This is the first post in a (likely) 3 part series going through the data/methods/results which I presented at the Pittsburgh Hockey Analytics Workshop. If you’re interested in seeing the slides or hearing the presentation (or the other presentations, which I highly recommend), they’re available on the WaR on Ice website here.
One of the biggest challenges facing the hockey analytics community right now is getting beyond the player analysis stage and starting to look at how analytics can impact team and player strategy. While possession based metrics (CF%, FF-Rel) and their derivatives (dCorsi, xGD20) have vastly improved our ability to identify those players who are truly driving on-ice performance, it won’t be long until every team is more of less working with the same baseline of player identification data, eliminating any edge that analytics might have provided for early adopters.
Applying analytical techniques to on-ice strategy is one area where teams can begin to regain that advantage, and what’s more, it’s one that teams need to constantly re-evaluate as the effectiveness of strategies often changes (and sometimes drastically) over time. Perhaps the best recent example of applying data to look at team-level strategy is Eric Tulsky and company’s pioneering work on zone entries. While almost every coach from Peewee up to the NHL has probably preached the “Dump and Chase” methodology at some point, Tulsky’s work on zone entries showed that this was far from an optimal way to play the game; in fact dumping the puck in was a significant detriment to generating shot attempts for most teams. Not only was Tulsky’s work lauded by the analytical community, it has made a huge impact with many teams and players now explicitly aiming to generate more controlled entries.
The dump and chase is just one example of how a data driven approach can lead to potentially valuable new ways to play the game, or to put a team or lineup together. There are dozens of other age-old hockey wisdoms and questions that can be addressed by analytics: Is it beneficial to play 4 forwards on the powerplay? Hint: probably. When should teams pull their goaltender? Hint: earlier than you’d think. Do teams get a momentum boost after a fight? Hint: no, and it’s worse than you’d think.
One question that comes up rather frequently (at least in my mind) is whether coaches should focus on balancing their lineup so that players don’t play on their off-hand side (i.e. a left shot playing RW or a right shot playing LD). Last year, as people were debating whether PK Subban should make the Canadian Olympic team if he’d end up playing on his off-hand, I took a look at the offensive performance of off-hand defencemen. While I found that there was a slight drop-off in all-situations offensive performance, ultimately the difference was often negligible when compared to the difference in talent between players.
That study, however, only focussed on offensive play, and didn’t look at how well defencemen performed in their own end. While that made sense when looking at whether Subban, one of the greatest offensive players in the league, would struggle on his off-hand, if we want to make broader decisions about lineup construction we need to know what’s happening at both ends of the ice. If we have an up-and-coming left-handed defenceman that we want to get more minutes by moving him to his off-hand on the first pairing we need to know what kind of a drop in performance (if any) we expect to see in order to properly weigh the cost and benefits of the change.
In order to look into this, however, we first need to figure out who is playing on what side of the ice. One way we can tackle this for defencemen (and the same idea applies to forwards, although we have to factor in the fact that there are centers to deal with) is to look at who is shooting from what side of the ice when 2 players are on together. The basic idea is that the player playing left defence should be taking most of his shots from the left side, while the player playing right defence should be taking most of his shots from the right side.
To quantify this, we can use the NHL’s shot location data to find each defencemen’s “Side Bias”, which calculates the percentage of shots that a player takes from a given side of the ice:
Side Bias = (# of Shots Taken from Left Side) / (# of Shots Taken from Left or Right Side) – 50%
Side bias numbers that are greater than 0 indicate that a player took most of his shots from the left side of the ice, while side bias numbers that are less than 0 indicate that a player took most of his shots from the right side of the ice.
In order to use these numbers to figure out which defenceman was on which side of the ice for a given pairing we’ll use a simple decision system:
- If a pairing has played together a significant amount (which I’ve defined as at least 10 shots taken by each player while they were on the ice together), we’ll use the side bias data from when they played together.
- If a pairing has rarely played together (if either player has taken less than 10 shots when the pairing were on the ice together), we’ll use their overall side bias numbers to figure out which player was on which side.
There’s obviously flaws with this method – the 10 shots that we use as our cutoff is entirely arbitrary, and I suspect that you could probably get by using only 5 shots. In addition, for extremely rare pairs, we’re almost certain to guess wrong some of the time, although this will only have a small effect on our analysis overall.
So how do coaches tend to use their defencemen when we look at the data? Well the first thing we see is that coaches prefer, when possible, to play defencemen on their on-hand, with 64% of total shots occurring when a pairing was on their on-hand, and only 0.2% coming with both defencemen on their off-hand (this number may be overstated as well, as we may have misclassified some of the rare pairs).
|Pairing (L-Side/R-Side)||% of Total Shots|
The second thing to note, is that L/L pairs are significantly more common (10X more common in fact) than R/R pairs. Obviously this makes sense in a league where left handed shooters are more prevalent than right handed shooters, but the size of the difference will allow us to proceed by breaking the data down into simply same-handed or opposite-handed pairs without worrying that we’re missing anything.
With that in mind, we can take a look at our 3 primary possession measures broken down by same-handed vs. opposite-handed pairs to see whether defencemen on their off-hand may be holding their teams back.
|Same (L/L or R/R)||49.32%||49.42%||49.52%|
I should make note of a few things before I dig too deep into the numbers here: first, this data covers 2008/09-2013/14, as I chose to ignore the possibility of both defencemen playing on their off-hands so I could use the RTSS rather than the shot location data (since the data above suggests it’s extremely rare). Second, the shots for percentage numbers here are slightly different than in the slides I presented in Pittsburgh as I had to adjust my analysis to exclude situations where there were 3 defencemen on the ice.
Nevertheless, we see that in aggregate the opposite handed pairs perform better from a possession point of view than the same-handed pairs (and in fact, both analyses show a 1% bump in SF%). We also see that the advantage tends to decrease as we exclude blocks and misses, going from a 1.4% gap in CF% to a 1.2% gap in FF% and down to just a 1% gap in SF%. While we can’t say for certain, it seems likely that this is driven primarily by fewer blocks and misses in the offensive zone, as it wouldn’t make sense that defencemen would be significantly better at blocking shots or forcing misses on their off-hand (this also agrees with the research I’ve done in the past).
So if opposite handed pairings are outperforming same handed pairings, what’s driving it? Are off-hand defencemen having trouble preventing shots in their own end, or is it something else that’s holding them back possession wise? To get a better sense, we can go back to the shot location data and take a look at where the shots against are coming from.
|Shots Against – Left Side %||48.2%||48.1%|
|Shots Against – Right Side %||49.6%||49.7%|
What this table tells us is that there isn’t really a difference between the opposite or same-handed pairs when it comes to defending one side of the ice or the other. If a left-defenceman playing on the right side of the ice is hampering his team defensively, it certainly doesn’t show up when we look at where the shots against are coming from when he’s on the ice.
So if the drag on possession numbers isn’t being driven by defencemen having trouble defending their side of the ice on their off-hand, where is it coming from? One suggestion that was brought up at the conference was that defencemen on their off-hand have trouble both exiting their own zone and setting up controlled entries into the offensive zone, and I think in the context of this data it makes a lot of sense. After all, playing on your on-hand is going to significantly increase the ease at which you can make or take a pass in the neutral zone, which should translate into more opportunities for controlled entries. Similarly a pressured player on their off-hand is probably more likely to dump the puck in than attempt to make a backhand pass through a tight opening. We don’t have definitive evidence, but it is a theory that makes sense and seems to agree with the numbers we have available.
So we can conclude that teams should never play defencemen on their off-hand, right? Well, not quite. Since we’re looking at aggregate data, it’s still possible that there are other factors driving the differences that we see, and that the delta that we’re attributing to off-hand play may really be a function of some other variable. In particular, one potential issue that Arik Parnass suggested in his write-up of the conference for Hockey Prospectus was that the differences between the pairings could be related to how coaches are deploying their pairings. After all, if coaches are aware of, or at least believe there to be an advantage to keep defencemen on their on-hand, we should expect them to avoid same-handed pairings whenever possible. And it would follow then that we’d expect most of the same-handed pairings would be the 3rd pairings, who we naturally expect to post weaker possession numbers as the worst players on the team.
We can test out Arik’s theory by breaking up the pairings into 3 buckets by Even Strength TOI Rank, and seeing whether our results still hold when we control for a coaches view of talent. The first thing we should look at though is whether coaches really are avoiding same-handed pairings where possible. We can do that by taking a look at what % of the total Corsi events (both for and against) for a pairing-bucket are taken when opposite-handed pairs are on the ice vs. same-handed pairs. Looking at the % of total Corsi events should give us a pretty good proxy for time on ice, as we wouldn’t expect the overall shot attempt rates (i.e. the game pace) to vary substantially between opposite and same-handed pairings.
|% of Corsi Events|
What we see when we dig into the data is that while coaches appear to favour opposite-handed first-pairings slightly more than 2nd or 3rd pairings, the difference isn’t significant at all, and it certainly doesn’t appear as if coaches are avoiding playing same handed pairs as their first unit.
Now let’s take a look at how same and opposite-handed pairings tend to do from a possession standpoint when we’ve broken it down by even strength time on ice.
There are two things that stand out to me in this table: First and foremost, opposite handed pairs still outperform same-handed pairs in every grouping, so it appears as if playing on your off-hand for a defencemen does have a detrimental effect on puck possession, even after we’ve controlled for differences in talent level (or at least coaches views of talent level).
The second thing that’s interesting is that the difference appears to be significantly smaller for 2nd pairing defencemen. While same-handed 1st and 3rd pairs experience a drop of 1% or greater in almost every possession metric, same-handed 2nd pairs see their numbers fall by less than 0.4% in each metric. This difference isn’t easily explainable with the data we have available, but one theory I have is that it might be related to the fact that the 2nd pairing is generally not relied on to contribute heavily in the offensive side of the rink. If most coaches are using their 2nd pairing as a shutdown pair, and if the difference in possession numbers is related to the ability to generate offense as I’ve hypothesized above, then the inability to generate offense likely doesn’t matter as much to a 2nd pairing as it would to a 1st or 3rd pairing.
One thing to keep in mind (as I’ll go over in more detail in part 3) is that players playing on their off-hand do tend to post higher shooting percentages than those shooting primarily from their on-hand. So while a defenceman playing on their off-hand may be giving up some ground on the possession front, they’re getting at least part of that back by having more of their shots get past the keeper. Whether this trade-off is worthwhile obviously depends on the team and player, there are certainly circumstances where the shooting benefits outweigh the costs, but all else being equal most teams would be better off taking the possession boost rather than the shooting boost since most defencemen tend to shoot the puck relatively infrequently and at lower overall percentages than forwards.
The other factor that needs to be mentioned is that ultimately teams should look to play their best players most, regardless of structural factors like this. If a team is choosing between playing a 50.5% Corsi defenceman on his off-hand, or promoting up a 50% Corsi player so he can play on his on-hand, then going with the on-hand player is obviously the better choice. But if the choice is between a 55% off-hand player and a 50% on-hand skater the team should stick with the better player. As we’ve seen here, lineup balance is obviously important, but not so much so that you should put your best players out for less time (or your worst players out for more) just to maintain the balance.