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# Olympic Triathlon

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Age 14 to 16

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- Problem
- Getting Started
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This problem offers a context to analyse some real world data and invites students to use their knowledge of sport to explain what they notice.

"The triathlon consists of a 1.5km swim, a 40km cycle and a 10km run. Do you think the triathlon will be won by someone who is very strong in one event and average in the other two, or someone who is strong in all three disciplines?"

Give students some time to share their ideas.

Display the spreadsheet.

"We have some data showing the times for each leg and the overall times from the Men's 2008 Olympics. What are you going to look for in the data to see if you are right about what makes a good triathlete?"

Give students time to plan what analysis they will carry out. Suggestions might include scatter graphs to look for correlation between individual event times (or rank) and overall time (or rank), or an investigation of the average and spread of times for each leg.

If a computer room is available, students could work in pairs with the spreadsheet. If no computers are available, the data is on this worksheet.

Finally, invite students to share what they found in the data, including any unexpected results, together with their explanations drawn from their knowledge of swimming, cycling and running.

If time allows, further work could be done with the results on the second spreadsheet, so that comparisons can be made with the Triathlons that took place in previous years.

How well do each of the individual event times correlate with the overall times?

Your explanations might want to take into account the following:

How long do athletes spend on each leg of the Triathlon?

How might the order of the events in the Triathlon affect the final times?

What are the advantages of staying with other athletes over breaking away early?

How do the energy requirements differ for each leg of the Triathlon?

Who's the Best? offers a simpler context for exploring Olympic data.

David and Goliath invites students to look for correlations and explain what they find in the data in the context of the men's shot put.