Every October, the same pattern repeats. A team starts 7-1-0 and people declare them Stanley Cup contenders. Another starts 2-6-0 and fans demand the coach be fired. The betting markets overreact to these small samples, creating opportunities for disciplined bettors who understand regression to the mean.

Small Sample Size Variance

Eight games represents less than 10% of an NHL season. Teams can run hot or cold for short stretches purely through luck - favorable bounces, timely saves, fortunate schedules. The public treats early results as predictive when they're largely random noise. Smart bettors fade the extremes and bet on talent reverting to expected performance levels.

I actively look to fade teams that start extremely hot, especially if their underlying metrics don't support the record. A team winning one-goal games at an unsustainable rate will regress. Similarly, talented teams starting slow often present value as betting underdogs before the market corrects.

Schedule Impact

Early season schedules vary dramatically in difficulty. A team might play their first 10 games against weak opponents and look dominant, while another faces playoff teams exclusively and struggles. Lazy analysis looks only at records rather than strength of schedule context.

I maintain a spreadsheet tracking each team's opponents through their first 15-20 games. When a hot team is about to face a brutal schedule stretch, their odds haven't adjusted yet. Conversely, struggling teams coming off a tough schedule facing easier opponents create value betting opportunities.

Goalie Variance

Early season goalie statistics are wildly unreliable. A goalie might post a .950 save percentage through eight starts and regress to .915 by December. Betting teams because their goalie is running hot early leads to painful losses when regression hits. Focus on process and talent, not results-based small samples.