How do we know?

Some of the more regular readers of this blog know I occasionally go off on some wild big-picture discussions from time to time, the greatest being perhaps my snooze-fest post where I applied the guidelines for higher-ed accreditation to junior hockey programs. Unfortunately, it’s long, boring, and probably didn’t mean much to the vast majority of readers who lack the background in this kind of thought. So I’m going to take a step back and try something that might be a little easier to relate to, and discuss business scorecards and key performance indicators. In many ways this is jumping ahead, because you can’t develop these until your vision/mission/values/goals have been established. However, I am going to assume there is already a set of these elements in play, all existing within the envelope of developing hockey players and advancing them to the “next level.” I don’t want to bog this down in specificities related to some league or level (that’s what the comments function is for, so fire away there), and regardless I feel there is probably 80% congruency of thought anyway, the other 20% existing in that realm of constructive and destructive politics that makes this sport interesting (and helps make this blog possible). So, assuming there are some generic goals out there, both on the ice and off, how do we know if/when they have been met/accomplished? Well, that’s what measures are for. I’m not psychic and I don’t have a nifty tricorder from Star Trek, so there’s no way currently to glance at a program and determine their health. Yes, we have some general sense… but intuition is not knowledge. JUNIOR HOCKEY KEY PERFORMANCE INDICATORS Product Measures Winning Pct. League Rank (an ordinal ranking based on playoff and regular season performance) Total Players (those who saw action in at least one game last season) Average Age of All Players Weighted Average Age of All Players (weighs age against games played) Average Age of Rookies (those not playing at this level or higher for more than 20 games the previous season) Average Age of Veterans Source of Rookies - Midget AAA Source of Rookies - Midget AA Source of Rookies - High School Source of Rookies - Junior B Source of Rookies - Other (International, Jr. C, etc) Percent of All Players (Final Roster) Whose Home Town or Last Team is Within 200 Miles of Your Team D1 Commitments – Total D1 Commitments – Hockey East D1 Commitments – CCHA D1 Commitments – WCHA D1 Commitments – ECAC D1 Commitments – Atlantic Hockey D1 Commitments – CHA D1 Commitments – Independent D3 Commitments Number of NHL Draft Picks Number of Players Ranked by Central Scouting Bureau (Final Rankings) Percentage of D1 Commitments Still Rostered with Two Seasons Later (those still playing for their college team and have not washed out) Percentage of D3 Commitments Still Rostered Two Seasons Later (those still playing for their college team and have not washed out) Average HS GPA of Rookies Average HS GPA of All Players Average Top Combined ACT Score of All Players Average Top SAT Score of All Players Total Alumni in Pros – NHL Total Alumni in Pros – AHL Total Alumni in Pros – AA Total Alumni in Pros – International Average on-ice practice time per week Average off-ice practice time per week Number of Tryout Camps Held (Including Final Camp) Total Players Attending All Camps Total Unduplicated Players Attending All Camps Business Measures Total Staff FTEs Total Hockey Staff FTEs Total Business Staff FTEs Total Game Night Operations Staff Total Budget Total Staff Payroll Total Travel Expenses Total Recruiting Expenses (Including Camp Expenses) Total Ice Expenses (practice/game ice only, do not include tryout camps) Total Daily Game Operations Budget (include cost of officials) Total Net Gain/Loss Total Paid Attendance Average Paid Attendance Total Drop Count Attendance Average Drop Count Attendance Percent of Facility Capacity (Paid) Percent of Facility Capacity (Drop) Totals Comps Average Comps Total Paid Season Tickets (exclude billets and other comps, include corporate package deals) Total Ticket Revenue Total Merchandise Revenue Total Sponsorship Revenue Total Camp Revenue Total Concessions Revenue Note: this is not a complete list Most of these measures are what I consider to be of the first order, in that they are basic sums (totals). There are a few second order derivatives, such as the averages, but the ones I listed are pretty common to most people (like average attendance). The next step in performance evaluation is to pit some measures against each other. If a specific goal is to win with a younger team, then divide your winning percentage by your average age. Want to measure efficiency in the front office, then ratio your revenues against your FTEs, or corporate revenues per sponsorship. Also, earlier in this post I said that intuition is not knowledge. Well, data is not knowledge, either, so I caution readers who might interpret these measures (and the data that would come from them) as knowledge. This, in part, goes back to me jumping ahead to measures before first establishing the required vision/mission/values/goals. Data is never knowledge until one asks of the data, “why?” WHY is the data what it is? When you know why the data is what it is, and what impacts it both positively and negatively, then you have something of value. Of course, none of this means anything unless you have something to compare it to, and that’s where transparency comes in. How do you know you’re a high-level performer if you don’t have any peer data? Thus we arrive at the punch line: If an individual team does this, the usefulness is limited. But, if an entire league shares their data, the value of the information increases markedly. For one thing, teams are competitive, and if driven (ideally) by the same vision/mission/values/goals, they are immediately going to step it up both on and off the ice. A basic truth of performance is that it really does matter when/if you’re keeping score. Secondly, for the business measures, it’s in everyone’s best interests to share their best practices. “How did one team improve so much in a given area,” followed by “could their change in process or strategy improve my performance, too?” I’m not saying teams/leagues share all their results with the public. Heaven knows I drown in enough of data as it is, so I’m not particularly interested unless someone wants to put me on the payroll (hint hint, nudge nudge, say no more). Anyway, that’s my school of thought on this. There’s plenty more to discuss, like why these measures, and it’s my hope these types of discussions take place as leagues and USAH go into their summer meetings.