The last several days I have been playing around a fair bit with team data and analyzing various metrics for their usefulness in predicting future outcomes and I have come across some interesting observations. Specifically, with more years of data, fenwick becomes significantly less important/valuable while goals and the percentages become more important/valuable. Let me explain.

Let’s first look at the year over year correlations in the various stats themselves.

 Y1 vs Y2 Y12 vs Y34 Y123 vs Y45 FF% 0.3334 0.2447 0.1937 FF60 0.2414 0.1635 0.0976 FA60 0.3714 0.2743 0.3224 GF% 0.1891 0.2494 0.3514 GF60 0.0409 0.1468 0.1854 GA60 0.1953 0.3669 0.4476 Sh% 0.0002 0.0117 0.0047 Sv% 0.1278 0.2954 0.3350 PDO 0.0551 0.0564 0.1127 RegPts 0.2664 0.3890 0.3744

The above table shows the r^2 between past events and future events.  The Y1 vs Y2 column is the r^2 between subsequent years (i.e. 0708 vs 0809, 0809 vs 0910, 0910 vs 1011, 1011 vs 1112).  The Y12 vs Y23 is a 2 year vs 2 year r^2 (i.e. 07-09 vs 09-11 and 08-10 vs 10-12) and the Y123 vs Y45 is the 3 year vs 2 year comparison (i.e. 07-10 vs 10-12). RegPts is points earned during regulation play (using win-loss-tie point system).

As you can see, with increased sample size, the fenwick stats abilitity to predict future fenwick stats diminishes, particularly for fenwick for and fenwick %. All the other stats generally get better with increased sample size, except for shooting percentage which has no predictive power of future shooting percentage.

The increased predictive nature of the goal and percentage stats with increased sample size makes perfect sense as the increased sample size will decrease the random variability of these stats but I have no definitive explanation as to why the fenwick stats can’t maintain their predictive ability with increased sample sizes.

Let’s take a look at how well each statistic correlates with regulation points using various sample sizes.

 1 year 2 year 3 year 4 year 5 year FF% 0.3030 0.4360 0.5383 0.5541 0.5461 GF% 0.7022 0.7919 0.8354 0.8525 0.8685 Sh% 0.0672 0.0662 0.0477 0.0435 0.0529 Sv% 0.2179 0.2482 0.2515 0.2958 0.3221 PDO 0.2956 0.2913 0.2948 0.3393 0.3937 GF60 0.2505 0.3411 0.3404 0.3302 0.3226 GA60 0.4575 0.5831 0.6418 0.6721 0.6794 FF60 0.1954 0.3058 0.3655 0.4026 0.3951 FA60 0.1788 0.2638 0.3531 0.3480 0.3357

Again, the values are r^2 with regulation points.  Nothing too surprising there except maybe that team shooting percentage is so poorly correlated with winning because at the individual level it is clear that shooting percentages are highly correlated with goal scoring. It seems apparent from the table above that team save percentage is a significant factor in winning (or as my fellow Leaf fans can attest to, lack of save percentage is a significant factor in losing).

The final table I want to look at is how well a few of the stats are at predicting future regulation time point totals.

 Y1 vs Y2 Y12 vs Y34 Y123 vs Y45 FF% 0.2500 0.2257 0.1622 GF% 0.2214 0.3187 0.3429 PDO 0.0256 0.0534 0.1212 RegPts 0.2664 0.3890 0.3744

The values are r^2 with future regulation point totals. Regardless of time frame used, past regulation time point totals are the best predictor of future regulation time point totals. Single season FF% is slightly better at predicting following season regulation point totals but with 2 or more years of data GF% becomes a significantly better predictor as the predictive ability of GF% improves and FF% declines. This makes sense as we earlier observed that increasing sample size improves GF% predictability of future GF% while FF% gets worse and that GF% is more highly correlated with regulation point totals than FF%.

One thing that is clear from the above tables is that defense has been far more important to winning than offense. Regardless of whether we look at GF60, FF60, or Sh% their level of importance trails their defensive counterpart (GA60, FA60 and Sv%), usually significantly. The defensive stats more highly correlate with winning and are more consistent from year to year. Defense and goaltending wins in the NHL.

What is interesting though is that this largely differs from what we see at the individual level. At the individual level there is much more variation in the offensive stats indicating individual players have more control over the offensive side of the game. This might suggest that team philosophies drive the defensive side of the game (i.e. how defensive minded the team is, the playing style, etc.) but the offensive side of the game is dominated more by the offensive skill level of the individual players. At the very least it is something worth of further investigation.

The last takeaway from this analysis is the declining predictive value of fenwick/corsi with increased sample size. I am not quite sure what to make of this. If anyone has any theories I’d be interested in hearing them. One theory I have is that fenwick rates are not a part of the average GMs player personal decisions and thus over time as players come and go any fenwick rates will begin to vary. If this is the case, then this may represent an area of value that a GM could exploit.