It seems almost unnecessary and somewhat obtrusive to rehash this point again, but Moneyball didn’t argue simply that walks were to be desired, but rather that baseball put an undue premium on batting average that devalued on-base percentage, allowing the astute (Billy Bean in this instance) to acquire above average players for below-average-player prices.
While the book – and the subsequent movie – were not the impetus for the rise of analytics in professional sports, they’ve served as a foundational pillar for the average fan’s acceptance and belief in their “mysterious” and “mythical” powers. While baseball has garnered the most attention, basketball may be the most advanced with camera technology that can actually track every single player’s movement on the court in real time. The NFL’s “All-22” game film allows for a similar dissection, though its constant moving parts and detailed play schemes suggest a greater gap between advanced statistics and the public’s acceptance of such.
Of the four major sports, the NHL seems to lag behind. Recent analytical history has concluded that the “best” way to ensure long-term success (as much as any analytical tool can), is to possess the puck. This seems like hockey’s best John Madden impression – “to win the game you have to score the most [goals]” – but the sport’s pace, on-the-fly substitutions and varying rates of scoring opportunities make deeper dives extremely difficult.
Read about hockey over the summer and the term CORSI likely made its way into the article (and every journalist’s lexicon). The statistic is a simple measurement of puck possession where no precise measurement is available. A CORSI above 50 means that this particular player or team attempted more shots than the opposition when this player (or team) was on the ice. As of now, it’s impossible to track granular puck possession down to the second – at least until the NBA’s cameras invade the NHL – so CORSI serves as a decent substitute. Fenwick – CORSI’s younger brother – applies the same formula, but removes blocked shots in an attempt to decipher those shots that have a chance of finding the back of the net.
Regardless of one’s feelings on analytics, the movement’s most important feature is its verifiability. Studies have confirmed that attempting more shots than the opposition has a positive correlation with winning. This was the holy grail of the industry – the discovery of a crucial component of success. But it’s just the start of what will hopefully be something much greater in scope.
We know what good teams do that make them good, but we don’t know how those teams do what they do. Tracking CORSI or shot-suppression or shooting percentage or anything else shows after-the-outcome effects that can be used to predict future success. But it’s like saying that a MLB player who racks up seven WAR in one season will automatically do it again, simply because he just did. How did he get there? What makes those particular skills – desired by advanced metrics – stand out above the fray?
Simply put, why are the players who consistently post incredible CORSI percentages able to possess the puck longer than their teammates or opposing players? What types of offensive and defensive systems produce the best results? Most importantly, if every team now understands that puck possession is a fundamental key to success, what comes next?
If the NHL becomes more homogenous – a potential result of general managers identifying players who portend puck possession success – where will the next market inefficiency be? A recent study held up as an analytic bullet point suggested that carrying the pick into the offending zone leads – on aggregate – to a higher likelihood of scoring. Therefore, most teams will likely look to improve in this regard, like a chess game designed with a trap door. Defensive systems have likely already planned to counteract this shift, creating more neutral zone turnovers until the entire game is played in the 50×85 center portion of the ice.
Ex post facto analytics can only scratch the surface. What the sport needs are dedicated people that can blend the research with understanding of hockey system functions in order to explain how players such as Jonathan Toews and Drew Doughty excel. Justin Bourne – a former player turned writer – has been instrumental into looking at organizational philosophies that may dictate why their possession numbers skew one way or the other. Tyler Dellow – a former lawyer turned blogger – did so as well, but his blog archives and future studies were purchased by the Edmonton Oilers in an effort to limit the negative feedback to the organization’s servers. Others, such as Corey Sznajder, have opted to manually track individual games, in hopes that providing the necessary data leads to a wave of innovative uses.
Most organizations, at least after this summer’s influx of analytics hirings, have an idea of where to turn next, how to blend the statistics into their scouting departments to create projectable reports for amateur and current major league players. But we’re not yet at the point where analytics have overtaken old-school mentalities, in large part due to the former’s fallibility when it comes to understated variability. If you know the end result, why should you even play the game? For those invested in advancing hockey toward its sabermetric future, the how is just as important as the why.