It is beginning to become a regular occurrence but someone over at Hockey Graphs has attempted to debunk a theory/stat/opinion of mine and once again failed in their procedure for doing so. This time Garret Hohl tried to debunk Sv% RelTM as a useful statistic by looking at the persistence and predictability of Sv% RelTM over time despite the fact that just a month ago I suggested that evaluation of the past and predicting the future are two different questions. The reason for this is due to the fact that lack of persistence might be due to players changing teams or changing roles. What Garret didn’t do in his analysis was control for players changing teams or players changing roles.
Let me step back here and stat my theory as it pertains to a players ability to positively (or negatively) impact his teams save percentage. My theory for this is aligned with what we see in score effects. With score effects, when defending a lead (and presumably playing more defensive hockey) teams see their save percentage rise. My theory for individual players is when players are assigned defensive roles it is likely that their style of play will result in a boost to their teams save percentage.
To test this theory we need to be able to define the role a player is playing. Specifically, are they playing a more defensive role or a more offensive role. I am going to propose two statistics for doing this.
- LTIndex. I haven’t talked about LTIndex much and I don’t yet have it available on Puckalytics.com but it can be used for this purpose. Lets define LeadingTOI% as the percentage of time that his team is defending the lead that he is on the ice for (players TOI in 5v5 leading situations / teams TOI in 5v5 leading situations). Essentially this is what percentage of ice time defending a lead does his coach trust the player to be on the ice for. TrailingTOI% is the same except for in 5v5 trailing situations, or what percentage of ice time the player is given when his team is playing catch up. LTIndex is calculated by taking LeadingTOI% and dividing it by TrailingTOI%. The result is any value greater than 1.00 indicates the player is given a higher percentage of his teams ice time defending a lead than playing catch up. In other words, players with an LTIndex greater than 1.00 are biased towards playing a defensive role while players with an LTIndex less than 1.00 are biased towards playing an offensive role.
- DZone%. This is calculated much like OZone% but with DZone faceoffs in the numerator. DZone% = DZ faceoffs / (DZ faceoffs + OZ faceoffs). Players with more defensive zone face offs can likely be considered to be given more defensive roles while players with more offensive zone face offs can be considered to be given more offensive roles. I am using DZone% so that correlations (positive or negative) will mean the same for both DZone% and LTIndex as higher numbers will imply more defensive roles for both stats.
The next thing I did was grabbed 8-year statistics for forwards in 5v5close situations (using close to remove most/all score effects). The stats I grabbed are CF60 RelTM, CA60 RelTM, CF% RelTM, CSh% RelTM, CSv% RelTM, CF60 Rel, CA60 Rel, CF% Rel, CSh% Rel, and CSv% Rel. I am going to look at both RelTM and Rel stats just to see how they compare (in theory Rel should give stronger correlations as it is potentially a more pure comparison with players in opposite roles to them).
Here is a chart of correlations which perfectly summarize the the findings.
To summarize the above shows that:
- Players in more defensive roles generate fewer shots on offense while offensive players would generate more (makes sense).
- Players in more defensive roles have a worse CF% rating while offensive players would have better CF% ratings (makes sense as they generate more shots for).
- Players in defensive roles have a worse Corsi shooting percentage while offensive players have better shooting percentages (makes sense, we know offensive players can drive shooting percentage).
- Players in defensive roles have a better Corsi save percentage while players in offensive roles have a negative impact on save percentage (as predicted).
- Aside from CA/60 there is good agreement between Rel and RelTM stats though as expected Rel stats have slightly stronger correlations.
- The correlations for CSv% is about 60% of the correlations for CSh% which means the ability to drive shooting percentage is stronger than that the ability to drive save percentage.
So I used two different methods for identifying players who play defensive roles. I ran correlations and the correlations with CF60, CF% and CSh% for both methods of defining defensive role make sense and the correlation with CSv% for both methods of defining defensive role matches with my prediction in my theory. That is strong evidence in support of the theory that players have an ability positively influence save percentage and that Sv%Rel and/or Sv%RelTM are measures of that ability.
None of this surprises me and none of it should surprise you either. We have seen it in score effects and I have provided numerous examples of players that seem to do so (and they typically are tied to players with defensive roles such as Brandon Sutter). If Hohl, or anyone else wants to debunk Sv%Rel or Sv%RelTM as a useful stat using a method surrounding predictability they must first take roles into account. If a player’s Sv%Rel is not persistent because of role changes you cannot use that against as evidence to debunk Sv%Rel as a useful stat. Remember, how to evaluate the past and how to predict the future are two different problems. Don’t mix them up.