A topic of growing interest in the NHL is the recent influx of players with high-end talent. While hockey is known for being a team game, where individual players tend to have a more marginal impact on results than other sports, team models that emphasize retaining a core of high-end talent have had success and is becoming the blueprint for cup contention. Namely we’re thinking of the Penguins and Blackhawks, who’ve together have won 6 of the last 10 Stanley cups.
This isn’t necessarily anything new – teams have always needed high end talent to win a cup and they always will need it too. However, these teams have shifted to a model that places more importance on players at the top the skills distribution at the cost of depth. The Blackhawks were forced to let good players go to keep their core intact, while the Penguins paid handsomely for a star-studded forward group at the sacrifice of a deep d-core.
How exactly this plays out for the leafs moving forward will be interesting to see. The team is still looking to sign Nylander this year, and have negotiations with Matthews and Marner coming up next year. It’s highly possible they will need to let Gardiner walk, and as the team progresses, they may lose out on depth players that are good, not great, and thus not a part of the core. Edmonton is finding themselves in a similar situation with McDavid, Draisaitl, and Nugent-Hopkins but have a few more bad contracts that could crunch them.
So this begs the question, have teams responded to the success of this new model by paying more for superstar players? We’ve seen McDavid be rewarded with a 12.5M contract – how has this shaken out with the rest of the NHL?
The Gini Coefficient
To determine whether or not inequality is growing in the NHL, we are going to use the Gini coefficient, which provides an index we can use to measure inequality by looking at income distributions.
The Gini coefficient is a way of comparing how distribution of income in a sample compares with a similar sample in which everyone earned exactly the same amount. Inequality on the Gini scale is measured between 0, where everybody is equal, and 1, where all the country’s income is earned by a single person. In our case, we will compare income distribution between NHL teams to see which are more equal, and which are more top heavy.
More details on the Gini coefficient can be found in the Methodology section at the end of this story.
An Example
Using this calculation, let’s take a quick look at some of the more equal and unequal teams in the NHL to see if the index is properly capturing inequality. Looking at the 2017-2018 season, the last full season, we can see the most unequal teams based on the Gini coefficient are Chicago, Pittsburgh, Dallas, Washington, and Nashville:

This certainly passes the eye test that the Gini is accurately finding teams that are top heavy – our Chicago and Pittsburgh examples are both included, along with other teams with superstar talent – Dallas, Washington, and Nashville. Let’s take a quick peak at the top of their distributions.

These teams all have high salaries at the top of their distribution, and tend to have more players on ELCs and cheap contracts at the bottom. Another way we could look at this is the % of the CAP the top players are taking up – but we’ll leave this methodology for a separate blog post and focus on the Gini for now. We also notice that these teams span decent to great, not surprising considering their top end skill – but supports our working hypothesis that teams with a higher Gini are more successful.
The bottom of the distribution is a little more interesting – we find the Islanders, Ducks, Red Wings, Leafs, and Devils at the bottom:


As expected for the Gini bottom feeders – we’re seeing contracts at the top of the distribution that are lower than the more unequal teams – topping out around $6M. Two other interesting points – what seemingly makes the NYI the most equal is their lack of ELC’s coupled with the smaller top end contracts. Around 2/3rds of the team makes over $2M – while teams like Toronto and Detroit are relying more on entry – level deals.
Do Unequal Teams Make The Playoffs More Often?
The table below shows the data for the 2018 season – and I’ll now take the time to take a descriptive look at how inequality may impact results. Using playoff qualification as a sort of crass measurement – we see there isn’t a clear relation between Gini and qualifying for the playoffs. There is a dynamic evident between teams who are further into their development and have had to pay out large contracts to their core. Gini doesn’t seem to impact success with making the playoffs, perhaps because having young players on their ELC is beneficial, as is having top end talent resulting in a higher Gini. There are a lot of factors here we are passing over – how much cap space teams are using to name just one – and the analysis would benefit from a more statistical approach which we will save for the next post.

Back to the Question!
Finally, we will look to answer the question we set out at the beginning – has the NHL become more unequal in recent years? Compiling data from 2014 to 2019, the average Gini coefficient in the NHL increased steadily from 2014 to 2018, before dropping back down to 2014 levels this year.

This result is not too surprising considering the variety of factors that could be impacting results. One, as good teams become great and pay out contracts for their top players – we also expect rebuilding teams to filter to the bottom of the list as they rebuild and shed those large contracts. And second, our hypothesis was that high-end skill would claim more money – but it’s also possible that the bottom of the distribution has become more skilled, offsetting any salary gains at the top of the distribution.
Next Steps
It’s difficult not to caveat such a high-level analysis, but in our next post we will look to apply some statistical methods that may give us a better answer in our inequality influences results.
Methodology
Salary cap data was pulled from Cap Friendly, and Corsica Hockey was used for accurate team names for 2014 – 2019.
Gini coefficient explanation: The Gini coefficient is computed using a Lorenz curve, where population is plotted on the x axis and cumulative share of income is plotted on the y axis. The “Lorenz curve” represents the plotted points of the actual distribution in question, and the perfect distribution line represents a society where everyone earned the same amount.
Finally, we calculate the Gini coefficient by taking the area underneath the actual income distribution and the line of perfect income equality. In the diagram pictured, this is A/(A+B)
The coefficient doesn’t capture very explicitly changes in the top 10% – which has become the focus of much inequality research in the past 10 years – an area we may be able to further explore after this analysis
