Team winning percentage

The first thing I looked at was year over year comparisons:Looking at this without knowing any details it would appear that the shootout results are in fact perfectly random, you can sort of see some clustering in the middle, but other than that there are too many anomalies to make sense of anything. Let me however make a few key points:

1. LA: Lost Garon (80% shootout save percentage after 2005-2006)

2. Carolina: After the playoffs dumped Gerber and Ward has 1 win in three seasons

3. Florida: Lost Luongo

4. Philadelphia: switched to Niittymaki

5. Pittsburgh: started to use Fleury more often

6. San Jose: Toskala thankfully only played 1 shoot-out game in2006-2007 (0 wins in his career)

Remove the above points and you have a data set that has so much more structure. Of course I could probably think of good reasons to exclude every point on this graph, I'm simply trying to show that there are some key points that shouldn't be included because too much changed.

Since it appeared that Goalies were a big part of the shootout results. I figured I'd best compare goalie's save percentages in the shootout. Now the graph below BIG = BAD, small = GOOD. The graph below shows player's Z-scores. A Z-score of -2 means that only 2.5% of players will do better, a Z-score of -1 means that 16% of players will do better than that player. A score of 0 means that 50% of players are better, 50% are worse. A score of +1 means that 16% of players are worse and a score of +2 means that 2.5% of players are worse. [Very simplistic explanation]. These Z-scores are necessary because each goalie sees a different number of shots and it's easier to stop 100% of the shots if you only see three shots than it is to stop 100% of the shots when you see 100 shots. The main point I want to make is that having a Z-score of 2 one year suggests that you will have a Z-score of 1 the next (this is called a regression to the mean).This regression to the mean is quite small, as it is saying that half of the average goalie's save percentage results are due to luck the other half come from skill (in a given season). [Individuals could be quite different]

In translation: Garon's 90% save percentage on the shootout, should be closer to (0.9-0.66)/2+0.66 = 78%r 78% (his career save percentage is: 46/58 = 79%).

Save percentage By Round

Now there are a few things to keep in mind:Statistics by round:

1: 616 Shots. Shooting%: 0.3669

2: 616 Shots. Shooting%: 0.3344

3: 460 Shots. Shooting%: 0.2978

4: 83 Shots. Shooting%: 0.3494

5: 40 Shots. Shooting%: 0.3500

6: 24 Shots. Shooting%: 0.2917

7: 14 Shots. Shooting%: 0.286

8: 11 Shots. Shooting%: 0.364

9: 4 Shots. Shooting%: 0.250

10: 2 Shots. Shooting%: 0

11: 2 Shots. Shooting%: 0

12: 2 Shots. Shooting%: 0

13: 2 Shots. Shooting%: 0

14: 2 Shots. Shooting%: 1

15: 2 Shots. Shooting%: 0.5

Note Round 9+ 16 shots, 25% shooting percentage.

In general later rounds are easier to stop. So if a goalie plays until the 7th or 8th round his numbers should be better than a goalie who only sees the first two shooters.

Shooters:

On the surface it would appear that shooting percentage is all luck as there is no correlation between years. That being said, the graph below could just be the result of playing different opposition.

What is a guy like Garon worth in just the shootout. Well assuming average number of shootouts (10) he wins you an additional 1-2 points (or about $0.75M - 1.0M). [Based on this image , 35% shooting percentage and 80% save percentage]. Of course if you're Edmonton and discovered a way to get to the shootout 3x as often as expected, well then Garon is even more useful.

## 1 comment:

An interesting plot would be a teams shootout winning percentage over the past 2 seasons with their winning percentage in games that don't end in a shootout.

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