So I was up the other night thinking there has to be a good new age way to analyze hockey players, ala Sabermetrics in baseball. All I kept thinking is that Plus/Minus could be a very useful statistic, but it doesn't truly measure the goals a player contributes to his team and directly measure the ones in the other team's favor. So I came up with a formula using a player's goals, primary and secondary assists, powerplays drawn and the effectiveness of those powerplays, giveaways and the results of those giveaways, and penalties taken and the result of those penalty kills. The resulting formula is as follows.
((Goals * .5 (assuming that the player scoring the goal is 50 percent responsible for it's actual being scored)) + (Primary Assists * .35 (assuming the primary assister is 35 percent responsible)) + (Secondary Assists * .15) + (Powerplays Drawn * (League number of powerplays drawn / league number of powerplay goals)) + (Shots on drawn powerplays * (league number of shots on drawn powerplays / league number of powerplay goals) + (goals on drawn powerplays)) - ((giveaways * (league number of giveaways / league number of giveaway goals)) + (shots off giveaways * (league number of giveaway shots / league number of giveaway goals)) + (giveaway goals) + (penalties against * (league penalties against / league penalties against goals)) + (penalties against shots * (league penalties against shots / league penalties against goals)) + (penalties against goals))
In my estimation this formula should measure the true number of goals a player contributes to his team or against is, weighted appropriately against league averages. Unfortunately the statistics I require are not available or recorded or I just could find them, so I went through play by plays by hand of the Stanley Cup Finals and recorded stats from those seven games. The results of my formula are as follows.
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| Justin Abdelkater 0.9605 |
| Ville Leino 0.6605 |
| Brett Lebda -0.7473 |
| Ruslan Fedotenko 0.5569 |
| Valtteri Filppula -0.1044 |
| Marc Andre Fleury -1.3834 |
| Brad Stuart -0.4334 |
| Brian Rafalski 0.6717 |
| Chris Osgood 0.1322 |
| Evgeni Malkin 1.6803 |
| Niklas Lidstrom -0.7536 |
| Niklas Kronwall -3.6576 |
| Mikael Samuelsson 0.2632 |
| Phillipe Boucher -0.0395 |
| Henrik Zetterberg 1.8202 |
| Hal Gill -2.0148 |
| Jonathan Ericsson -0.9140 |
| Maxime Talbot 2.2195 |
| Bill Guerin -1.7104 |
| Jordan Staal 0.5421 |
| Segei Gonchar -0.9666 |
| Kris Letang 1.1525 |
| Matt Cooke 1.1789 |
| Craig Adams -0.0341 |
| Marian Hossa 0.3079 |
| Johan Franzen 3.1554 |
| Sidney Crosby 0.6894 |
| Brooks Orpik -1.6174 |
| Mark Eaton 0.3765 |
| Jiri Hudler 0.6482 |
| Tyler Kennedy 1.1920 |
| Tomas Holmstrom 1.3554 |
| Dan Cleary 2.3177 |
| Darren Helm 1.4912 |
| Chris Kunitz -2.4622 |
| Miroslav Satan -0.3753 |
| Rob Scuderi -0.9018 |
| Kirk Maltby -0.1580 |
| Mathieu Garon 0.3807 |
| Pascal Dupuis -0.1534 |
| Kris Draper 0.8807 |
The average is somewhere around 0.2. So by my estimation the most effective player was Johan Franzen, the least effective was Niklas Kronwall, the most effective Penguin was Maxime Talbot, the least effective Penguin was Chris Kunitz.
This is only a rough formula come up with at about four in the morning last night. It seems to skew towards forwards and be innacurate towards defensemen, but seems to work well enough for me. The results are also probably pretty skewed due to the extremely small sample size, but I was too lazy to look at any more play by plays.
Let me know what you guys think.
Connor


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