The document discusses analyzing running data from Strava to predict half marathon times and how runners can improve. It uses linear regression, ensemble methods, and random forests on 22 features from 10,000 runners to benchmark models. The best model was ensemble partial least squares regression with an R^2 of 0.72 and RMSE of 6.6 minutes. Validation on held-out data showed an R^2 of 0.63 and RMSE of 7.2 minutes. Monthly pace was found to be the most important predictor variable.

1. Can you run faster?
Alexis Yelton

2. Runners want to run faster
How fast can I run a half marathon?
How can I improve on that time?

3. Demo

4. Data from Strava.com
Pace
Time series, demographic, and aggregated running data on
10,000 runners. 1,000 with half-marathon times.
75
100
125
150
1 2 3 4
Rests per week
HalfMarathonTime
"
1
8.4
8.6
8.8
9.0
0 2 4
Log Month Distance
LogHalfMarathonTime
10
10
10
20
clean5$mnth_pace
8.4
8.6
8.8
9.0
0 2 4
Log Month Distance
LogHalfMarathonTime
10
10
10
20
clean5$mnth_pace

5. Data from Strava.com
60
80
100
120
140
160
20 30 40 50 60
Age (years)
HalfMarathonTime
"blue"
blue
10
10
60
80
100
120
140
160
120 140 160 180 200
Weight (lbs)
HalfMarathonTime

6. Analysis
Benchmarking with a linear model 0.73 6.5 min
Reducing number of features
Ensemble partial least squares regression 0.73 6.4 min
5-fold cross-validation
Regression r2
RMSE
Validation:
72 runners 0.63 7.2 min

7. About me: Alexis Yelton, MIT postdoc
Chitinase in marine cyanobacteria
Chitinaseactivity
My first half
marathon:
1:56:30
Personal best:
1:47:56

8. 22 Features
Month distance Weight Range
Month Runs Gender
Month Elevation Rest Days / Week
Month Pace Fast Days / Week
Month Time Long Days /Week
6 Month Distance 5K Time
6 Month Runs Marathon Time
6 Month Elevation Minimum Pace
6 Month Pace Minimum Pace > 2 mi
6 Month Time Minimum Pace > 3 mi
Age Range SD Pace

9. Results
0
500
1000
1500
4000 5000 6000 7000 8000
train_sub$HALF.MARATHON
sqrt(errorstrain^2)
Half Marathon Time
Errors vary with half
marathon time.
A larger data set
would allow for
better predictions
for faster and slower
runners.

10. Results

13. Analysis
Benchmarking with a linear model 0.73 6.5 min
Dealing with collinear features (and reducing number of features)
1. Ensemble partial least squares regression 0.72 6.6 min
2. Linear model 0.71
6.7 min
3. Lasso regression 0.69 6.8 min
4. Ridge regression 0.72 6.7 min
5. Random forest regression 0.67 7.1 min
3-fold cross-validation
Regression r2
RMSE
Validation:
69 runners 0.63 7.2 min

14. Analysis
Benchmarking with a linear model 0.73 6.5 min
Reducing number of features
1. Ensemble partial least squares regression 0.72 6.6 min
2. Linear model 0.71
6.7 min
3. Lasso regression 0.69 6.8 min
Other models with these features
1. Ridge regression 0.72 6.7 min
2. Random forest regression 0.67 7.1 min
3-fold cross-validation
Regression r2
RMSE
Validation:
69 runners 0.63 7.2 min

15. 0.0e+00 4.0e+07 8.0e+07 1.2e+08
rf$finalModel
IncNodePurity
Your average pace over the past month is the most
important feature by far.
Results
Variable importance
Increase in node purity
Pace past month
5K time
Pace past month
Rest days
SD pace
Weight
Long days
Age
Gender