NHL betting how-to: Understanding moneylines to find value
It's not as dramatic as the popular 2015 Netflix documentary "Making a Murderer." Instead of exposing the police and judicial system in rural Wisconsin, we're merely going to bring to light how an NHL moneyline is made.
We started the NFL season with a similar exercise, but football is predicated on point spread wagers, while NHL betting is done primarily on the moneyline. It's more difficult to assign odds for a team to win in hockey than it is to guess what a point spread should be in a football game.
What's behind a moneyline?
Moneylines are a function of win probability. If your team is -200 to win a game, it is deemed 66.6% likely to win since you have to put up $200 to win $100. If you think there's better than a two-thirds chance the favorite is victorious, the team would be worth a bet. The opponent would likely be around +170 on the moneyline or 37% likely to win. If you think the likelihood of winning is closer to 50/50, it's worth a bet on the underdog.
*If you're doing the math and realizing that the two percentages above don't equal 100%, that excess percentage is the sportsbook's rake, otherwise known as "vig" or "juice," and is how sportsbooks make money.
What goes into team ratings?
You can guess the percentage likelihood a team wins each game, or you can be more informed. Moneyline odds are created off a team's market rating, with a starting point of how likely the team is to beat an average NHL team.
There are three main pieces of information you can use to rate teams.
- Last season's results/metrics
- The regular season point total betting market
- Current season results/metrics
Obviously, with days to go before the regular season starts, we have no information for the third piece. As the season progresses and more data points are accrued, that category can become more important and take up a larger piece of the information pie.
Last season's metrics are relevant because they're tangible on-ice results. Most teams see them change each season, but last year, only the Blues, Ducks, Blue Jackets and Blackhawks saw their underlying metrics crater. On the positive side, the Hurricanes, Devils, Maple Leafs, Lightning and Kraken had massive upward changes in how they played at even strength from 2021-22 to 2022-23.
What's an average team?
In football, baseball, or basketball, an average team is expected to finish at .500. In the NHL, because three points are assigned for a game that goes to overtime, that number is weirder. Last year, the average NHL team accrued 91.43 points. In the Eastern Conference, you needed 92 points to make the playoffs. In the Western Conference, meanwhile, 92 points resulted in barely missing the playoffs because there were three teams with 60 points or less, which boosted everyone else's total on that side of the league.
To rate each team relative to the league average, we look at a handful of sportsbooks' regular season point total markets and take the average for each team. Carolina led the way with a projected point total of 107.5.
We'll then take each team's total and divide it by 91.43. Here's how that looks for the Hurricanes:
- 107.5 / 91.42 = 1.176
An average team - like the Jets or Senators - is lined at 91.5 and a 1.00 rating. So, the Hurricanes have been assessed as 17.6% above average, while the Sharks' super-low point total of 66.5 has them 27.2% below a league-average team.
Translating regular season points into market ratings
| Team | Regular Season Point Total |
Rating |
|---|---|---|
| Hurricanes | 107.5 | +17.6% |
| Devils | 107 | +17.0% |
| Avalanche | 106.5 | +16.5% |
| Maple Leafs | 106.5 | +16.5% |
| Oilers | 106 | +16.0% |
| Stars | 105.5 | +15.4% |
| Golden Knights | 102.5 | +12.1% |
| Rangers | 102 | +11.6% |
| Kings | 100.5 | +10.0% |
| Bruins | 100 | +9.4% |
| Panthers | 98.5 | +7.8% |
| Penguins | 97.5 | +6.7% |
| Wild | 96.5 | +5.6% |
| Lightning | 95.5 | +4.5% |
| Flames | 94.5 | +3.4% |
| Kraken | 93 | +1.8% |
| Sabres | 92.5 | +1.2% |
| Islanders | 92.5 | +1.2% |
| Jets | 91.5 | 0.0% |
| Senators | 91.5 | 0.0% |
| Canucks | 89 | -2.6% |
| Predators | 87 | -4.8% |
| Red Wings | 85.5 | -6.5% |
| Capitals | 85 | -7.0% |
| Blues | 84.5 | -7.5% |
| Coyotes | 76.5 | -16.3% |
| Flyers | 75.5 | -17.4% |
| Blue Jackets | 73.5 | -19.6% |
| Canadiens | 72 | -21.2% |
| Blackhawks | 71 | -22.3% |
| Ducks | 67.5 | -26.1% |
| Sharks | 66.5 | -27.2% |
Creating moneylines
Here's the math portion of the program.
Now that we have each team's percentage against an average team, how do we compare them to each other?
- Let's use the season-opening matchup between the Predators (-4.8) and Lightning (+4.5), take the rating difference (9.3) between the two teams, and apply it so that the difference is reflected across a 50/50 proposition:
- Lightning's chance of winning: 0.5 + (9.3 / 2) = 0.546
- Predators' chance of winning: 1 - 0.5465 = 0.456
- Since these games are not played on neutral ice, a home-ice advantage should be applied. Last season, home teams went 687-625 or 52.3%. That's actually lower than the previous season of 704-608 of 53.6%. If we split the difference over 2,624 games, home-ice advantage has meant 3% to the home team's win probability (WP).
- Lightning's chance of winning: 0.546 + 0.03 = 0.576
- Predators' chance of winning: 1 - 0.576 = 0.424
At that point, using a standard win probability to moneyline calculator found online, we know that a fair price for the Lightning is -136 and the Predators +136.
Here's how that game breaks down relative to the currently available line:
| TEAM | FAIR PRICE | WIN PROB. | ODDS | IMPLIED PROB. | EDGE |
|---|---|---|---|---|---|
| Predators | +136 | 42.4% | +150 | 40% | +2.4% |
| Lightning | -136 | 57.6% | -170 | 63% | -5.4% |
At the price of +150, betting on the Predators would return a 2.4% profit over 100 games. It might not be worth backing the underdog in Game 1 of 1,312, but finding an edge isn't easy, even when you know how to look for it.
Matt Russell is the lead betting analyst for theScore. If there's a bad beat to be had, Matt will find it. Find him on social media @mrussauthentic.