later stage in subsequent analysis, so keep them coming.
The raw data are presented in the table below, in the order in which they appear in my news reading software (Free Agent 1.21/32). I have assumed that people who responded in rec.sport.unicycling implicitly agreed to republication on this website, from the other people in the table I have requested permission to publish - all agreed.
Some responses were ambiguous in one way or the other, but you can check in the table how I reduced your data to simple, 'hard' facts. Data that are too 'vague' to be used are printed in a red font - those individuals unfortunately had to be excluded from the analysis. In addition to raw data, I also included in the table (if available):
Note that some of the numbers in the table appear rounded. However, in the analysis I used all available decimal places.
In the figure below, I have plotted the learning time versus age for all respondents except the few that were unusable. For some people, both age and hours fully coincided. For those, I have shifted some points in the plot over a distance of 0.3 years (+ or -) from their true age. (I know that this is unconventional practice but I wanted all datapoints to be visible. Note that for all calculations, true ages were used.)
The blue line in the picture is a 'typical' fit, based on another bit of unconventionality involving weighted median values of logaritmically distributed bins. (I have favoured this approach over e.g. a least squares fit of some sort, to lessen the influence of the few people who required very long or short times to learn. I think such cases could have appeared at any age.)
The main trend is that older people take longer to learn, which confirms our common assumption.
Just for fun I include the age distribution histogram of the people who responded (or who was responded for). The 'hump' around 10-15 years of age is remarkable now that we know that this is a slightly difficult age to learn. I guess unicyclists are challenge seekers after all. Also remarkable is the concentration of learners around 35-40 years. Maybe these are the parents of the 10-15 year olds?
OK, so now we know that age is an important determinant of learning time even though the effect may be different from what most expected. But what else matters? Well, simply everything. In order of importance:
Moral: find an instructor.
The people in the database learned on wheels ranging from 16" to 26". Adults (defined as age 16 and up) on a 16" or 20" (13 in total) typically learned 30% quicker than adults on a 24" or 26" (9 in total). (Based on learning times corrected for age and quality of instruction.)
Moral: pick a small wheel to learn on.
Note that there were no small children in the dataset who learned on an unreasonably large wheel (or so I think). The youngest learner on a 20" was 9 years old, the youngest on a 24" or 26" was 11 years old. C'mon parents or club leaders, who's gonna put his learning child on a Coker from day 1, for statistics' sake? And did any adults learn on 28" or larger wheels, besides Scott Kurland (of whom I have no data)?
There was an appreciable difference in expected learning time for males (50) and females (13). The (median) expected learning time of females was 18% quicker than that of males. (Based on learning times corrected for age, quality of instruction and wheel size.)
I'm hesitant to give you a moral for this one...
Hours spent per day
Time spent per day on learning varied from about 7 minutes to 8 hours. One might expect an effect of that on learning but the data (note that both scales are logarithmic) look like a amorphous cloud. Yet I had the nerve to throw a linear fit through the data (in the log-log domain) and use that as a basis for correction.
I hate to say it but the more you practice per day, the more hours you will spend in total. If you double your daily practice, you will spend about 13% more total hours. Bad news huh? Well, the good news is that the data on this particular issue are so noisy that the effect is probably not significant anyway.
And moral? The more time you spend per day, the more fun you have!
OK, this was all boring but now the interesting stuff. Find out how long YOU will need to practice!
Based on the described relationships I've built a numerical model that can predict the typical learning time for any individual. It is reasonably accurate: for about 60% of the people, their actual learning time will be less than 50% different from the prediction, while only 14% will need more than twice the practice predicted by the model. The uncertainty stems from things like previous experience, determination, physical condition, talent and attitude; such factors are difficult to quantify and have not been included in the model.
To use the model, download the spreadsheet hour_est.xls. Don't left-click to open in your browser, but right-click, Save As and then open the saved file. You need Excel 4.0 or higher to open it. Answer five simple questions and hey presto! The spreadsheet returns your estimated time to learn and ride 50 metres on a unicycle. You will also get a probability histogram so that you can estimate how much uncertainty there is in the predicted number of hours.
Oh and if you think that your estimated time is discouragingly long, you could have a look at this discussion in rec.sport.unicycling, where the experiences of very fast learners are discussed. I will not include such data in this analysis though, because they are biased.
Get your talent score!
Can you ride already? Then download the Unicycling Talent-o-meter from this page and get your personal unicycling talent score!
(Bonus: that spreadsheet makes it also very convenient to e-mail me your personal learning data for inclusion in a future update of this page, if you desire to contribute to science!)
Back to Unicycling statistics.