Last year, I published an analysis that ranks NHL’s best shooters with Bayesian multilevel modeling. Now that the 2017–2018 regular season is over, I have repeated the analysis with an additional year’s worth of statistics (it starts from season 1967–1968). Some highlights are shown below, and the full results table is available here.
I’ll start with a brief recap of the methodology. More details are available in the original blog post.
There are multiple ways to model players’ shooting ability. The simplest is to use the shooting percentage, which is the number of goals scored divided by the number of shots on goal. The metric is simple to define and straightforward to use, but it has two shortcomings.
The first shortcoming is that while it works well for players with a large number shots of goal, there’s more room for random chance for players with few shots. For this reason, the official career shooting percentage leaderboard only includes players with at least 800 shots. What if one wants to evaluate the shot of a young player who has only played for a couple of seasons?
The second point is not as clear of a shortcoming, but many have argued that the gameplay and level of goaltending have changed over the years. As a result, so has the difficulty of scoring goals, and this change is not reflected in shooting percentages of players from different eras.
To take these two issues into account, I have analyzed shooting percentages with Bayesian multilevel modeling. As always with modeling, there are assumptions that are simplifications of reality. The most important one here is the assumption that each player has an innate level of skill, or shooting ability, that does not change throughout their careers. While this is naturally not exactly realistic, it gives us a nice metric that can be used to rank players. And since players’ careers typically span multiple seasons, it also allows us accommodate scoring difficulty not being constant between seasons.
If you’re interesting in more details, please read the original blog post. It also contains full R and Stan code to perform the analysis, and some plots to evaluate its performance.
After updating the model with data from season 2017–2018, here are the overall top 10 shooters. Any changes in ranking from last year are highlighted with arrows and color.
|3 ↑ 1||Andrew Brunette||forward||1995–2012||1,516||268||17.68%||18.84%|
|4 ↓ 1||Steven Stamkos||forward||2008–current||2,088||348||16.67%||18.81%|
|9 ↑ 48||Patrik Laine||forward||2016–current||445||80||17.98%||18.06%|
|10 ↑ 7||Paul Byron||forward||2010–current||388||70||18.04%||17.96%|
As the most interesting changes from last year are for active players, here is also their top 10.
|2 ↑ 10||Patrik Laine||forward||2016–current||445||80||17.98%||18.06%|
|3 ↓ 1||Paul Byron||forward||2010–current||388||70||18.04%||17.96%|
|4 ↓ 1||Brad Marchand||forward||2009–current||1,426||226||15.85%||17.77%|
|5 ↓ 1||Adam Henrique||forward||2010–current||1,068||166||15.54%||17.20%|
|6 ↑ 2||Mark Stone||forward||2012–current||613||95||15.50%||16.55%|
|7 ↑ 4||Sean Monahan||forward||2013–current||929||138||14.85%||16.42%|
|8 ↑ 44||Auston Matthews||forward||2016–current||466||74||15.88%||16.42%|
|9 ↓ 4||Sidney Crosby||forward||2005–current||2,843||411||14.46%||16.30%|
|10 ↑ 8||Mark Scheifele||forward||2011–current||760||113||14.87%||16.24%|
The full results table with 5,573 players is available here.