OnThe Forecheck
Registered User
- Jan 7, 2017
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A teams shooting and save percentage are unrelated so why must they revert to 100? Why is that a sign of unsustainable luck and not the sign of a good team?
A teams shooting and save percentage are unrelated so why must they revert to 100? Why is that a sign of unsustainable luck and not the sign of a good team?
The big problem with PDO is that too many people think that 1000 is the bar for under/over performance for any player/team.
That's simply not true at all.
What is the truth then? There have to be different expectations for different teams? The Coyotes aren't unluck, they're just truly terrible
It is the worst analytics stat out there.
A good goalie or defensive system is probably going to have a higher save percentage than a poor team. That number should be sustainable for the year and if players/coaches dont change then it can be sustainable multiple years.
Shooting percentage has a better chance to be measured since you play various defensive teams (good and bad), but also has too many variables that affect the result. Individual shooting percentage may be easier to gauge when comparing previous years, but even then you can get wild fluctuations.
Dont use it
I think PDO is a good tool if you are juat taking a glance at it and using other stats to justify why a team's PDO is either high or low. For example, if a teams shooting percentage after half the season is at 6% and their save percentage is at 91%, their PDO would be at 97% and some people would jump to the conclusion that they are just unlucky. But that team could just be taking the majority of their shots from low danger scoring zones and allowing more high danger chances than the average team would. Because of this, there is no reason to believe their PDO would rise. Again, PDO can be a useful tool, but it can't be the "end all be all" stat.
http://stats.hockeyanalysis.com/rat...0&teamid=0&type=shots&sort=ShPct&sortdir=DESC
7yr sample with players sorted by 5v5 SV%. Are we surprised to see the best player in the league at #1 with 11% SH%, and conversely Craig Adams last with 5%?
Did Crosby just get lucky those 7 years and over-perform his expected 8% SH%?
Using PDO to estimate a player's future is only acceptable if you already know that player's mean PDO. It somewhat works because the majority of players will naturally be close the bad assumption of "PDO should = 1 for all players" because the player population is bell curved around the mean. Just because an assumption can be close for the majority of players, does not make it a correct assumption.
We cannot assume each player is the same and would be expected to have the same SH% (and also SV% from goaltending).
PDO forecasting on a individual level will be difficult because you need a large sample to determine a player's true SH% talent. Goals are a rare event. It defeats the purpose of trying to use "PDO should = 1" as a shortcut when you don't have the volume of data about a player.
A similar analogy is to get the average MPG of the entire new car fleet made by every automotive company (probably around 25MPG), and then try to compare either a Prius's MPG or a V12 Ferrari's MPG to it. You already made an incorrect assumption that every car is "the same".
Regression towards the mean is only valid if you are dealing with samples of the same thing. Crosby's SH% is not the same as Craig Adam's. Both are centered around their own means and will vary around that.
PDO does not "regress" to 1.0 for anyone or any team This is mathematical "phenomenon" when looking at a league SH% and SV% averages in whole. league average (SH%+SV%)=1. It only regresses to 1.0000 for an individual (team or player) if they lie perfectly on the mean (or a sum of) in both components. A league is not a sum of equal teams. They are distributed about the mean. Probability of an analog sample falling exactly on an individual value is 0.
Please understand this, and help stop people from claiming it. If (PDO =1) were "true" then SH% would have to be equal among individual players and we all know that is not true. Nothing is in place (SV% wise) balancing Crosby's consistently high SH% to ensure his PDO regresses to 1.0.
A team is a sum of players so if any rule must by true at the team level, it must also be true at the individual level. It is entirely possible for a good team to have a "regression PDO" of 1.03 and another less skilled team to have a "regression PDO" of .98 based on their rosters of unequal players.
For the PDO = 1 falsity to be true at the team level, a league would require you to ice an equally poor player to counter weight your good players and play him an equal amount. "PDO regressing to 1.0" is an assumption that is improper, and has 0% of being correct (see above).
Team PDO = SUM [player PDO x % ice time]
The points above are fair critiques of PDO. You can't simply say that every team will regress to 100.0. But, I do think it's useful in evaluating teams at the extreme ends of the spectrum in smaller smaple sizes.
For some clarity:
http://stats.hockeyanalysis.com/teamstats.php?disp=1&db=201417&sit=5v5&sort=PDO&sortdir=DESC
Over the last three seasons, the top 5v5 PDO is the Rangers at 102.1 and the worst is the Hurricanes at 97.8.
25 of the 30 teams have three year PDOs in the range of 99.0 to 101.0. So 83% of teams have a PDO within 1.0% of the average over the last three years.
Let's look at teams PDO at about the haflway mark of 2016-17:
http://stats.hockeyanalysis.com/teamstats.php?disp=1&db=201617&sit=5v5&sort=PDO&sortdir=DESC
The Wild and Capitals will have a hard time sustaining PDOs around 103.0. Colorado's PDO will likely increase a bit. We won't have seven teams with a PDO above 101.0 by season's end, nor will we have eight teams with a PDO below 99.0.
So PDO is good for evaluating the outliers. It's more useful at the beginning of the season, say 20 games in, when teams are riding percentages a bit more (certain teams will be at 105.0 and 95.0, and we know that those simply aren't sustainable). As the sample size increases, the value of using PDO decreases.
So PDO is good for evaluating the outliers. It's more useful at the beginning of the season, say 20 games in, when teams are riding percentages a bit more (certain teams will be at 105.0 and 95.0, and we know that those simply aren't sustainable). As the sample size increases, the value of using PDO decreases.