andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-245 knowledge-graph by maker-knowledge-mining

245 andrew gelman stats-2010-08-31-Predicting marathon times


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Introduction: Frank Hansen writes: I [Hansen] signed up for my first marathon race. Everyone asks me my predicted time. The predictors online seem geared to or are based off of elite runners. And anyway they seem a bit limited. So I decided to do some analysis of my own. I was going to put together a web page where people could get their race time predictions, maybe sell some ads for sports gps watches, but it might also be publishable. I have 2 requests which obviously I don’t want you to spend more than a few seconds on. 1. I was wondering if you knew of any sports performance researchers working on performance of not just elite athletes, but the full range of runners. 2. Can you suggest a way to do multilevel modeling of this. There are several natural subsets for the data but it’s not obvious what makes sense. I describe the data below. 3. Phil (the runner/co-blogger who posted about weight loss) might be interested. I collected race results for the Chicago marathon and 3


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 Frank Hansen writes: I [Hansen] signed up for my first marathon race. [sent-1, score-0.715]

2 The predictors online seem geared to or are based off of elite runners. [sent-3, score-0.135]

3 I was going to put together a web page where people could get their race time predictions, maybe sell some ads for sports gps watches, but it might also be publishable. [sent-6, score-0.466]

4 I have 2 requests which obviously I don’t want you to spend more than a few seconds on. [sent-7, score-0.13]

5 I was wondering if you knew of any sports performance researchers working on performance of not just elite athletes, but the full range of runners. [sent-9, score-0.259]

6 There are several natural subsets for the data but it’s not obvious what makes sense. [sent-12, score-0.107]

7 I collected race results for the Chicago marathon and 3 shorter races: Chicago Half Marathon, Soldier Field 10 Miler, Ravenswood 5k. [sent-16, score-1.284]

8 Within each year I matched results for finishers between each shorter race and that year’s marathon based on full name and age. [sent-18, score-1.294]

9 I used python to scrape web pages for the results. [sent-19, score-0.161]

10 Of course in a particular year a given marathoner may have run more than one of the shorter races. [sent-20, score-0.298]

11 At this point I am ignoring that, treating them as independent records even though they have the same marathon finish data. [sent-21, score-0.666]

12 I would think that knowing several shorter races to predict a marathon time would help, but demanding several matches really cuts down the data. [sent-22, score-1.15]

13 I also collected weather data, so I know the temperature, humidity, wind speed near 8 am for each race (in Chicago). [sent-23, score-0.662]

14 A record contains a marathon time, a short race time, the type of short race, the temperature, humidity and wind speed difference between the short race and the marathon. [sent-25, score-1.968]

15 I also know the age and sex of the marathon finisher. [sent-26, score-0.812]

16 2e-16 In the regression results the marathon and short race “pace” variable is in seconds per mile, so the short. [sent-95, score-1.221]

17 typehalf equal to 82 means roughly add 82 seconds to your half marathon mile pace to get the marathon mile pace, and so on for the inde[endent variables. [sent-97, score-2.059]

18 Marathon day for 2009 was really cold, predicting pace for 2009 based on a fit of the other years has larger errors than predicting 2008 using a fit for the non-2008 data. [sent-99, score-0.324]

19 My main piece of advice is to never ever ever ever ever use “summary” to display regression outputs in R. [sent-100, score-0.455]

20 Unless, that is, you care that your standard error is “4. [sent-102, score-0.065]


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tfidf for this blog:

wordName wordTfidf (topN-words)

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Introduction: Frank Hansen writes: I [Hansen] signed up for my first marathon race. Everyone asks me my predicted time. The predictors online seem geared to or are based off of elite runners. And anyway they seem a bit limited. So I decided to do some analysis of my own. I was going to put together a web page where people could get their race time predictions, maybe sell some ads for sports gps watches, but it might also be publishable. I have 2 requests which obviously I don’t want you to spend more than a few seconds on. 1. I was wondering if you knew of any sports performance researchers working on performance of not just elite athletes, but the full range of runners. 2. Can you suggest a way to do multilevel modeling of this. There are several natural subsets for the data but it’s not obvious what makes sense. I describe the data below. 3. Phil (the runner/co-blogger who posted about weight loss) might be interested. I collected race results for the Chicago marathon and 3

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Introduction: Frank Hansen updates his story and writes: Here is a link to the new stuff. The update is a little less than half way down the page. 1. used display() instead of summary() 2. include a proxy for [non] newbies — whether I can find their name in a previous Chicago Marathon. 3. graph actual pace vs. fitted pace (color code newbie proxy) 4. estimate the model separately for newbies and non-newbies. some incidental discussion of sd of errors. There are a few things unfinished but I have to get to bed, I’m running the 2010 Chicago Half tomorrow morning, and they moved the start up from 7:30 to 7:00 because it’s the day of the Bears home opener too.

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