Archive for July, 2017

Team True Spirit are back again!

Team True Spirit are the Help for Heroes long distance challenge team and is made up of 19961554_1676060579102092_2766951157671718974_n.jpgwounded, injured and sick (WIS) serving soldiers and veterans.

Originally formed at Headley Court, to give the injured guys returning from Afghanistan something to train for and for the seriously injured guys (traumatic blast injuries, amputees, bullet and shrapnel wounds etc.) showing that their life was not over but was just going to be different and they could still be involved in training, challenges and sport.

Last year the team took part in the Lakeland 50 and the Lakeland family accepted them with open arms. The teams are made up of both males and females and there are a huge range of injuries, from ABI (acquired brain injuries), blast injuries, Post-Traumatic Stress Disorders (PTSD) to visual impairments and hearing problems.

Last year, several of the team completed the 50 mile challenge within the qualification time and will be on the start line for the 100 mile event in 2017. Having just completed the Bolton Ironman event (last weekend), they are ready for whatever challenges the Lakeland 50 & 100 will throw at them.

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Lakeland 50/100 The Goal Setting Process – Final Installment ‘Get The Pacing Right’

The team at Lakeland 50 & 100 are pleased to have Dr Ian Boardley as a guest blogger. Ian10-peaks-1_0508 is a sports psychology lecturer at the University of Birmingham and a 5 time Lakeland 100 finisher. He will be writing a monthly blog post to help you prepare for the 2017 event. You can follow him on Twitter HERE

In the first five blogs in this series, we have introduced goal setting and why athletes use it, discussed different types of goals, which goals to set, key characteristics of effective goals, key aspects of the goal-setting process, potential barriers to goal achievement and how to overcome them, how to monitor and evaluate progress towards your goals, when it may be appropriate to revise your goals, and how we can use training data to monitor and evaluate goal progress and inform and your race-day goals. Hopefully by applying these techniques to your training and preparation races you are feeling well prepared for the upcoming event and now have a clear time goal in mind for race day. Also, you should now have a clear set of process goals that you plan to implement in your race. In addition to these process goals many athletes also like to have target splits for each stage of the race both to work towards and to help monitor their progress along the way.

However, given the changes in terrain throughout the course, just dividing your overall race target time by the race distance and multiplying this value by the distance for each stage is likely to be largely unhelpful in determining realistic time targets for each stage. So how should one go about determining realistic achievable time goals to guide efficient pacing? A useful analogy here – taken from an article I read in UltraRunning Magazine last year – is spreading peanut butter on a piece of bread. You don’t want to spread it so you have a big glob of peanut butter for the first bite and then little left on the rest of the slice. Equally, you don’t want to leave any on the knife! So how can we plan our pacing strategy to get that nice even spread across the entire slice?!

In terms of pacing strategies, clearly there are marked differences between road marathons and trail ultra-marathons. The most obvious of these is the difficulty involved in quantifying intensity. For a flat road marathon with few tight turns and little wind then minutes per mile is an effective – and measureable – indicator of intensity. This brings up an often missed point when pacing strategies are discussed; that it is intensity – and not pace itself – we should be looking to monitor and control if we want to optimise our performances as this is what actually impacts the physiological systems in a runner’s body. Whilst pace can be a useful indictor of intensity for road marathons, it is pretty much meaningless as an indicator of intensity for ultra-marathons held on hilly and technical terrain.

So what information can we use to assess – and plan – our pacing strategies for races such as the LL50/100? One option is to look at historical data for your target race, and look at how more – and less – successful runners have paced their races. When doing this the first issue to overcome is finding a way to compare runners who differ vastly in their levels of performance. On the road we can do this by looking at how pace varies as a function of a runner’s overall pace for the race. For instance, if a three-hour marathon runner is close to 6:52/mile or a four-hour marathon is close to 9:10/mile throughout a marathon we would say both of these runners have paced their races pretty well. An equivalent approach for ultra-marathons is to calculate the ratio between a runners’ average pace for each stage and that for the entire race. This gives you a value that varies around 1.0 depending on whether the pace for an individual stage is slower (i.e., ratio >1.0) or faster (i.e., ratio <1.0) than the overall pace for the race for any given individual. Estimating a line of best fit for these ratios across the race demonstrates whether relative pace was increasing or decreasing across the race; if this line has a negative slope (i.e., is sloping down) then the runner has run the equivalent of a negative split and vice versa for a positive slope (i.e., sloping up).

Having calculated these ratios for runners who completed the 2015 LL100, I have conducted two analyses using these ratios to try to determine what appears to have been the best pacing strategy for the event. The first of these analyses compares the pace ratios for the first and last ten finishers of the 2015 race (see Figure 1). One thing this analysis shows is that the top ten finishers ran a much more even pace across the race than the bottom ten finishers, evidenced by their much flatter line of best fit in Figure 1 for the top ten runners compared to that for the bottom 10 runners. Interestingly, the winner of the race ran an even more even-paced race (i.e., his line of best fit is flatter than that for the top ten overall) in comparison to the top ten as a whole, suggesting the potential benefits of further evening out relative pace across the race beyond that seen for the top ten. This suggestion was supported further by a separate analysis (not shown in Figure 1) in which I calculated the ratios and line of best fit for Terry Conway when he ran the course record. This analysis showed an even flatter line of best fit for Terry Conway in comparison to the 2015 winner. Also shown in Figure 1 are my pace ratios for the 2015 race. Collectively these analyses support the potential benefits of evening out your efforts across the race (i.e., spreading the PB evenly), in comparison to going out hard and trying to hold on (i.e., big glob of PB in your first bite).

fig1
Figure 1 – Comparison of Pace Ratios between Top and Bottom Ten Finishers

So this is all well and good you may say, it is not particularly ground-breaking to show faster runners ran a more even paced run than slower runners. However, an alternative analysis of the ratio data demonstrates that the potential benefits of an evening out of pace are not constrained to the elite (see Figure 2). In this second analysis I took the ten runners with the highest pace ratio for the opening stage (i.e., those who set out most conservatively) and compared them with the ten runners with the lowest pace ratio for this stage (i.e., those who set out most aggressively). This figure shows a clear difference in pace degradation for runners with the ten lowest initial pace ratios compared to those with the ten highest. An outcome of potentially greater importance though is the finding that it was not only faster runners who demonstrated the most evenly paced races, as the runners with ten highest initial ratios came from runners who finished between 6th and 107th. Thus, there was a large spread in finishing position for runners with conservative opening intensities – and resultant even pacing – meaning you don’t have to be elite to pace evenly. You may therefore be thinking is a conservative opening intensity effective then? In response to this it is important to consider pacing strategy is just one factor in determining performance (along with fitness, navigation, nutrition, etc.) and also to consider what is shown in Figure 1 alongside what is shown in Figure 2. In combination these two analyses show the fastest finishers run a fairly even intensity, but also that the ability to do this is not restricted to the fastest runners. Further, if one compares the positions of the ten runners with the highest opening pace ratios (i.e., 6th to 107th) with those for the runners with the ten runners with the lowest initial pace ratios (i.e., 67th to 209th [i.e., last]) one can see that although high and low initial pace ratios are both associated with a wide spread of finish positions, the high ratios are spread largely across the top half of the field, whereas those with the lowest initial ratios are spread mainly across the bottom half of the field. This adds further support to the argument that on average a more even pacing strategy appears to be advantageous in comparison to setting out aggressively and slowing later.

fig2
Figure 2 – Comparison of Highest and Lowest 10 Pace Ratios for Stage 1

So assuming having read the above and looked at the two figures you agree that a fairly even pacing strategy is the likely to be optimal, how would you go about calculating target splits for each stage for your overall target time? To do this, all you have to do is: 1) identify your overall target time based on your training data, race history and recent race performances, 2) decide which profile you want to apply (i.e., average top 10, 2015 winner, Terry Conway), and 3) multiply your race target time by the pace ration for each leg of the profile you have selected. Doing this will give you target splits for each stage of the race. For instance, the average ratio for the first stage for the top ten was .82, which for someone targeting a 30-hour finish (i.e., overall average pace 17:09 minutes/mile) would equate to an average pace for stage one of 14:03 minutes/mile (i.e., 17:09 x .82). This would give a time of 1:38:24 as a target for stage one. Using this method, you can apply this process for you own target time using the information in Table 1.

Stage Top Ten Average My Data
  Overall Time Stage Split Leg Pace Ratio Leg/Overall Pace Ratio Leg/Overall Pace
CP1 Seathwaite 01:11:00 1:11:00 00:10:09 0.76 0.93
CP2 Eskdale 02:28:56 1:17:55 00:11:08 0.83 0.96
CP3 Wasdale Head 03:30:12 1:01:16 00:11:21 0.85 0.98
CP4 Buttermere 05:16:12 1:46:00 00:15:22 1.15 1.26
CP5 Braithwaite 06:52:05 1:35:53 00:14:45 1.11 1.14
CP6 Blencathra Centre 08:37:51 1:45:46 00:12:27 0.93 0.97
CP7 Dockray 10:15:11 1:37:20 00:12:38 0.95 0.91
CP8 Dalemain 12:13:40 1:58:29 00:11:44 0.88 0.87
CP9 Howtown 13:46:34 1:32:55 00:13:05 0.98 1.00
CP10 Mardale Head 16:07:41 2:21:07 00:15:01 1.13 1.10
CP11 Kentmere 17:45:16 1:37:35 00:15:01 1.13 1.07
CP12 Ambleside 19:33:58 1:48:42 00:14:53 1.12 1.00
CP13 Langdale 20:45:47 1:11:48 00:12:49 0.96 0.85
CP14 Tilberthwaite 22:24:17 1:38:30 00:15:09 1.14 1.02
CP15 Coniston 23:15:17 0:51:00 00:14:34 1.09 0.92

To do this using the average pace ratios for the 2015 top 10, all you have to do is follow the three-stage process described above using the values in the fourth column. However, based on the data presented in Figure 2, my own experiences, and the fact most people (even those finishing in the top ten!) tend to set off too fast I would actually advocate a more conservative approach. Also, it is worth bearing in mind that the analyses presented here only include the finishers of the race. It is possible – indeed quite likely – that the support shown for an even pacing strategy would have been even more marked had the ratios been included for those runners who failed to complete the course! A very conservative approach (i.e., the equivalent of a negative split), would be to adopt the ratios from my data in the fifth column. Alternatively, if this is a little extreme for you, you may wish to design your own set of pace ratios to work towards based on those presented in this blog. However, if you do this a good check at the end is to make sure your average pace ratio equates to 1.0 – otherwise there are errors in your calculations! If you are running the Lakeland 50 rather than the 100, the data from legs 9-15 should help inform possible strategies for your race.

To conclude, hopefully the above information and figures have helped you reflect upon and identify what may be an effective pacing strategy for your race. Whatever process, performance, and outcome goals you set for your race, I hope you have a great race and manage to achieve them. When it comes to your pacing strategy though, try to spread the peanut butter evenly so you can enjoy your final bite as much as your first!

See you in Coniston!
Ian Boardley

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