What Makes a National Football League Team Successful? an Analysis of Play by Play Data The National Football League (NFL) is one of the most popular sports in North America. The league showcases many strong athletes and winning is very important to all teams; however, for every winning team, there is a losing team, and it is the coachesu0027 responsibility to decide what plays to call in order to help their teams win. Data mining play data can help show trends and areas where the top teams in the NFL excel by looking at questions like how often certain plays are run, how many yards do the plays get and where on the field are touchdown scored. With the help of data mining intelligent tools such as Naive Bayes, decision tree algorithms and association rules, we worked to isolate the areas where the best teams in the league separate themselves and produce winning franchises. By classifying the teams into two categories  the top teams and bottom teams  we were able to compare the two classes for differences which explain what results in more success in the league. Although we found that most teams  both top and bottom teams  use similar plays, there were also factors that distinguished the two types of teams. This research highlights these specific factors and some overall distinctions found between the more successful versus less successful teams to the NFL community.

Spatial and Temporal Entropies in the Spanish Football League: A Network Science Perspective We quantified the spatial and temporal entropy related to football teams and their players by means of a passbased interaction. First, we calculated the spatial entropy associated to the positions of all passes made by a football team during a match, obtaining a spatial entropy ranking of Spanish teams during the 20172018 season. Second, we investigated how the playerxe2x80x99s average location in the field is related to the amount of entropy of his passes. Next, we constructed the temporal passing networks of each team and computed the deviation of their network parameters along the match. For each network parameter, we obtained the permutation entropy and the statistical complexity of its temporal fluctuations. Finally, we investigated how the permutation entropy (and statistical complexity) of the network parameters was related to the total number of passes made by a football team. Our results show that (i) spatial entropy changes according to the position of players in the field, and (ii) the organization of passing networks change during a match and its evolution can be captured measuring the permutation entropy and statistical complexity of the network parameters, allowing to identify what parameters evolve more randomly.