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The Wolfram|Alpha Team

Personalized Activity Results and Tools for Runners and Cyclists

July 23, 2010 —
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Runners and cyclists can now get personalized physical activity and fitness results from Wolfram|Alpha. Our team has added enhanced activity formulas to provide specific results that account for the individual differences among all types of runners and cyclists. Whether preparing for a race or monitoring regular routines, athletes and enthusiasts alike can now calculate actual performance results and compute performance predictions and the impact of exercise on personal physical fitness.

You can calculate your own results in Wolfram|Alpha by using a natural language input such as “cycling 72.13 miles for 240 minutes” or you can type in “cycling” to explore all of the formula’s options. For example, a cyclist who is preparing for, or who has just completed, a race can calculate a variety of user-specific metabolic properties, like the amount of fat and the number of calories burned, by taking into account factors such as age, gender, height, weight, incline, resting heart rate, and wind speed and direction. Below are sample results from Wolfram|Alpha when calculating the speed a 25-year-old male cyclist needs to maintain to complete a race in 240 minutes:

Calculating the speed a 25-year-old male cyclist needs to maintain to complete a race in 240 minutes

To complement the results of Wolfram|Alpha’s calculations, cyclists can compare their speed or pace with world record times by clicking the “Show comparisons” link.

Runners can input similar information and calculate calories and fat burned; oxygen consumed; heart rate; equivalent activities; conversions for speed, pace, distance, and time; and performance predictions. For this example, we convinced a member of our team to share his post-race results from the 2009 Chicago Marathon:

2009 Chicago Marathon results

There are a number of existing Wolfram|Alpha tools that come in handy when preparing for a casual ride or a major race. Wondering when you need to start your rigid 18-week training program for the Chicago Marathon? Ask Wolfram|Alpha to calculate “October 10, 2010 minus 18 weeks“. If you’re running a few weeks behind, enter the date of the event, and Wolfram|Alpha will count down the number of days until you cross the finish line.

Wondering about the weather forecast for the day of the race? Take a look back in history and ask Wolfram|Alpha about the race location’s weather from the previous year.

Need to fuel your body before hitting the road? Ask Wolfram|Alpha for “nutrition information for 3 servings of pasta with marinara sauce” and get a breakdown of calories, fats, carbohydrates, proteins, vitamins, and minerals.

These are just a few handy tools in Wolfram|Alpha for runners and cyclists. Which tools are you already using? Are there other running and cycling related tools you’d like to see in Wolfram|Alpha?


Some of the information is not good ( or at best useless)… I typed swimming and then entered 27.63 s 50m 14 years old 5ft11 172 lb as parameters, and I read that the Riegel model ( whatever that is) says that it would be a swimming equivalent to a 3 min 55 s on the 400 m…
Anybody who has any sense of what a given swimming time is worth would know that these 2 performances are not equivalent ( 3 min 55 s on the 400 m is significantly better than a 27.63 s on the 50m: For example , the top teenage girls can do a 27.63 at the regional level, but it will probably take a few decades before a woman of any age anywhere can do a 3min55s on the 400 m ).

It would have been much better to use some of the tables of equivalence ( or calculator) found on the USA swimming site or the FINA site to find equivalences not only between distances in the same stroke but also between strokes and types of pools (Times are better in a 25 m pool than in a 50 m pool)… Time calculators for swimming are common, so it would be trivial for WolframAlpha to do it… And that would be much more useful than the Riegel model.
( I checked that the Riegel model doesn’t make too much sense either on the running time )

Posted by Sylvain Flamant July 23, 2010 at 4:19 pm


    The empirically-based power function we are using takes into account fatigue vs endurance across time and distance. For a given pace and distance, an exponential fatigue factor is used to estimate the degradation of the starting pace over time. For example, if a runner starts off running at a 5 min/mile pace for 5 miles, the model estimates that they probably started off faster than they will finish and, over time and distance, they will gradually get slower. Like any exercise model, there are some assumptions being made to complete the calculation. Most notably the model assumes that the pace that is input is not a sprint pace and the “effort” (or work) put forth for 3 miles can be maintained over a marathon distance. Accordingly, the model assumes a heightened level of physical fitness. The model also assumes a continuous path, which obviously plays a part in the finish times displayed for swim race distances.

    Our model is not perfect, and we are always looking for ways to improve the data we provide. Regardless of our confidence in the model we are currently using, we will be reviewing its implementation an effort to correct any errors that may have been made. If you have any notable models in mind that would better represent swimming/running endurance over time, please feel free to submit them for our review.

    Thank you for sharing your feedback.

    Posted by The Wolfram|Alpha Team July 28, 2010 at 11:25 am

This is great! Thanks for these great tools! I’ve been using similar tools at for years. But I like Wolfram’s interface and integration much better. How about some aerodynamic and rolling resistance calculators as well? Thanks for this great tool!

Posted by Sean Langford July 26, 2010 at 9:27 am

I’m voting for aerodynamics and rolling rasistance too!

Great use of data!

Posted by Andrew July 26, 2010 at 2:56 pm

Very useful tool, this will come in handy.

Posted by Paul Stone November 21, 2011 at 12:51 pm