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Around SBN: The Week In Worst: When Baseball Goes Wrong

Red Wings vs. Ducks, WCF Preview

The battle for the Clarence Campbell Bowl has come down to the top two seeds in the West, Detroit and Anaheim. I've cranked out the numbers, so let's see who will make it to the Stanley Cup Finals...

How the Red Wings can score: The story with Detroit, as always, is a reliable shooting advantage - the projection is for them to outshoot the Ducks by roughly seven shots per game. Unlike many other matchups, however, these extra shots are coming from close range, medium range, and long range; they're not just taking harmless tosses at the net and hoping for the best. They'll need every shot they get, as J.S. Giguere has been his usual solid self during these playoffs, with a .952 save percentage entering the series, alongside his net-mate Ilja Bryzgalov's .929 mark.

How the Ducks can score: The projections here show that Anaheim's shooting percentage from various ranges is very close to Detroit's, and unless that changes, overcoming the gap in total number of shots would seem to be a daunting task. Note the "Sht % Factor" row in Anaheim's chart, and you'll see that Dominik Hasek has been clamping down on close and midrange shots to a slightly greater degree than even Anaheim's goaltending. Combine that with the limited number of shots that Detroit's allowing (see the "Shots Factor" row), and the Ducks will clearly need to maximize the opportunities that do come their way.

Summary: Toss these two teams together, and you get a gap of 0.40 Goals Per Game in favor of Detroit (2.08 - 1.68).

Outside The Numbers: There are two major factors at play here - the most obvious being the recent injury knocking Red Wings defenseman Mathieu Schneider out of the playoffs. Presumably that's going to impact Detroit's performance at both ends of the ice. The other factor is that Anaheim has outperformed these predictions in each of the first two rounds - against Minnesota in the first, my offensive assessment was on the mark, but their defense shut down the Wild more than expected, and against Vancouver in the conference semifinal, they outscored projections, and clamped down on the Canucks offense at the same time. Whether that's due to their numbers being hampered by that unusual stretch when Giguere, Pronger, and Beauchemin were injured at the same time and they went into a temporary tailspin, or whether the Ducks' playing style just adapts better to playoff hockey, I'm not sure. But I'm likely to give them more credit here than this gap would normally indicate.



Prediction: Even with Schneider out, Detroit's blueline out-Norris's Anaheim's with seven trophies to two. I think Chelios plays a solid series picking up some marginal playing time, the goaltenders at both ends play very well, but Detroit's power play makes the difference. Red Wings in 7 games.
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Table Key:
Shots For = average of shots per game by that team, from the range specified.
Shots Factor = a factor representing how many shots the opposing defense yields in that range (1.24 = 24% more than average, 0.89 = 11% less than average).
Exp. Shots = "Shots For" times "Shots Factor", how many shots are expected to occur within each range.
Sht % = The fraction of shots from within that range result in goals.
Sht % Factor = a measure reflecting how the opposing goaltender handles shots from a given range (0.74 = 26% fewer goals than average, 1.53 = 53% more than average)
Exp. Sht % = "Sht %" times "Sht % Factor", the expected shooting percentage for this matchup.
Exp. Goals = "Exp. Shots" times "Exp. Sht %", the number of goals per game expected from each range.
Values indicative of significantly higher goal-scoring are shaded green, values for lower goal-scoring shaded pink.
All figures represent exponential moving averages, giving greater weight to recent performance. Empty-net goals are excluded.

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Comments

Display:

how "flat" is your exponential decay?

In other words:
We're currently at about game 95, what does:
weight(game 95)/weight(game 0) equal?

Another way of putting this is: how many games in October are equivalent to a game in April?

Is a goal scored in October "worth less" than one scored in November and why?

Do you realize your "chasing" strength of competition due to inclusion of recent playoff series.

by JavaGeek on May 11, 2007 1:24 PM EDT reply actions  

The weighting factor I'm using is 0.97 (each value going back gets multipled by 0.97 per game), so at the start of the playoffs, Game 82 meant about 12 times as much as Game 1 in the rolling averages.

Admittedly, that factor was chosen arbitrarily, and one to-do over the summer is to perform some backtesting against this playoff and last year's to determine what would be a more desirable figure.

As to the strength of competition, I'm not factoring that into these averages, and that's another to-do. As we go deeper into the playoffs I think that becomes less of an issue, in that everybody's playing good teams. Nobody's getting a chance to light up the Kings for six goals in a night...

Bottom line is that this is a "first-crack" for this form of analysis, and if it turns out to have some promise, I'll take a run at revising it further.

by The Forechecker on May 11, 2007 11:09 PM EDT reply actions  

Percentage of the table that comes from the previous series (6 game).
sum(0.97^x,x=0..5)/sum(0.97^x,x=0..93)
= 17.7%

San Jose had a "shot % factor" of 0.53. for 0-9'
The average team should have a shot % factor of 1
If they score as expected (at 50% their rate) their shooting percentage will fall from 22.9% to 22.9%*0.82+12.1%*0.18 = 21.0% or a drop of 10%. With no change in ability. (It fell to 20.0%)

Since there are so few shots taken at 0-9' this isn't too important of an issue, but it crops up in every section. And since one playoff series accounts for almost 1/5th of the results you get a data set that is chasing the previous opponent significantly.

Every team in the playoffs is very different and when you start multiplying different factors these differences can grow exponentially.

I'd be shocked if you could find a weighting that was statistically significantly different than 1.

by JavaGeek on May 12, 2007 12:32 AM EDT reply actions  

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