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OPINION: Advanced statistics too often overlook most important number—wins

J.D. Killian Avatar
November 18, 2015

“Figures often beguile me…There are three kinds of lies: lies, damned lies, and statistics.” – Mark Twain

Hockey statistics lie.

Before my much anticipated lynching, allow me to clarify my position. While they may be fun to discuss, advanced statistics fail to tell a complete and accurate story about a game, as well as a team. Saturday’s Avalanche game against the Montreal Canadiens presents a prime case.

The Colorado Avalanche, seventh place in the Central Division, faced the league leading Montreal Canadiens at Bell Center. If one missed the first period and merely looked at the statistics, the information made no sense. The Avalanche led 3-1 on only eight shots against the Canadiens’ 18 shots. The shot count alone would indicate the Avalanche were dominated throughout the first period and that somehow the Avalanche managed to just get off a couple of lucky shots. Looking at the statistical models on War on Ice, the Avalanche appeared dominated.

However, watching the game provided a very different experience. The game showed lots of back and forth motion. Possession between the teams appeared relatively even, with Montreal getting more dangerous shots in bunches, while the Avalanche countered, only having to really fight for positioning. But the statistics told a different story.

I decided to re-watch the first period, keeping track of only two things: the amount of time the puck stayed in the neutral zone or Avalanche’s offensive zone and the number of shots from near the blue line for both teams. Despite the high number of shots logged against Avalanche goaltender Reto Berra (leading one to think the Canadiens had set up camp outside of the Avalanche net), the puck actually spent 10:39 in neutral territory and the Montreal end of the ice, despite the Avalanche having the only penalty kill of the period.

As for blue line area shots, the Avalanche threw only three shots from there on net, while the Canadiens managed more than ten. There are two ways to look at the blue line shots. One, the Avalanche defense kept the Canadiens from getting high percentage shots down low by the net. Two, the Avalanche need to put more shots on net. Yet, the Avalanche scored well in the first period (three goals), so it’s hard to fault their shot choices.

This exemplifies the fault within most modern day hockey statistics. Many of the calculations center on shots taken, blocked shots, shots that went wide, and scoring chances. The challenge? These are all subjective. There is an official scorer who determines what qualifies as a shot or a scoring chance. However, during the preseason when there were no official scorers, even between television stations showing the same game, there were marked differences in shot totals.

During the broadcast of the first period of Saturday’s game, at one point the official scoring showed the Avalanche had four shots compared to twelve for the Canadiens. Nearly five playing minutes later, the official scoring showed the Avalanche only had three shots. Marc Moser, play-by-play announcer for Altitude Radio, even wondered how the Avalanche could have lost a shot in the game. Obviously, there’s plenty of room for error with counting shots.

Shots on goal are technically supposed to be counted any time the puck approaches the net in such a way that it would score a goal if it wasn’t touched by the goaltender. Shots that are deflected or blocked, hit a goalpost, or sail wide are not counted as shots on goal, but fall into different subgroups such as Blocked Shots. However, what qualifies as a shot seems to vary. When I counted the Avalanche’s shots in the first period, I came up with ten shots on goal: three ended in goals, four sailed wide, and two were blocked. It became very clear shot count was subjective. Yet even that doesn’t tell the whole story.

One of the impressive aspects of Saturday’s game centered on the number of Avalanche players fighting for possession in the neutral zone. Avalanche players sprawled on the ice to prevent the Canadiens from interrupting an Avalanche drive into the offensive zone. Others battled to keep the puck and pushed opposing players off the play. I saw two Avalanche players dive to block offensive turnovers. Colorado showed they could be tough in fighting for possession. Montreal, one of the best teams in the NHL, contested Colorado’s pressure and tipped lots of passes.

Neither of these efforts counted in statistical analysis, especially because those actions happened in the neutral zone. Effective neutral zone positioning functions as a key to some teams’ strategy, yet most statistical measurements overlook neutral zone efforts and focus instead on shots.

Corsi, the much touted statistical measurement for teams and players which is based on shots (i.e., attempted, blocked), showed some very interesting data concerning the Avalanche/Canadiens game. According to War on Ice, Blake Comeau suffered one of the worst ratings among Avalanche players in terms of Corsi measurements, yet he was one of the more impressive players in the game(see Avs Game 17 Grades).

Also, according to the Corsi rankings, Colorado defensemen Erik Johnson and Francois Beauchemin ranked similarly as Comeau even though they both had a plus four rating (meaning the team netted four points over the time they were on the ice). Of the Avalanche defensemen, Brandon Gormley had the best Corsi number. While Gormley adds good value for the Avalanche, I don’t know of anyone who thinks he should have the top pairing minutes of Beauchemin or Johnson at this juncture.

Only three Avalanche players ended up in positive Corsi numbers for the game: forwards Matt Duchene, Nathan McKinnon, and Mikhail Grigorenko. Corsi analysis fails to allocate appropriate values for good defensive effort, and while shots are one measurement tool, they tell an incomplete story. A player’s numbers lack the ability to accurately reflect the level of competition they played against. For example, the first line defensive pairing will frequently encounter the best scoring line for the opposing team, skewing their ratings.

The biggest challenge with statistical analysis for hockey relates to the ability to manipulate the numbers to support any argument. Corsi analysis works best for evaluating performance over the long term. However, one could make the argument, after looking at the cumulative Corsi numbers for the Chicago Blackhawks and the Los Angeles Kings for the past five years, the teams’ play dropped off during the playoffs. One could also argue that their regular season statistics weren’t a reliable indicator of whether they would win the Stanley Cup. In fact, last year the Los Angeles Kings’ Corsi numbers charted excellently, yet the team failed to make the playoffs. There’s a large grey area around both the interpretation and the proper application of advanced statistics

The ultimate number to consider has been, and will remain, wins verses losses. That’s the only statistic that matters.

Hockey statistical analysis has a long way to go before it can really give an accurate assessment of a team, a game, or even a player. The analytics for the Montreal/Colorado game tell a very different story from what one saw watching the actual game. Maybe it’s because hockey is such a complex, fast-moving game, with lots of variables; math fails to accurately represent the events. While baseball has had over 150 years to develop it’s statistics—and still struggles to accurately reflect the course of a game and/or a season—hockey analytics is still in its infancy. Hockey needs more time to develop a better way of evaluating teams’ and players’ levels of play. Until then, people need to remember that the numbers may be fun, but they can’t compare to actually watching the game.


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