The National Hockey League as a Transaction Network: Part 1

Throughout the sports world some organizations have a reputation for growing and grooming players from within. In soccer, this can be through youth academies. In the MLB or NHL, this could be through minor league affiliations. In the NFL or NBA this could be through NCAA pipelines. Is this an effective strategy? It seems in conflict with the typical flurry of activity that one might see on trade/transfer deadline days as teams wrestle with each other making adjustments before the playoffs or cup play. This project consists of a series of visualizations that show the National Hockey League(NHL) as a transaction network. I plot network characteristics of teams against team performance metrics with the hope of finding a relationship. I also classify each team as having one of twelve possible strategies and look for patterns here. This is a novel approach to examining transactions in an American sports league and builds upon work previously done with European soccer teams. Generally, the result here is that teams that are less connected and less involved in the network have better performance results. None of the metrics generated statistically significant p-values, however there were trends that are worth exploring further.

This project is supported by an interactive network which is located at https://d141.github.io. The thickness of the edges that are drawn correspond to the weight of the link. Click on a node to see features about that team from the selected season. Recommended browsers (Safari and Firefox)


Considerable work has been done on the performance of individuals following a trade. Some work has also been done on team performance but primarily focuses on European football transfer networks. There is negligible work done on team performance dynamics of American leagues. The question of team performance in America is more interesting because the leagues are closed. This will lead to more interesting dynamics as players are much more likely to play against former teams and teammates. It’s also quite common for groups of players to be traded together and so they can work together against their former team.

American sports leagues do not promote or relegate between flights and have little competition with leagues in other countries. The nearest league to an exception would be the KHL which is the professional hockey league in Russia. The quality of play is quite good, but there are very few transactions conducted between the NHL and KHL. There have been a total of three KHL vs. NHL games played in the last 30 years and none since 2010. So these leagues exist in near isolation. This means that the teams within these isolated leagues are much more connected than they would be otherwise. For example, in the NHL all teams are guaranteed to play against every other team at least twice per season. This means that any player who is traded or relocated for any reason within the NHL is guaranteed to play against their former team in the near future. This introduces many more opportunities for network effects and makes for a more interesting study, at least in establishing a baseline analysis that could later be extended to the more complex European networks.


The conclusion in several papers on individual performance is that being traded has a positive effect on a player. This is the case in the NHL, as illustrated in Figure 1.

The same phenomenon was observed in MLB players who showed increased batting percentages following a trade. Again, the phenomenon repeated itself in the NBA as illustrated by Figure 2. Players that were traded mid-season had significantly elevated performance when playing against the former team in their former arena.

With respect to viewing sports leagues as transaction networks, there is existing work but it primarily focuses on European football. Within these papers, there are few compelling visualizations. The papers reached interesting conclusions and provide a great starting point in thinking about this topic. The most compelling visualization and one that is most similar to the work in this paper is below in Figure 3.


Figure 3. This is a network of transfers conducted in European soccer leagues over the summer of 2014.
Figure 4. The abbreviations stand for from-same-country (fsc), from-other-country (foc), to-other-country(toc), and (to-same-country).

Another interesting visualization is Figure 4 above which categorizes different strategies of European football clubs. The conclusion reached by Rossetti and Caproni is that in soccer competitions, it’s better to trade globally, recruit locally, and minimize turnover by being less active in the transaction network. With respect to minimizing turnover, this is consistent with what was found in the NHL.

    The sole paper analyzing American sports leagues as networks or complex systems came from a group in Brazil who looked at the NBA and tried to predict team success by examining network characteristics. The paper is quite interesting and they did have some success predicting success with the characteristics. Their work uses the data of individual players in order to evaluate transactions and then they predict performance in the following season. They use some basic features that are similar to what are used here such as number of nodes and measures of centrality. They also engineer new features such as roster volatility and experience metrics. Some examples of visualizations in their work that are roughly compatible with the study here can be found in Figure 5 below.

Figure 5. On the left, the growth of the NBA in the green nodes and the upward trend of transactions per season in the purple nodes. On the right, clustering coefficients plotted with ages of teams and individuals.

In Part 2, I’ll outline my contribution and methods

-David Van Anda

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