In order to empirically calculate the most miserable fan base in the NHL, a lot of variables were input into a formula that gave us a fan misery localizer, because I hilariously wanted to abbreviate it as FML (who said stats aren’t fun?). For that to happen, several calculations had to be made; but first, I had to gather my raw data.
As I mentioned in the main article, I used average ticket price, average attendance percentage figures, average beer prices, home record, and meticulously combed through each individual team’s records to calculate how many goals were scored at home.
The average beer prices chart was helpful enough that it provided a solid amount for the cheapest beer, and its smallest available container; so I simply divided the amount by number of ounces to yield the cost per ounce of beer.
In order to calculate the cost of wins per home game, I followed a fairly simple formula; I took the average ticket price, multiplied it by the number of home games in the regular season (41 to be precise), and divided by number of wins at home. Overtime losses and regulation losses were discounted, as they did not fit the strict definition of a “win.” I followed the same formula for cost of goals per home game. This was intended to be a simulation of how much, on average, it would cost a person to see their team win and score should they attend every single home game, sit in an average seat, without purchasing a season ticket. These are the amounts you see in the main article.
Once I had that data, I calculated the average league-wide cost of watching your favorite team win and score at home (for those who are wondering, $344.57 and $63.42 respectively).
From there on, I used the average cost per win, compared to each individual team, and integrated the attendance figure as part of the calculation. This is meant to simulate what your share of the team’s success is monetarily, so for an arena that is regularly attended at 75% capacity, the fans are responsible for 125% of the cost. I used the following formula:
(100 / attendance) x (team cost of win / average cost of win) = relative cost per win (RCW)
This was integrated into the main calculation formula, with the resulting score accounting for 45 per cent of the FML.
And I followed the same formula for goals:
(100 / attendance) x (team cost of goal / average cost of goal) = relative cost per goal (RCG)
The goals area accounted for 15 per cent of the FML, as teams can win by a one-goal margin, and the excitement of scoring a goal is integrated into the excitement of a team getting a big win.
In the beginning stages of planning this article, I jokingly said that I would include the beer price as a metric, but the more the article got traction, the more I thought of including it and taking it seriously; since it makes a loss or goal against easier to swallow, no pun intended. In my very rudimentary model, I had to compare number of wins over number of losses, while not discounting altogether that people also drink during wins; therefore, I used the following formula:
(price of beer per ounce x (1 + (number of wins at home / number of losses at home (including OT)))) = Beer variable (BV)
I fixed the formula so that it would subtract 5 per cent of the total score in order to make it reflect the ease of handling a loss, leaving us with a dynamic formula accounting for 55 per cent of the FML.
Lastly, the x-factors, or “worry factor”; which were thoroughly debated and assigned a numerical value between 1 and 100, added onto the calculation as a simple percentage. For the sake of thoroughness:
(assigned number / 100) = worry factor (WF)
Since the FML is a slight variation on a weighted score, the final amount for the FML was calculated as follows:
((RCW x 0.45) + (RCG x 0.15) + (WF x 0.45)) – (BV x 0.05) = FML
And that, kids, is how you science.
Pedro Rengel
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