Mark Barbour (@18sktrs, patreon.com/18skaters)
In my first few articles here at Apples & Ginos I’ve been looking at how the statistics that are relevant to fantasy hockey are distributed among NHL skaters based on age. This type of analysis can be helpful when projecting future skater performance. We can use it to identify skaters who could be about to hit their prime, or to identify skaters who are likely to see a decline.
My previous articles discussed the distribution of goals, assists, and shots. This article looks at something that isn’t discussed as frequently when it comes to the “ageing curve” of an NHL skater: the relationship between a skater’s age and his hits.
How Were Hits Distributed By Age In 2021-22?
The hits data from last season are plotted below. Each square in the plot represents a skater, and its location shows the skater’s age as well as his total hits for the season. The squares will overlap when skaters who are the same age register the same number of hits, and when that happens the squares get darker. The effect is essentially a “heatmap” where the dark areas represent the highest density of skaters.
Comments on this plot:
- This looks different than the age distribution for goals, assists, and shots. Many of the top hitters were age-30 or older, and the “best” skaters were not clustered around the mid-20s.
- The top under-25 skaters were Tanner Jeannot (318 hits), Brady Tkachuk (270 hits), and Martin Fehervary (251 hits). You probably don’t need me to tell you this, but Brady Tkachuk is a rare skater. He had the fifth most hits in the NHL last season while also scoring 30 goals. The next closest 30-goal scorer was Andrei Svechnikov who had “only” 189 hits. Tanner Jeannot is also pretty interesting. He scored 24 goals last year and had the most hits among skaters who play at the forward positions. It will be interesting to see if Jeannot can put up similar scoring numbers again this season.
- As noted, many of last season’s big hitters were age-30 or older. What happens if we look at a larger sample of data? Have the league’s oldest skaters always been so ornery?
How Were Hits Distributed By Age From 2014-19?
The time period of 2014-19 represents the five most recent full-length seasons prior to COVID-19 affecting the NHL schedule. What does this historical data look like?
Comments on this plot:
- This looks different all over again. There were bangers in their mid-20s through to about age-30 with a drop-off starting after that. As was the case with the statistics examined in previous articles (goals, assists, and shots), skaters who were age-34 or older really faded out of the top ranks.
- Curious about who had the highest hit totals in this data sample? Matt Martin occupied the top two spots with 382 hits (age-25 season) and 365 hits (age-26 season). Now that you know that, are you curious about how many hits Matt Martin had last year? In his age-32 season Matt Martin recorded 235 hits. Note too that his average time-on-ice last year (11:26) was actually a little higher than it was back in his age-25 and age-26 seasons. So his hit rate has dropped over the years even if it remains high relative to the rest of the league (he had the 13th most hits in the NHL last year).
- What does this mean for last year’s data which had all those hitters who were age-30 or older? It seems reasonable to project some decline in hits for those skaters even though the single-season data suggest that skaters in their 30s are among the most frequent hitters in the league.
Just Show Me Some Numbers!
Maybe scatter plots that look like heatmaps aren’t your thing. Perhaps you want some simple numbers that tell you what’s going on here. OK, here’s a count of the number of skaters who had at least 200 hits in a season, separated by age.
AGE DISTRIBUTION OF 200+ HITS
| AGE | COUNT |
|---|---|
| 20 | 1 |
| 21 | 4 |
| 22 | 5 |
| 23 | 11 |
| 24 | 13 |
| 25 | 10 |
| 26 | 10 |
| 27 | 16 |
| 28 | 14 |
| 29 | 13 |
| 30 | 16 |
| 31 | 8 |
| 32 | 6 |
| 33 | 4 |
| 34 | 2 |
| 35 | 1 |
| 36 | 2 |
| 37 | 2 |
The End Of The Article
That’s all for now. I’ll be back soon with my final article about the age distribution of fantasy-relevant statistics.
Apples and Ginos 