Four weeks into the NFL season and teams have begun to realize exactly how successful they can be this year. Some teams are undefeated, while others have yet to record a single win. How exactly do team executives and coaches decide on signing or re-signing a player they believe can improve their team? One obvious way is to use player statistics from prior seasons. However, what if there was a way that did not involve using any player statistics. A study by Salim & Brandão attempted to use a NFL social network to predict team success.
In the study by Salim & Brandão, nodes in the network represented quarterbacks and teams. Edges represented the labor relations between quarterbacks and teams. The figure below is the NFL social network the researchers came up with.
The researchers calculated the clustering coefficient of the NFL social network and compared it with that of the Erdös-Renyi network. Since the clustering coefficients of these two networks were very different, the researchers were able to determine that the NFL social network was not a random network. In class, it was explained that the Erdös-Renyi Random Graph can be used to determine if networks are random or not, which is exactly what Salim & Brandão demonstrated in this study.
Using the Pearson coefficient, the researchers determined a positive correlation between node degree from the NFL social network and team success in one of two NFL conferences. Figure 11 displays the positive correlation between node degree and team success in the American Football Conference (AFC). This result is significant considering the researchers compared this model’s findings with another model that used a quarterback performance statistic called passer rating, to predict team success. In that model, there was no pattern of correlation between passer rating and team success (Figure 10). This meant that the passer rating metric did a poorer job in predicting team success, compared to the model that used the property taught in class called node degree.
In my opinion, the results from this study can have major implications in the sports world. What intrigued me to read and write about this study was how the researchers used concepts taught in our class, in order to make meaningful observations about one of my favourite sports. In the competitive world of professional sports, teams are always looking for an edge over their competition. Instead of using player statistics to make roster decisions like every other team in the league, team executives could start utilizing social networks to determine if they should sign or re-sign certain players. It will be fascinating to see if social network analysis can change the way professional sport teams conduct their business in the future.