Originally Posted by lhbernhardt

I would propose n km before puncturing would be a more meaningful starting point. The idea would be that you'd have a 95% probability that the tire would go n km before going flat. That last 5% accounts for completely random behavior. But the closer you get to km n, themore likely you are to puncture!

Originally Posted by Dan The Man

I think I tend to get more flats after recent heavy rains. All the water washes around debris towards the curb and leaves glass and wires pointing upwards in the pavement. That's my theory at least.

Originally Posted by Andy_K

There's definitely something to that. Wet rubber is much, much easier to cut than dry rubber, and debris absolutely accumulates on the side of the road, making designated bike lanes flat tire factories.

Originally Posted by Andy_K

The trouble here is that riding location is a huge factor in flats. I'm convinced that how many flats you get on your route has essentially no numerical correlation to how many flats I would get with the same tires on my route. Only by comparing two different tires on the same route do I trust the data.

Originally Posted by old's'cool

...uncontrolled variables... dominate the observations reported in this thread.
When uncontrolled variables dominate, you don't have an experiment worthy of the name. Next...!

Originally Posted by Andy_K

Like many bike commuters, I have a tendency to obsess over flat tires. Like many bicyclists, I'm also a nerd. As a nerd who obsesses over flat tires, one of the things that intrigues me is the problem of understanding flat tire rates, particularly as it applies to comparing various tires.

It's well known among bike commuters that flat tires are essentially random events. You'll go eight months without getting a flat tire, then you'll get three in two weeks. It's just totally random, right? Well, I'm not giving up that easily.

One of the main problems with flats being random events is that it calls into question the possibility of comparing two different models of tires without using both for a long, long time. Nevertheless, as humans we all form opinions based on small sample sizes and can't be convinced otherwise. If I try tire A and get a bunch of flats then switch to tire B and don't get a bunch of flats you won't be able to convince me that tire A wasn't significantly more flat prone than tire B.

But is that really true?

That's one of the questions to which I wanted the answer. So, being a pseudo-scientific type, I set out to collect data. For the last three years I've been compulsively recording all information that seemed relevant about my flat tires -- the date, where I was riding, what the weather was like, how many miles were on the tire, front or rear, cause of the flat, etc. Now with three years worth of data, I'm starting some analysis.



So, I've got two tires, which I will call tire A and tire B. I used tire A for about 1900 miles and got 6 flats. I used tire B for 2000 miles and didn't get a single flat. Obviously tire B is more flat resistant, right? But how to quantify that?

What I decided is that I'd imagine a simplified probability model. I'd choose a somewhat arbitrary probability that I'd get a flat in any 10 miles of riding and then apply that probability to these two tires to see how well it would explain the data.

Let me say that I am aware of the crudity of this model. For one thing, the probability of getting a flat isn't actually consistent over time but seems to increase with tire wear. It also varies with weather and riding location. I'm ignoring these factors.

So, returning to my model, I made the guess that for any 10 miles of riding there was a 3% chance that I'd get a flat tire. Applying that (by means of the binomial formula), I find that in any given set of 200 10-mile trips, there is about a 60% chance that I'd get 6 or fewer flats, so that seems like a reasonable fit for tire A. However, with that probability, there is only a 0.2% chance that I would get zero flats in 200 10-mile trips. If both tires actually had this same probability of getting a flat, there would be about a 1 in 1000 chance that I would get more than 5 flats with tire A while getting no flats with tire B.

Conversly, in order to get as much as a 1 in 4 chance that I could have used tire B for 2000 miles without getting a flat, I have to assign a probability of 0.7% for a flat in any given 10 mile trip. Applying that value to tire A, there would be a 99.7% chance that I'd get fewer than 6 flats. This yields less than a 1 in 1000 chance that I would get more than 5 flats with tire A while getting no flats with tire B.

So, my conclusion is that given two tires both used for 2000 miles in similar conditions if one tires gets 6 flats while the other gets 0 flats then I can, in fact, trust my belief that the tire that got no flats has better flat protection.

The next thing I'd like to know is how many flat tires you need to get before you can conclusively say that a tire is not as flat-resistant as another tire that got no flats.

Yes, I have too much time on my hands.

Originally Posted by Andy_K

Nonsense. Granted the variables are a bit uncontrolled, but given a large enough sample size order emerges from chaos.

Originally Posted by Andy_K

Besides, I never claimed there was even an experiment in progress, merely data analysis.

Originally Posted by tjspiel

I've concluded that the black tires get more flats than blue tires and I didn't have to work nearly has hard at it as you.

This is of course no help if your bike is red because the blue tires would clash. Imagine the horror if you did get a flat in a blue tire that was mounted on a red bike. I bet no one would stop to help you.

Originally Posted by oldskoolwrench

Flat tires have nothing to do with tubes, tires, protection, etc... they're directly attributable to the Bike Gremlins that accompany all of us
on every ride, waiting to pounce and muck up a perfectly good ride. Good relations with the bike gods, and good karma in general help
alleviate the chaos and misfortune a puncture (or several) can create...



Alan

Originally Posted by Andy_K

Like many bike commuters, I have a tendency to obsess over flat tires. Like many bicyclists, I'm also a nerd. As a nerd who obsesses over flat tires, one of the things that intrigues me is the problem of understanding flat tire rates, particularly as it applies to comparing various tires.

It's well known among bike commuters that flat tires are essentially random events. You'll go eight months without getting a flat tire, then you'll get three in two weeks. It's just totally random, right? Well, I'm not giving up that easily.

One of the main problems with flats being random events is that it calls into question the possibility of comparing two different models of tires without using both for a long, long time. Nevertheless, as humans we all form opinions based on small sample sizes and can't be convinced otherwise. If I try tire A and get a bunch of flats then switch to tire B and don't get a bunch of flats you won't be able to convince me that tire A wasn't significantly more flat prone than tire B.

But is that really true?

That's one of the questions to which I wanted the answer. So, being a pseudo-scientific type, I set out to collect data. For the last three years I've been compulsively recording all information that seemed relevant about my flat tires -- the date, where I was riding, what the weather was like, how many miles were on the tire, front or rear, cause of the flat, etc. Now with three years worth of data, I'm starting some analysis.



So, I've got two tires, which I will call tire A and tire B. I used tire A for about 1900 miles and got 6 flats. I used tire B for 2000 miles and didn't get a single flat. Obviously tire B is more flat resistant, right? But how to quantify that?

What I decided is that I'd imagine a simplified probability model. I'd choose a somewhat arbitrary probability that I'd get a flat in any 10 miles of riding and then apply that probability to these two tires to see how well it would explain the data.

Let me say that I am aware of the crudity of this model. For one thing, the probability of getting a flat isn't actually consistent over time but seems to increase with tire wear. It also varies with weather and riding location. I'm ignoring these factors.

So, returning to my model, I made the guess that for any 10 miles of riding there was a 3% chance that I'd get a flat tire. Applying that (by means of the binomial formula), I find that in any given set of 200 10-mile trips, there is about a 60% chance that I'd get 6 or fewer flats, so that seems like a reasonable fit for tire A. However, with that probability, there is only a 0.2% chance that I would get zero flats in 200 10-mile trips. If both tires actually had this same probability of getting a flat, there would be about a 1 in 1000 chance that I would get more than 5 flats with tire A while getting no flats with tire B.

Conversly, in order to get as much as a 1 in 4 chance that I could have used tire B for 2000 miles without getting a flat, I have to assign a probability of 0.7% for a flat in any given 10 mile trip. Applying that value to tire A, there would be a 99.7% chance that I'd get fewer than 6 flats. This yields less than a 1 in 1000 chance that I would get more than 5 flats with tire A while getting no flats with tire B.

So, my conclusion is that given two tires both used for 2000 miles in similar conditions if one tires gets 6 flats while the other gets 0 flats then I can, in fact, trust my belief that the tire that got no flats has better flat protection.

The next thing I'd like to know is how many flat tires you need to get before you can conclusively say that a tire is not as flat-resistant as another tire that got no flats.

Yes, I have too much time on my hands.

Originally Posted by Medic Zero

Regular offerings of ca$h to the Gear Gods, and the occasional sacrifice to N+1 help keep the Gremlins in check as well...

Originally Posted by tarwheel

I enjoy riding too much to use heavy, poor handling tires with high rolling resistance. I would much rather fix a few flats than have to slog up all the hills on heavy tires.

Originally Posted by Cyclaholic

I can't comment on your dataset since you haven't published it but I don't think the binomial model is the correct one to use. I think you should model it as a Poisson distribution as the of a flat per unit mile (or 10 miles if you want to stick with that), then you can compare the lambda of each different tire and I think that would give you a much better indication of its flat resistance.

Originally Posted by tarwheel

- Riding in the "crud line" of gravel, glass, etc. that accumulates on the edge of roads and at intersections.

Originally Posted by old's'cool

I doubt the extra mass of "heavy" tires is holding you back as much as you seem to think, but rolling resistance certainly is a critical parameter no matter what the riding condition.