sat, 27-jul-2013, 08:03
Gold Discovery Run, 2013

Gold Discovery Run, 2013

This spring I ran the Beat Beethoven 5K and had such a good time that I decided to give running another try. I’d tried adding running to my usual exercise routines in the past, but knee problems always sidelined me after a couple months. It’s been three months of slow increases in mileage using a marathon training plan by Hal Higdon, and so far so good.

My goal for this year, beyond staying healthy, is to participate in the 51st running of the Equinox Marathon here in Fairbanks.

One of the challenges for a beginning runner is how pace yourself during a race and how to know what your body can handle. Since Beat Beethoven I've run in the Lulu’s 10K, the Midnight Sun Run (another 10K), and last weekend I ran the 16.5 mile Gold Discovery Run from Cleary Summit down to Silver Gulch Brewery. I completed the race in two hours and twenty-nine minutes, at a pace of 9:02 minutes per mile. Based on this performance, I should be able to estimate my finish time and pace for Equinox by comparing the times for runners that participated in the 2012 Gold Discovery and Equinox.

The first challenge is extracting the data from the PDF files SportAlaska publishes after the race. I found that opening the PDF result files, selecting all the text on each page, and pasting it into a text file is the best way to preserve the formatting of each line. Then I process it through a Python function that extracts the bits I want:

import re
def parse_sportalaska(line):
    """ lines appear to contain:
        place, bib, name, town (sometimes missing), state (sometimes missing),
        birth_year, age_class, class_place, finish_time, off_win, pace,
        points (often missing) """
    fields = line.split()
    place = int(fields.pop(0))
    bib = int(fields.pop(0))
    name = fields.pop(0)
    while True:
        n = fields.pop(0)
        name = '{} {}'.format(name, n)
        if re.search('^[A-Z.-]+$', n):
            break
    pre_birth_year = []
    pre_birth_year.append(fields.pop(0))
    while True:
        try:
            f = fields.pop(0)
        except:
            print("Warning: couldn't parse: '{0}'".format(line.strip()))
            break
        else:
            if re.search('^[0-9]{4}$', f):
                birth_year = int(f)
                break
            else:
                pre_birth_year.append(f)
    if re.search('^[A-Z]{2}$', pre_birth_year[-1]):
        state = pre_birth_year[-1]
        town = ' '.join(pre_birth_year[:-1])
    else:
        state = None
        town = None
    try:
        (age_class, class_place, finish_time, off_win, pace) = fields[:5]
        class_place = int(class_place[1:-1])
        finish_minutes = time_to_min(finish_time)
        fpace = strpace_to_fpace(pace)
    except:
        print("Warning: couldn't parse: '{0}', skipping".format(
              line.strip()))
        return None
    else:
        return (place, bib, name, town, state, birth_year, age_class,
                class_place, finish_time, finish_minutes, off_win,
                pace, fpace)

The function uses a a couple helper functions that convert pace and time strings into floating point numbers, which are easier to analyze.

def strpace_to_fpace(p):
    """ Converts a MM:SS" pace to a float (minutes) """
    (mm, ss) = p.split(':')
    (mm, ss) = [int(x) for x in (mm, ss)]
    fpace = mm + (float(ss) / 60.0)

    return fpace

def time_to_min(t):
    """ Converts an HH:MM:SS time to a float (minutes) """
    (hh, mm, ss) = t.split(':')
    (hh, mm) = [int(x) for x in (hh, mm)]
    ss = float(ss)
    minutes = (hh * 60) + mm + (ss / 60.0)

    return minutes

Once I process the Gold Discovery and Equnox result files through this routine, I dump the results in a properly formatted comma-delimited file, read the data into R and combine the two race results files by matching the runner’s name. Note that these results only include the men competing in the race.

gd <- read.csv('gd_2012_men.csv', header=TRUE)
gd <- gd[,c('name', 'birth_year', 'finish_minutes', 'fpace')]
eq <- read.csv('eq_2012_men.csv', header=TRUE)
eq <- eq[,c('name', 'birth_year', 'finish_minutes', 'fpace')]
combined <- merge(gd, eq, by='name')
names(combined) <- c('name', 'birth_year', 'gd_finish', 'gd_pace',
                     'year', 'eq_finish', 'eq_pace')

When I look at a plot of the data I can see four outliers; two where the runners ran Equinox much faster based on their Gold Discovery pace, and two where the opposite was the case. The two races are two months apart, so I think it’s reasonable to exclude these four rows from the data since all manner of things could happen to a runner in two months of hard training (or on race day!).

attach(combined)
combined <- combined[!((gd_pace > 10 & gd_pace < 11 & eq_pace > 15)
                       | (gd_pace > 15)),]

Let’s test the hypothesis that we can predict Equinox pace from Gold Discovery Pace:

model <- lm(eq_pace ~ birth_year, data=combined)
summary(model)

Call:
lm(formula = eq_pace ~ gd_pace, data = combined)

Residuals:
     Min       1Q   Median       3Q      Max
-1.47121 -0.36833 -0.04207  0.51361  1.42971

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.77392    0.52233   1.482    0.145
gd_pace      1.08880    0.05433  20.042   <2e-16 ***
---
Signif. codes:  0***0.001**0.01*0.05.0.1 ‘ ’ 1

Residual standard error: 0.6503 on 48 degrees of freedom
Multiple R-squared:  0.8933,    Adjusted R-squared:  0.891
F-statistic: 401.7 on 1 and 48 DF,  p-value: < 2.2e-16

Indeed, we can explain 65% of the variation in Equinox Marathon pace times using Gold Discovery pace times, and both the model and the model coefficient are significant.

Here’s what the results look like:

The red line shows a relationship where the Gold Discovery pace is identical to the Equinox pace for each running. Because the actual data (and the prediced results based on the regression model) are above this line, that means that all the runners were slower in the longer (and harder) Equinox Marathon.

As for me, my 9:02 Gold Discovery pace should translate into an Equinox pace around 10:30. Here are the 2012 runners who were born within ten years of me, and who finished within ten minutes of my 2013 Gold Discovery time:

2012 Race Results
Runner DOB Gold Discovery Equinox Time Equinox Pace
Dan Bross 1964 2:24 4:20 9:55
Chris Hartman 1969 2:25 4:45 10:53
Mike Hayes 1972 2:27 4:58 11:22
Ben Roth 1968 2:28 4:47 10:57
Jim Brader 1965 2:31 4:09 9:30
Erik Anderson 1971 2:32 5:03 11:34
John Scherzer 1972 2:33 4:49 11:01
Trent Hubbard 1972 2:33 4:48 11:00

Based on this, and the regression results, I expect to finish the Equinox Marathon in just under five hours if my training over the next two months goes well.

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