Intro to CHIP, Part 2

Part 1 summarized the injury experience of every NHL team between 2008-09 and 2012-13, as measured by man-games lost (MGL) and Cap Hit of Injured Players (CHIP), as well as analyzing the relationship between (regular season) success and injuries.

In Part 2, I look at another couple of simple questions that the data can help go some way towards answering.

Does good/bad health persist from season to season?

If you are Marian Gaborik, Carlo Colaiacovo, or Rick DiPietro, the answer is clearly “Good: no. Bad: yes” and you can stop reading here.

Alternatively, as an assessment of whether avoidance of injury is a repeatable “skill” at the team level, we can look at a plot of pairs of MGL/GP figures from successive seasons for each team (i.e. four x 30 data points over the five-year sample):

Part 2 diagram 1

Figure 1 – Click to view

The same data in tabular form, grouped into buckets of 10 ranked by MGL/GP in the first year of each pair (Y1):

Part 2 diagram 2

Figure 2 – click to view

Despite the data including a few instances of single injuries spanning across multiple seasons, the correlation here is weak, supporting the premise that injuries are essentially luck driven. However, it would perhaps be interesting to see if some of the apparent outliers suggested by the team experience figures seen in Part 1 (e.g. Pacific teams) remain so over a longer period.

Do injuries increase with age?

The next chart shows total MGL distributed by the age of the player (as at January 1 during each season/lockout), plus the total number of games played by all players.

This is admittedly a reasonably crude measure. For example, I have not attempted to make any adjustment for the fact that some of the games missed are attributable to season-long or career-ending absences, or that the games played number for goalies doesn’t reflect the number of games they were available and dressed for.

Part 2 diagram 3

Figure 3 – click to view

Perhaps a fairly useful illustration of the age profile of the NHL player population and the distribution of injury absences, but to make this a bit clearer, the next chart shows the crude proportion of games missed by each age cohort, expressed as MGL/(MGL+GP):

Part 2 diagram 4

Figure 4 – click to view

Sample sizes get pretty sketchy below age 20 and towards the late thirties, unsurprisingly, but enough to suggest that old people get hurt more for longer, supporting the ultra-controversial hypothesis first presented by Dr. M. Recchi, Professor of Hockology at the University of Carlyle.

Thomas Crawshaw is a UK-born/bred/based hockey obsessive, creator of the CHIP injury metric, occasional blogger and since watching the NHL, winner of as many Stanley Cups as Glen Sather. He doesn’t write often, but when he does, it’s always almost good.

Follow Thomas on Twitter at @LW3H.

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