Once or twice a season we’ll have our distance runners run a time trial on a well known trail in town. Joan hardly ever repeats the same workout, but there are a handful of important ones that we keep track of over time. We can go back and see over time how kids improve on specific workouts throughout their high school careers. This is really important to kids – they can see just how much they’re improving over time – a very motivating thing to do.
Of course for most races and workouts you start everyone at the same time and the finish times end up being naturally staggered. There are plenty of times where runners very quickly get segmented (through ability, current fitness level, pecking order, etc.) – sometimes lots of runners spend the entire workout running alone.
Having done the Medoc Trail Race which staggers start times based on age and gender, I thought a similar approach would be good for the XC team. Instead of using age and gender to stagger the start times, we used a recent mile time trial on the track to determine how to time advantage each runner received.
I ended up creating a spreadsheet to translate the mile time into the predicted time for our 2.4 mile time trial loop. I then figured out the how much of a head start each runner should receive compared to the fastest runner on the team. I printed out the spreadsheet with runner and time advantage. The slowest runner on the team ended up with a 7 minute head start on the fastest runner.
We had never tried anything like this and I was really hoping I did the math right and made good estimations about fitness levels. It would be pretty embarrassing for me to have the fastest runners finish minutes ahead or minutes behind the eventual winner!
What ended up happening was pretty cool. The slowest girls on the team were still leading the race with about a quarter mile to go! The slowest girl ended up in 7th place in the “race.” What really surprised me though was how much faster almost everyone ran! Our top boys beat the all time record for this loop by about 20 seconds – a huge improvement. Up and down the line people had significant course PRs!
It’s easy to see how there would be this kind of improvement. Imagine you’re the fastest guy on a team. In every normal workout you pretty much run alone – nobody passes you and you don’t pass anyone the entire time. Now imagine the “chase race” – the pressure is on the entire time to pass almost 80 people! It’s similar for the slowest runner – every step you feel like you’re getting ready to get passed by 80 kids, but you also feel like there’s a definite chance you can win the time trial!
Here’s some videos of the start and finish of the chase race as well as the top 11 finishers.
I’ve often been curious about transit levels in the United States, especially cycling. Commuting by bicycle has been dropping since 2014 in the U.S. There are lots of theories about why that is.
One question I’ve asked fellow cycling advocates in Carrboro: what is the highest level of cycling ridership we could possibly hope for in the next, say, 10 years? Assume we could implement our entire bike plan overnight. How would you even go about answering this question?
It’s hard for people to even venture a guess. Here’s how I would do it. Find the best currently available data on ridership in the U.S. and look for cities with the highest cycling numbers. Narrow that list to ones that have similar characteristics with an emphasis on qualities that affect ridership: number of workers, cycling infrastructure, climate (average number of rainy days, average temperature), “hilliness,” proximity to local areas of employment, college towns, average income – there are many factors. Use the cities at the top of this list as a guide – how much higher is the ridership there? How much of the difference can we attribute to each factor? Is it too hilly in your town to ever really get much cycling ridership?
I was surprised that there was not a convenient way to do this already, so I created my own. I realize data from the American Community Survey isn’t perfect, but it’s way better than anything else we have and it’s what the League of American Bicyclists uses to determine usage (in fact, it’s the only factor they use to determine actual “usage” on their application for Bike Friendly Community rankings.)
Some things I’d like to add:
A hilliness factor – how many hills does each town have?
Climate related number – average temp, rainfall, snow, etc.
Average commute times
If you’d like to contribute to the project in some way or have ideas on adding hilliness or climate numbers, I’d love to hear from you. Contact me on Twitter.
I’ve always thought that we could do a lot better displaying XC race team results. The cold, dry text format that we’re all used to is nice for a quick score, but pretty bad for most anything else. Just how did our fifth runner stack up against everyone else’s? Sure, you can scan the column and compare a couple teams at a time, but there should be a better way.
Another thing that should be easily visualized is the differences in the scores. Let’s say we were behind a team by 10 points. Is that a lot? In a dual meet it’s not really that close but in the state meet or a big invitational that’s a razor thin margin!
Here’s my attempt at making cross country team results way more fun to look at. These are the results from the North Carolina XC State meets from November 3rd, 2018. Each column represents a team. Each colored section in the column represents the points for one of the five scoring runners. The total column height is the team score for the team. Hover your mouse over the columns to see a popup that shows the runner’s name, place, and time.
Take a look and let me know what you think. How would you improve it? Any reason Milesplit shouldn’t do this? If you like this, follow me on Twitter.
My wife Joan is the head coach of Chapel Hill High (I’m the assistant). Although I’m the “data guy,” Joan is simply amazing at remembering all the PRs and times of the entirety of both the boy’s and girl’s teams. As soon as the runners finish a race she instantly knows if they ran well or not with high accuracy. I quickly realized that there was no way I could remember the PRs for 80 kids each season. I knew I needed a technological solution.
Here’s a list of things that I wanted to be able to do:
Be able to record the finishing times of each runner on the team as they cross the line without having to write anything down on a clipboard.
Easily keep track of mile splits during a XC race for each runner on the team.
Tell me at a glance how well the runner ran compared to their personal best and their season best.
Keep track of the big, notable workouts we do each year to be able to compare runners’ progress across years against themselves and against runners that graduated seasons ago.
The Web Site
I created a web site using PHP and a database that can store times, splits, and rosters from races and workouts. You can do all sorts of queries and reports to compare 5k results across the program’s history or see a particular workout report that shows athlete’s improvements over time.
This was quite an effort but not because of the coding as much as collecting as much race history as we could from Mile Split and our archives. Of course this is mostly a manual effort and Mile Split doesn’t make it easy since they only allow you to show one race at a time instead of being able to download your team’s entire history. I made this request to them but they were not interested in allowing this.
My Android Timing App
The web site is pretty nice, but by itself it’s not particularly unique. In my mind the real convenience is the Android app that I created to integrate with the web site for efficiently timing the actual races. I call it Coaching Timer (such an amazing name I realize!) and here’s some screenshots to show how it works.
Timing the Race
Here’s a screenshot of the main timer view. You start the timer with either volume up or down button. The 04:45 is the current elapsed race time.
As they come through the mile split, I press the button for that particular runner as they cross the mile mark. The runners are in descending order by their PR (more on how this happens later – it’s easy!) When I press a runner’s button, that button goes to the bottom of the list of buttons. See this screenshot that shows Ben Hawley and Owen Rogers having dropped to the bottom of the list. Why is this important? It minimizes the hunting I have to do to find the name as someone crosses the mile mark. It also allows me to quickly determine if every team member has crossed the mile mark. Also, if I end up having to click a button out of order that’s a mild indication that someone might be having a good day or a bad day.
As I click each button the timestamp is recorded along with the name in the left hand column.
As They Finish
I record the 2 mile splits similarly, but then for the finish the app sees that it’s within a normal range of a 5k finish and it adds some important details to the timestamp.
In the above screenshot, Amelia Maughan, Lilly Crook, and Sydney Runkle just crossed the finish line and I clicked their buttons (which moved to the bottom of the list, remember.) In this case, though, their PR and SB (season best) data was added to the timestamp. I can quickly see that Amelia tied her PR, Lilly was 20 seconds off a PR and 10 seconds off a season best, and Sydney beat her PR by 6 seconds.
Send the Results
In the 3 dot menu on the top right of the app, there are several options, two of which allow you to send results.
The Email Times and Text Times menu items let’s you send the results to another app for sending. Email Times brings up the following email message ready to send. Text Times let’s you send them via text message.
So How Does The App Know the PRs?
Here’s where some magic happens that I really like. You create groups in the app for each race that’s happening – for example, Boys Varsity, Boys JV, Boys JJV, etc. When you initially start a timer you choose the group to use for the race.
I initially created the groups by hand – typing in each runner’s name individually. I realized quickly how much better it could be, so I added an Import function for creating the groups.
The import function pulls a list of runners from the web site. It stores three pieces of data for each runner: their name, their PR, and their season best. The runners are listed in descending order by PR which lines them up just right for selecting varsity, JV, and JJV.
Just check the box for each runner to include in this group.
Then to start a timer for a race, just choose the appropriate group and click Start Timer:
Additional Nice Features that I’ve Added
Phone Sleep Prevention: As I’ve used the app over the years I’ve added some features to improve it. When waiting for the start, sometimes the phone would time out and I’d have to unlock it and, dang, I just missed the start of the race. So now there’s a feature implemented that prevents the phone from going to sleep with the Coaching Timer app is active.
Volume Buttons Start Timer: Also I realized it was hard to tap a button and look at the starter’s pistol in the distance across a field since there’s no tactile feedback from a virtual button on the screen. I added a way to start the timer via either volume control – problem solved and it’s much, much easier.
Disabled Phone Rotation: At first I thought it would be important to be able to rotate the phone and have the app switch from portrait to landscape since some names can be long and starting wrapping in portrait mode. This mostly ended up being disorienting when trying to get splits quickly and I disabled it.
Multiple Simultaneous Timers: Most races end up completing before subsequent race begin, but occasionally that’s not the case and they overlap. This is why it’s important to be able to keep multiple timers running simultaneously.
Adjust Start Time for Running Timer: Another critical feature that I added that has saved my behind multiple times is the ability to adjust the start time of an already started timer on the fly. Super useful! Let’s say you missed the start of the race. No more quick math to adjust each time after the fact – as long as anyone got the start of the race on their stopwatch, this function let’s you set the timer to any running time. This has been probably the most useful feature I added – it happens a lot and this is an awesome backup.
Adjustment Factor for Certain Race Venues: There’s an optional “adjustment factor” that you can apply on the web site for certain race venues/meets. Let’s say there’s a course one day that’s cold and muddy and the times are very slow compared to a “normal” course on a good day, or perhaps a course is short. You can apply an adjustment factor that provides an adjusted time for the purposes of the PR calculation. This prevents the PRs being skewed if we run a short course where true PRs shouldn’t really be valid.
The State of the App
The app is pretty stable right now and I’ve thought about releasing it to other coaches. But in order to do that I would have to put a good amount of work into generalizing it to work with other teams. Also, it’s highly dependent on the web site which is another layer of complication for someone to set up. To be honest I’ve always thought this would be a good thing for MileSplit to do since they have a good bit of the data required to make something like this work for any coach.
If you have any comments or suggestions I’d love to hear some feedback. Comment on this post and also follow me on Twitter while you’re at it.
I indicated the source as “US Census Data,” but I should have been more specific as one comment suggested that since the “census” is only done every 10 years that these numbers are completely made up. These numbers come from the American Community Survey and they are updated annually.
The Decrease Isn’t Really Real
A small number of responses were of this type. They said that the ACS is a survey and there is insufficient data to reflect a real decline. It’s theoretically possible that the numbers for the last 3 years are erroneous and that the number of bicycle commuters is actually growing, but I doubt it. The ACS is commonly used by cycling advocacy groups to point to successes – see this report by the League of American Bicyclists (LAB) which comes from the ACS data.
In general I think the LAB’s approach is a good one – attempt to measure municipalities’ cycling friendliness score and then use the ACS data to report successes. This can be a powerful argument for cycling advocacy – sprinkle some cycling infrastructure improvements and watch it grow.
Given the success in building cycling infrastructure in lots of areas in the US, are we bumping up against a theoretical ceiling for cycling? I don’t think so but I admit these ACS numbers make me less sure. One comment suggested that looking at bicycle sales numbers as evidence that the decline is an anomaly. A quick search for those numbers doesn’t look optimistic to me.
Commute Times are Increasing
A few comments suggested looking at workers’ commute times which are, in fact, increasing over time. Interestingly I couldn’t find any data on commuting distances. I would imagine that there should be data that analyses congestion over time by looking at commute times and commute distances over time.
By the numbers, all of the new workers over the last several years are choosing to commute by car. They apparently aren’t bothered too much by the longer commutes/congestion or maybe they don’t feel like they have a choice. As commutes get longer, at some point along that continuum the choice to bike to work is no longer a realistic option.
The Places Where Cycling Is Easy Are More Expensive
A few folks responded with this argument including economist Arnold Kling:
Perhaps rents went up in some locations, forcing some would-be bike commuters to move out of range
This is not something I thought of prior to writing the post, but this makes a lot of sense. The areas where cycling infrastructure is excellent or improving are also affected by the housing crisis. Could it be that local zoning laws are indirectly working against cycling? Seems like a reasonable theory worth exploring.
The Connected Generation Can’t be Disconnected Long Enough to Ride a Bike
Also, it is much harder to text on your phone when cycling, versus Uber (or even sneaking in texts at stoplights). For the ‘connected generation’ on-road cycling may be an unacceptable interruption. If they want exercise they can use a stationary cycle.
This seems logical to me although I have to way to verify it or gauge how important its effect is. I’ve always thought of this as an advantage to riding a bike, not a downside. I’m sure not everyone feels the same way.
lenore skenazy and i found some data on the sharp decline in biking to school for kids, i think it happened in the 1990s. 2015 would not be a special year, for adults; it was special on campus. i would not interpret the 3 year decline for adults as anything yet, could be random fluctuation. unless you can get data broken down by age. if you find no change for those over age 30, but yes for under 25, then it is Gen Z.
I looked for data on bicycle commuting and age via the ACS. Oddly enough, you can get raw bicycle commuting numbers normally, but when you group by age they are lumped together in a single group with “Taxicab, motorcycle, bicycle, or other means.” This category is increasing over the timeframe of the bicycle decline which as far as I can tell makes this impossible to isolate bicycle commutes unfortunately. If anyone knows how to use the ACS to isolate bicycle commuters please let me know.
Here are a couple charts that show the difficulty. Even though there is a drop in bicycle commuting, this larger group is increasing over time. Bicycle commuters are only a small part of this group so it’s hard to tease out any effect here.
Randal O’Toole Seems to Doubt the Work from Home Theory
I knew Randal O’Toole was an avid cyclist, so I emailed him to see if he had any ideas. Here’s how he responded:
Those are good ideas. Here’s an additional datapoint: people who work at home earn the most money. Drive alones are second, transit third, and “other” (which includes cycling) well below that. Walking is lowest.
So if work-at-homes captured cyclists, it was the high-end cyclists. I’m not sure how many of those there were.
A more significant phenomenon: the census form asks people how they “usually” get to work. The National Household Travel Survey found that people who usually drive in fact drive 98 percent of the time but people who say they usually bicycle in fact only bicycle 70 percent of the time. Maybe some of those people who bicycled, say, 60 percent of the time in 2014 went to 40 percent in 2017.
Randal said a couple things that surprised me here. One is that cyclists as a group earn less money than the average worker. I tried to confirm this with the ACS survey and it’s true that the group that contains bicycle commuters earn less than average, but I suspect that, like age, this is similarly confounded by grouping “Taxicab, motorcycle, bicycle, or other means” together. A quick search shows that in Australia cycling commuters are wealthier than average. I also discovered from survey data from the NC DOT that cyclists that use greenways strongly skew rich and white relative to the local population.
Randal’s theory about percentage of time commuting seems to jive with the housing costs theory previously mentioned.
General Safety Concerns/Helicopter Generation
A large number of comments suggested that cycling is, in fact, getting more dangerous over time due to drivers texting, more cars, more cycling-intolerant drivers, etc. I agree that it seems there is a general sense among cyclists that of course it’s getting worse on the roads. This has always struck me as somewhat of a moral panic. I’ve had my share of cars purposely buzzing me on my bike and yelling crazy things at me but I don’t get the sense from my own experience that these incidents are increasing over time.
Cycling advocates consistently talk about the large number of people who WOULD bike if they could be made to feel safer on a bicycle. This approach seems to have been successful in getting municipalities to devote resources to large cycling infrastructure projects. The current generation seems to be especially convinced by appeals to safety but there’s a bit of a paradox. If people believe cycling is TOO dangerous they’re not going to do it no matter how good the infrastructure is. (Safety first!) But if people feel that cycling is safe then there isn’t much demand for new infrastructure to be built.
Are the roads getting more dangerous over time? I decided to try to answer that question. It doesn’t feel like it to me but let’s look at the data. The National Highway Traffic Safety Administration has kept statistics on crashes for a long time. They produce an annual report that has all sorts of data on traffic deaths.
First off, I would have intuitively expected to see deaths from distracted drivers increasing over time (texting, obviously), but that doesn’t appear to be the case. (Not sure why we see that sudden decrease from 2009 to 2010 – anyone know?)
OK, well how about deaths from drunk driving? Are those increasing? No – it turns out they’re way down.
What about motorists that exceed the speed limit and cause a crash that kills someone? Getting worse over time? No – those are down too.
Note that the charts above are pessimistic since I didn’t normalize on vehicle miles traveled which has been steadily increasing over time. That is, if we accounted for miles traveled the charts above would look even better than they already do. Here’s a chart of vehicle miles traveled by year. (Note I adjusted the y-axis minimum for better readability.)
Most Unique Response: Fade Out of the Lance Armstrong Effect
From my local cycling friend Brendan:
The post Lance Armstrong cycling era taking effect.
I think the high end cycling world has definitely taken a hit – at least anecdotally from my perspective in a cycling heavy area. I’m reasonably confident (65%) USA Cycling could produce some reports based on road race participation over the last decade and it would show a steady decline. Would this have an effect on bicycle commuting? Probably but most likely the effect would be small.
Where is the Cycling Ceiling in the US?
It ultimately boils down to this for me as I alluded to earlier: how much room is there to grow cycling in the US? Of course I don’t have the answer to this but these numbers make me think that perhaps we’re closer to the ceiling than I would have guessed. And given the headwinds of safety consciousness, housing costs, the price of gas, and the rise of telecommuting, how big could gains from infrastructure improvements be?
I’m starting to think about a way to come up with an answer to this question on my own from some other data sources. Look for a post soon.
Offer to Bet
If you believe the drop in bicycle commuting is temporary, I’m offering a bet at 1:1 odds that the bicycle commuting number won’t exceed the number from 2014 in years 2018, 2019, or 2020. Any takers? Send me a DM on Twitter: @davemabe.
Imagine the year is 2014. We’re in the midst of an era when lots of cities across the US are working hard to promote cycling. By many counts it’s never been easier to ride a bike. Our family was already three years into getting by with just one car – this would not have been possible without the ease of cycling in my hometown of Carrboro, NC. During and since that time many major cities have implemented lots of cycling initiatives to build new bike lanes, make more areas accessible by bike, and created bike shares to make it as easy as possible to bike. So what would have been your prediction in 2014 about what US bicycle commuting numbers would look like over the next three years? I think it’s safe to say we’d be haggling over how much of an increase in bicycle commuting there would be in 2014-2017.
This makes it even more striking that the number of people commuting by bike in the US has steadily decreased since 2014. This is over a time period where the total US workforce has steadily increased, so in percentage terms the cycling numbers are even worse than the chart above appears (down from a peak in 2014 of 0.62% to 0.55% in 2017).
So why has this drop in bicycle commuting occurred? I can’t say for sure, but here are some theories about what might be playing a role.
Americans Really Like Travelling by Car
The percent of US workers commuting by car (alone or carpool) has remained remarkably consistent – it’s currently 85.3% of the workforce. Commuting by public transit is down in percentage terms from it’s peak in 2015 – 5.232% then and 4.998% now.
As much as people complain about traffic congestion, it’s apparently not enough to change people’s behavior too much.
Gas Prices are Down
US gas prices are close to their lowest in a decade, making driving relatively more attractive at the margins.
Popularity of Electric Vehicles
Electric vehicles continue to become more widespread in the US. When you think about the target market for these vehicles, a significant portion of this group might also consider biking to work. It’s becoming easier to signal environmental consciousness AND drive a stylish car. I suspect this is crowding out some would-be bicycle commuters. (Hat tip to my wife Joan for this one)
Working from Home is Exploding
If the percent of the workforce that is commuting by car is basically unchanged and the percent of transit use is down, what’s left?
It turns out working from home has been quietly exploding. After a four year run of basically no change from 2008 through 2011, there have been solid increases and now it’s up over a third from 2011. In fact 2017 was the first year where more people work from home than commute by public transit. This occurred during a period of lower gas prices – seems reasonable to assume that these numbers are positively correlated. That is, if the price of gas were higher over the last 3 years or in the future, these work from home numbers likely would be higher still.
Kids of Helicopter Parents Enter the Workforce
Another theory that seems like it might be true is that kids of so-called helicopter parents are coming of age and entering the workforce. I expect there’s not great data to look at for the “helicopter-ness” of the last couple decades, but it certainly seems to be true that safety (over) conscious parenting has increased dramatically. (Here’s a daycare that’s suggesting kids wear helmets for recess, for example.) It’s hard to deny that this has been the case and in fact we’re starting to see responses to this problem.
The time period for the decline in bicycle commuting also might roughly coincide with a generation of safety conscious kids entering the workforce. Will kids of this generation be more of less likely to commute by bike to work? The answer seems obvious.
Any other theories?
Predictions on Bicycle Commuting in Next 4 Years?
Anyone willing to offer a bet on what future numbers of bicycle commuters will be in the census data? If so, contact me.
“Price Gouging” has a negative connotation. The public broadly supports the vague laws that prevent it. Here’s a list of the states that have laws preventing some form of price gouging along with the language used in the statutes. You’ll notice that fewer than half of the states that have such laws actually specify a percentage increase in the price – most of the statutes use the vague, “weasel wording” of “grossly excessive,” “unconscionably high,” or similar highly subjective language. In my state of North Carolina, they use the term “unreasonably excessive.” Putting aside the problem of putting a precise definition on just what price gouging is, the public supports these laws.
It’s hard to find a public poll directly on this topic – the closest I could find was a Gallup poll about what was to blame when gasoline prices were high in 2008 – 58% blamed “price gouging by oil companies.”
There is a poll, however, of people with the most knowledge of the effects of “price gouging” – economists. They are overwhelmingly against laws preventing price gouging. If your knee jerk reaction is that “economists are just partisans that love the rich” – keep in mind that the average economist is a moderate Democrat.
In another setting, though, the public apparently is very supportive of price gouging. Uber and Lyft are extremely popular with their “surge pricing” being very effective during busy events whether that be a sporting event or a disaster.
I am holding out hope that the public will slowly come around to soften their support of laws against price gouging as they more routinely encounter it via Uber and Lyft and even Airbnb. In fact, as the sharing economy continues to grow more and more of us become the “gougers” – for example, renting out a room in one’s house during a major sporting event.
What other issues have such a large split in support between the public and economists?
I asked that question to economist Mike Munger who quickly answered “rent control” – an issue that is popular politically but where economists are almost universally against.
How do I feel about it? I like to say I’m pro choice – for everything. Especially voluntary transactions.
After a discussion with a friend who’s quite concerned about voting rights, I decided to take a look at what voting data was available. Depending on which side of the issue you are on, you probably think that voter fraud is the most important issue and under reported or you think that voter suppression is the most important issue and under reported. A lot of discussions about voting get stuck on these points so I was interested in looking at the data and perhaps somehow determining for myself the magnitude of “voter fraud” and “voter suppression.”
There are several zip files that are freely downloadable anonymously which contain almost every piece of voter registration data and voting history for every registered voter going back over 20 years. This includes voter affiliation changes, which ballot you used to vote in the primaries, whether you voted early, regular, or absentee in every election, your mailing address, your age, your race and gender, your phone number – basically EVERYTHING associated with your voting history for 20 years EXCEPT what boxes you ended up checking on the ballot.
Here’s a list of things that you can easily do without logging in or paying any fee – just by simply downloading a couple files:
The party affiliation of anyone
When and how anyone changed their party affiliation for the last 20+ years
Whether someone voted or not in each election over the past 20+ years
What ballot someone used in each election for the past 20+ years
Using street names, look up how your neighbors’ affiliation and whether they’ve voted in recent elections
With a little work you could create a list of people in your town that didn’t vote in the last election or who recently changed party affiliation. You could easily include their address, age, phone number, gender, and race. All this can be done by anyone who has access to the internet from anywhere in the world.
Here’s one example in Pennsylvania where a shady political group sent mailings to registered voters who failed to vote in the last election with a threat of a followup so that “you and your friends, your neighbors, and other people you know will all know who voted and who did not vote.”
Why do we have to give up this much privacy just to register to vote?
How many people would be less likely to register to vote if they knew they were giving up this much privacy to do so?
Imagine if private companies treated their customer data as carelessly as this.
The best reason I can come up with to support this invasion of privacy is to ensure transparency and therefore faith in our democracy. The current status quo seems to go far beyond what would be required to fulfill that standard though.
I happened to notice that our local public transportation organization is giving awards for smart commuting that they call the Golden Modes. Here’s how they describe the awards:
The Golden Modes Awards recognize companies, organizations and people who best use their resources to influence Triangle employees and university students to pursue smart commuting options. Ultimately, they reduce the number of people who drive alone, reducing traffic congestion, alleviating air pollution and improving our quality of life.
My work situation is tailor made for this award! I went back and looked at previous award winners – taking public transportation from Raleigh to Chapel Hill, commuting by bike to work, commuting by bus to Durham, etc. How can I not nominate myself for this award given how much better my commute is? I just self-nominated just ahead of the deadline which is today. Here’s what I submitted:
Having worked from home for 17 years now, I believe I exemplify the smartest of smart commuting – no commute at all. My carbon footprint is minuscule compared to previous years’ award winners. Working from home has huge benefits – I figure I save 2.5 hours per day for almost 2 decades. This allows a completely different lifestyle than I otherwise would have. I am able to run and cycle with friends, spend more time with family, and get more work done – all without contributing to congestion or requiring a subsidy. Raining? Snowed in? It doesn’t matter to me – my commute is a breeze.
I’m pretty sure there’s no way I can lose this one.
Although I got no takers on my last offer to bet on the light rail project, I’m offering a new one based on the effects of the net neutrality rules being phased out. This change officially went into effect yesterday 6/11/2018.
I’m offering to bet $100 that the cost of internet services as measured by the US Bureau of Labor Statistics will not increase more than the level of inflation on 6/11/2023 (five years from the rule change going into effect). I’m offering even odds.
If the net neutrality rules are put back in effect prior to that time, then the bet will be nullified.
Any takers? DM me on twitter if you’re interested.