Today on the merch to the dirt podcast episode number 34. Do you have a system for collecting data about your races? Do you look at those numbers analyze your situation, make sure everything is going as planned. If you've remoted more than 1 race, you have plenty of data from your tips. But if you've never sat down and turned that data into information, you may not be seeing the trends that make that data actually useful. Worse yet, you may miss opportunities
that good data analysis will give you. In this episode, I'm going to show you why measures and metrics. Are important. How you can turn that data into useful information, and then how you use that information to make data driven decisions that matter when they need to matter. Or you could just keep doing what you've always been doing, you know, use your gut feeling to make all those decisions. That's never let you down. Right?
Greetings, and welcome to the under podcast. I am Kyle Bondo, the Rechinir. That's the recreational engineer. Here to take recreation and engineering, combine it together as something to be used full for you when you're promoting races, directing races, or building things that matter to you in the racing world. And today, we're gonna talk about measures and metrics. Measures and metrics. Measures is taking a measurement of something, and a metric is finding out what that measure actually means.
That's a variance, you know, 0 to 1. Is it 0? Is it 1 or somewhere in between? So you think about measures and metrics. The reason why those are important is because there are the principles behind data driven decisions. And what is a data driven decision? Well, it's just like it sounds. It's some information, data, actually, the raw that you look at. And you analyze and by analyzing retrieving information, you'd learn something about the information in front of you.
So that data becoming information is the learning process that it now process, that now I can take that information and do something with it and make a decision with it. Look at this and say, you know what? This race is not working. I should probably stop doing that, or this race is going crazy. Maybe I should be doing way more of this. Those are kind of examples of debt. Different different decisions. There's a couple other data driven decisions.
A lot of times, they use the words quantitative and qualitative with that too. Quantitative is like numbers. Like, I have a race that 40 people come to every year. Okay? There's a number. That's a that's quantitative. I've counted them. Of kind of the quantities. Qualitative is those 40 people have the greatest time racing because there's none of the other stuff that happens in bigger races. It's a little race. It's a it's a intimate race. They could trail
access like never before. And actually have chances of the podium. Now when this race blows up and gets bigger and bigger, which it has from time to time, you now have, you you know, that quality control where maybe it's a quality for a different type of customer. What's quantitative and qualitative. Then you have measures and metrics. Right? Collect it. I have a variance. I have a of a range I'm looking for. A medium,
a mean. You know, a lot of this statistical analysis puzzle words they could throw in there. But, realistically, data driven decision has a lot to do with collecting measures, collecting data, that actually means something. That has quality. There's also quantitative. So I've counted certain things. I've done the bean counting. Qualitative. I've understood you know, 10 is the is pain, 0 is happy, or maybe 0 is sad, and 10 is super happy.
I've done those kind of things. I've kind of done some of the information that makes it actually important to me. And this is something that a race program and a race director should be doing on a constant basis. And if you've been around you've been around You've been around the podcast anytime.
You've noticed that I've done a restrictors inside the restrictors studio, parts 1 and 2. Well, I talked a lot about this this race series I did this summer called Wednesday's at Wakefield. Now, Wednesday's Wakefield is this 4 race series that's been going on for 15 years. And I've been personally directing it for 5. And when I thought about to think about this 1, I was blown away about how much how much data I have that I haven't turned in information.
And 1 of the things that I noticed when doing this is I thought about, okay. Well, this race has been going for 15 years. Was this year a good year? That's a really simple question. It's a good place to start. What's this year a good a good year? Did we have enough racers? And in doing the counts and looking at past races, because I had to go back and find out, did we even count people? Yeah. Kinda. We know we know we counted heads because there are certain payoffs. Park goes usually headcount.
So we have to go back and and look at the headcount. But did we look at the headcount from year to year and no? Did we know we had a good year? How do how do we know? Well, we base it off of last year. Last year was a lousy year. The weather mostly is a big factor in that. Basically, I have last year. Last year, we had lost a bunch of people, so we had sort of volunteer wise. So we had a lot more work to do. Looking back on that,
I think it was last year, it was a horrible year, and this year was a great year. Why is it a great year? Better last year. But is that really? Quality analysis? No. Why? Because what does it mean? Does it matter if it was a good year or not? What was my targets? What was my goal? How many people did I really wanna have? How much money did I need to make? How much money did I make? Did I need to make just to pay the bills and break even? Did I even make any money?
I don't know. Why don't I know? Because all that data, like I said, it's laying out there. Hasn't been analyzed yet. So it was 1 of those races where it was it was good because because, anecdotally, it felt like there was a lot more people there, a lot more vibe, There was a lot more camot camaraderie, a lot more new faces and some old friends. I hadn't seen in a while. So Overall, the race series was really successful.
Not only that, only had 1 injury in broken collarbone, which sucks But the grand scheme of things, of 4 races with 200 racers at each race, 1 broken collarbone, You know? That's that's kind of that's kind of fantastic. When you look at the risk management situation of all the things that should have gone wrong, that didn't go wrong, That was the only thing to go wrong. Sucks for the guy who who had happened to, granted. But put that aside for a minute.
Should I have had like 5 or 6 broken collarbones, couple smashed helmets, broken bikes? Well, if you you add up 200 people per race for 4 races, And let's say uniquely. Let's just say they were all unique. Let's say there's no 2 people showed up though. That's what? Do simple math. 200 times 4. 800 times, there was a racer on the course. Should have been more than just a broken collarbone. Right? Okay. Well, that's that's just 1 way look at the data is, wow, successfully
very safe. Okay? Was there any factors in that? What do we do? Do we do something different? We change things up. Answer happens to be yes. Answer happens to be that we had more course marshals. We had we had actual communications.
We had radios that were functioning, and people know how to knew how to talk on them. So that was 1 big thing. Number 2, we had a centralized location for race launch and race finish, that has a big factor in it because it's since we're right in the middle of the park, that's a big deal. Okay? The number of races were out there we had a lot of dry days. There's only 1 race where it got a little wet. But other than that, trail's very dry. Which makes tires grippy a little better, maybe.
Okay. So maybe there were some environmental situations that took place that made this race a little more safe. So that's just looking at that kind of data. Now let's go back to the the money and the dollars thing. K? We didn't raise the price. The price is still 20 bucks ahead. Which is unheard of in mountain biking. But we like the idea of this backyard race where we offer pretty much pretty much a black a bit older bootstrapped club event.
This is where the club I belong to, the Potomie feel club. Put on this for a series every year, as our our little fundraiser. And a lot of times, it's experimental because we teach a lot of our club members certain aspects of of racing, of volunteer of of doing trail maintenance, of supporting local clubs and having just an environment for mountain biking that isn't really kind of overblown
with a lot of pompous circumstance, which isn't or this isn't a good or bad thing. It's just how we put it on. It has this ambiance of this local backyard kind of thing. Which is what it is. Right? The weekday race, train race is what we do. K. So so putting the numbers together though, At 20 bucks ahead, we had a pretty good year. There's a lot of people who raced. A lot of people who preregistered this for this race too, which was really interesting. What does that mean? Why did that happen?
Why this year more than other years? What changed? What's the variances? Within my data that would let me know if I'm assuming something right, or I may just the beneficiary of some circumstances. Turns out after doing some analysis, we were the beneficiary of some circumstances. 1,
by company in the area, British pro in the area, stop doing mountain bike races. Okay. That's a big deal, especially with doing mountain bike races in the same parks or roughly around the same area. They split that mountain biking off to another group who was doing it for a while. They stopped doing mountain bike races. Okay. So you have 2 of them out of out of the mountain bike business in Northern Virginia. And then there's a third company that's primarily
focused more on adventure racing, was also doing mountain bike racing. They stopped doing mountain bike races. That's 3 of them. Completely, they changed their business model, they moved on to the things. They they 1 of them went out of, I guess, could say, went out of business.
Of the the VAT that turns out that guess what? As far as mountain bikers as there are, there is this Wednesday's wake field we put on. And then there's the wolf bouncer we put on in September. That's it. That's all there is in what in Northern Virginia. The market just cleared itself out. So we were the beneficiaries of people who wanted a mountain bike race, but didn't wanna drive a hundred miles to go do it, or worse, didn't wanna go to 1 of the ski resorts and do it. Because they weren't downhillers. They're cross country racers.
So this is this is what I mean by the data analysis. You have to take a look at this stuff to find out what matters and what didn't. So we can change anything more or less about the race.
We added a couple of things. We fixed some things. We fine tune a lot of the process in the back end. We definitely fix the way that we keep the trail up and running and the way that we clean up the trail and the way that we produce our race and put her for a registration in, and a lot of the the fine 2 things that go in there that none of the races have received. Fix a lot of those things. A lot of bumps in the road on that, but that got fixed. So data is important,
measuring that data is important, and knowing that you need to measure the data is important. And then knowing what matters and what doesn't matter when you're looking at data, because this is you've you've ever heard of the term big data,
there can be a lot and a lot and a lot of stuff coming at you. It can come at you quick too. And you have to know how to deal with it till it matters. So you don't wanna you you definitely collecting everything. Sometimes you collect data and even know it. But you don't want to overwhelm yourself with too much data because then you don't have no idea what the heck to do with it all. And that's that's an important thing too, is you want to be able to collect the right data.
Right data to make the right decisions at the right time. I don't know if it sounds like a catchphrase or tagline, isn't it? Right data, right time, if I make the right decisions, and I just mixed it up. Right. Right. Decisions, the right time with the right data. See?
It works out when you know what you're looking for, but you have to know what you're looking for. Let's let's let's talk about some some some problems that we had with this race. That I use data to make a decision. Okay. So we're gonna get into this. Okay. We're gonna talk about the problem of too many racers in the tight trail at 1 time. That's a big problem that everyone seems to have. We're gonna talk about massive category turnout.
That's a whole another problem. That maybe you've maybe experienced or you've had just too many people in 1 category. I mean, how do you how do you work with a group like that? And then we'll talk about a little bit about data cleansing or just garbage in, garbage out. That's that data is only as good as you make it. We're gonna get into this. We're gonna dive into this this data stuff and find out how you find a balance in all of this. How do you How do you work that? And then what happens if
the data tells you 1 thing and you go with that decision and find out that people hate that change? People hate the decision you just made. You wanna do that. So then you have to find out, not only do people hate the change, but maybe you don't have time to roll the change out, that people really do want.
So you make it change too soon, people hate it, or you've got all the data and you figure out what does he do, but there's just no time. There's no capability to actually roll those changes out. This is all what all involved inside big data analytics and what we're calling what I'm calling today. You know, how to turn those guesses into data driven decisions? Okay. Let's get into problem number 1. Too many racers on the trail at 1 time. So this year, we had record turnout.
It was 200 plus racers just in the 1st race. That's good. That's a great problem they have. Right? But it also presents this problem of capacity. When you're working at a park, They've only handled so many bikes on the trail at 1 time. I mean, the course length is only 5 miles. So you have 200 racers, that first wave, we do it in 2 waves, 1 at 6:1 at 6:55 PM. So we have 2 waves. So the first wave was like looking at a 120 to 140 riders.
2nd wave is always smaller, not the experts in the client sales, bad bike, things like that. But the first wave is masters 35 plus. Masters 45 plus. We have beginners with single speed, the juniors. All that's in the first the 1st 6 o'clock race. So that's a lot. So how many bikes can you fit out of course? That's only 5 miles long. Well, the solution, of course, is gauging your categories.
So there's there's some timings involved and that's some orchestration that takes place. In order to get people in and off the course and get reg you know, get results and get podium to get people home before it's pitch black because you really hard to pick up a course in pitch black. So in order to do this successful and get people in and out and be safe about it, we're talking about you have to have enough sunlight
to to be able to do what you need to do. So there's only so many stages that can take place before it gets dark. This leads you only about 2 start times that are packed with a bunch of categories writing in each stage. So at 6 PM, single speeds, max 35, max 45 beginners, like I said. This gives you about an hour to ride 2 to 3 laps, 5 miles each. So before the next stage can safely be launched because you don't wanna have what Like I said, 120, 140 riders out there, and then launch another 80.
That just that's a recipe for disaster. So you don't wanna do that. So the 6 PM racers are not really off the course yet. However, because we're doing these waves, the expert man really don't really reach the end of the first lap until, like, 20 minutes after the start. It's like 20 minutes left. So they're they're going fast. It's still remember, it's still 5 miles. So I just wanna trap. So 99% of the 6 o'clock people, they're done.
So the tail end, the 6 PM race is really kind of getting done. When 65 rolls around and they launch it. So the staging really kind of reduces registration stress. Those registrations at 6 o'clock. I mean, they have to arrive at 5 to 5:45 if they wanna get into the race. So this allows registration to handle the crowd and not get overwhelmed. That's advantage number 1. When the race is being run by volunteers, not getting them overwhelmed isn't an important advantage.
So definitely wanna throw that into your planning. The second advantage, it reduces the trail the the the trail dress, the trail dress at the starting line. So if everyone was packed in down the start no matter how staggered the launch times were, the number of bikes on the trail would still make it difficult for racers to pass slower riders. When you have a ton of single track out there with only some very particular strategically placed passing lanes.
Having a lot of bikes on the trail is tough. That's a tough race. When you're stuck behind slow pokey for a mile or 2, sure you're getting a break, but that guy ahead of you that you're trying to catch, he's going further. He's getting further and further away from you. And that's tough, and some people get very frustrated with that. So you need a good understanding of which categories have the highest registrations to divide them up evenly.
The strategy you have to really consider is that you need to consider size of the category. How many prices are going to to be in that category. And speed, how fast are they? Because you don't want your brand new beginner riders going in front of the master's 35 grizzled, you know, hardcore super speed demon guys.
They're gonna run them down. That's a that's a bad jujamins. You don't want to do that. So the faster riders, the more laps, you put those guys up front. And the slower riders, fewer laps, Put them in the back. Makes sense. Right? So some of the big categories, however, the 6 PM, we're really, really, really big. So the faster riders are going to lap some of the slower riders in this massive monster pile all of your riders going around. You can't help the
slower the slower riders being being lapped. You can't even be help having them not lap by the 65 where you have people out there who are doing 45 minute lasts. They're doing 5 miles 45 minutes, which is fine. Some beginners get that way. That's why we only do beginners 1 lap. So you think about it is that if the beginners doing a 40 minute lap and they launch and they launch a, what, 10 minutes after the fast guys go.
That means the fast guys are doing 20 minute laps. They're gonna get back to the they're gonna get back the finish 10 minutes after you launch the beginners. The beginners have a 10 minute head start. By the time the math works out, beginners are done. So this is how you build in safety in your course. Okay? So make the stages as staggered by speed and experience as possible.
This will reduce the total field during each start into a manageable size. And hopefully, everybody's trail incidents that result from her feelings because in collisions, I mean, 1st feelings are the best. Right? Someone being like, someone passed me, and I didn't really like them. They don't mean to me. Okay. You know? That sucks. I've had that happen to me too. A lot of guys who who pass you, super nice. Some guys who pass you, they're not nice at all. Or at worst collisions.
This is where the guy's going super pet fast. He's upset. He rides weird and the beginner or whoever panics. And turns the bike into the person coming. Done this too. Done this where I didn't know the guy was passing me. He he said he's pass on the right. When I got over, he decided to cut to the left, put an elbow into him. Talk to him later. You know, he's like, oh, hey. No. I understood. I told you right. I went left. My bad.
Think about that. So that's how you kind of break up your trail, your trail stress to something a little more manageable. Alright? And that's that's this kind of dealing with that that problem the first problem is 2,000,000 razors on the trail at 1 time. Right? So staging your categories. That's solution number 1. Alright. Let's talk about problem number 2. Massive category turnout. The second problem I had was this explosion of number of registrations.
So when you have a record turnout, You've learned very quickly who your main customer is. And I went to Zillow Field, my main customer, Deborah Craig, turns out to be a majority of men between ages 3 to 55 years of age. That was very obvious with who showed up.
B, interest the people are interested in racing against a similar mountain bike types, which is why some of these categories are super big. And c, they're interested in racing against similar ages is why 1 category in particular was the biggest. Record turnout is always a good problem to have. And it certainly impacts underrepresented categories. More of those people come out too, but when an underrepresented category,
tends to have 5 riders in it. And when you have record turnout, it's 10 riders. That's pretty easy to deal with. When a record turnout goes from a category that has 30 riders to 60 riders, that's a problem. So unrepresentative categories only see like that 10 to 15% increase, and the categories are favored by my main demographic, see the 20 to 30% increase. So the result is this large concentration registration.
It happened to be the master's 35 master's 45 plus categories. These are men in those ages. 35 to 39 or 44, I guess, and then master's 45 plus. So the 6 p n race, a total 128 racers registered at the start with half of them in only 2 categories.
It might not seem like a large number of riders, but for a small race and a relatively small park, a 128 riders is a year record. You're gonna have a lot of these 45 plus guys, eventually getting mixed up with other rider types. That's gonna be a problem. So what's a race director to do? Well, the solution,
we go back to what is what is a way that I can use data to drive a decision about what to do with the category. It's just so dang big. So, of course, these are great problems they have, and these are great problems to solve. Requires you to to use your race data to figure out what it can tell you about your situation. So in this problem, too many master category writers in 1 race. That seems to be pretty obvious. The solution is really break it up into smaller race groups. Right? But which ones?
So there in lies the rub. There in lies why you needed data to information to drive the decision you needed to make about where do you make the cut? Which which age categories do not get to be in categories they were in? This is remember, this is this is a problem
that just started in this particular race series. Last race series, they all race next to each other. Now granted people have birthdays, people age out of certain categories. When you get that age, you know, master's 45 plus, same people are showing up, so they wanna race with each other. They wanna be part of that 45 plus Kabal or, you know, mafia fighting for the podium because some of these guys aren't just fighting for the race day podium. They're fighting for the series
series podium. So you have to these don't even think very carefully about which age groups you need to cut. You
know, not cut, but, you know, where you make the cut to the age groups to create new age groups. In order to begin to solve this problem, you may first look at the breakdown as whole ages. So to do this, you need some specific data that includes the ages of the writers' category. So you should create a simple spreadsheet and do the following. Put the age list starting with 30 and an in 60 in the first column, and then you have 2 columns.
Right? The top of 2 columns, you have the master 3 by master 45. And then you count the number of riders each category. They have the same race day age as the age listed. And the total number of each category at the bottom. That's what I'd k. So I started with the master's 25 plus category and the master's 35 plus category. But many people do I have are of what age? So look at it. There are 7 where age is 45. 6 where age is 47. 5 where age is 36, 44, 51, 353, had 4 ages 37, 50.
And then 321 broken up the everything else. So you should have a complete accounting of how many of what age you have in each race category. So the goal was to create a good customer experience. And break those categories up into the groupings that would reduce those trail that trail congestion. And everyone was for it, but it wasn't the how. So would it be divided by the fives or the tens? In other words, would we simply add masters 55 plus to our current masters?
Category list? Or do we change those categories into 30 plus, 40 plus, 50 plus? The solution, go back to the data. So by going back to the data, you start to look at what are your groupings, what kind of data can you find out from combining the ages into certain groups to see what groups are more represented than others. So we tried this with the with on the 0s, the 30 to 39, the 40 to 49, and the 50 plus. We find out kind of about 12 in the 3039. At 32 in the 4049.
It's pretty big. But we had 26 and 50 plus. That's kinda interesting. But you can't just do it with that. You have to go back to the fives and look at the fives as well. So we went back to the fives. We started at 35 to 44. We got, like, 24. 45 to 54 was, like, 38. Big fat category.
And then the 55 plus, that was only like 8 or so. So by looking at these 2 groupings, it starts to look like dividing it on the thirties is the good way to go. But there's a there's a caveat to this because, like, I'll talk in the the next segment, that is only as good as what I do to collect it. So if I don't do good collection, I don't have good data.
So having a way to collect the data is important. So where's the hole in my data? Well, it's obvious that the people who are racing 35 plus are hopefully 35 or older. People who are racing 45 plus are, hopefully, 45 or older. That means understanding the ages of people who race in the master's category or between the ages of 30 34, tough to do because I wasn't collecting it. In fact, you know, I have I have 0 in that category because I'd have to go back and
dig through all my my waivers to find out if there was even people in that edit category. And I know they are, but they don't race masters. So that's a problem. So if I go to 30 to 39 as a category,
does that open that up for more people able to come, maybe people who thought that Master's 45 wasn't for them. Maybe they will now. Maybe with the 40 fives, the forties and fifties pulled out of that category, they'll start to think that that's a category for them. So this is the kind of decision making you need to make. You have to go through and crunch these numbers to find out where these people fit inside your divide.
So dividing on the fives or dividing on the tens to break this category up was difficult, and we had division in the ranks. Because 1 of the parts of doing this is you can't just do this in a vacuum. Put this out to the staff. Put this out to the people involved. Put this out to your your partners. And the people who are helping you put on this race, and get their feedback. What do they think? Try to understand the categories. And not only that, put it out to the racers themselves.
In fact, on different race days, I went around the 35 plus and the 45 plus and asked them, What would you think if I made this 40 plus in a master's 50 plus category? And most were kinda open to it. In fact, what's funny is the 40 plus people are kinda like, no. Well, you know, it'd be kinda cool, I guess. I don't know. Because they're they're kind of racing in that group. It was the 50 plus people
who are like, yeah. Finally, a category that I can be in to get those young punks out of my category. So this is this is the kind of decision making to make. And we haven't actually made the decision yet. We're gonna do that in the in the winter, where we decide when we do our race planning, for 2018
how to break up this category. But right now, the data looks very, very good to create a master's 50 plus category and then change the other 2 master's categories in 23040, which historically after 15 years of doing this race, would be a, you know, a deviation from the historical norms of the Master's 35, Master's 45.
But sometimes change is good. And this race in particular and this series helped us determine that it was time for a change. And this was the change that happened. The demographics had shifted to where people in their forties and fifties were mountain biking more in the master's category, felt more comfortable in the master's category than any other category. And we had to adapt to the change. So by adapting to this change, by understanding
what our data said, and then making decision off that data. We're able to come to some very intelligent decisions on, do we add 1? Do not add 1? And where do we make the cut? So problem number free is your data is only as good as you make it. This is garbage in. Garbage out is a good way to look at this. If you do not collect the metrics that you need to make decisions like this, then reverse engineering, your race to find this data will be hard, especially
If you're not recording this at all, then you don't have it. If you don't have it, there is no data to make an analysis with. So you need to be thinking in the future of what kind of decisions you're gonna have to make. And that's the whole point of the merchandiser podcast to give you those those lessons and those those tools to understand what you're going to need, what are the things that you're gonna have to look at. In order to make good decisions.
So 1 of these type of lessons is understanding that you're gonna have to make decisions like how many people fit in a category, for safety, for management, and just for good control of your trail congestion,
You're gonna have to know where to break categories up, and you're gonna guess at first, and then you're gonna run a race, and you're gonna see things not working out the way you thought they would work out. This is the difference between developing a race and operating a race. You're gonna plan all these cool things for your race. And then you're gonna go and actually put it into operations and actually execute on your plan and find out that Wow.
Way too many people showed up for 1 category and knew and showed up for another category. When you can't really make a cut, right there on race day. You could try, but a lot of people sign up for a category and have this this vision of where they're racing and who they're racing against. Some people sign up with their friends, and they wanna race against those friends or with those friends. And if you start making tweaks on race day, you're gonna start upsetting your customer.
So understanding that you need to start collecting data and what data to collect is a very important point to add to your planning preparations.
So the data is only as good as you make it though. If you all you also discover how good or not good your data is. In this case, found out that most of the ages that determine either the race's birth date found on the waiver or the race age, which is determined by some arbitrary rule. Right? We manage our races based on USA Cycling race age rule, which asking the racers racers aged based on what age they would be on December 31st.
Pretty simple. Yeah. This removes the mystery of the age category and what you qualify for. Oh, my birthday is next week. Can I race 1 character? If that goal goes away. K? However, most racers are have horrible penmanship. So if we're using the waiver as the decision point, making a handwritten handwritten birth date and a waiver is not an easy task nor is it easy to determine if the age
They write on the form. It's the age they are when they sign the waiver, or the age they will be in December 31st. Now why was this? Because we didn't dictate it. We didn't go out and tell everyone, look, your race age is December 31st. Even if it says it on the waiver, says it on the form, we weren't enforcing that because no 1 was asking. K? Lesson learned.
When it went through the waiver of my of my race, I found a mix of both, then when I began to crunch the numbers for this exercise, I discovered that 2 of the racers were in the wrong category. Wow. Wow. This is another issue you have to work out with considering your age group categories. If you decide to break your ages into groups, you'll need a policy in a process for determining who can legitimately register for that category.
So in my race, I discovered that there was a 36 year old rider racing in the master 45 plus. Well, that's a problem because he obviously isn't 45 plus. Not for another 9 years. And then while there was a 48 year old racing in the Masters 35 plus, Now granted, master 35 plus is what? 35 and older. Until you hit the 45 plus category, then you have to ship categories. But we never told anybody they didn't have to. So technically, the 48 year old guy is legit. That's a problem.
He's no longer in a proper category. He's in a he's in a different category. So meanwhile, we've never had an existing rule that only states only those ages 35 to 44 can race in the master's 35 plus category. And since no 1 has reportedly done it, we didn't really have to enforce it. There's no rule
This technically doesn't allow the 45 year old to race in that category because he should have been in the other category. But again, we never said anything. This is something else that needs to get fixed. It's a lesson learned. So although these are small numbers and they're easily overcome, Imagine if this category had been 200 plus or 300 plus or 400 plus riders where
not asking the right age and not getting the right people in the right category or not checking to make sure that people were somewhat legitimate. I mean, They're not cheating in the sense.
They're they're just racing probably with friends. It's usually the the re usually the result. When you do some investigation, you find out the reason why Someone races in a category that's the wrong age is because they wanna race with their friend, and they they just don't tell anybody, and no 1 asks, So they get in, they fall under the cold, slip underneath the radar. This happens a lot. It's not cheating, but when you're doing data analytics,
this provides anomalies. This provides these strange outliers where your data is not 100%. This is what I talk about garbage in, garbage out. If you're first, if you have no collection, you're not collecting ages, or worse, you're relying on the person filling out the waiver to tell you what their age are, their age is. And The handwritten age is the only data you have, and half of them are unreadable. That's a data collection problem.
How do you solve something like that? Well, there's a whole bunch of different solutions. You could have data entry being done by by your own staff or your volunteer sitting there at the registration table where they don't fill out anything. They just sign something at the very end. That's 1 way, but you start adding cost. The minute you start changing your process like this. So let's think about my data collection process right now. Eraser comes into the race, they go and they fill out what we use as the standard USA cycling waiver, which has all the the
amendments and and deviations from that, you know, what what's protected, what's not protected, things like that,
which is which is how we how we operate as a USA Cycling club. They fill out that waiver by filling out their information, their race information, what category they're in. Their date even says on the form, you know, what date what age are you at December 31st this year, So they fill all that out, they walk over, they pay their money, if they're race day, they go over and they get a bib,
and then they move on their way, they go on their they put it on the bib on their bike, they go stretch out, and they go race. So looking at that, the only interaction that I have of knowing that a customer is on my course is 1, I have their waiver with their information on it that I use to put in registration. 2, they collected a Bib. We write their name down, what Bib number they took as well. That's our second collection. 3,
they paid money. If they paid cash, I don't have a connection. But if they paid with a check, then, you know, we don't do credit cards, not yet.
So there might be a third 1 in there. That's like a maybe. So I get their waiver. That gives me pretty much the most information I'm never gonna get out of them. And the BIM number, at least I can connect the name with a BIM if I have to to backtrack that way. That's it for data collection. So if I wanna do something like how many people were in the master's 45 plus category. I just count the number of waivers. They give me a number. But are they right? Are they legit?
And then what about the 2nd race? The 3rd race? The 4th race? What about last year? The year before that? Year before that? Year before that. If I've never crunched these numbers, if I've ever looked at this data before, That's a lot of waivers. I got sitting in a box in my garage. I have to go dig through because we save them. We save them for years.
So I've got enough waivers to go back 5 years. I can go dig through all that data and find it. Man, that's hard. That is really hard to do. If you've ever had to go through a 1000 waivers over 5 years. That is a lot of bad penmanship. Not to mention because people are sweaty and it's the summer. So there's this wet and it's hot and it's dirty. Those papers are just gross. So you're digging through 5 years of people's aged
water stain, dirt stain waiver forms. To you imagine, you need to you know, I did this on my kitchen table. Wow. My wife was not thrilled about dirt and garbage. She's like, this has to happen in the garage. You're not doing this on the kitchen table. But that's the kind of thing to to have to experience the pain of that kind of data collection. Now, granted, you only have to do it once.
But would it be nice if before you even started your 1st race, before you ever even opened the door to allow people the come in for registration that you already had thought this through, that you already decided, that you're going to collect data, And these are the data your data points you're gonna collect based upon a criteria that you think in the future or even in the present or the near present, you're gonna need that information for later. Here's an easy 1. Okay.
I have a race series or I have a race coming up in September called the wolf bouncer. You'll find that at wolfbounceer.com. There is an email list I have for that that I'd have yet to populate with last year's racers. Why? Didn't get around to it? Doing all the direction of planning stuff and things falling through and things, you know, and patching things up?
It got it got slipped off my list. So I have to go back through those those waivers and find those email addresses so I can tell everybody, hey, there's a race coming up. That you might be interested in coming back to because a lot of people had a lot of fun. So
in order to have planned that, if I had planned that in from the very beginning, If I had said, I need to collect these email addresses so I can tell them about my race for next year. While I'm planning the race this year, I wouldn't have this problem. I wouldn't have to go find those waivers, dig them out of a box in the garage, lay them out, figure out if I can decipher their babylonian hieroglyphics, which happens to be their email address.
You know, hopefully, it's still active because I haven't reached out to them. So now they might even you know, they might have moved on email addresses, so I might have a bunch of of bogus email addresses. So I had done that, if I had computed that information on race day, and then a week or so after that race reached out to them to say thanks for coming
in a in a different way rather than using my registration software, which they keep this they keep the email addresses a little more close to the close to the vest. This is a way I can control it. I can put it in a mail chimp and hit my own list, and that can reach out to me anytime I want to without having to worry about the registration software.
And we're about a third party controlling that. I control that. If I had planned that in, would have this problem. This is the kind of thinking you need to have. When it comes back, let's go back to our example of breaking up these categories into their different parts, knowing
what the people that are in your categories are legit is important, especially if you're dealing with big numbers. If you start getting up to the 1000 and tens of 1000, like marathon level, external level type stuff. I mean, that could be a full time job for somebody to make sure that the data is correct or to go back and reverse engineer. You want to prevent the reverse engineering because having good quality data upfront allows you to make decisions faster.
It allows you to make more accurate decisions. And it allows you to base those decisions off of data that you know is correct. That is the key. So to go through this data driven example, and to understand how we went by the process for figuring out next year's category breakup. You have to understand that your data collection has to start before the first gun goes off
then the first racer starts the course. You have to be doing this long before that. You have to be doing this long after that. And you have to build it into your process. You cannot do reverse engineer metrics and be comfortable with the decision or enjoy the reverse engineering process. Because let me tell you, any fun. So do yourself a favor. Take this lesson learned to heart.
And understand that the data cleansing and the data collection and the measures and metrics that you need you need to make a data driven decision have to happen before your race. And now you know. So on this episode, we talked about data driven decisions. But what about the times you don't have data or even worse when you have just guesses, when you're just kind of thinking, oh, maybe this will work.
Well, what's why do you get around that? Well, experience, of course, is 1 way. But in the next episode, I'm gonna teach you another way, and it has everything to do with surviving what I call the 4 horsemen of the race planning apocalypse.
These are Mister Murphy's best friends, and we're gonna talk about all 4 of these guys who go about just kinda just smashing your race, and it's gonna get really kind of interesting. And I hope you stick around to see it. That's that's in the next episode of the VirTra's Durb podcast. Thank you so much for listening to the VirTra's Durb podcast. I would love to hear from you. Please reach out to me via email at [email protected] or to find me on Twitter. I'm at merchantsadirt,
and I'd love to know what you're thinking about data driven decisions, and how you go about kind of thinking about what you're going to need when it comes to what data you need to collect before your race. Because there's no real standard way to do this. So everyone kinda does their own thing. Wouldn't it be great if we all got together as a community and thought about, hey, this is the data we could use because if we all collected the same kind of data in the same way, what kind of macro things can we look at? Now if you go back to 1 of my past episodes where I talked to Sisack, from adventurerace hub at adventureracehub.com.
He gets into kind of the analytics of adventure racing and what adventure racing details and understanding of an interest tree he was able to collect just with 2 years of death. Do you imagine if the trail runners and the mountain bikers and the ultra runners and the orientators all collected data this way and were able to share it in a big almost like almost like thinking about an annual report for a corporation of the the state of off road racing,
which is Okay. You know, you heard it here first, the state of off road racing, trademarked Reconier, trademarked Calabrio. So I'm gonna try to think about that, to kind of think about what kind of data every 1 of these organizations, every 1 of these race style disciplines, could put together to give everyone an idea of is for growth. These are not growth. Where is the money being made? Where is the money being lost? Would that be interesting? I think it would.
So please reach out to me and let me know. And by the way, if you're an avid merchant of your podcast listener,
you'll know, kinda been off for a little while and take care of some family stuff. And if you listen to some of my past episodes, you talk about the the choke point, the bottleneck, you know, the single point of failure. Well, I'm the only host, so I'm the only 1 who can put out the podcast. So when something happens in my family, that's something I gotta take care of. I I just had to added it to turn it off for a couple weeks, but I think I'm back. I think we've kind of reached some stability and
gonna start putting out some more mercysogroup podcast episodes, and I'm gonna start putting out some more get lost racing. Started working on another idea called Rekineer Media. Has to do with kind of putting together a little network of off road and outdoor type podcast groups. A little bit more of that in the future. But for now, just know, hey, the Rechinir, File Bondo, merch is
is back. Hopefully, until the end of the year, we're gonna start knocking out some episodes and really get into how to start races and how to build a better race through your ability to go from becoming a racer,
to becoming a race director, or a racer from another group. That's my goal. At least my goal for the end of the year. So I hope you stick around and listen to more episodes. I appreciate you listening, and I look forward to to seeing some more subscribers or maybe you share my podcast out there for other folks.
And maybe even asking me some questions. I'd love questions. I get them all the time of what kind of things you guys are working on that are stuck on or things you're not sure about. I can help you find that answer. So any other questions you have, go to wreckingyear.com,
got plenty of articles up there to read, to get kind of an idea of how to fix your race. And then, of course, the whole backlog of emergency or podcast will get you up and running. And until then, it will build better races. Take care. No Mister Murfrees were harmed during the making of this podcast.