INTRODUCTION TO QUALITY OF COMPETITION
Look at the scale. It’s tiny. It’s from 2.1 goals/60 to 2.4 goals/60. Let’s plot this with GF/60 and GA/60 on a more ordinary scale.
STRENGTH OF COMPETITION ADJUSTMENTS
(Oct 2006)
In general, the scoring rate decreases when there is more talent in the league. The best players do not have as many weak opponents to score a lot of goals against. This is a simple reason why the NHL has had troubles keeping scoring up.
When the average age of players is younger, in general the NHL is a better league.
A lower quality league allows aging players to hang around longer.
We are now in the era of enforced parity. There are no more really good or bad teams.
One method of measuring the quality of opposition in a given year is by using the percentage of players in the league that had the best season of their career in a given season as a proxy for calibre of opposition. In a lower calibre year, more players will have their best year of their career than in a higher calibre year.
SOME ISSUES WITH QoC
(April 2007)
It is the average On/Off-Ice +/- of the opposing players a player faces.
Desjardins is effectively assuming that each player’s offensive contribution is equivalent when he does this – he adds together the five players On ice/off ice +/- and divides by five. I doubt that this accurately reflects the contribution – I suspect that forwards, at the very least, impact the offensive side of the +/- equation more than the defenceman.
Consider a player who played only against terrible players and therefore had good numbers. The players who played against him would look like they were playing hard minutes when judged by this player’s numbers when, in actuality, they weren’t.
MEASURING QoC
(Aug 2008)
calculating the time average on/off ice adjusted +/- rating of the players who play against a given player
The adjusted +/- of all five players on the ice in opposition to a player is averaged between the five players and in time and then compared to the time average for their team’s opponents
A player who consistently plays against hard opposition is a better player than his +/- shows and one who is protected from hard minutes is not as good.
Quality of opposition can be measured using the on/off ice adjusted +/- framework.
does a good job of identifying players who play against high and low quality opposition.
CORSI INSTEAD OF +/- TO CALCULATE QoC
(Jan 2010)
Instead of using relative +/- (which includes just goals when a player is on or off the ice), it would be better if Qualcomp used the total shot volume while players were on the ice. Why? We already know that shot differential (aka Corsi) is a better predictor of future goal differential than goal differential itself is. Corsi also includes a much higher number of events than simple +/- does – approximately 25x.
QUALCOMP & LINE MATCHING
Which one will win out depends on how important the competition effect is and, importantly, how much the coaches focus on the matchups.
In the playoffs, matchups get a lot of focus by everyone
Coaches do close to everything they can to win the game they’re in. A top line facing a bottom line is rare, especially on face offs that don’t follow icing. In the playoffs we would expect the matchup effect to dominate the competition effect.
LEAGUE WIDE DEPLOYMENT
My hypothesis is that coaches tend to lean either toward matching competition (CORSI rel QOC) or matching zone (zone%).
While it seems clear that coaches try to match forwards by either line or zone, we can’t tell with certainty that this is the case for defensemen.
Interestingly, some teams seem to zone match their forwards, while line match their defense, suggesting that it’s possible (and potentially advantageous) to do both.
QoC 5-5 CLOSE SITUATIONS
(July 2012)
My hypothesis is that teams that are really good will play more time with the score close against other good teams and less time with the score close against significantly weaker teams.
My hypothesis is that players on good teams will have a tougher QoC during 5v5 close situations than during overall 5v5 situations and players on weak teams will have weaker QoC during 5v5 close situations than during overall 5v5 situations. Let’s put that hypothesis to the test.
Good teams have on average tougher 5v5 close opponents than straight 5v5 opponents and weak teams have tougher 5v5 opponents than 5v5 close opponents which is exactly what we predicted.
QoC metrics vary very little across players or from situation to situation (from my perspective QoC can be ignored the majority of the time).
TOI AS QoC METRIC
they start by assigning each player some kind of score to assess how tough an opponent he is; then to calculate a player’s quality of competition, you average his opponents’ scores together.
A player’s ice time is a direct reflection of the coach’s opinion of the player, and at this relatively early stage in the evolution of analytics, the coach’s opinion is more accurate than any one individual statistic. So why not try to build a quality of competition metric using ice time as the measure of how good each opponent is?
The most commonly used competition metric right now is Corsi Rel QoC, so we’ll compare the results of our TOI Qualcomp to that measure.
a team’s best line tends to face opponents who get a lot of ice time, even if those opponents don’t tend to outshoot their opponents.
It looks to me like we are separating out not just the quality of competition, but the type of opponents a player faced
Thus, by using ice time as an indicator of player strength, we can eliminate the complications that zone starts and competition have on the shot-based metrics. We then find indications that top line players may face stronger competition than is suggested by the existing competition metrics.
HOW OPPONENT STRENGTH AFFECTS TEAM PERFORMANCE
· Single-game shooting and save percentages are pretty much impossible to predict, so it’s not surprising that in-game Sv% doesn’t vary predictably with
. When it comes to shots for, however, teams appear to put more shots on net when facing teams that have allowed more shots going into the game
The rates at which teams allow these events don’t correlate with the rates at which their opponents have created them, yet teams do create more shot attempts when playing teams that have allowed more shots.
These findings suggest that the rate at which NHL teams allow shots is one of the more consistent features of their game: the rate of shots against doesn’t appear to vary dramatically by opponent shots-for, though score effects may be partly responsible for this.
The implication, which we see reflected in these results, is that the rate of shots your team attempts is likely to be somewhat dependent on the defensive skill of their opponent.
HOCKEY NEEDS A BETTER QoC METRIC
(July 2016)
At the individual level in hockey, the most important context is provided by:
• Teammates
• Zone starts
• Competition
They’re important, because each of them drastically affect the count of good/bad things. Again, this is hockey, not stats. Who you play with, where you start, and who you face makes a huge difference to your success.
Teammates:
WOWY analysis is always useful when looking at players, and in my opinion, is mandatory when trying to assess defensemen.
Zone Starts:
The second aspect of context we talked about is how the coach uses a player – whether they’re starting in their own zone a lot, or gifted offensive zone time, or neither (or both).
Turns out, this doesn’t matter nearly as much as you’d think
There’s reason to believe that zone starts affect a player’s numbers less than you’d think; and when they do – we have an idea of how much, and can adjust for them.
Competition:
On-ice competition is a big deal, and a critical part of measuring players.
Notice something about the QoC though. See how it has such a narrow range? The weakest guy on there is Clendening at -0.6. The toughest is Klefbom at a shade over 1.0.
it should be clear that the validity of this QoC metric is almost entirely dependent on the validity of the ‘competition value’ assigned to each player.
If that competition value isn’t good, then you have a GIGO (garbage in garbage out) situation, and your QoC metric isn’t going to work either.
There are three different data values that are commonly used for calculating a QoC metric,
Using Corsi for QoC:
Corsi is a highly valuable statistic, particularly as a counterpoint to more traditional measures like boxcars. But as a standalone measure for gauging the value of a player, it is deeply flawed. Any statistic that uses raw Corsi as its only measure of quality is going to fail.
Using CorsiRel As QofC:
RelCor is a very valuable metric in the right context, but suffers terribly as a standalone metric for gauging the value of a player.
Like raw Corsi, despite its widespread use we should rule out relative Corsi as a useful standalone basis for QoC.
Using 5v5 TOI As QofC:
This is probably the most widely used (and arguably best) tool for delineating QoC.
So once again, we find ourselves concluding that the underlying measure to this QoC, TOI, tells you a lot about a player, but there are very real concerns in using it as a standalone measure.
A Malfunction In The Metric:
· QoC measures as currently used do not show a large differentiation in the competition faced by NHL players. This is often at odds with observed head to head matchups.
· Even when they do show a difference, they give us no context on how to use that to adjust the varying shot metrics results that we see
STATSPORTS - QoC
The use of QoC comes from trying to analyze data at the player level rather than at the event level.
It is possible to address QoC and QoT systematically at an appropriate level for analysis. One way to do this is through a multiple regression that has a term or terms for each players on the ice for a given event or shift. This area of research is known as adjusted plus-minus in the statistical analysis of sports literature.
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