Scoring opportunities from turnovers

It seems that we as fans just love to lay blame on a variety of players for bad plays that lead to scoring opportunities and every team seems to have one or two players that fans seem to love to pick on. But is it really justified? In an attempt to find the answer to that I decided to take a look at every players giveaways and whether they led to a shot on goal, or worse yet, a goal. In order to eliminate as much of the bias that exists in the giveaway stat that results from game monitors

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Adjusted hit totals

The NHL keeps track of a large variety of statistics, many of which are subjective in nature. The most subjective might be the hits statistic. What really is a hit? There is the pound the opposing player through the glass hit and then there are the almost incidental bumps which depending on severity may be classified a hit by some and not a hit by others. And looking at the stats, there is no real standard because some stats keepers in some cities seem to give players credit for a ton of hits while other cities are far more stingy

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A blast from the past

This may show my age here but I thought it might be interesting for everyone to see one of my first ventures into statistical analysis of hockey statistics. Back in March of 1996, the Leafs traded Darby Hendrickson, Sean Haggerty, Kenny Jonsson and a first round pick (turned out to be Roberto Luongo) to the Islanders for Wendel Clark, Mathieu Schneider and D.J. Smith. Clearly the first round pick and the lost chance at Roberto Luongo made this a bad trade but at the time Toronto media and especially Maple Leafs fans were outraged at the Leafs trading of young

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Even Strength Player Ratings for this season

Yesterday I presented my even strength player ranking system and rankings for last season which clearly created some controversy. Hopefully when I present this years rankings I can clear up a bit more of the concerns that people have with the system. I know some people found some of the results difficult to believe but if I can show some consistency from year to year I think that should help show the value that this rating system can have. So first let me start off by showing the top 40 rated players from this season. Player Team Offense Rating Offense

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My Player Ranking System and last years ratings

When developing my player ranking system I wanted to isolate the ability of an individual player as much as I can and best factor out both who the player is playing with and who they are playing against. I don’t know how many times I hear things like ‘but he has to face the opponent’s best players’ and things like that when people try to analyze how a player is doing. So I am trying to eliminate that. For now I am going to spare you all of the gory details of the process I took but basically I looked

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The need for a Player Ranking System

Hockey is, maybe more than any other sport, a team sport. A hitter in baseball can be evaluated based on a series of one on one battles with pitchers which are largely independent of the ability of the hitters teammates. In hockey this is not the case. Jaromir Jagr may be a great goal scorer but if he didn’t have quality teammates around him the number of goals he scores would be significantly impacted. A great defensive play by Marek Malik causing a turnover in the defensive zone followed by a great breakout pass might be just as important in

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Where are the additional goals coming from?

Jame Mirtle has an interesting post regarding the increase in offense from 2003-04 to this season and where the goals came from. James concludes: It’s also no wonder Gary Bettman wants to keep the number of penalties called per game high for the postseason — without the increased scoring generated on the power play, the notion of a radically transformed, higher-scoring (read: better) league goes up in smoke. Now, as much as I love bashing Gary Bettman digging deeper into the numbers will bring out a different picture. According to James, power play goals have increased 48.2%, short handed goals

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Making Predictions on Final Standings

The push for the playoffs is on and with the extremely unabalanced schedules it becomes important to look at which teams have the more favourable schedules between now and the end of the season to see who has the best chance of making the playoffs. So what I did was look at how many points a team has as of games through last night and compare that to the difficulty of their schedule so far and then based on the number of games and the difficulty of their remaining schedule make a prediction on how many points they should get.

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Beating the best

To win the Stanley Cup, you have got to win in the playoffs and that means beating the best of the rest. That led me to ask the question, who is the best at beating the best? To answer that question I started with my power ranking calculation based on games through last night. Just for reference, here are the updated power rankings. Rank Last Week Team AdjWinP SchedStr Power Rank 1 1 Ottawa 0.696 0.511 0.723 2 4 Dallas 0.604 0.522 0.639 3 2 Colorado 0.565 0.538 0.634 4 3 Carolina 0.683 0.476 0.632 5 5 Calgary 0.594 0.518

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New Algorithm Game Predictions – 1/27/2006

It is a very quiet day on the hockey schedule today so I figured it would be a perfect time to unveil version 2 of my prediction and power ranking algorithms which I have spent the last day or two tweaking. The new algorithms are much more robust and have allowed me to improve my success rates by a couple percent as well as allow me to make predictions on all games. That means no more pick-em games. Here are my (would be) success rates under the old and new systems. Old: Strong: 94 of 129 – 72.9% Good: 144

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