If you coach long enough, you realise most athletes are trying to answer two different questions that get mashed together into one.
Question one is prediction: what finish time is realistic for me on this course, this year, given my current fitness and how I usually race.
Question two is pacing: how do I distribute effort so I do not blow up like a cheap tent in a southerly.
They are related, but they are not the same. You can predict a time and still pace it like a clown. You can also pace brilliantly and still finish slower than you hoped because the course is brutal, the day is hot, or nutrition went sideways. The goal of this post is to give you two real world, data driven ways to predict finish time, then show how to turn that prediction into a pacing plan that is less guesswork and more “I have receipts”.
The first method is the simplest and, in my experience, the most reliable for everyday trail and ultra runners.
It is the percentile method. Instead of obsessing over pace, you look at where you typically finish in a field, then you apply that same relative placing to the target race and read off the likely finish time from last year’s results. I have previously explained in more detail here.
Why this works is pretty boring, which is exactly why it works. Most runners are surprisingly consistent in relative performance when the event category is similar. If you are usually a top third finisher, you tend to keep doing top third things. If you are usually mid pack, you tend to keep doing mid pack things. Not always, but often enough that it is a handy forecasting tool.
Here is how you do it.
Start with at least three past races that represent “you on a normal day”. Exclude the day you walked with a mate, the day you bonked because you forgot to eat, and the day you were clearly injured but did it anyway because your brain went offline. For each race, record your placing and the number of finishers. Convert it into a percentile.
If you finished 100th out of 300, you finished at about the 33rd percentile. You were ahead of about two thirds of the field. Do that for a few races and look for a pattern. Most athletes show a tight band. You might be a 25 to 35 percentile athlete. Or a 55 to 65 percentile athlete. Or you might be all over the shop because you keep racing radically different distances and terrain, which is a clue in itself.
Now take your target event and find last year’s results. If the race has 600 finishers and you typically finish around the 33rd percentile, you are roughly aiming for around the 200th finisher. Go to the results and look at the time for the 200th place. That is your baseline prediction.
Then, adjust slightly for context. If the target race is longer than anything you have done before, add a small “new distance tax”. Usually this is only a few percent if you have trained sensibly. It is mostly about inexperience and overexcitement. If the course is dramatically more technical or the vert is much higher, you might add a bigger adjustment because your relative performance might shift if your strengths do not match the course. If conditions last year were mild and this year looks hot or wet, you might adjust again. This is where coaching judgement comes in, but the anchor is still the data.
Where this method breaks is when the race itself is a different beast year to year. Some courses are consistent. Some are chaos. If a river crossing turns into a swim, all bets are off. In those situations, the best you can do is say “my likely placing might still be similar, but the time could swing wildly”, which is annoyingly honest.
Now for the second method, which is useful if you run in the trail world where everyone has a ranking score floating around like a permanent tattoo.
If you have an ITRA score or a UTMB index, you can use last year’s results plus those databases as a matching tool.
The logic is simple. If you can find runners in last year’s race who have a similar ranking score to you, their finishing times are a decent clue about what you might do on a similar course, assuming comparable conditions and that you are not coming off a huge breakthrough or a massive setback.
This is basically “find my statistical cousins”.
The workflow looks like this.
Step one, pick last year’s race results and select a bunch of finishers around the time you think you might do. If you have no idea, grab a spread, say 30 people from the quarter of the field you usually sit in.
Step two, look those athletes up in the database and note their ranking scores.
Step three, identify the cluster of athletes whose score matches yours, or is close. Now look at their range of finish times.
If you are surrounded by athletes with a similar score who finished between 6:50 and 7:30, and your percentile method predicted 7:05, you have two independent estimates pointing at the same neighbourhood. That is confidence. If one method predicts 6:30 and the other predicts 7:45, that is a big red flag that something is different. Maybe the race attracts a deep elite field. Maybe you are stronger on runnable courses than technical ones. Maybe your previous races were smaller local fields. Or maybe your ranking score is stale and does not represent your current fitness. Either way, the disagreement is information, not failure.
This is why using both methods together is better than treating either one as gospel. You get a cross check.
At this point you have a predicted finish time range. Not a single magic number. A range. That matters because endurance racing is not a lab test. It is a long argument with your own decision making.
Now we shift from prediction into pacing. (Nerdy sciencey bit)
The research on pacing in ultras is basically screaming one message in different accents.
The best performers tend to pace more evenly and avoid early surges. In a 100 km race analysis, faster runners started faster, maintained their speed longer, and finished within 15 percent of their starting speed, while slower runners showed bigger decreases and more variation. In the discussion, the authors describe that faster runners held initial speed until roughly 50 km, then reduced speed only slightly, while slower runners dropped earlier and more sharply. Researchy-Linky
In a 24 hour track ultra analysis, athletes generally show a reverse J shaped pattern. They start relatively quick compared to their average speed, slow through the middle, then lift slightly late, before a final dip at the very end. The key part for everyday runners is this: the fastest group started more conservatively relative to their own mean race speed and paced more evenly than slower groups. The authors also found a moderate inverse correlation between early relative speed in the first two hours and total distance covered. In plain English, the more you overcooked the start relative to your average, the worse you tended to go overall. In their practical applications section they pretty much spell it out. Start conservatively and limit speed fluctuations if you want the best outcome. Other Researchy-Linky
So the science and the lived experience line up. Don’t go out like you are trying to win the first 10 km of a 100 km race. That is like flooring the accelerator leaving the driveway because you are excited about the road trip. You will still be stopped at a servo later, sad, eating something you found in the glove box.
This is where your finish time prediction becomes useful. It gives you a framework for what “conservative” actually means.
If you think you are a 7 hour finisher, conservative does not mean jogging the first hour like you are on a Sunday recovery run. It means running the early section at an intensity you can actually sustain, with enough restraint that you can keep making good decisions at hour five.
If you think you are a 12 hour finisher, conservative means something different again. It might mean you are planning for a lot more hiking, a lot more time on feet, and a much bigger nutrition and hydration load. The pacing mistakes at 12 hours are usually not about 10 seconds per km. They are about ego surges, heat mismanagement, and forgetting that calories are not optional.
Now let’s put the whole thing together with an example.
Say you are targeting a 50 km trail race you have never done before.
Your past results look like this.
Race A, 42 km. 310 finishers. You placed 102nd. That is about the 33rd percentile.
Race B, 30 km. 420 finishers. You placed 140th. That is also about the 33rd percentile.
Race C, 50 km but flatter. 280 finishers. You placed 90th. That is about the 32nd percentile.
You are basically a one third of the field athlete when you are fit and racing properly. That is your pattern.
Now the target race last year had 600 finishers. One third of 600 is 200. You find 200th place last year was 7:12. That is your baseline predicted time.
But this course has more climbing than anything you have raced. You are not terrible uphill, but you are not a mountain goat either. You also know you tend to start too hard when you are nervous. So you apply a small tax. Call it 3 to 5 percent. That puts you around 7:25 to 7:35 as a conservative prediction.
Now you cross check with ranking.
Your UTMB index is 520. You pick 25 runners from last year’s results around the 6:50 to 7:50 range, look them up, and you notice that the runners with indexes around 510 to 530 mostly finished between 7:10 and 7:45. That overlaps your percentile prediction nicely. Now you can be fairly confident that 7:25 ish is a good planning number, with a bit of wiggle room.
So what do you do with that.
You build a pacing plan that protects the middle and late race, because that is where finishing time is actually decided. The early race feels like free money. That is the trap.
A solid ultra pacing plan is basically a budget. You have a fixed amount of intensity you can spend, and if you blow it on something dumb in the first half, you go into debt and the interest rate is savage.
Practically, that means you want the first part of the race to feel almost boring. You should be able to eat. You should be able to drink. You should be able to think. If you are already doing maths in your head at 15 km about how you can still salvage the day, you did not pace. You panicked.
This is also where the sex difference pattern often shows up in real races. Women, on average, tend to pace more evenly. Men, on average, are more likely to go out hot, feel like heroes, then spend the second half paying it back with interest. That is not a moral failing, it is just a common behavioural pattern. Plenty of men pace brilliantly and plenty of women blow up, but if you are the type who gets sucked into early hero running, you should assume you are not special and build guardrails.
Now, guardrails are easier if you use a pacing calculator. A tool like UltraPacer can take your predicted finish time and show you estimated time on course and splits. The key is not treating it as prophecy. Use it as a sanity check and a planning template.
You can set your target finish time, then deliberately plan early splits that are slightly slower than the calculator would suggest, especially on runnable early terrain, because the cost of being a few minutes down early is tiny compared to the cost of detonating later. Bossi’s 24 hour data basically supports this principle. Faster outcomes are associated with a more conservative early relative intensity and fewer fluctuations. 2017-Bossi-PacingStrategyDuring…
If you are doing a mountain course, you also need to accept that pace is a rubbish metric on steep climbs and technical descents. The better metric is effort. That might be heart rate, it might be RPE, it might be power if you have it. Or it might just be “can I breathe through my nose and eat a gel without choking”. The point is that your pacing plan should be effort based on climbs and terrain based in technical sections, not pace obsessed.
A simple, practical way to structure it is this.
Early race, cap effort. You should finish the first quarter feeling like you have been held back. That is the point.
Middle race, settle into your actual sustainable rhythm. This is where you keep the machine running, fuel steadily, and stop doing dumb stuff.
Late race, increase effort if you can, but only if it is real. A lot of athletes try to “push” late and what they actually do is shuffle harder while forgetting to eat. If you want to lift, lift by staying on top of nutrition, keeping cadence up, and being smooth.
The Lambert 100 km paper gives a useful mental model for why this matters. Faster runners held speed better and were still within 15 percent of their starting speed at the end, while slower runners faded more and varied more. That does not mean you should start fast. It means the good performers avoid the big fade. They protect the back half.
So in our 7:25 target example, your “win condition” is not running 4:50 per km at 5 km in because you feel good. Your win condition is being able to run when everyone else is walking at 42 km. That is where minutes turn into chunks of time.
Now, a prediction model without acknowledging uncertainty is just cosplay science.
Here are the big things that can blow up your prediction, and how to account for them.
Course change. If the race route has changed, your last year comparison might be misleading. Still useful, but less precise.
Weather. Heat is a performance tax that compounds over hours. If you expect a hot day, be conservative with predicted time. Your best pacing move might be slowing down early to keep core temperature under control. This is not weakness, it is physics.
Field composition. Some races have a deep elite field one year and a softer field the next. If the top end changes a lot, percentiles can shift slightly, especially near the front. For most mid pack runners it usually stays fairly stable, but it can still move.
Your fitness trend. If you have clearly improved or clearly declined, old percentiles might not represent current capability. You can still use them, but you should weight your most recent races more heavily, and be honest about whether your training supports the prediction.
Distance jump. If the target distance is significantly longer than anything you have finished, assume you will under perform your usual percentile slightly. Not because you are weak, but because there is a learning curve in pacing, hiking efficiency, and nutrition. The Lambert paper even notes the idea that pacing can be a learned skill that benefits from practice.
Now, once you have your predicted finish time and a pacing template, the final step is turning it into something your brain can actually execute on race day.
The easiest way is to create a pacing script with three simple rules.
Rule one. The first hour feels too easy. If it does not, you are lying to yourself.
Rule two. Eat and drink early, before you feel like you need it. Pacing falls apart when fueling falls apart.
Rule three. No surges to prove a point. Surges are for the last quarter, if you have earned them.
If you want an even more practical guide for the early race pacing problem, there is a solid pacing article by Mile 27 that emphasises the danger of going too hard early and the value of patience. That kind of advice lines up nicely with the observational data from 24 hour and 100 km races, where early over effort and big fluctuations are associated with worse outcomes.
So now we have some waste time predictions and some pacing guidance you can have a play with a tool like UltraPacer to get a feel for how an event might go including aid station delays, terrain and elevation changes across the course, consideration for endurance fatigue and fade, and more.
This is an excellent tool and I highly recommend checking it out!
One last note, because athletes love to argue about this.
Yes, pace, power, and heart rate are useful. They are not the villain. The point is that for predicting finish time, real world result data is often more stable than any single physiological metric. Fitness indicators tell you what you could do. Past results tell you what you usually do when the gun goes off and your brain starts making questionable decisions.
Use both.
If you are the kind of athlete who wants a simple order of operations, here it is.
Use the percentile method to get a baseline finish time.
Use ITRA or UTMB matching to cross check and tighten the range.
Use a pacing calculator to turn finish time into splits, then make the early plan slightly more conservative.
Use the research as your behavioural reminder: conservative early, smooth and even, limit fluctuations, protect the back half.
Then go and practise it in training, because pacing is not a belief system. It is a skill.
