- I think understanding the "pull" tendency is worthwhile, because misses to the glove side are frozen at almost 6 times the rate of misses to the blocker side (not surprising at all)

Backhand (n = 5,222) Finally, backhands look almost like the inverse of wrist shots. There's a tendency to miss to the right, counteracted at a significant level by being a right shot. Shooting from the left side of the ice increases leftward pull. No effect from goalies

Slap (n = 9,380) The coefficients for slapshots are directionally the same as snap shots, except for goalie handedness. Right shots miss to the left much more, as do shots from the left side. Goalies catching on their right tends to force shots to the right at a statistically significant threshold

Tip In (n = 10,317) Tips are quite different, the two strongest predictors are side of the ice with shots from the left side much more likely to miss left and shots from the right much more likely to miss right. Right shooters tend to miss right slightly more. Goalie handedness has no effect

Snap (n = 13,962) The shooter being a right side shot is again the strongest predictor, but this time it increases the chances a shot misses to the left. Shooting from the left side of the ice is the other only statistically significant predictor, also increasing the odds a shot misses to the left

Wrist shots (n = 35,466) The intercept suggests a tendency to miss to the left. The shooter being a right side shot has the highest significance and decreases the odds of a shot missing to the left. Goalie handedness and side of the ice are not statistically significant predictors

iykyk

Net schedule impact on overall team xG. Estimates based on outputs of an RAPM model for the 2025-26 season. Included with and without time zone changes, because I'm less sure I'm capturing the impact correctly

In total Dallas travels to 61 of their games (T-10th and crosses 38 time zones (T-13th), an average of ~0.6 time zones per travel game (13th)

Since the NHL started re-classing missed shots that were contacted by the goalie as misses, the share of missed shots that were frozen has ballooned. This doesn't look settled to me either. Something interesting to monitor next season will be whether this stabilizes or continues to increase

Reminds me of my all time favorite photo of my cat

I remembered that! Didn't figure out a way to work this into the thread, but share of rush shots by odd man situation has been fairly steady, and share of goals roughly match EDGE data (except 3-on-2s for some reason, could be my method for id'ing odd man situations)

This appears to be a PBP issue, not a tracking issue. A3Z shows a fairly constant share of attempts and goals off the rush while PBP is consistent on share of attempts but declining for goals. A3Z is consistent with ~5 sec cutoff on time since entry via NHL EDGE (~40% of goals off rush)

This doesn't explain declining shooting percentage though, which starts before then (23-24 is the first season rush shots are less dangerous in PBP). Declining shooting percentage is also at odds with A3Z's data, which shows rush shooting increasing proportionally with other shot types

Continuing on rush offense: there has clearly been a change in how giveaways and takeaways are recorded, a much smaller percentage are followed by a rush starting in 24-25 and takeaways make up a much smaller share of preceding events starting that season bsky.app/profile/lady...

Rush shots and follow-up shots have not experienced a change in timing behavior, despite longer delays being associated with better outcomes in recent seasons

Interestingly, there's a similar, though reversed, shift in shots off an OZ faceoff a season earlier. Starting in 2023-24, teams take many more shot attempts in the first three seconds after a faceoff. This could be tactical, but the shift is so sudden and persistent it raises my eyebrows

I'm not totally settled on this as an explanation, but I think this does makes some intuitive sense and aligns with another data point: in the last two seasons, more shots "off turnover" are delayed. Again the shift is so sudden and in the 24-25 season I don't think playing style accounts for it

Switching focus from rushes to the related predictor of turnovers (though I'm not done with rush shots), I think some of the drift is explained by changes in event recording. Giveaways, takeaways, and hits all see a sudden change in distribution by zone

So, I was wrong, and Corey does include odd man markers in his tracking data. It doesn't tell a clean story, but it does look like there's a bit of a decline over the last 3 seasons in the share of rush shots that are odd man, esp breakaways. It'll take some time to look further back than 23-24 tho

Using a 30 game cutoff for NHL regulars, there's a persistent pattern that better finishers take more shots per minute, but also take slightly *worse* shots on average. Presumably because they have more confidence they'll score on those shots

However, that means the output is actually a weighted average since (usually) better goaltenders and better shooters get more ice time and therefore face/take more shots (focus is on forwards as they take ~70% of shots and are typically better finishers than defensemen)

It's interesting to see most of the predictors that have changed trending negatively, while the intercept has trended positive over the same time

Follow-up to this: did a bunch more testing on a model that updates each day of the season to track trends within the season (with a slightly stronger weight on more recent games) and found the following variables improved performance out of sample the most

As a group, McDavid, MacKinnon, and Kucherov have received a higher share Hart Votes than any other player since the first season all three received votes (2017-18). Matthews and Draisaitl are the only players close, but they'll fall further off pace after this year's ballots are revealed

Ah, ok, that makes sense. What if you flipped the rink vertical and moved the trends to the right side and arranged them so they’re laid out the same as the z-scores?

Saw some discussion about John Chayka's drafting record with the Coyotes, so I wanted to use that as a springboard for some ideas about how to evaluate draft performance The easiest way to evaluate drafting is to take a single number value stat (EH xSPAR/goalie WAR here) & sum it getting total WAR

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