Data analysis has become increasingly ubiquitous, and the sports arena is no exception. There are few career paths where such a high premium is placed on performance. As if millions of viewers weren’t enough, there are also huge salaries at stake for a win on the field, court or ice. The question remains, like in so many other realms of data analysis, what will be done with this data? The NFL and NBA are experimenting with that question beginning this year.
The NFL recently announced a program in which players will wear tracking devices during select practices and games to better understand the movements of the players. All 32 NFL teams will participate in this program and the technology has already been tested by some players on the Buffalo Bills team in Buffalo, New York. Stats such as distance covered, and explosiveness in running will hold players even more accountable for their decisions on the field. It may then be possible to piece together an “ideal model” for football players, what should a player have done in a certain situation based on the data analysis?
This ideal model has already been toyed with in the basketball largely thanks to the Toronto Raptors and their data analysis. They were one of the first teams to invest in cameras that track a player’s movement, which is then rendered in a 3D model that analyzes basic stats like distance run by a certain player, but also more nuanced questions like where and when pick-and-rolls occurred and whether the picks actually hit defenders. From this analysis, an ideal model was built that was consistently much more aggressive than how the players performed in reality. The overall goal in this analysis is to find systematic ways to improve the game, so with the insight that players were not as aggressive as the ideal model, it is possible for coaches to methodically recommend specific situations in which players should play more aggressively. The NBA recently announced a partnership with STATS LLC to install SportVU Player Tracking technology that will collect data in all NBA courts beginning this upcoming season, so we can expect to see much more data and insights soon.
The NHL isn’t moving quite as quickly as its professional sports counterparts in the data realm, which only further proves the point that data’s role in sports is new. The classic Corsi system is used in the NHL, but it’s quite low-tech. It tracks shots on net while each player is on the ice, so even if I did not have the shot on net, but I was on the ice while a teammate of mine had a shot on net, it would count as a positive for me. This is thought to be a more accurate measure of success than just goals. However, other stats such as zone exits and entries, ice time, passes attempted and completed for each player would be incredibly valuable to start out with, and would probably require something similar to what the NFL or NBA are implementing.
The use of data analysis in the sports world is just ramping up, so we can expect to see a lot more of it in the years to come. To empower your own data analysis, you can give DataHero a try today. For example, I created a graph of goals and assists by position for the Colorado Avalanche using our new custom color palettes to match official Avalanche colors.
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