Player Overall Value

Player Overall Value

An all-encompassing stat intended to evaluate the value of a hockey player.

The new trend in the statistical analysis of sports is all-encompassing stats. It began in baseball, the most statistically developed sport, with the introduction of WAR, or Wins Above Replacement. This stat is a calculation of a multitude of statistics, calculating the amount of runs that they add, and then this is linked to the amount of wins that they add. This is better explained here, by Fangraphs. 

WAR trickled down once it was developed by baseball, with different versions of all-encompassing stats making its way into basketball, with PER (Player Efficiency Rating), and VORP (Value over Replacement Player) becoming more mainstream statistics. These stats are popular because they make it easy to compare two players, and these stats can be applied to any era, making for interesting debates (Is Mike Trout better than Babe Ruth?). 

With the fluidity of hockey, statistics like these have not caught on in the mainstream discussion. A WAR model has been developed by @DTMAboutHeart, a writer for Hockey Graphs, which you can read here. Additionally, a metric called Game Score was developed by Dom Lusczcysyzn, a writer for The Athletic. These are the early efforts of making a one-number-covers-all in hockey, but neither has had much success in catching on, Dom's being slightly more successful in the analytics community. This is largely in part because of a reluctant leadership group in hockey, a group that is reluctant to mention Corsi as a valuable indicator of a player's success, but more than willing to tout a player's grit and toughness as valuable aspects of a player's game. 

While single number statistics have their limitations, they can be an extremely useful tool in determining what a player is worth. Player Overall Value is an attempt to join in on the single stat metrics. POV follows a similar mold as Dom's Game Score, with a few added wrinkles, and with different weights. To make this statistic, I used the following stats:

• Goals
• Assists
• Shots
• Hits
• Blocked Shots
• Giveaways
• Faceoff Differential
• Penalty Differential
• +/-
• Corsi Differential

A few things of note about how these statistics played themselves out:

• Corsi ended up being a driving factor in POV. For the uninitiated, Corsi is the most popular statistic to measure possession while a player is on the ice. It is measured by shot attempts, which includes blocked shots and shots that miss the net, as well as shots on goal. To calculate this, it is simply the amount of shot attempts by the player's team while he is on the ice, subtracted by the amount of shot attempts the opposing team has. To contextualize this, a player with a 60% Corsi For Percentage means that while he is on the ice, his team takes 60% of the shot attempts. 
• This stat played a role relatively equally to goals and assists in determining the value. While this might be a red flag to some, I believe Corsi can ultimately be used to explain most events that happen in a game, because if you control shot attempts, you will generally have more scoring chances, which leads to more goals. This can be a good indicator of future success. 

Here is what the equation ended up being, with the weights:

.75*Goals + .65*Assists + .1*Shots + .05*Faceoff Differential + .075*Blocked Shots + .05*Hits + .05*+/- + .05*Corsi Differential - .1*Penalty Minutes - .1*Giveaways

Once the weights were determined, I ran this for the 2016-17 season, to do a test to see if it lined up with perception of players. Here is the rankings of players sorted by Player Overall Value for the 2016-17 season. 

For the most part, this lines up about how one would expect. Some things that might jump out at people is David Pastrnak being so highly ranked. This goes back to Corsi explaining a lot of the statistic. The Bruins and the Kings dominated possession metrics, being 1st and 2nd by a healthy margin over the 3rd place team, the Canadiens. Therefore, good possession metrics combined with a good season by counting stats has David Pastrnak among the elites in the sport. 

To evaluate goalies, it was a little bit simpler, as their sole job is to stop the puck from entering the back of the net. The two numbers used in this were saves made and goals allowed. On the first run of this, Sergei Bobrovsky was ranked first by a decent margin, which makes sense, as he dominated the league last year. 

While this stat is far from perfect, it is a good tool that can be used to evaluate players and compare them to each other.