Numbers Game – Statistical Analysis & Management

Working with a company that has been family-owned and operated for several decades has provided me with a sort of second-hand history lesson in business-practice and management. Through the lunch-room and water cooler stories of the family's octogenarian patriarch and his son (the company’s current President), all with a heavy coating of my father’s 35 years of overlapping experience on both sides of the retail game and his tendency to comment on and contextualize everything I am exposed to, I’ve been privy to a mini crash-course in the experiences, culture and evolution of a rapidly changing and increasingly challenging business landscape.

A micro sample even within our respective market, the lessons I have learned are nonetheless universally applicable to competitive business models, which must perennially react, evolve and strive to be ahead of the curve in their given sector. Among the leading trends of the past couple decades, in direct correlation with the advancement and proliferation of computer capabilities of course, is the increased emphasis on and sophistication of market research and statistical analysis. Gone are the days in which sales was a game of throwing mud at the wall to see what would stick; today, every item must be carefully studied and micro-managed in order to exploit every shred of opportunity available in a crowded and inherently limited marketplace.

Similarly, NHL hockey has become increasingly professionalized at all levels (from management to coaching, scouting, even player personnel) in response to a progressively more competitive market for all involved. Magnifying and intensifying these pressures, the CBA and its Salary Cap structure place even greater onus on roster decisions, as shortcomings and mistakes can no longer be corrected by throwing money at the best available free agents. Where assets remain limited and funds are suddenly capped, a system that was once dominated by deep wallets suddenly and necessarily converts to an obsessive search for value.

Evinced by the emergence of a burgeoning field of study devoted to the statistical analysis of hockey (see Hockey Analytics, Puck Prospectus, Behind the Net, Hockey on Paper, Objective NHL, Hockey Numbers, etc.), the trend towards more sophisticated means of talent evaluation has also spawned the solicitation of NHL organizations by consulting groups such as Coleman Analytics. The pioneering endeavour of statistician, Richard Coleman, and former Winnipeg Jets and Chicago Blackhawks GM, Mike Smith, Coleman Analytics is breaking ground in what could prove to be a viable long-term resource for all NHL organizations.

The key to analytics, which Smith himself is careful to distinguish, is that they are not simply statistics. Stats are available in near infinite variations, limited only by the ability to objectively measure some facet of the game, but raw data can only tell you so much: the significance of analytics, on the other hand, lies in the ability to shape, contextualize, and draw relevance from the numbers. Analytics look to dig beyond the surface to discern the objectivity behind the numbers and to engineer complex new criteria by which a player’s relative impact or value can be assessed. A terrific complement to the work of an NHL GM (already tasked with formulating such guesses), analytical studies effectively present mathematically supported conjectures.

Numbers on a spreadsheet can never replace the eyes and instincts of managers and evaluators though, nor should they; according to Smith himself, his services are not to be relied upon independently of traditional forms of evaluation, but as an added layer of intelligence and confirmation (or refutation) of what you already think you know. The bottom line is that NHL organizations, like other businesses, have a responsibility to seek out every possible edge they can in order to best position themselves within their marketplace, and this new breed of analytical consultant may offer just that (Smith claims that, in 3 seasons in the NHL, 17 of 20 teams using his services have reached the playoffs).

Most infamously instituted by Okland A’s manager, Billy Beane, in the form of Sabermetrics, the analytical movement has been slower to take hold in hockey than in other major team sports. This, Smith suggests, is at least in part the result of a ‘conservative’ hockey world that is often slow to change, but one whose general acceptance of analytics as a practical tool is bound to increase as the field of study reaches new levels of depth and refinement and, more importantly, as it becomes associated with success.

At the heart of it all, however, the Salary Cap is the catalyst that made analytics a viable and desirable resource for NHL teams in the first place: putting the onus on GM’s to assemble a winning roster with capped funds, the new structure of the NHL turns GM’s from simple spenders (particularly in the larger markets) to high-risk investors who must very selectively divvy up their resources in order to obtain the highest possible returns. Players thus effectively become stocks, thoroughly studied in terms of their relative market value, expected yield and cost-effectiveness.

Pivotal in determining a solid investment? Structured analytics, of course.