What is Fenwick?
Like Corsi, Fenwick is a shot derived statistic, but unlike Corsi, it does not include blocked shots. These means that fewer events on the ice are measured when creating the statistic, just goals, shots on goal, and shots that miss the goal. Fenwick is most often presented in a percentage or plus/minus format, just like Corsi.
Fenwick can be used at both the team or individual level and is calculated by situation, meaning separate numbers for even strength and special teams.
When expressed a percentage, Fenwick is calculated by dividing positive unblocked shot attempts by total unblocked shot attempts by both teams. The resulting number representing the proportion of positive unblocked shots in a given situation.
When used in plus/minus format, it calculated by subtracting negative unblocked shot attempts from positive ones, usually expressed as a differential for every 60 minutes of play in a given situation.
While putting Fenwick in a percentage format gives a clearer picture of a player or team’s performance, the plus/minus format can add additional context if a player or team takes part in more events than another. New Jersey for example, limits shots both for and against, which is why their games are typically low scoring.
As with all possession metrics, Fenwick can also be broken up into for and against statistics, which is a better usage if you want to specifically measure specifically offense or defense.
Practical Examples
While looking at the Corsi piece, it was suggested that examples could help elucidate the way these statistics are calculated, so I’ll do one for both Fenwick plus/minus and Fenwick percentage using the following made up numbers for “Player A” and “Player B”.
| Name | Goals for | Goals against | Shots for | Shots against | Missed shots for | Missed shots against |
| Player A | 2 | 1 | 14 | 7 | 3 | 3 |
| Player B | 1 | 3 | 22 | 18 | 12 | 6 |
The easiest way to explain this table is to add all the columns with “for” together, and all the columns with “against” together. This gives you your Fenwick for and Fenwick against for both players.
| Name | Fenwick for | Fenwick against |
| Player A | 19 | 11 |
| Player B | 35 | 27 |
Now that we have simpler numbers, we can plug in our equations. We’ll start with Fenwick plus/minus:
From this we can see that both players had a good possession game, though Player B was involved in far more events. From there we can express it as a percentage as well:
(Fenwick for} / (Fenwick for + Fenwick against) = Fenwick percentage
Player A: 19 / (19 + 11) = .633
Player B: 35 / (35 + 27) = .565
Looking at these two players’ performances in percentage format, you can see that Player A was actually more effective in his ice time than Player B. This is why it’s helpful to have these two different expressions of these stats.
Why is it called Fenwick?
What does Fenwick tell us?
Fenwick has a great correlation with scoring chances, meaning a team that dominates unblocked shots is usually generating more scoring chances, and has a much greater chance of scoring, and therefore winning, the game.
The natural correlation of more shots meaning more chances to score a goal means winning the Fenwick battle carries with it predictive value. This predictive value on the team level can be shown in Chris Boyle’s piece earlier this year, where he showed that strong Fenwick teams are far more likely to make the playoffs than weak ones.
Fenwick correlates with time of possession, but not as strongly as Corsi does. However Fenwick carries a much stronger correlation with future scoring, which is why I prefer it as a metric to Corsi. A player can be a strong Fenwick player and not as strong in Corsi, and still be more effective than a player who’s stronger from a Corsi standpoint. Case in point for this would be a player like Josh Gorges, who isn’t a fantastic time of possession player, but due to how many shots he blocks, his team ends up getting more scoring chances than his opponents, and his Fenwick numbers are better than his Corsi numbers. This is part of why we count both.
What are Fenwick’s limitations?
Like we said about Corsi, you can’t use one statistic to evaluate a player. All the comments on the role that usage plays that we listed when discussing Corsi also apply here, but Fenwick has another limitation that applies less so to Corsi, and that’s sample size. Because Fenwick uses fewer data points than Corsi overall, its predictive power is weaker in small sample sizes than Corsi is. Once enough games have been played though, Fenwick is the better statistic from the research that I’ve done.
Criticisms
Because both Fenwick and Corsi are possession statistics, the criticisms are largely the same.
As always if there’s something you’d like to expand on, that’s what the comments are for.
