Explaining Playoff Goaltending

Are goaltender’s big-time playoff performances best explained by their regular season or historical playoff results? Can some goalies just turn it on when it counts more?

Clutch Off the Bench

The 2018 1st round series featuring the Columbus Blue Jackets and Washington Capitals was an interesting case study in playoff goaltending performance.

Starring for Columbus was Sergei Bobrovsky. The reigning Vezina Trophy winner was coming off another very good season, hoping to continue rolling in the playoffs. However, despite Bobrovsky’s accolades he has never advanced past the 1st round and has been uncharacteristically below average preventing goals in all of his prior playoff appearances.

In another subplot, Washington actually began the series with Philipp Grubauer. He had been excellent in the regular season but relatively untested in the playoffs (he played parts of two clean-up games, neither went great). This decision put Braden Holtby on the bench, who had a very pedestrian regular season but had been at or above average in all 5 of his previous playoff appearances. Cumulatively playoff Holtby has prevented about 1.1% more goals than expected, 2nd only to Jonathan Quick of goalies entering the playoffs with at least 1,000 playoff shots to their names.

Everyone knows what happened next. Grubauer wasn’t great, while Washington dropped their 1st two games at home. Holtby came in and delivered 4 straight above average performances, while Bobrovsky ended the series with 3 straight below average games. Washington took the series 4-2.

A few interesting questions can come out of this series I hope to explore. Was Washington coach Barry Trotz right to go with the ‘hot-hand’ over the ‘proven-vet’ by starting Grubauer? Is it likely that a goalie might be good in the regular season, but below average in the playoffs? More generally, if we are trying to explain goaltending performance in the playoffs, what matters more? Past playoff performances, regular season results, or just career results?

Can someone simply turn it on in the playoffs after a below-average regular season?

High Stakes Noise

Let me preface most of this exploration with the understanding the idea of ‘clutch’ or ‘performance-when-it-matters’ is problematic from a statistical perspective. A few bounces over a playoff series might dictate whether the outcome is perceived as ‘clutch’ or ‘choking.’ In reality, a good game or bad game doesn’t have much effect on the outcome of the next game, but if you flip a few heads in a row (bad games) you are out of the playoffs, while a few tails mean you advance. Someone has to advance, so a ‘clutch’ narrative might be created from chance outcomes alone.

With a small sample like a playoff series, a bounce or two can change the narrative of those outcomes. Analysts can deal with this by framing the outcome with a range of uncertainty. Fewer shots or games mean more uncertainty. Ultimately, we can’t be too sure the outcome of a series reflects the ‘truth.’ Holtby could have come into the playoffs with his game in top condition and his vitals in the optimal range to deliver a clutch performance, but if a few Seth Jones’ shots bounced off of someone’s ass in game 3 or 4, the narrative is completely different. Drilling down further (tied in the 3rd period only, etc) only compounds the problem of insufficient sample size.

Is Winning a Skill?

It’s important to the scientific process that we assume our hypothesis is null then work to prove it with data. A ‘clutchness’ factor is no different, we should assume it doesn’t exist. It might not exist as a differentiator at the NHL-level for good reason, a propensity to fold in critical moments would likely prevent them making it.

However, this doesn’t feel right. I’ve played with the pressure of losing the game 1-0, and it’s certainly easier than winning 2-1. Goaltending can easily be the equalizer between a dominant team and a dominant win, possibly even flipping the script to a loss. Goal prevention is the best way for a goalie to win games. However, it’s possible that some goalies might be consistently better in crunch time than their goal prevention would suggest.

Regardless whether you think being clutch might be an innate skill some have or whether those differences are incredibly tiny at the NHL-level, we have to acknowledge that the finite and imperfect nature of the data will likely be a limiting factor.

What Does the Data Look Like?

The objective of this analysis is to explain goaltender playoff performances using data available prior to round 1, game 1. The target of interest is playoff goal prevention per shot, save % less expected save %. If a goaltender faced 25 expected goals on 250 shots, but only conceded 20 actual goals, this would be a 2% lift (5 / 250 or 92% – 90%). Actual save % may deviate wildly from expected save % in small sample sizes like the playoffs. A few bad goals and/or unlucky bounces against will likely prevent a chance of redemption.

To help explain the selected measure of playoff performance for each season, the save % lift can be calculated for:

  • the regular season performance prior to that playoffs
  • entire career regular season performance prior to that playoffs
  • entire career playoff performance prior to that playoffs
  • a proxy for goaltender workload at the onset of the playoffs

Visualizing the relationship between the save % differences we see a small relationship and correlation between each. As predicted, the variance in playoff results (y-axis) is higher than the explanatory variables (x-axis) with a higher sample size. Initially, it appears regular season results are most correlated with playoff success (a perfect correlation would be equal to 1 with each point falling along the grey diagonal line). Career regular season results have the least variance and lowest correlation.

playoff explained
High Variance Results

Do these any or all of these metrics matter when explaining playoff performance?

The Weight of the Playoffs

In order to understand how each of the explanatory inputs matter we can use a multiple linear regression. This helps us quantify the direction and strength of the relationship between the explanatory variables and playoff performance.

Running a regression of 122 goalie-seasons facing at least 100 shots in the playoffs and 1000 shots in the respective regular season results in the model below.

Variable Coefficient Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0011 0.00 0.253 0.801
Career Playoffs Sv% Lift 0.2176 0.11 1.987 0.0494*
Regular Season Sv% Lift 0.8485 0.27 3.15 0.002**
Career Regular Season Sv% Lift 0.1203 0.33 0.37 0.712
Weighted Shots in 15 Day Window Prior Playoffs 0.0000 0.00 -1.131 0.261
Playoff Rookie 0.0004 0.00 0.089 0.929
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01536 on 124 degrees of freedom
Multiple R-squared:  0.1342, Adjusted R-squared:  0.09341
F-statistic: 4.292 on 4 and 124 DF,  p-value: 0.002752

Notably, this is a pretty weak model, confirming the intuition that playoff performance is tough to explain. But directionally, regular season results are more significant and the coefficient is larger than career playoff results. Also noteworthy, career regular season results have no significant effect (though directionally positive) on the playoff results once the current season and career playoff results are controlled for. Workload has a no significant effect, though directionally negative. Being a playoff rookie also has no effect, but is directionally negative too.

Formula For Success

Dropping the insignificant variables and re-running the regression creates the formula below to (loosely) calculate the expected playoff results.

PO \triangle Sv% = -0.3% + (0.21 * Career PO \triangle Sv%) + (0.83 * RS \triangle Sv%)

So, for example, Holtby entered the 2018 playoffs with a save % lift of +1.14% in prior playoffs, but only -0.18% in an uncharacteristically mediocre regular season. The regular season results are weighted about 4 times more important in the formula, resulting in an expected -0.2% save % lift in these playoffs, which he’s exceeded to date.

Bobrovsky’s prior playoff results (-2.27%) pulled down his regular season results (+0.5%), expecting a -0.33% performance. He finished with a -1.22%.

Despite a great regular season, Grubauer’s expected save % was about 0% due to poor prior playoff appearances pulling it down.

playoff projections
Subject to Change


If there’s anything to take away from this analysis is that explaining playoff performances is difficult. This was likely obvious to anyone who’s watched playoff hockey. Small sample sizes, survivor bias, and out of control narratives, playoff hockey has everything to confound a good analysis.

That said, some things do matter directionally. Entering the playoffs after a good regular season is probably more important than a good playoff track record. Braden Holtby may have bucked this trend playoffs-to-date, but it was probably more likely his regular season results were lower than his true talent suggests.

The results also suggest that waiting for a goalie’s playoff results to regress to a career average is generally fruitless. This makes sense intuitively, a goaltender may change teams and systems. They develop and regress. Regular season results likely give enough of a snapshot of where their game is at that entire career regular season results are unnecessary. Marc-Andre Fleury entered the 2018 playoffs with excellent regular season results, average career regular season results, and below average playoff results. This was a recipe for success based on the basic model (expected +0.5%, chart above) and he’s subsequently delivered with excellent results (he currently has the best save % lift of goalies with over 500 shots in the dataset going back to 2011).

With all of these considerations, there is nothing to suggest a goalie can simply turn it on for the playoffs. Proven experience certainly helps, but it’s more important to have posted good results with the most current team and defensive conditions.

Washington Re-Visited

Was Trotz right to start Grubauer? Probably. Playoff series are short and Grubauer had played excellent during the regular season. However, past playoff results do have a partial explanatory effect, partly because there are other considerations in the playoffs. Playing styles can change, physicality around the net can increase, and facing a well game-planned opposition for 4 to 7 games means that tendencies and tempers can amplify. Holtby had experience in those situations, not enough to completely offset the difference in their regular season, but close.

Bobrovsky can take comfort in the fact that his playoff results should have been better than they turned out this season. There’s likely no use in him re-visiting these playoff letdowns, his best bet is to look forward, focusing on another big season and carrying that performance forward. Either the results will come naturally or maybe he will be carried up by some positive unexplained variance.


Thanks for reading! I update goalie-season data using expected goals, it can be downloaded or viewed in my goalie compare app. Any custom requests ping me at @crowdscoutsprts or cole92anderson@gmail.com.

Code for this analysis was built off a scraper built by @36Hobbit which can be found at github.com/HarryShomer/Hockey-Scraper.

I also implement shot location adjustment outlined by Schuckers and Curro and adapted by @OilersNerdAlert. Any implementation issues are my fault.

Analyzing the Impact of the Reverse VH Tactic

Goaltending tactics have evolved considerably in the last 30 years, confirmed by rising save percentages. The Reverse Vertical Horizontal (RVH) is a relatively new goaltender tactic, now widely-adopted. Is there a meaningful impact in the data?

What is the RVH?

Growing up almost every coach I had wanted me to stand-up ‘more’ – more being a relative term. Most made peace with the fact that I was going to try to make the same type of saves as Patrick Roy or Dominik Hasek but since I was still a kid, it would probably help if stood up once in a while. Still, they had to choose their battles wisely, most picked the same hill to die on – bad angle shots. It was simple geometry really, with the right stick positioning an adolescent goalie could stand there and cover 100% of the net. However, this just led to the terrible experience of having people hack away at your feet waiting for either a goal or a teammate to save you – glued to the post you couldn’t cover the puck and dropping to your knees with the puck that tight would create a hole anybody could hit.

By the time I got to junior and had a goalie coach we worked in the Vertical-Horizontal (VH) tactic to deal with shots from sharp angles. The short-side pad would seal the post (vertical) and the back leg would drop sealing the ice (horizontal). There was always a risk of getting your stick tied up and/or getting beat between the post and skate, but used properly it was pretty tough to beat from range, however, there were trade-offs. Leading with the pad tied up the hands a bit, meaning rebounds were more difficult to control. If there was a rebound the VH was configured to push off the post, but only in one direction. If you had kept your knee tight to the goal line, but needed to push to the top of the crease, too bad, you were pushing across the goal line.

Me using the Vertical-Horizontal (VH) or just really slow to recover?

The Reverse Vertical Horizontal (RVH) flipped the configuration of the pads, so the strong pad seals the ice (horizontal) and back leg remains anchored (vertical), freeing up the hands and stick more to make plays and allowing rotation with the back leg and push off with the post leg (I would have never dreamed of this, most nets growing up were easy to knock off whenever you needed a convenient whistle). The back leg can anchor or drop into a butterfly quickly which gives the RVH more flexibility when repelling a play originating from a sharp angle compared to the VH.

This added flexibility has meant RVH has mostly supplanted VH as a tactic for sharp angle shots, but it’s not perfect either since it leaves a few holes along the post above the pad, particularly over the shoulder. Additionally, because of its flexibility, some goaltender’s become too reliant on it, defaulting to it prematurely or in situations that don’t call for it. Shooters are also able to pick up on trends. After all, throughout the VH and RVH it was always an option to play sharp angle shots more passively by standing up as long as possible (perhaps anticipating a pass or change of angle) or more aggressively by moving off the post and squaring up. The RVH is a great tactic, but it’s up to the goalie to assess the shooter speed, handedness, passing options, and defensive support and making a read rather than simply defaulting to the RVH.

RVH, by a pro

What does the data say?

As early as 2014, InGoal Magazine’s Greg Balloch discussed the RVH being over-used situationally and improperly, including at the NHL level. You don’t need to watch too many highlights to see someone who’s 6’5″ inexplicitly getting beat over the shoulder from a bad angle shot because they were leaning on the post in the RVH. Is this is a growing problem or just some unfortunate anecdotes and the benefits outweigh the negatives?

Looking at NHL play-by-play from 2010-2018, we can isolate shots where the RVH has been presumably been used properly and possibly improperly to see if there are any patterns in the:

  1. Share of shots resulting in goals (obvious why this matters)
  2. Number of shots attempted per game (perhaps RVH has encouraged or discouraged bad angle shots)
  3. Share of shots resulting in rebounds (are some tactics more prone to rebounds than others)
  4. Shooting % on rebounds, or calculated expected goals on rebounds if the sample size is too small (are some tactic more prone to bad rebounds that are more likely to be converted in goals or possibly leave the goalie less likely to make the rebound save)

Observing these metrics over the last 8 seasons might reveal a meaningful change in success rates, but it important to caution that while this might appear to be a testable tactic, in a complex game like hockey, effects can be hard to pin down. We don’t have passing data to reveal if, for example, a more aggressive tactic led to more passing from the sharp angle and consequently more dangerous locations, though the number of attempts per game might lend a hint.

Either way, it’s possible macro trends don’t reveal anything meaningful since there’s much that unobserved and the data itself is imperfect (though the coordinate data has been adjusted to hopefully improve the accuracy of shot location). That said there may be potentially meaningful and interesting information in the data that might inform a more concentrated deep-dive later.

What does the data look like?

For this analysis, we will focus on bad angle shots where a goalie might select the RVH tactic, either properly or improperly. To do this we can limit to shots taken from a 45° angle or less and within 10 feet from the goal line (visualized below). Further, we will want to breakout combinations of:

  1. ‘Close’ vs ‘Long’ Shots – using the cut-off of 12 feet from the net, look at how goalies have dealt with shots where they wouldn’t have time to react, and compare to longer shots.
  2. ‘Poor Angle’ vs ‘Decent Angle’ Shots – the RVH is generally recommended on poor angle shots (0°-22.5° from the goal line) but could be over-used when the puck is at a decent angle(22.5°-45°).

Identifying these combinations of angle types for analysis can be visualized on a rink (cumulative shooting percentage labelled). The average shooting percentage across all shots is about 6.6%, so shots closer than 12 feet from a poor angle are about as dangerous as the average shot while getting a few feet out to a decent angle improves the shooting percentage by 2%. Another important consideration is that I crudely bucketed this data, which is generally not ideal, but for the purpose of the analysis helpful (. The coordinate data itself isn’t perfect either, but some home-rink bias adjustment has been applied, so hopefully won’t be systemically biased across zones or time.

The four areas of analysis (note: the points are jittered, so areas ‘bleed’ into each other)

Trends By Season

A quick note about the charts below. They focus on shots at 5v5 and 5v4 play since the distribution of the type of shots we might see from each of the zones above would be different on a 5v4 or 4v4. On a 5v4, we’d expect to see more one-timers as a share of total shots from these zones, increasing the expected shooting percentage and might be the result of changing powerplay tactics. Shots while gameplay is 4v4 or 3v3 are also more likely to be dangerous, if a shooter shoots from a sharp angle in 3v3 overtime, for example, it’s probably because they expect to score.

We must also deal with both signal and noise in the data, are fluctuations in shooting percentage caused by anything material or just randomness? Our default assumption is that the RVH likely hasn’t had any impact on bad angle shots and the burden of proof would be on the analysis to discover a statistically significant difference in the data. Ideally, we’d have some sort of intervention period where all NHL goalies adopted the RVH. Unfortunately, this would never be the case, so we can only observe loose trends over time at the macro-level.

Without a clear way to compare a “before and after” period for all goalies, we can create uncertainty bars for each season by considering sample size. Say we had observed 10 goals on 100 shots from a particular area, we wouldn’t be too sure in that 10% shooting percentage, a post here or there, it may have been 6% or 14%. What if we observed 100 goals on 1,000 from the same spot? We can be increasingly sure in that 10%. To reflect the impact sample size has on certainty, the analysis will use the standard deviation of beta distribution to convey uncertainty by using error bars +/- 1 standard deviation. 

Shooting Percentage

The primary job of a save or tactic selection is to stop the puck, so naturally, the first trend to look at is shooting percentage from each segment of the ice.

Starting with 5v4 shots, the first trend that jumps out is the rise in shooting % on shots within 12 feet up until after the lockout-shortened season, falling dramatically, and then slowly rising again. This presumably reflects a cat and mouse game between shooters and goalies, but may also involve powerplay and penalty kill strategies countering each other. Shooting percentages from longer bad angle shots followed a more muted version of this trend.

At 5v5, trends are less pronounced. Interestingly, shooting percentage on close shots from a poor angle jump above those from a decent angle in 2014-15, which is strange, before normalizing again.

Shooting Percentage by Season

Rebound Percentage

It’s also important that the goalie prevents rebounds on bad angle shots. Rebounds are a bit tricky to define in the play-by-play data but can be estimated by flagging any follow-up shot with 2 seconds of the original.

Looking at just 5v5, rebounds have been generally less prevalent than the average shot (3.4% of shots in 2017-18 resulted in rebounds), but has been trending upward in the 4 areas of the ice. It’s tough to infer a definite trend because we have some more uncertainty (rebounds are rarer than goals) but it seems the rebound rate is not falling.

Rebound % by Season

Rebound Shooting Percentage

Rebounds are a problem because they are very dangerous, they are converted to goals about a quarter of the time, 4 times as dangerous as a non-rebound shot. Are rebounds on shots from poor angles getting more dangerous?

At 5v5, two things are apparent. Rebounds from poor angles are generally ‘safer’ than average, the shooting percentage has about 10% lower than average. This suggests goalies have generally done a good job of keep rebounds on the strong side, preventing pucks from getting to the middle of the ice or weak-side and more dangerous extra chances against.

Secondly, because we are dealing with a fraction of a fraction our sample size is quite small and the error bars are large.

Rebound Shooting % by Season

Alternatively, we could look at the expected goal value of the rebounds to reclaim some of this sample, where we can calculate factors such as the total distance the puck travels between shots and the angular velocity the goalie might have to deal with.

Expected Goal % on Rebounds by Season

Both of these views suggest that there isn’t really a definitive trend since we are working with 3% of the original data (already limited to bad angle shots) making the results pretty noisy. An interesting finding is that rebounds on shots from poor angles can be more dangerous on shots from slightly better angles, possibly due to goalies not being square to the initial shot in these cases.

Bad Angle Attempts as Share of Total

It’s also important to check to see if shooters are attempting more shots from bad angles as a share of total shots. This might be the result of defensive pressure, but it also might signal shooter’s seeing and testing holes.

Shoot your shot?

It appears most 5v4’s are moving away from bad angle shots, notably on shots over 12 feet. However, at 5v5 there had been a modest increase in attempted bad angle shots from further than 12 feet away (until this season). Some players are definitely happy to test goalies from the seemingly impossible angle – and why not? They don’t have to chase down the puck if they miss.

Shooter Handedness

We can also look at shooter handedness and how it has impact goal, rebound, and attempt rates over time. Generally, shooters are more trigger happy when they are on their strong side (meaning the shooter’s stick blade is closer to the centre of the ice, if they are on their forehand), though success rates for shooters on their weak side are in the same range. Shooters have become less successful on their weak side on the powerplay, but attempts haven’t fallen considerably to reflect this.

Handedness Impact on Bad Angle Shots

Goaltender Specific Trends

These general trends might have some interesting nuggets and reveal things we might want to explore further, but they can’t reveal much regarding tactical usage of the RVH because each goaltender will have implemented it at different times, if at all. While it would be nice to have a definitive list of when goalies might have adopted the RVH tactic that might be a little simplistic. Early adopters might have had an advantage since shooters hadn’t picked up on its relative weakness. It’s also possible (and pertinent to our analysis) that goalies have become over-reliant on it in more recent seasons, defaulting to it in improper situations which could have an undesirable effect.

Without a completely clean solution, one way to test individual goaltender effectiveness from poor angle shots is to treat each off-season (where tactical changes would normally be implemented) as a divider between a ‘before’ and ‘after’ period. We can calculate save percentage in each period and compare them, testing for statistical significance in each sample. Where the save percentage has a statistically significant difference from the before to after period it might draw interest and warrant a deep look. Was there a tactic change or something else causing a meaningful change in results?

For this part of the analysis, we will limit to the 24 goaltenders that have faced at least 100 bad angle shots a season in at least 5 of the 8 seasons we have data for. Each off-season will be treated as a ‘split’ or intervention period. We’ll only focus on save percentage since rebounds are rarer, making the task of finding meaningful differences tougher.

Saga Bobrovsky

Sergei Bobrovsky had quite the change in the 2012 off-season going from the Philadelphia Flyers to the Columbus Blue Jackets (along with some time in the KHL waiting for the lockout to end), and ultimately winning the 2013 Vezina Trophy. In Columbus, he also had a new goalie coach, Ian Clark (I both attended and worked at Ian’s goalie schools in the past, full disclosure). Among other things, Clark helped Bobrovsky implement the RVH. If we look at Bobrovsky’s 5v5 save percentage on all bad angle shots 2010 – 2012 and compare it to 2013 – 2018 is it materially different?

In the ‘before’ period, Bobrovsky allowed 14 goals on 194, for a 92.8% success rate. Since then, he’s conceded 25 goals on 743 shots, his save % rising to 96.6%. If we run a test of statistical significance to check to see if there is enough evidence (shots) to determine that these proportional are meaningfully different, we get a p-value of 0.029. Stated otherwise, this difference would happen by chance alone about 2.9% of the time (showing my work below).

2-sample test for equality of proportions with continuity correction
data: c(180, 718) out of c(194, 743)
X-squared = 4.7966, df = 1, p-value = 0.02852
alternative hypothesis: two.sided
95 percent confidence interval:
-0.080419509 0.003384363
sample estimates:
prop 1 prop 2
0.9278351 0.9663526

In the soft sciences, convention suggests the ‘cutoff’ for statistical significance is a p-value is 0.05, so we can say with some certainty that this difference is likely not due to chance. We can never be sure from the data alone, but it seems that it’s likely that some combination of the move to Columbus, new goalie coach, and adoption of the RVH probably had a positive effect on his save percentage from bad angles.

Complete Goalie Splits and Save Percentage Results

We can do the same thing with Bobrovsky’s other 6 off-seasons and all 163 unique goalie-offseason splits. The results below will label any goalie-offseason where the p-value is less than 0.05. Goalies that experienced a significant change received their own color, the rest of the 24 qualifying goalies are represented by the ‘Other’ green.

Holtby, Rask, Elliott, Miller, and Bobrovsky all saw a notable rise in save percentage occurring somewhere between 2011 and 2014. Some of this might be attributable to tactical changes, though without talking to goalies, their coaches, and/or grinding on video we can’t necessarily assert for sure. However, it’s possible that RVH adoption helped drive some of this effect.

Luongo, Varlamov, and Price have all experienced a notable drop in success rate. Luongo stands out because he likely adopted the RVH in 2013, but saw a drop in the 2014 split.

Save % Before and After for 163 goalie-season splits

Price particularly struggled this past season on shots >12 feet and <= 22.5°. He gave up more goals from that area last season (5) than from 2010-2017 (4). This helps identifies a particular pain-point in Price’s poor season. Bad angle goals are easily preventable, and going back to the tape would help identify if tactics, luck, and/or laziness were at fault, which can help inform the proper adjustments.

Save % Before and After by Area of Ice

If you are stricter and insist on a p-value of 0.01, only 2012 Brian Elliott in the ‘Close-Decent Angle’ area, 2012 Jonathan Quick in the ‘Long-Decent Angle’ area and aforementioned 2017 Price in the ‘Long-Poor Angle’ area saw significant changes at that level.

A weakness of this analysis is the ‘bucketing’ of data into specific areas, so it is possible a lot of borderline goals or shots from one area ended up in other area or another by chance. Unlikely, but something to keep in mind.


Capturing the full impact of the RVH is a near impossible task since we don’t observe when it is actually deployed. But we can look at a proxy for when it might be deployed and investigate if there were any meaningful impact on results. While incomplete, it might help ask smarter questions and help concentrate the proper video application. Carey Price struggled from poor angles last season? Those clips can be isolated and analyzed to re-affirm the trend and possibly reveal why.

Analyzing tactical usage in hockey is often frustrating since everyone on the ice is basically playing a complicated version of rock-paper-scissors on skates trying to gain an advantage. We can observe goals, rebounds (kind of), and total attempts, but what if shooters have adapted by making more passes that lead to even more dangerous shots? There are rarely clean test and control cases we can use to attribute some change in results to a specific tactic.

We can, however, attempt to use data to help guide a more informed approach and use the framework above to begin to create and explore additional questions. Often looking from just a video perspective misses part of the equation. If you looked at all bad angle goals against when goalies where using the VH and compared to the RVH, you wouldn’t have the complete picture. You want to look at all bad angle shots using each tactic then look at the success rate of each. Of course, we can’t do that easily, so identifying proxies and exploring the data can help paint a more comprehensive view and sharpen the focus on where meaningful differences may exist.

Goaltending can frustrate fans and coaches alike because results from game to game can be inconsistent. Goalies can’t necessarily dictate the game, rather have to let the game come to them while employing tactics that give them the best chance to succeed – ‘playing the percentages.’ The evolution of goaltending tactics has largely been positive, as save percentages suggest. It appears the RVH has probably helped goaltenders deal with bad angle attacks, but this isn’t a one-way effect. There is evidence to suggest that rebound rates are rising and some goaltenders have had notable falls in save percentage from poor angles. Shooters will always adapt, so it’s important for goaltenders to critically assess the tactics they employ and continue to stay a step ahead.

Thanks for reading! A notebook with code for the analysis can be found here. Any custom requests ping me at @crowdscoutsprts or cole92anderson@gmail.com.

Code for this analysis was built off a scraper built by @36Hobbit which can be found at github.com/HarryShomer/Hockey-Scraper. I also implement shot location adjustment outlined by Schuckers and Curro and adapted by @OilersNerdAlert. Any implementation issues are my fault. The rink plot is adapted from @iyer_prashanth code.

My code for this and other analyses can be found on my Github.