You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


TLDR;

Starting too fast too early may wreck your marathon by as much as 60-minutes based on data from more than 400,000 Chicago Marathon finishers.

Going out too fast also makes you much more likely to hit the dreaded wall later in the race; over 50% of Chicago Marathoners who hit the wall go out too fast in the first 5k.

And yet lots of people continue to go out too fast; almost 40% of Chicago Marathoners have the first 5k as their fastest race segment. Women and older runners are slightly more likely to do this but ability and experience play a major role.

Starting more conservatively can improve your finish time; about 10% of Chicago Marathoners run the first 5k as their slowest of race and they gain a whopping 50 minutes on those who’s first 5k is their fastest. And, according the data, none of these slow-starters hit the wall!

This is part of a series of posts that I am writing as part of an ongoing analysis of marathon data. So if you are interested in this sort of thing then you will find a growing number of articles here.

Seriously. Don’t Go Out Too Fast in a Marathon

One of most common pieces of advice for marathoners — the number one race time if you will — is: don’t go out too fast. If you do you’ll suffer later, or so goes the conventional wisdom, and yet every year a significant percentage of partipants throw caution to the wind and fly off the starting line. Some simply get caught up in the excitement of the start and neglect to monitor their pacing before realising their mistake. Others plan to bank some time early on while they are still fresh. Yet others adopt a “go out hard and hang on for dear life” strategy. These are all a bad idea according to the lore of running, but what does the data say?

Our data is drawn from the last 12 years of the Chicago Marathon (2005–2016). This data provides a wealth of rich data including 5k split-times for each runner; that provides us with 8 x 5k segments (5k, 10k, 15k, 20k, 25k, 30k, 35k, 40k) plus a final 2.2k segment from 40k to the finish-line (which we will refer to as final). Roughly speaking each 5k segment is the equivalent of 3.1 miles and the final segment is just under 1.4 miles.

All of this this makes it possible to conduct a fairly fine-grained analysis of pacing throughout the race; for comparison I’ve completed a similar analysis on data from the Dublin Marathon where 10k split-times are available. So that’s the data. Now, what does it say about going out too fast?

Race in Haste, Repent at Leisure

The graph below shows the average finish-times depending on which segment is the fastest. In other words, for each of the 9 race segments we group the runners who ran that segment as their fastest and we compute an average finish-time for these runners. This gives us the graph denoted by All below. We do the same for males and females and for younger (under 40s) and older (over 40s) runners too.

In what follows we will refer to those people who has the first 5k as their fastest segment as fast-starters. While they may be enthusiastic marathoners, things dont seem to go their way. For a start, we see above that their average finish-time is about 295 minutes, which is much longer that the average finish-time when any other segment is the fastest. It’s almost 25 minute slower than when the second (10k) segment is the fastest, for example, and its more than an hour slower than those runners who have the 35k segment as their fastest! That’s quite a penalty to pay for going out too fast.

The graph below looks at the relative pace variation between the segments based on which is the fastest. Not wanting to get too technical but the pace variation is based on the coefficient of variation of the individual segment paces. Suffice it to say that a higher pace variation value means that pace is varying a lot across the segments. We can see how the fast-starters (and also those who run the 10k segment as their fastest) suffer the most in this regard with 2–3 times the pace variations of runners who avoid going out too fast in the first-half of the race.

But that’s not all …

So it certainly looks like the conventional wisdom is correct: fast starts make for slow finishes. Its the same for men and women and for younger and older runners. But there’s more because we see a similarly worrisome pattern when we look at how often fast-starters hit the wall; in our analysis hitting the wall is based on a 33%+ slow-down in the second-half of the race as discussed here.

From a previous post we found that about 10% of people bonk or hit the wall in the Chicago Marathon based on this 33% slow-down. That’s about 40,000 runners! In the graph below we look at how many of these poor souls ran the different segments as their fastest. It is clear that when the first segment is the fastest it has a major impact on how likely a runner is to hit the wall. A full 50% of people who hit the wall in the marathon have run the first 5k as their fastest segment. In the earlier post we found that women were much less likely than men to hit the wall in general but nevertheless a full 60% of female bonkers ran their first segment as the fastest.

This goes a long way to explaining the deterioration in finish-times for these fast-starters. Fast-starters who hit the wall have an average finish-time of over 320 minutes while those who manage to avoid this fate have an average finish-time of just 290 minutes. This is still much slower than those who don’t go out too fast, so hitting the wall is not the only reason why fast-starters have a slow race.

So, going out too fast is a big risk because you are much more likely to hit the wall, in which case your finish-time will explode. Even if you avoid the wall you still suffer from a much slower finish-time than might otherwise be the case.

Who are These Fast Starters?

If going out too fast is really such a bad idea then surely it rarely happens, right? Not so I’m afraid. The graph below shows the percentage of runners with different fastest segments. The most common fastest segment (with 37% of runners) is the first 5k at the start of the race. In other words, fast-starters account for more than 1 in 3 Chicago Marathon runners. There are minor variations based on age and gender but by and large it is the same story for everyone.

What about ability or experience? Are faster runners more or less likely to be fast-starters? Are repeat marathoners more likely to avoid a fast start?

The graph below shows the percentage of fast-starters based on their finish-times. Faster (more able) runners are much less likely to be fast-starters; only about 5% of those finishing within 180 minutes are fast-starters, for example.

As finish-times increase so too do the number of fast-starters,, all the way up to the 5-hour mark. After 5 hours the percentage of fast-starters falls off again. Clearly ability has a bearing on whether someone will have the first segment as their fastest, and those in 210 to 360-minute finishing range are much more likely to start out fast compared to either faster or slower runners.

What about marathon experience? If we go out too fast as a marathon newbie, do we learn our lesson for future races? Yes we do. The graph below shows the percentage of fast-starters based on the number of marathons they have run. Over 65% of first-timers go out too fast but less than 20% of those on their second marathon are fast-starters and fast-starters all but disappear as the number of marathons increases.

Slow Start = Fast Finish?

If going out too fast is such a bad thing, does it mean that a conservatively paced start — e.g., running the start segment as the slowest segment — will produce a faster overall finish-time? The graph below compares the average finish-time for runners with different slowest segments. We can see that there is indeed some benefit in being a slow-starter because the best finish-time (244 minutes) is achieved for slow-starters, at least compared to running any of the other segments as your slowest.

It is also worth pointing out that according to the Chicago Marathon data, no slow-starters hit the wall! However, slow-starters are not that common and less than 10% of Chicago Marathon runners adopt this conservative early-race strategy. Still, all other things being equal, starting slow is certainly better than starting fast.

But why is a Fast Start so bad?

Why is starting fast so bad and so much worse than having a fastest second or third segment? Why is it that when the first segment is our fastest then the rest of the race goes so badly? There must be something about the first segment that makes it special, right? Yes there is.

In fact there are a couple of things about the first segment that make it particularly problematic if we loose track of our pacing early on. Firstly, because it is the first segment there is more of the race remaining than for any other segment. So if we go out too fast, and use up a lot of energy, then what remains in us has to get us through a much longer distance.

Secondly, because first segment is bound up in the excitement of the start-line it is more susceptible to more erratic pacing. We are raring to go with adreneline coursing through our system and a hair-trigger controling our pacing. If we don’t pay close attention to our speed then are we liable to go out much faster in the first segment than in any other segment? Does the data support this? Yes, it does.

The graph above shows the pace for different fastest segments as a fraction of overall pace. So, for example, when the first (5k) segment is the fastest then it’s average pace is just over 87% of the average pace for the race. In other words it’s 13% faster than the runner’s average pace.

By comparison, if later segments are faster then they are faster than the average pace by a decreasing degree. When the second (10k) segment is fastest it is only 11% (100–89%)faster than the runners average pace. When the 40k segment is the fastest then it is only fractionally faster (<1%) than the average pace; not so surprising at the end of a long race. The exception is the final segment (which is a little over a mile) because when it is the fastest segment then our fast-finishers tend to run about 7% faster than their average pace. It’s probably surprising that it is not faster. For instance it is not faster than the first segment for fast-starters; even a sprint-finisher does not out-run a fast-starter.

Going out too fast is especially bad because there is a natural tendancy to go out much faster than for any other segment and because we will have to pay for this excess speed over a much longer remaining race distance.

Pacing Strategies & Race Plans

In this post we have focused on the dangers of starting too fast and it is pretty clear that getting caught up in the excitement of the start-line, or going out hard and hoping to hang on, is a recipe for disaster. Simply put, if you don’t watch your pace at the start you are liable to go our very fast not just a little fast.

If you want to run your best race then at the very least it will be important to control your pace at the start of the race and running with a pace group can be a very useful way to do this; in a recent article we took a more in-depth look at the benefits of running with pacers; at the very least they can facilitate even pacing and thus a more controlled start.

Of course this doesn’t mean that taking it too easy in the first part of the race will get you across the finish-line with a new PR. In a future article we will take a closer look at some of the nuances of so-called pacing strategieswith a view to building a better race-plan for the big day.