Monday, 1 February 2016

Marathon prediction and junk mile calculator

Race predictor Version 0.2
Age: Male Female
Previous race distance:
Time achieved (h:m:s):
Pace (mm:ss):

Pace: per km per mile
Distances: Standard Custom

Average weekly distance:
Average weekly pace:

Saturday, 30 January 2016

Can we calculate what might constitute a junk mile in marathon training?

Runners often worry that they are wasting their time and effort doing slower additional miles - distance that is referred to as junk miles. But, how do we know when we are heading towards junk miles and the quantity no longer makes up for a lack of quality? What follows is a simple logical extension of a marathon performance prediction equation and how it can be used to calculate when it is not worth running an additional mile - just how slow does it need to be before it is junk?

Friday, 29 January 2016

What makes you a faster runner - pace or distance?

Before I show what actually happens if one trains in the lands where 'dragons may lie' (long distance slow running), I thought it might be a good idea to consider what we know or think we know about those lands. Common phrases suggest that running high mileage at slow pace is not a useful strategy for a performance runner - Running slowly makes for a slow runner - Junk miles - Quality not quantity - Race pace training - Tempo running is a key session - He's a plodder - No pain, no gain!

However, we also know that elite runners engage in high mileage - or at least relatively high mileage - compared to most club runners. So, what does that training space look like when plotted on a graph?

Thursday, 28 January 2016

Filling the training parameter space

The marathon prediction equation produced by Tanda (2011) did not look at the performance of any sub-elite runners, his fastest was 2:47. In this post I have added in some data from a few faster runners - the results are surprising.

Tanda (2011) - A viewpoint

Giovanni Tanda (2011) looked at the marathon performance of 22 runners who had run a total of 46 marathons (over a 5 year period) at near flat-pace race effort (halfway splits <±4 min) - i.e. near optimal aerobically limited efforts. He looked for correlations between marathon performance time and the following elements of the training diary (warm-ups and recoveries were included):

Wednesday, 20 January 2016

Predicting marathon performance from training data

Those who train for a marathon following a pre-prepared plan, of which there are many available, should have a reasonable expectation of achieving their goal: a 3:15 marathon plan should get you a 3:15 marathon time if you execute both the plan and the race appropriately. Unfortunately runners train in the 'real-world' where sessions get skipped and targets missed. The effect of failing to precisely execute 'The Plan' is hard to predict. Can missing one day/week really have a measurable effect? The lack of predictability presents a serious problem to many runners and can lead to injury as they attempt to make-up for missed sessions or bonk badly in the race by failing to scale back their speed to match their lack of diligence in training.