The very first Olympics in ancient Greece mainly consisted of what we would today call track and field sports: running, jumping, and throwing competitions. Today, of course, the Olympics is a bit different and so is the sport of track and field, with one of the most profound changes, being the emergence of the field of big data and analytics. Track and field data, therefore, consists of historical and current athletic performance data.
The running disciplines are: sprints, middle distance, long distance, and relay races. Jumping: long jump, triple jump, high jump, and pole vault. Throwing: shot put, javelin throw, discus throw, and hammer throw.
Notably, this data category may include cross-country, racewalking, and road running data since these are all included under the sport of athletics umbrella with track and field.
The main sources of this data include the World Athletics Organization, college-affiliated clubs, Olympic teams, and sport media. Other sources may include medical research facilities and open-source databases. These databases receive regular contributions from amateur athletes who, due to the nature of most of the disciplines in this sport, can participate without joining local clubs when none are available.
Wearable technologies have also made a major impact on this sport, with the first wearable devices (fitness watches) ideally suited to collecting running and racing data.
Each discipline generates—and uses—unique data. However, basic data attributes include time of race completion, distance jumped, distance a javelin was thrown, and so on. Other common attributes are sex and the age bracket of participants.
One common aspect of this data category are world records; not every sport regularly tracks world records nor can every sport present world records as examples of the limits of human capability.
Additional sources of data include weather and GPS data. Since most track and field sports take place outdoors, weather will impact athlete performance and the data provide valuable insight for athletes, coaches, and fans. Further, if a dataset includes cross-country racing, GPS or course condition data may also appear.
Finally, track and field dataset may also include meet attendance and fan and athlete demographics. Companies catering to these population segments can find great use of this information.
Coaches, athletes, businesses, and fans all use this data. Coaches and athletes use it to measure and improve performance. Businesses use it to market products to athletes and fans. Fans use it to discuss athlete performance, place bets, ponder the limits of humanity’s physical abilities, and so on.
A quality dataset is accurate, complete, comprehensive, up-to-date, and relevant to the needs of the researcher. Track and field data is no different. For those who want to collect this data to improve their own performance as amateur athletes, a number of wearable athletic performance tracking devices provide personalized data as well as immediate analysis and visualizations. Those who want to track more general information, however, may have a harder time with data collection and analysis. Using a wide range of data sources and ensuring the resulting dataset is clean and standardized, however, will go a long way toward ensuring the data is ultimately of good quality.
The problem is that once you step off the treadmill into the real world, the relationship [between mechanical and metabolic power] changes. When you head uphill, for example, your stride gets less bouncy and as a result you get less free energy from your tendons.
…When you go from level ground to a 10 percent uphill gradient, your efficiency drops from roughly 60 percent to 50 percent. At a steeper gradient of 20 percent, efficiency drops even more to 40 percent.