Fastest Team, Ever!

Can mushers use mathematical functions to build the strongest possible dog team?

Math, Statistics

How do you build the fastest team… is it best to create a machine of 16 65-lbs males? Or, is the winning team a unit of different individuals with different strengths and different personalities coming together in the ‘right way,’ including the human? The Iditarod keeps a data base of all the dogs that have run the race dating back to 1996. The database has more than 15000 records! Can mushers find answers by looking to data on the dogs that have already run in the Iditarod?

Scroll down for activity! Or click to download Fastest Team, Ever! Activity (PDF)

ACTIVITY

Procedure

1) A mathematical function is a relationship between a set of input parameters and a set of output parameters. Each input is related to the output.

If our function is Racing the Iditarod, what sort of input parameters might affect the output of doing well during the race? [Values can include: dog health, not catching a bug, proper training, weather conditions, trail conditions, having the right/working equipment packed, run/rest cycles, attitude, where to take rests, pace set, storm cycles, unexpected problems (moose, rain, foxes looting checkpoint bags, etc).]

2) As a class try to give relative (subjective) weight of importance for the input parameters listed!? Identify which factors the musher has control over (food, training, equipment, etc), and which the musher does not have control over (illness, weather, moose, etc).

Consider mushers favoring importance of some input vs. the race conditions for each year being different. For example, mushers may favor heavy or light-coated dogs and thus their team may have advantage in specific weather conditions: Dogs that are adapted to the cold and wind of the coast may do better in a colder race than in a warm race; dogs that have been training in deep snow and difficult trail conditions will do generally do better overall with a race with deep snow.

3) Every dog that runs the Iditarod must be microchipped. This information is kept in a large database that contains over 15,000 records for individual dogs dating back to 1996. What is it possible to learn from this data, if anything? Can the mushers use this data as they work on building their team?

Excel spreadsheet data Irod_Dogs.xls is data taken from the master Iditarod Dog database for four dog mushers that have placed in the top 10 in the past 10 years: John Baker, Aliy Zirkle, Mitch Seavey, and DeeDee Jonrowe. The data has been filtered to represent dogs that have competed between the years of 2002 and 2012. Note that not all 2012 data was included in the database. Also, the dogs are listed based on the first year and first team they ran – the number or races shows how many times that dog ran subsequent to their first year. So if a dog ran with Aliy’s team in 2008, 2009, and 2010 and she was the first team the dog ran with, the dog will show up in 2008 and that it has run 3 races.

4) As a class, review the dog data from the four representative teams. Statistics are only as good as the initial data used in analysis. Create a list of possible data errors and omissions that you see with this data set. [This can include misspellings of names, transposed numbers, not knowing exact years (a dog that raced 3 years could have raced any 3 years after the initial date, so may have skips in years or ran with another team), etc.]

5) Split the class into four teams. Each group should be assigned one of the mushers data sets.

6) Teams go to the Iditarod race archives at www.iditarod.com and find the listing of all mushers that have run the Iditarod. Each team locate their respective musher and answer the “Musher Analysis” questions:

* How many total years has this musher been competing in Iditarod?
* Where is this musher from/where do they train their teams?
* Has this musher had any years that they have NOT competed between 2002 and 2012?
* Has this musher had any years that they have had to scratch (not finish) the race between 2002 and 2012?
* How many top 10 finishes has this musher achieved during 2002-2012? Make a list of the top 10 finishes and the year.
* What is the highest place the musher has achieved during 2002-2012? What year was that in?
* What was the lowest place (and year) the musher achieved during 2002-2012?
* Make a graph of the musher’s finishes between 2002 -2012. Do you see any trends in the musher’s overall performance over time?
* What was the AVERAGE finish this musher had during 2002-2012? Indicate this AVERAGE with a line on your graph.

7) Have each team review the dog dataset for their musher for the years between 2002-2012 (Excel spreadsheet data Irod_Dogs.xls) and answer the “Dog Team Analysis”

* Why do you suppose some dogs are listed from years prior to 2002? [The number of races indicates that the dog may have run during the time window we are interested in.]
* How many dogs does this musher’s data set include? Do you think this is an accurate number? (Remember, mushers can register up to 20 VERIFY dogs, but only 16 are allowed to start on a team). Explain why or why not.
* What is the age of the youngest dog that has run on this musher’s team?
* What is the age of the oldest dog that has run on this musher’s team (remember to think about the number of races the dogs have done, not just their age when they started racing).
* What is the AVERAGE age of this musher’s team? If you compare the average age from the first 3 years and the average age from the most recent 3 years, has there been any change in average age over time?
* What is the percentage of male dogs vs. female dogs on the team? Do you think there are advantages to having a team of mostly male or mostly female dogs? Explain.
* Look at the dogs that ran the year your musher placed the best between 2002 and 2012. What is the oldest, youngest, and average age of the team? What is the experience level of the team (have the dogs competed before, or are they all new to racing?) Was this year a northern route year or a southern route year? How many male vs female dogs were on the team?
* Look at the dogs that ran the year your musher placed the lowest between 2002 and 2012. What is the oldest, youngest, and average age of the team? What is the experience level of the team (have the dogs competed before, or are they all new to racing?) Was this year a northern route year or a southern route year? How many male vs female dogs were on the team? How do the statistics from this year compare to your musher’s best year?

7) Teams create a chart or table to graphically display your findings for your musher. Teams compare findings with those from the other groups with the other mushers. Can teams draw any conclusions from this analysis? Do teams think the analysis of these four mushers is representative for the entire race? Why or why not?

8) What other factors may affect the teams’ performances that are not represented in this data set?

Download Irod_Dogs.xlsx

Musher Analysis

* How many total years has this musher been competing in Iditarod?

* Where is this musher from/where do they train their teams?

* Has this musher had any years that they have NOT competed between 2002 and 2012?

* Has this musher had any years that they have had to scratch (not finish) the race between 2002 and 2012?

* How many top 10 finishes has this musher achieved during 2002-2012? Make a list of the top 10 finishes and the year.

* What is the highest place the musher has achieved during 2002-2012? What year was that in?

Go to the Iditarod race archives at www.iditarod.com and find the listing of all mushers that have run the Iditarod.

Locate your respective musher and answer the “Musher Analysis” questions:

* What was the lowest place (and year) the musher achieved during 2002-2012?

* Make a graph of the musher’s finishes between 2002 -2012. Do you see any trends in the musher’s overall performance over time?

* What was the AVERAGE finish this musher had during 2002-2012? Indicate this AVERAGE with a line on your graph..

Dog Team Analysis

Review the dog dataset for your musher for the years between 2002-2012 (Excel spreadsheet data Irod_Dogs.xls) and answer the “Dog Team Analysis” questions:

* Why do you suppose some dogs are listed from years prior to 2002? [The number of races indicates that the dog may have run during the time window we are interested in.]

* How many dogs does this musher’s data set include? Do you think this is an accurate number? (Remember, mushers can register up to 20 VERIFY dogs, but only 16 are allowed to start on a team). Explain why or why not.

* What is the age of the youngest dog that has run on this musher’s team?

* What is the age of the oldest dog that has run on this musher’s team (remember to think about the number of races the dogs have done, not just their age when they started racing).

* What is the AVERAGE age of this musher’s team? If you compare the average age from the first 3 years and the average age from the most recent 3 years, has there been any change in average age over time?

* What is the percentage of male dogs vs. female dogs on the team? Do you think there are advantages to having a team of mostly male or mostly female dogs? Explain.

* Look at the dogs that ran the year your musher placed the best between 2002 and 2012. What is the oldest, youngest, and average age of the team? What is the experience level of the team (have the dogs competed before, or are they all new to racing?) Was this year a northern route year or a southern route year? How many male vs female dogs were on the team?

* Look at the dogs that ran the year your musher placed the lowest between 2002 and 2012. What is the oldest, youngest, and average age of the team? What is the experience level of the team (have the dogs competed before, or are they all new to racing?) Was this year a northern route year or a southern route year? How many male vs female dogs were on the team? How do the statistics from this year compare to your musher’s best year?

We usually reply with 24 hours except for weekends and holidays. All emails are kept confidential. We do not spam.

Thank you for contacting us !

Enter a Name

Enter a valid Email

Message cannot be empty