Stats Based NHL Betting Profile: SDQL

Today’s hockey system looks at NHL teams with low penalty minutes and uses a summative technique to isolate high probability Over/Under plays!

In a recent thread at the EveryEdge sports betting forum, I listed NHL teams who had been called for the fewest number of penalties during the 2014-15 season and started looking for similarities between these clubs based on locations (home or away) and listed odds. The “Under” in hockey betting had been hitting at its highest rate since 2010, not including the lockout shortened 2012 season, and it seemed to me that clubs who took fewer penalties would be good candidates for betting the under. After all, fewer penalties mean fewer power plays for the opponent.


Initial research isolated home underdogs as the most likely candidate and the concept made sense: Home teams in general have fewer fouls called against them thanks to the officials’ human nature, and if a home team is given just a slight edge against an obviously stronger opponent (strong enough to be a road fave), it would even the playing field which could lead to fewer scoring opportunities. The SDQL from the first set of games produced a 62-percent record seen with the following SDQL text at HD and season = 2014 and tA(penalties) < 4 and game number > 20


To bolster play, I was also handicapping the officials for each game using parameters such as HD, dog, home, team specific and then team specific at site.


Using a new summative technique for grouping the ‘N’ value, I decided to have a look at the ‘year’ and ‘month’ tendencies for home dogs who are taking fewer penalties for the past 1/3/5/10/15 games. My theory was that there could be value today on teams who were dirty earlier in the year that were now playing more disciplined hockey.

Research revealed that the “Under” gets progressively stronger up to 10 games in the summative and then falls by a few points between 10-15 games. The questions were whether the fall-off continued further beyond a 15-game range up to a full season game-count and which group created the greatest potential for profit.

Here is a comparison between the 10-game group and my original home dog group that were averaging low beyond ‘game 20’ on the season. The third SDQL combines the ’10-game avg’ with ‘game number>20’ and note that these numbers have adjusted since the research done on March 22, 2015:


tA(penalties, N=10) < 4 and season = 2014 and HD

SU:         54-88 (-0.52, 38.0%)          avg line: 127.9 / -141.7   on / against: -$2,338 / +$1,660    ROI: -16.4% / +8.3%

OU:        47-85-10 (-0.21, 35.6%)    avg total: 5.3


HD and season = 2014 and tA(penalties) < 4 and game number > 20

SU:         23-38 (-0.46, 37.7%)          avg line: 126.8 / -140.5   on / against: -$907 / +$597           ROI: -14.8% / +7.0%

OU:        19-36-6 (-0.45, 34.5%)      avg total: 5.3


tA(penalties, N=10) < 4 and season = 2014 and HD and game number > 20

SU:         50-82 (-0.55, 37.9%)         avg line: 129.3 / -143.3   on / against: -$2,164 / +$1,531    ROI: -16.4% / +8.1%

OU:        43-80-9 (-0.23, 35.0%)      avg total: 5.3


Waiting beyond 20 games was a good idea, eliminating 10 early season games that went 4-5-1 (.555 UN). The full-season average produced a slightly better percentage (.5-percent UN), but the game count for the third group (131) was more than double the games in Group 2 (61).

The average juice for playing these games “Under” would likely be in the ballpark of -125 or -130. It could be lower, but we can’t be 100-percent sure so for the sake of argument, let’s say it was -130. The implied probability (IP) for a bet at -130 is .565 so at .650, we’re getting .85 basis points of value on these plays.


The total outlay on 131 bets at avg. juice of -131 is $17,030…


43 losses at -130 = -$5,590 (that money is gone)

9 pushes at -130 means +$1,170 back

80 wins at -130 = +$8,000 in winnings along with the +$10,400 stake (total = +$18,400)

Push + returns = +$19,570

Profit = +$2,540

ROI (profit/total outlay) = 14.9-percent


… with a 14.9-percent return on our investment, playing 131 games


By running a ‘penalty’ summative anytime at the free sports database, we can get a look at each team’s average penalties per game on the season. It’s worth the extra step to look at each team’s specific performance in certain situations (conference, line range, days of rest, etc.) and you can always stop by the SDQL discussion forum to ask questions about these and other sports betting queries.


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