Welcome to my Second Quarterly Hockey Betting Report of the 2021/22
season, also marking the end of the first half (cut off at Jan 21). Unlike my weekly reports, the
quarterly report delves deeper into my team-by-team results. It
should be noted that I’m not betting with real money. These are all fictional
wagers in a spreadsheet. If you’re betting with real money, you should not be
betting on every game, only the games you like the most. Whereas I’m betting on
every game, every over/under, because it provides a complete dataset for
macroeconomic analysis. To view my first quarterly report, click
here.
In the second quarter (henceforth referred to as Q2),
I started using Draft Kings lines more often because they are released early
and tend to offer a little extra value. Though traditionally, I’ve gravitated
to Vegas casino line offerings, where only one team is listed +1.5 or -1.5 on
the puckline. Some books do offer a more diverse set of options, but I’ve been
doing it this way since the beginning (this whole experiment started after a
trip to Vegas with my dad in 2019). For the sake of continuity and simplicity,
I prefer the “one or the other” method for pucklines. Whichever teams gets
listed -1.5 goals is referred to by me as the “favorite”, even if the
moneylines are the same. When you see “Washington -1.5 goals” that only refers
to those games where they were “favored”.
My 1st Quarter Profit: $8,927
My 2nd Quarter Profit: $7,206
After remarkably few Covid postponements in Q1, it
was chaos in Q2 as the latest Omicron variant swept through North America. I
always record my bets the day before games, which is a questionable practice in
a pandemic environment. It’s tough when you make a bet, only to see the team
you’ve bet on lose key talent on gameday. If you’re betting real money, there
are ways to “cash out” of your position when important pieces go into Covid
protocol. Some books literally offer a “cash out” option (likely for a small
loss), but you have the option of just betting on the other team to get most of
your money back.
Since I’m not betting real money, I don’t cash-out
bets after Covid announcements. It would violate my betting experiment parameters to place opposing
wagers on the same game. The advantage of betting 24 hours before a match is
that you can get better line offerings. Sure, you often don’t have a confirmed
starting goaltender, and players can test positive for Covid the next day. In
the long run, you should benefit from Covid/starter announcements as often as they hurt
you. In week 10, the medical situation wounded me, with some favorites losing
key players after I had already logged my bets. But paying attention to medical
reports absolutely benefitted me in the 3 weeks that followed, banking over $3,700
in week 11.
It wasn’t just individual players who were shut
down, but over 90 games were postponed entirely. Yes, I do give myself a full
bet refund when a game gets moved to a later date. Games were not only being
delayed because of outbreaks, but also because of attendance restrictions at
certain arenas. In week 12, Ottawa, Seattle, Vancouver, Montreal, and the
Islanders all played zero games. That caused bigger problems in fantasy hockey than it did for bettors.
The two teams that climbed the most in my betting
power rankings in the second quarter of the season were Montreal and Vancouver,
but for very different reasons. I was a little too bullish on this Habs team
entering the season given their Stanley Cup final appearance, but was also
aware that there would be some regression without Price, Danault, or Weber in
the line-up. Once I was fully aboard their downward spiral, they became extraordinarily
profitable. The Canucks on the other hand got red hot after firing their coach,
and I was aboard that movement quickly.
The team that dropped the most in my power rankings
was the Ottawa Senators, when they improbably followed up a 1-12 stretch with 5
wins in 6 games against some of the league’s best teams. It’s always perilous
when a team pulls out of a nosedive that quickly and spectacularly. Of course,
that’s when I started laying more money on the Sens to win, and they reverted
to sucking. It was a perfect storm. Washington and Carolina each dropped 10
spots, attributable to 10%-12% drops in winning percentage. You can get a more
detailed breakdown of my results for each team below.
My 2nd Quarter Results: (L2S is the rate of
return for the previous 2 seasons)
The first half was dominated by home teams and
favorites, which is somewhat of a redundant statement given that home teams
tend to be favored. It would be more accurate to say that favorites are
delivering dough aided by the success teams have had on their own ice
(winning 55%, higher than either of the 2 previous seasons), but road favorites
are also getting the job done. Granted, there generally needs to be a
significant disparity between two teams for the visitor to be favored. It makes
sense that home ice advantage would increase once fans were allowed to return, after so many games were played in empty buildings.
If you bet $100 on every moneyline favorite up to
January 29, you won nearly $6,000. That’s substantially higher than this point
in the 2 previous seasons. The 2019/20 season was underdog wonderland, with a
straight line pointing consistently downward for the last ¾ of that schedule. Ever
since Covid hit in March 2020, there was a complete reversal of that trend, and
favorites became much more profitable, though did trend downwards in the 2nd half. One of the big contributing factors was decreasing payouts on favorites.
As bettors were flocking to the teams most likely to win, sportsbooks are
forced to tax the lines above expected value, otherwise they’d lose too much
money.
The most glaring characteristic of the second
quarter were favorites and overs. If you just continuously laid money on
favorites, both moneyline and puckline, you would have been rewarded. Goal
scoring didn’t start to really pop until the last week of November, but prior
there had been an increase in empty net goals that was driving pucklines -1.5
goals. In 2021, I only really hit those favorite pucklines against teams
who were nosediving (like Buffalo). Smashing pucklines was also the
primary driver of my Q1 Coyotes profit.
Increases in goal scoring have been helping
favorites -1.5 as the biggest periods of sustained growth correlate to weeks of
increased scoring, most especially late November and the post-Christmas period.
That growth did eventually level off as the scoring started trending down. If
you put the overs on the same chart as faves -1.5, you notice the similarities. (More on over/under discussed below). I only banked $673 on the puckline -1.5
goals in the second quarter, versus $6,711 on favorites moneyline. Those
pucklines caused me some heartache, whereas favorites moneyline have been
crushing it all season.
For Christmas Santa delivered me a hot hand for
betting on NHL games that lasted deep into January. Actually, my hot run
started approximately Dec 18, which coincides with the creation of my “GameSum”
worksheet in Excel, which breaks down the probabilities of all the different
outcomes and measures them against the implied probability of the betting
lines. That was also when my over/under algorithm was born (existing within
GameSum). It’s all just a very basic estimate for measuring line values. Keep
it simple.
My new methodology ostensibly became highlighting a
game, pressing a button, reviewing probabilities, and making a judgement call.
I mostly blindly followed the recommendations for over/under, but in terms of
winners and losers, it was more of a “consultation”. This didn’t lead to a
major strategy shift, as I laying heavy money on favorites throughout December
and January, but it would convince me to lower my bet size when the line was
“off”. It also improved my decision-making process on games involving teams who
were evenly matched.
Over/Under
This has been my inaugural foray into over/under
betting, and I waited until week 10 before creating my own algorithmic method
to guide my selections.
My October picks were basically educated guesses. In November, it
evolved to checking my previous results, and making course corrections based on
previous performance. It was important to build up a sufficient sample size
before attempting to construct an algorithm. On Wednesday December 15, I
started a new “Game Summary” worksheet that summarizes probabilities for each
team for each game.
Whenever I’m creating a new algorithm for anything,
I always start with very simple models. If the results are good, then there is
no need to add layers of complexity. For my first over/under method, take the
last 10 games played by each team, and measure what % of games they went above
or below the total on the line. Then you calculate the implied probability of
the betting line and subtract it from the percentage over or under (pushes
don’t count). Bet on the positive value. Bet double when there’s significant
positive value. That’s it. Pretty simple, anyone can do it.
As goal scoring starts to trend in either
direction, the sportsbooks will adjust their lines higher or lower to
compensate. If betting lines start moving up, then the win-loss record of either
overs or unders from the previous games becomes misleading. For example;
pretend every game gets set at 5.5 goals and 70% go over. If every line then
gets bumped to 6.5 goals, you can’t then say there’s a 70% chance of going
over. You need to recalibrate. A team might go over 5.5 goals 70%, but over 6.5
goals 55%. That’s why my November method of checking account balances on overs
or unders was a flawed method. That bias needed to be eliminated.
The graphs above chart goals per game per week, and
my own over/under betting results versus if you bet $100 on every over and
under. I came into the season ready to bet overs, but quickly jumped aboard the
under bandwagon. Week 3 was the lowest scoring week of the season, then the
goal scoring trendline started pointing up. There were 5.9 goals per game in
November, but the trendline began bending upwards in the final days of the
month. From doing my weekly reports, I saw the overs trending early in December
and jumped enthusiastically from one bandwagon to the other.
Looking at my results in the chart above, you can
see the 7 days of decline in mid-December where I was shamed into creating an
algorithm to guide my decisions. That led to a month of relatively steady
upward growth being driven by overs. The spread of Omicron seemed to be fueling
the increased scoring. When Sam Montembault is facing Maxime Lagace, the
sportsbooks almost need to stop accepting bets on the over for that game. So
bet the overs when goalies go into Covid protocol. That rule will have to be
added to my Gambling Manifesto.
Scoring decreased for 2 weeks after the
post-Christmas boom, but then it popped back up in the final week of the second
quarter. If you bet $100 on every under in the first half, you lost more than -$4,000. I closed out the 2nd quarter on Jan 21st, and my betting algorithm performed very badly
in the week that followed, forcing me to mute the optimistic tone of my performance.
Once this report gets published, I’ll have some free time during all-star
weekend to test some more models. I’ll verify if the last 10 games is the
optimal window to include in the calculations, or if that’s too far back. Recent
data is more relevant, but is also a smaller sample size. I’ll report my
findings in future reports.
Goalies:
At the halftime marker of the season, I copy-pasted
a complete list of goalie game logs into my spreadsheet, and summed some
totals. My own best/worst goalies to bet on/against, and some of the best
market picks if you’re betting $100 on every outcome. Keep in mind here that
the starting goalie has been unconfirmed in over 90% of the bets I’ve made, so
I’m often making my selections blind, or at least with only a guess at who will
be playing in goal. In many cases, it’s unlikely you’d be able to get the same
line price if you waited for the starter to be confirmed.
It’s ironic to see that I lost big money both when
betting Jonathan Quick to win and lose. Digging a little deeper into the game
logs, Quick played some of his best games against some of their best opponents,
and saved his blow-ups for lesser offensive teams (scroll down to the LA Kings
summary to for further explanation). On the flip side of that, I generated more
than $1,000 in profit both when betting Mikko Koskinen to win and lose, because the team abruptly went from really good to really bad, and I adjusted on the fly..
Carter Hutton started only 3 games, yet was my 2nd
most profitable goalie to bet against. It saddened me deeply when he was placed
on IR, and I’ve been eagerly anticipating his return ever since. Betting
against Jake Allen has also yielded riches, and he was also lost to a
significant injury. On the flip side, Petr Mrazek started only 4 games, but
cost me -$1,180.
My Best Goalies to Bet On: My Worst Goalies
to Bet On:
1) Darcy Kuemper, (+$3,229) 1) Alex
Nedeljkovic, (-$1,679)
2) Sergei Bobrovsky, (+$2,843) 2) Jonathan
Quick, (-$1,460)
3) Elvis Merzlikins, (+$2,053) 3) Stuart
Skinner, (-$1,408)
My Best Goalies to Bet Against: My Worst Goalies to Bet Against:
1) Karel Vejmelka, (+$5,009) 1) Anton
Forsberg, (-$2,220)
2) Carter Hutton, (+$3,258) 2) Tristan
Jarry, (-$1,790)
3) Jake Allen, (+$2,742) 3) Jonathan
Quick, (-$1,679)
Best/Worst Goalie “Market” Bets:
Best Moneyline Goalies: Worst
Moneyline Goalies:
1) Igor Shesterkin, (+$954) 1) Jake
Allen, (-$1,377)
2) Frederik Andersen, (+$920) 2) Philipp
Grubauer, (-$1,038)
3) Sergei Bobrovsky, (+$794) 3) Carter
Hart, (-$1,017)
Best Puckline +1.5 Goalies: Worst
Puckline +1.5 Goalies:
1) John Gibson, (+$454) 1) Jake Allen, (-$692)
2) Igor Shesterkin, (+$370) 2) Karel
Vejmelka, (-$555)
3) Ukko-Pekka Luukkonen, (+$343) 3) Semyon Varlamov,
(-$450)
Best Puckline -1.5 Goalies: Worst
Puckline -1.5 Goalies:
1) Ville Husso, (+$750) 1) Braden Holtby, (-$709)
2) Cal Petersen, (+$745) 2) Adin
Hill, (-$500)
3) Sergei Bobrovsky, (+$713) 3) Philipp
Grubauer, (-$455)
*Kings were favored 6 times with Cal Petersen
starting, covering 5 with nice payouts.
Best “Over” Goalies: Best
“Under” Goalies:
1) Robin Lehner, (+$1,011) 1) Karel Vejmelka, ($577)
2) Darcy Kuemper,
(+$923) 2) Igor Shesterkin, ($574)
3) Joonas Korpisalo,
(+$708) 3) James
Reimer, ($514)
Best and Worst Team Bets:
*Market Bets calculated by betting
exactly $100 on every outcome*
Market Best Bets +1.5 Goals: Market Worst
Bets +1.5 Goals:
1) Buffalo Sabres, (+$297) 1) Detroit
Red Wings, (-$536)
2) Chicago Blackhawks, (+$227) 2) Philadelphia
Flyers, (-$476)
3) Boston Bruins, (+$186) 3) New
Jersey Devils, (-$450)
Market Best Bets -1.5 Goals: Market Worst
Bets -1.5 Goals:
1) St. Louis Blues, (+$905) 1) Boston
Bruins, (-$771)
2) Minnesota Wild, (+$789) 2) San
Jose Sharks, (-$700)
3) Nashville Predators, (+$625) 3) Dallas
Stars, (-$479)
My 5 Best 2nd Quarter Over/Under Bets: Market’s 5 Best 2nd Quarter O/U Bets:
1) Colorado over, (+$1,332) 1) Columbus
over, (+$873)
2) Columbus over, (+$972) 2) Winnipeg
over, (+$744)
3) Rangers under, (+$947) 3) Florida
over, (+$735)
4) Washington over, (+$603) 4) Colorado
over, (+$696)
5) Nashville over, (+$578) 5) Minnesota
over, (+$621)
My 5 Worst 2nd Quarter Over/Under Bets:
1) Carolina under, (-$550)
2) Los Angeles over, (-$533)
3) Winnipeg under, (-$517)
4) Anaheim over, (-$429)
5) Arizona over, (-$418)
My 5 Best 2nd Quarter Teams to Bet
On: Market’s 5
Best Q2 Teams to Bet On:
1) Florida Panthers, (+$1,678) 1) St.
Louis Blues, (+$1,299)
2) New York Islanders, (+$1,318) 2) Minnesota
Wild, (+$1,168)
3) Vancouver Canucks, (+$1,018) 3) Pittsburgh
Penguins, (+$1,087)
4) Tampa Bay Lightning, (+$953) 4) Nashville
Predators, (+$1,054)
5) New York Rangers, (+$841) 5) Carolina
Hurricanes, (+$998)
My 5 Worst 2nd Quarter Teams to
Bet On:
1) Washington Capitals, (-$1,363)
2) Edmonton Oilers, (-$915)
3) Detroit Red Wings, (-$644)
4) New Jersey Devils, (-$631)
5) Philadelphia Flyers, (-$313)
My 5 Best 2nd Quarter Teams to Bet
Against: Market’s 5
Best Q2 Teams to Bet Against:
1) Arizona Coyotes, (+$2,481) 1) Edmonton
Oilers, (+$1,605)
2) Montreal Canadiens, (+$2,431) 2) Philadelphia
Flyers, (+$1,508)
3) Edmonton Oilers, (+$1,108) 3) Seattle
Kraken, (+$1,397)
4) Vancouver Canucks, (+$998) 4) New
Jersey Devils, (+$1,142)
5) New York Islanders, (+$762) 5) Calgary
Flames, (+$1,001)
My 5 Worst 2nd Quarter Teams to
Bet Against:
1) Ottawa Senators, (-$2,154)
2) LA Kings, (-$1,328)
3) Buffalo Sabres, (-$756)
4) Nashville Predators, (-$646)
5) Pittsburgh Penguins, (-$380)
Team By Team Power
Rankings
The team-by-team
gambling power rankings are ordered by the sum of all my bets on each team to
win or lose for the entire season. They are my own personal power rankings,
reflecting my own success picking the outcome of their games. These aren’t
necessarily the best teams to bet on, as some were swung by a few instances of
good luck or bad judgement. You’ll have to read the team summaries for a deeper
understanding of the replicability. If you are going to be betting on hockey in
the near future, it may help you to read about my own personal success and
failure over the month. For an unbiased look, I will include an overall rank of
account balances if you bet each team to win or lose every game and every
puckline, providing monolithic results of betting both sides consistently team
by team.
LR = League Rank
1) Arizona
Coyotes, ($8,860):
Last Quarter Rank: 1
1st Quarter Profit: $6,333
2nd Quarter Profit: $2,526
Q2 Win-Loss Record: 6-13
Q2 % Money Bet On: 2%
($336)
If you bet on them
every game ML+PL: -$268
(LR: 17)
Q2 % Money Bet Against: 98%
($2481)
If you bet against
them every game ML+PL: -$136 (LR: 15)
Q2 % Bet Over: 57% (-$418), Market Return
on $1: $0.87
Q2 % Bet Under: 43% ($127), Market Return
on $1: $1.05
The futility of the
Arizona Coyotes was the cornerstone of my Q1 success, and that continued into
December, though with a slightly altered strategy. I would still lay big money
when they played against top teams, reducing my bet size for weaker opponents.
Q2 started smashingly, winning 10 of my first 11 Arizona bets. However, there
were a few snags, starting with a road win vs the Ducks and later a stunning
upset of the Maple Leafs despite getting outshot 46-18. Goaltender Karel
Vejmelka was the Leaf Slayer, whom we saw steal a few games earlier in the
season before going cold.
Vejmelka lost 13 of
his starts in the first half, with a spread of at least 2 goals in 11 of them,
leading to the uncommon property of opponents having a higher rate of return on
the puckline than moneyline. If you bet exactly $100 on the moneyline for every
Vejmelka opponent in the first half, you lost -$8, but would have won $506 if you bet
the same amount on every puckline.
I’ve been gravitating
to Arizona overs for most of the season because I’m always expecting blowouts,
but they were rarely scoring enough goals to push the total over. That did
change in Q2. Unders were still the better bet to make, they were just less
profitable than in Q1 because the Yotes started scoring more goals. Their
average goals against went unchanged between quarters.
2) Florida
Panthers, ($3,583):
Last Quarter Rank: 9
1st Quarter Profit: $1,437
2nd Quarter Profit: $2,146
Q2 Win-Loss Record: 14-8
Q2 % Money Bet On: 85%
($1,678)
If you bet on them
every game ML+PL: $227 (LR: 13)
Q2 % Money Bet Against: 15%
($419)
If you bet against
them every game ML+PL: -$974 (LR: 25)
Q2 % Bet Over: 81% ($366), Market Return
on $1: $1.33
Q2 % Bet Under: 19% (-$317), Market Return
on $1: $0.57
The Panthers started
the season among my best teams to pick to win, until they went 0-4 in week 5,
which dropped them down my first quarter power rankings. That bad week proved
to be little more than a blip in the radar, and my confidence was never shaken.
One of the keys to unlocking this team betting-wise is their distinct home-road
splits, winning over 80% at home and under 40% on the road. They played a
majority home games in Q2, helping them become my #1 team to bet on for the
quarter.
Florida was the 3rd
best team to bet over in Q2, after a lacklustre performance in that category
for the first quarter. I only bet the under 5 times in 22 games and it hit just
once, reducing my overall Panthers O/U Q2 profit to $50. What helped increase the
profitability of their overs was both an increase in goals scored and goals
allowed. Spencer Knight was the least reliable of their goaltenders, as he cost
me 2 large bets vs Seattle and Ottawa, games that should have been easy wins
for the Panthers. I made a ton of money betting on Sergei Bobrovsky.
3) New York
Islanders, ($3,431):
Last Quarter Rank: 7
1st Quarter Profit: $1,513
2nd Quarter Profit: $1,918
Q2 Win-Loss Record: 9-8
Q2 % Money Bet On: 66%
($1,318)
If you bet on them
every game ML+PL: -$85
(LR: 16)
Q2 % Money Bet Against: 34%
($762)
If you bet against
them every game ML+PL: -$557 (LR: 21)
Q2 % Bet Over: 25% (-$309), Market Return
on $1: $0.71
Q2 % Bet Under: 75% ($147), Market Return
on $1: $1.19
The Islanders entered
December, on the long winding road to rock-bottom, on a losing streak that
would eventually hit 11 games (losing 6 of their first 8 Q2 games). That
nosedive proved positively profitable, helping the Isles climb up my power
rankings. Fortunately for Islanders fans, they bounced off that rocky bottom,
winning 7 of their next 9 games. Since NYI has been one of my best teams to bet
for the previous 2 seasons, it didn’t take me long to jump back on the
bandwagon. As a result, I posted fantastic returns both when betting them to
win and lose.
My results would have
been even better had it not been for over/under. They were one of the best
“under” teams in the league thanks to the tight defensive system deployed by
head coach Barry Trotz. I only bet the over 4 times, losing 3 of them. My
performance across all categories was unaffected by which goalie started.
Varlamov was injured early in the schedule, and struggled upon his return, but
eventually found his form.
4) Columbus
Blue Jackets, ($2,896):
Last Quarter Rank: 8
1st Quarter Profit: $1,483
2nd Quarter Profit: $1,414
Q2 Win-Loss Record: 7-14
Q2 % Money Bet On: 43%
($670)
If you bet on them
every game ML+PL: -$901
(LR: 25)
Q2 % Money Bet Against: 57%
($77)
If you bet against
them every game ML+PL: $461 (LR: 7)
Q2 % Bet Over: 82% ($972), Market Return
on $1: $1.42
Q2 % Bet Under: 18% (-$305), Market Return
on $1: $0.50
The Columbus Blue
Jackets exceeded my expectations in the first half of the season, a team that’s
been hovering around the .500 mark. But they didn’t rise this high in my power
rankings because I was betting them to lose. The secret to my success was betting
them to win at home, and betting overs. While they did lose their fair share of
games, I stopped laying big bets on their opponents after orchestrating
back-to-back wins against Colorado earlier in the season.
My results can best
be explained by the goaltenders. 100% of my profit betting Blue Jackets to win
in the first half came when Elvis Merzlikins was starting in goal +$2,053,
while losing -$906
betting on teams facing Elvis. All my profit betting the BJs to lose
came when Korpisalo or Tarasov were starting. Elvis might have been their best
goalie, but he only posted a .910 SV% in the first half, helping them become
one of the best “over” teams in Q2. Their average goals scored dropped in Q2,
but the goals allowed more than offset that decline.
5) New York
Rangers, ($2,511):
Last Quarter Rank: 14
1st Quarter Profit: $901
2nd Quarter Profit: $1,610
Q2 Win-Loss Record: 14-8
Q2 % Money Bet On: 77%
($841)
If you bet on them
every game ML+PL: $603 (LR: 8)
Q2 % Money Bet Against: 23%
(-$140)
If you bet against
them every game ML+PL: -$632 (LR: 22)
Q2 % Bet Over: 35% (-$39), Market Return
on $1: $0.70
Q2 % Bet Under: 65% ($947), Market Return
on $1: $1.23
Note to self: The New
York Rangers are good. For a brief moment in early January, they were the best
team in the NHL, finishing Q2 near the top of the standings. Looking at my
betting results, I figured out this team was good around week 6, and they
steadily climbed my power rankings in the weeks that followed. They were by far
the most profitable “under” bet of the first half, thanks in large part to
outstanding goaltending. There was no better team to parlay with an over or
under, cranking out greater returns than Colorado with the over.
All my Rangers
success was tied to goaltender Igor Shesterkin, who posted an outstanding .938
in the first half, ranking as my 4th best goalie in which to invest.
By contrast; I was a big net loser when betting Alexandar Georgiev to win
(keeping in mind, all but one of my bets were logged before their official
starter had been announced). The outstanding play of Shersterkin is what made
their unders so lucrative. There was not much disparity between their home and
road performance in Q2 (67% vs 62%).
6) Vancouver
Canucks, ($2,357):
Last Quarter Rank: 21
1st Quarter Profit: $40
2nd Quarter Profit: $2,317
Q2 Win-Loss Record: 12-8
Q2 % Money Bet On: 21%
($1,018)
If you bet on them
every game ML+PL: $732 (LR: 6)
Q2 % Money Bet Against: 79%
($998)
If you bet against
them every game ML+PL: -$1,269 (LR: 29)
Q2 % Bet Over: 45% (-$34), Market Return
on $1: $0.76
Q2 % Bet Under: 55% ($334), Market Return
on $1: $1.15
The Canucks were
among the worst teams in the league early in the season, leading to the
dismissal of both the coach and GM. The team caught fire under new coach Bruce
Boudreau, buoyed by the outstanding play of goaltender Thatcher Demko.
Unfortunately, Covid dumped some water on that fire as the team only played 3
games between Dec 16 and Jan 11. They had won 8 of 9 games prior to that
prolonged period of inactivity, then from Dec 30 to Jan 21, they only won 3 of
8 games. Covid killed the momentum that was being called “the Boudreau bump”
and haven’t been able to find it again in January.
I banked some nice
profit betting them to lose prior to the coaching change, then flipped and
started betting them to win when they got hot, hence how I was able to post
such strong returns on both sides. When they returned from that absence, my
money was back on their opponents. Vancouver was one of the biggest risers my
power rankings from Q1 to Q2, climbing from #21 all the way up to #6.
The big difference in
this team from the first to second quarter was a drop in their Goals Against
Average from 3.3 to 2.4. Bruce isn’t exactly known as a defensive coach, so
I’ll credit Demko heating up for that improved defending. Their unders had a reduced
rate of return from Q1 to Q2 despite the improved goaltending because the team
started scoring more goals.
7) Tampa Bay
Lightning, ($1,937):
Last Quarter Rank: 10
1st Quarter Profit: $1,400
2nd Quarter Profit: $536
Q2 Win-Loss Record: 16-8
Q2 % Money Bet On: 64%
($953)
If you bet on them
every game ML+PL: $457 (LR: 10)
Q2 % Money Bet Against: 36%
(-$83)
If you bet against
them every game ML+PL: -$679 (LR: 23)
Q2 % Bet Over: 75% (-$98), Market Return
on $1: $1.11
Q2 % Bet Under: 25% (-$237), Market Return
on $1: $0.79
The Lightning played
a chunk of the second quarter without either of their most dangerous offensive
forwards, Brayden Point and Nikita Kucherov. The team performed remarkably well
in the absence of greatness (winning 9 of 10 games in December), then both
players returned in early January. That’s when I decided to start laying big
money on Tampa wins, which was not immediately successful given Boston beat
them handily 2 games after the return.
My fantastic Q1
performance on their over/under proved unsustainable. They were my 3rd best “over” team and my 2ns best “under” team. In Q2, I lost money
on both sides. What happened? Well, Q1 was before the creation of my algorithm,
and my previous strategy was betting over against bad teams and under against
good teams. When the algorithm took control, it mostly recommended overs, and
was bad at picking unders. Perhaps in Q3 it would be smart to abandon the algorithm
for Tampa games and revert back to my good team vs bad team strategy.
8) Montreal
Canadiens, ($1,933):
Last Quarter Rank: 22
1st Quarter Profit: $7
2nd Quarter Profit: $1,926
Q2 Win-Loss Record: 3-15
Q2 % Money Bet On: 2%
(-$100)
If you bet on them
every game ML+PL: -$1,394
(LR: 29)
Q2 % Money Bet Against: 98%
($2,431)
If you bet against
them every game ML+PL: $609 (LR: 6)
Q2 % Bet Over: 59% (-$360), Market Return
on $1: $0.89
Q2 % Bet Under: 41% (-$46), Market Return
on $1: $1.00
You can be forgiven
if you entered this season expecting the Montreal Canadiens to be good. They
made it to the Stanley Cup final, so it’s reasonable to conclude they’re a good
team. Turns out, if you simply removed Carey Price, Shea Weber, and Philip Danault
from the roster, they suck. It didn’t take me long to figure out they sucked,
but what killed me early was the over/under bets. During the first 11 weeks, I
ran a negative O/U balance in 8 of them. That’s why the Habs finished #22 in my
Q1 power rankings despite banking $1,563 from their losses.
In the second
quarter, 98% of my money wagered (not counting O/U) was on Montreal opponents,
leading to $2,431 of profit. They were only a few dollars behind Arizona for
the title of my most profitable team to bet against. As bad as the Habs were,
there was an overall negative rate of return on their opponent pucklines -1.5
goals. 70% of my anti-Montreal money was on the moneyline. Due to attendance
restrictions in Quebec, several of their home games were postponed, meaning
most of their games played in Q2 were on the road. Though they’ve only won 5 of
16 home games on the season, so not sure increasing their road games was the
reason they struggled this badly.
9) Minnesota
Wild, ($1,912):
Last Quarter Rank: 3
1st Quarter Profit: $1,658
2nd Quarter Profit: $253
Q2 Win-Loss Record: 11-6
Q2 % Money Bet On: 79%
($319)
If you bet on them
every game ML+PL: $1,168 (LR: 2)
Q2 % Money Bet Against: 21%
($96)
If you bet against
them every game ML+PL: -$1,025 (LR: 26)
Q2 % Bet Over: 65% ($248), Market Return
on $1: $1.37
Q2 % Bet Under: 35% (-$409), Market Return
on $1: $0.57
The Wild entered
December near the top of my power rankings, and near the top of the NHL
standings. Betting Minnesota to win was paying off handsomely, especially when
they went on a 7-game winning streak to start the second quarter.
Unfortunately, those wins pumped up my confidence too much, leading to a big
loss of money when they followed up that winning streak with a 5-game losing
streak (including games vs Los Angeles, Buffalo, and St. Louis when I had laid
big bets on the Wild).
They have been among
the best teams to bet “over” in the first half of the season. In Q2, the goal
scoring went up and the goals against went down. I bet the under 6 times and
only won once (most of that coming before the introduction of my O/U
algorithm). My perception had always been that Minnesota is a stingy defensive
team, which they still are, but the firepower that Kaprizov brings to that
line-up has made them a force on overs.
Cam Talbot started
twice as many games as his back-up in the first half, but was a worse
goaltender (Talbot .909 SV%, Kahkonen .923). I made $1,897 betting Kahkonen
games, and $15 betting Talbot games (regardless of the outcome that I was
betting on, which was often Minnesota to win).
10) Chicago
Blackhawks, ($1,716):
Last Quarter Rank: 4
1st Quarter Profit: $1,632
2nd Quarter Profit: $84
Q2 Win-Loss Record: 9-12
Q2 % Money Bet On: 44%
($191)
If you bet on them
every game ML+PL: -$62
(LR: 15)
Q2 % Money Bet Against: 56%
(-$269)
If you bet against
them every game ML+PL: -$271 (LR: 19)
Q2 % Bet Over: 59% ($127), Market Return
on $1: $1.00
Q2 % Bet Under: 41% ($35), Market Return
on $1: $0.90
It’s perfectly
reasonable if you expected the additions of Vezina winner Marc-Andre Fleury,
Seth Jones, and the return of Jonathan Toews would elevate the Blackhawks into
a playoff contender. They won 32% of their games in Q1 and 43% in Q2. They
rapidly ascended my power rankings once I figured out they sucked and made a
substantial investment in their opponents. Marc-Andre Fleury adjusted slowly to
his new surroundings, but he eventually settled down and even stole a few
games.
I started to bet Chicago
to win more often once Fleury stabilized, but the bad games are scattered
throughout his game log. His rolling 5-game average save percentage fluctuated
between sub .900 and .940+. in Q2. You can’t necessarily trust him to be good
or bad. That mostly describes my second quarter of Chicago betting. They did
start scoring more goals in the second quarter, which increased the
profitability of their overs. I struggled with their over/under in Q1, but my
betting algorithm proved to be a good barometer for which side to wager.
11) Boston
Bruins, ($1,669):
Last Quarter Rank: 5
1st Quarter Profit: $1,620
2nd Quarter Profit: $49
Q2 Win-Loss Record: 13-8
Q2 % Money Bet On: 65%
(-$153)
If you bet on them
every game ML+PL: -$466
(LR: 20)
Q2 % Money Bet Against: 35%
($126)
If you bet against
them every game ML+PL: -$223 (LR: 17)
Q2 % Bet Over: 56% ($147), Market Return
on $1: $0.97
Q2 % Bet Under: 44% (-$71), Market Return
on $1: $0.95
The Bruins won 63% of
their games in Q1 and 62% in Q2, while average GF and GA barely changed. Yet I
won $1,183 when betting them to win/cover in Q1, and lost -$153 in Q2. There were 2
games specifically that dropped the Bruins down my power rankings, a loss to
the Red Wings at home on Nov 30 and their win against Tampa on Jan 8. That win
vs the Lightning helped convince me that this Bruins team is for real, and it
proved to be the start of a 5-game winning streak that included a win vs
Montreal that helped bring my balance back in black.
The Bruins were
favored in 17 of their 21 Q2 games, and only covered the puckline -1.5 goals 4
times. They’re a better bet on the moneyline, at least until the goaltending
settles down. Tuukka Rask returned to the team in January, and struggled. For
the first half, I won $2,050 in Linus Ullmark’s 19 starts, and lost -$582 in Swayman’s 16
starts. I produced strong returns both when betting Ullmark to win and lose
(although that was only because of a giant bet made on the Islanders when
Bergeron and Marchand were in Covid protocol).
12) Washington
Capitals, ($1,633):
Last Quarter Rank: 2
1st Quarter Profit: $2,294
2nd Quarter Profit: -$661
Q2 Win-Loss Record: 10-11
Q2 % Money Bet On: 86%
(-$1,363)
If you bet on them
every game ML+PL: -$682
(LR: 23)
Q2 % Money Bet Against: 14%
($122)
If you bet against
them every game ML+PL: $395 (LR: 9)
Q2 % Bet Over: 71% ($603), Market Return
on $1: $1.23
Q2 % Bet Under: 29% (-$23), Market Return
on $1: $0.69
The Capitals defied
the injury bug early in the schedule on the back of Alexander the Great, but
started slowing down in December and costing me some large wagers. They lost to
at home to Los Angeles (who was on a back-to-back), were beaten by Chicago
twice, even the struggling New Jersey Devils. The Capitals plunged down my
power rankings as I continued betting them to win, getting punished -$1,363 for my faith in
their effectiveness. They were officially my worst team when betting to
win/cover.
My Washington over/under
results in Q1 were terrible, with the unders generating slightly better results
than the overs. Then the goal scoring started to improve, and Ilya Samsonov
started to struggle. I vastly improved my output on Capitals over/under after
the implementation of my algorithm, which was strongly recommending the overs.
The diminishing quality of play from Samsonov was arguably the primary driver
for my Washington struggles in Q2, at least in terms of wins and losses. The
increased goals against enabled me to profit $580 on their over/under.
13) Edmonton
Oilers, ($1,488):
Last Quarter Rank: 6
1st Quarter Profit: $1,572
2nd Quarter Profit: -$84
Q2 Win-Loss Record: 4-13
Q2 % Money Bet On: 46%
(-$915)
If you bet on them
every game ML+PL: -$1,638
(LR: 31)
Q2 % Money Bet Against: 54%
($1,108)
If you bet against
them every game ML+PL: $1,605 (LR: 1)
Q2 % Bet Over: 67% (-$356), Market Return
on $1: $0.78
Q2 % Bet Under: 33% ($79), Market Return
on $1: $1.15
For the first 4 weeks
of the season, the Oilers ranked high in my power rankings thanks to their
winning ways. The offense was steamrolling the opposition, but started to show
signs of weakness by late November. On December 3rd, they lost to
the lowly Seattle Kraken, which kicked off a stretch with 2 measly wins in 13
games. I had been making a lot of money betting them to win, and it took a few
games for me to adapt, which is when most of my losses betting them to win
occurred.
One of the reasons
they started to struggle in Q2 was a reduction in average goals scored from 3.9
to 2.5 (including a 4-game homestand where they scored just 5 goals). This also
transformed them from a great over bet to a profitable under bet. My transition
from overs to unders was too slow, but improved after the introduction of my
algorithm.
14) Colorado
Avalanche, ($1,439):
Last Quarter Rank: 25
1st Quarter Profit: -$486
2nd Quarter Profit: $1,925
Q2 Win-Loss Record: 17-5
Q2 % Money Bet On: 97%
($737)
If you bet on them
every game ML+PL: $441 (LR: 11)
Q2 % Money Bet Against: 3%
(-$144)
If you bet against
them every game ML+PL: -$1,203 (LR: 28)
Q2 % Bet Over: 100% ($1,332), Market Return
on $1: $1.32
Q2 % Bet Under: 0% ($0), Market Return
on $1: $0.62
The Avalanche ranked
25th in my Q1 power rankings, despite being one of the best teams in
the league. They were a little slow out of the gate, and suffered a few upset
defeats in the first month that cost me some large wagers (same thing happened
last year). But in Q2 they caught fire and surged up the leaderboard, fueled by
high scoring games. Unfortunately, a decent chunk of that profit was reclaimed
by the bookie when they failed to cover a puckline -1.5 goals vs Arizona. Had
they scored an empty net goal, Colorado would have been my 2nd most
profitable team to bet in Q2.
The Avalanche did not
play any games from Dec 16 to Jan 2, but didn’t skip a beat. The biggest issue
with this team is that they are a very popular team to wager, so there tends to
be a tax on their betting lines. Hence why my profit margins on their wins were
so low. What really elevated them from #25 to #14 was the outstanding return on
their overs, going 14-6-2. It should be noted that the under hit in 3 of their
last 4 Q2 games, as the goaltending improved, so those overs may not be
entirely reliable in the third quarter.
15) Anaheim Mighty
Ducks, ($1,379):
Last Quarter Rank: 13
1st Quarter Profit: $1,167
2nd Quarter Profit: $212
Q2 Win-Loss Record: 10-13
Q2 % Money Bet On: 49%
($25)
If you bet on them
every game ML+PL: -$480
(LR: 21)
Q2 % Money Bet Against: 51%
($507)
If you bet against
them every game ML+PL: $328 (LR: 10)
Q2 % Bet Over: 38% (-$428), Market Return
on $1: $0.75
Q2 % Bet Under: 62% ($109), Market Return
on $1: $1.17
The Ducks have been
surprisingly mighty this season, and I profited from their wins when the market
expectations were still low. The longer they sustained their winning, the
higher the lines got priced, nerfing the rate of return when betting them to
win. In the first quarter I banked $1,193 when betting Ducks, but Q2 that
dropped to $25. The goaltending remained strong, but the goal scoring declined.
My bet sizes on Anaheim games were relatively small, never too confident they
would win or lose. My only big Q2 Ducks loss was when they failed to defeat the
Arizona Coyotes on Dec 17.
Once the offense
started to cool, the yield on their unders began to grow (going 13-8-2). The
overs only attracted 38% of my total O/U expenditure, yet still produced a
large loss. Most of those losses came prior to the introduction of my algorithm
and removing my preconceived beliefs from the selection process. It did not
make a big difference which goalie was in net, as Anthony Stolarz was every bit
as good as John Gibson. The strong goaltending is why I’ve stayed away from
their opponents -1.5 goals on the puckline in the past. I’ve nicknamed Gibson “the puckline
bandit” because that’s what he does (though more so in Q1).
16) Seattle
Kraken, ($1,329):
Last Quarter Rank: 11
1st Quarter Profit: $1,343
2nd Quarter Profit: -$14
Q2 Win-Loss Record: 6-15
Q2 % Money Bet On: 5%
(-$300)
If you bet on them
every game ML+PL: -$1,539
(LR: 30)
Q2 % Money Bet Against: 95%
($494)
If you bet against
them every game ML+PL: $1,397 (LR: 2)
Q2 % Bet Over: 88% (-$290), Market Return
on $1: $0.91
Q2 % Bet Under: 12% ($82), Market Return
on $1: $1.01
The Seattle Kraken
suck. I figured this out early in the schedule and profited from their losses,
while generating solid returns on their overs. The primary reason they dropped
in my Q2 power rankings is mostly due to 2 upset wins against Florida and Edmonton
(when people still thought the Oilers were good), costing me large wagers. After
those 3 wins in 4 games, they would lose 12 of their next 13. For the first 9
Seattle games of Q2, I lost -$1,651. The next 12 games yielded +$1,637
(100% invested in Seattle opponents).
The Kraken were the
#1 over team in Q1, thanks to decent goal scoring and bad goaltending. In Q2,
the goaltending stayed the same but the scoring cooled, neutering the
profitability of their overs. Grubauer was very erratic, posting an .883 SV% in
the first half, but he did have one good week where the unders went 3-0 while I
was still leaning heavily on those overs.
17) Dallas
Stars, ($1,106):
Last Quarter Rank: 16
1st Quarter Profit: $597
2nd Quarter Profit: $509
Q2 Win-Loss Record: 12-9
Q2 % Money Bet On: 55%
($397)
If you bet on them
every game ML+PL: -$495
(LR: 22)
Q2 % Money Bet Against: 45%
($368)
If you bet against
them every game ML+PL: -$262 (LR: 18)
Q2 % Bet Over: 35% (-$218), Market Return
on $1: $0.82
Q2 % Bet Under: 65% (-$38), Market Return
on $1: $1.06
The Stars spent most
of the first half in the mushy middle of my power rankings, between #10 - #15,
which generally means a mix of good and bad betting outcomes. Early in the
season, they were one the best teams to bet the under, then they went on a
scoring run, I started betting the overs, and they went on another run of
unders. 9 of their first 10 games in the second quarter went under, then 8 of
the next 11 went over. That’s not exactly a predictable pattern and helps to
explain my struggles in the O/U category.
In the first half Dallas
was a really good at home and really bad on the road (which seems to be a
common theme for a few teams). Overall, the Stars were a better team in the
second quarter, winning 57% up from 47%. For the first half, I won $1,700 when
Braden Holtby was in net, and lost -$874 when Jake Oettinger started (across
all categories). The predictable home-road splits helped me generate a decent
return both when betting them to win or lose. It always feels like a personal
victory when I can perform well betting both sides.
18) Philadelphia
Flyers, ($974):
Last Quarter Rank: 17
1st Quarter Profit: $595
2nd Quarter Profit: $378
Q2 Win-Loss Record: 5-17
Q2 % Money Bet On: 31%
(-$414)
If you bet on them
every game ML+PL: -$1,641
(LR: 32)
Q2 % Money Bet Against: 69%
($671)
If you bet against
them every game ML+PL: $1,508 (LR: 2)
Q2 % Bet Over: 59% ($99), Market Return
on $1: $0.94
Q2 % Bet Under: 41% ($22), Market Return
on $1: $0.94
The Philadelphia
Flyers fired head coach Alain Vigneault on Dec 6, but unlike the Canucks (who
hired Boudreau and went on a winning streak) the Flyers experienced no such
reversal of fortune. They did manage a big upset victory in Vegas Dec 10 that
cost me money, which kicked off a streak of 5 wins in 6 games. I’m not
classifying that as a “reversal of fortune” because the other wins came against
Arizona, Seattle, New Jersey, and Ottawa. They beat some bad teams, then ended
the second quarter with 10 consecutive losses.
The team did not have
a dominant trend one way or the other in over/under betting seemingly
alternating back-and-forth from one week to the next. Erratic scoring and
goaltending are problematic if you’re trying to predict goal totals. I was able
to generate a small profit on both the over and under. Which goalie started
didn’t make a big difference in my results, though Carter Hart did have a
better return on unders, while Martin Jones was more likely to go over.
19) New
Jersey Devils, ($752):
Last Quarter Rank: 15
1st Quarter Profit: $877
2nd Quarter Profit: -$125
Q2 Win-Loss Record: 6-15
Q2 % Money Bet On: 27%
(-$631)
If you bet on them
every game ML+PL: -$1,235
(LR: 28)
Q2 % Money Bet Against: 73%
($421)
If you bet against
them every game ML+PL: $1,142 (LR: 4)
Q2 % Bet Over: 81% ($293), Market Return
on $1: $1.24
Q2 % Bet Under: 19% (-$209), Market Return
on $1: $0.69
The Devils started
the season as a quasi-respectable team who won 47% of their games in the first
quarter. They had a 3-game winning streak in mid-November that included 7-3
victory versus the Florida Panthers, that convinced me they were a good team (I
even Tweeted “Note to self: The New Jersey Devils might be good”). It was a
costly deception, losing -$600 before realizing
their deceit. New Jersey lost 15 of their next 18 games after those 3 wins.
I was eventually able
to win some of that money back once I figured out they sucked. The goalie
situation has been a problem. MacKenzie Blackwood struggled to find his rhythm
after missing the start season with an injury, then later Jonathan Bernier was
lost to a season ending wound of his own. The best part of the porous goaltending
was that their overs became a reliable bet. Add in some talented goal scorers,
and you can profit from bad goalies. The Devils strangely had distinct
home-road splits for over/under. The overs went 9-1-1 on the road but 4-6 on
home ice.
20) Vegas
Golden Knights, ($747):
Last Quarter Rank: 24
1st Quarter Profit: -$353
2nd Quarter Profit: $1,100
Q2 Win-Loss Record: 12-9
Q2 % Money Bet On: 77%
($748)
If you bet on them
every game ML+PL: -$388
(LR: 19)
Q2 % Money Bet Against: 23%
($208)
If you bet against
them every game ML+PL: -$112 (LR: 13)
Q2 % Bet Over: 88% ($344), Market Return
on $1: $1.22
Q2 % Bet Under: 12% (-$200), Market Return
on $1: $0.69
My problem with Vegas
in Q1 was winning games I bet them to lose, and losing games I bet them to win.
They struggled with injuries to star players, which impaired my profit margin. When
Mark Stone got healthy, they became more reliable and started climbing my power
rankings. At least until Stone got injured again, then returned, then got
Covid. The team still managed to win 10 of 13 games in December where I was
mostly betting them to win. The Golden Knights started an 8-game homestand on
Dec 31, losing 5 of them, dropping the team down a few spots in my power
rankings as the quarter came to a close. You do have to wonder if the
predominance of Covid has lessened the effectiveness of the “Vegas flu”. Guests
aren’t visiting the casinos as much in this pandemic world (I’m assuming).
Goaltender Robin
Lehner had an uncharacteristically low save percentage in the first half at
.903, which actually helped make their overs very lucrative. My returns would
have been even higher had Lehner not posted a few quality starts towards the
end of the quarter that stole a few over bets. He returned from a 2-week
absence to allow only 3 goals in his first 2 game back.
21) Nashville
Predators, ($520):
Last Quarter Rank: 20
1st Quarter Profit: $199
2nd Quarter Profit: $321
Q2 Win-Loss Record: 15-8
Q2 % Money Bet On: 53%
($412)
If you bet on them
every game ML+PL: $1,054 (LR: 4)
Q2 % Money Bet Against: 47%
(-$646)
If you bet against
them every game ML+PL: -$1,078 (LR: 27)
Q2 % Bet Over: 74% ($578), Market Return
on $1: $1.26
Q2 % Bet Under: 26% (-$22), Market Return
on $1: $0.67
The Nashville
Predators have exceeded even the most optimistic expectations this season, due
largely to their outstanding goaltender and resurgent goal scoring. I’m up big
when betting them to win, and down big when betting them to lose. The same
thing happened in the first quarter, yet it did not seem to teach me any
lessons. Reviewing my game notes, most of those bets were road games, a few
back-to-backs, and some Dave Rittich starts.
I lost -$1,441 betting against
Juuse Saros, but I’ve got an excuse, something I’ve nicknamed “fantasy
hedging”. I own Juuse Saros on both my fantasy teams, so I’m acutely aware of
how well he’s been playing. I’m often tempted to bet against Saros to minimize
the emotional harm of a Nashville loss. If they lose, I win. Plus, sometimes it
also feels like there is a transitive “reverse jinx” effect that might maybe
increase the probability of my fantasy team collecting a W. I’m just disclosing
the bias in my Preds bet selection process. This is not something that I would
do with real money.
The one thing I’ve
been really good at this season is betting Nashville over/under. They were a
strong “under” team in the first quarter, but there was a shift in Q2 as the
offense got a significant boost in output. They were my 3rd best
under bet in Q1, and my 4th best over bet in Q2. I had already
shifted to overs before the birth of my O/U algorithm. But as a Saros fantasy
owner, I never wanted the over to hit. That may have been a hedge too, but at
least a profitable one…
22) Carolina
Hurricanes, ($361):
Last Quarter Rank: 12
1st Quarter Profit: $1,181
2nd Quarter Profit: -$821
Q2 Win-Loss Record: 13-6
Q2 % Money Bet On: 86%
($5)
If you bet on them
every game ML+PL: $998 (LR: 5)
Q2 % Money Bet Against: 14%
(-$224)
If you bet against
them every game ML+PL: -$1,343 (LR: 30)
Q2 % Bet Over: 48% (-$52), Market Return
on $1: $1.16
Q2 % Bet Under: 52% (-$550), Market Return
on $1: $0.75
The Carolina
Hurricanes dropped from #2 to #23 in my power rankings in the span of 3 weeks.
They were among the league’s hottest teams to start the season, and I was on
the bandwagon early. Then there was a 10-day period at the end of November when
they lost games to San Jose, Seattle, and Ottawa that cost me -$2,100. They would recover
and start winning games again, but they haven’t been able to gain traction in
my power rankings because they were by far my worst over/under team in the
first half.
My struggles with O/U
were justifiable. They were the 5th best under bet in the first
quarter, when the goaltending was extremely stingy. But when Q2 came along, the
goaltending got worse, and the goal scoring jumped from 3.2 per game to 3.9. I
lost a bunch of money early in Q2, sticking mostly to unders, heeding those Q1
results too closely. My performance did improve after the algorithm began
making the picks.
23) St. Louis
Blues, (-$52):
Last Quarter Rank: 27
1st Quarter Profit: -$824
2nd Quarter Profit: $771
Q2 Win-Loss Record: 14-7
Q2 % Money Bet On: 52%
($663)
If you bet on them
every game ML+PL: $1,299 (LR: 1)
Q2 % Money Bet Against: 48%
(-$362)
If you bet against
them every game ML+PL: -$1,491 (LR: 31)
Q2 % Bet Over: 45% ($431), Market Return
on $1: $1.10
Q2 % Bet Under: 55% ($39), Market Return
on $1: $0.83
The Blues ended the
first quarter on a 3-7 run, then lost goaltender Jordan Binnington to Covid one
week into Q2, then lost back-up Ville Husso shortly thereafter. Charlie
Lindgren became the starting goalie. Some might have seen an opportunity here
to short the Blues, but they were on a homestand with Charlie facing Detroit,
then Montreal. Binnington struggled upon his return, and Husso eventually
wrestled away top billing. Despite that volatility, the Blues had a great
quarter, and were the #1 best team to bet ML+PL. If you bet $100 on every St.
Louis moneyline and puckline, you banked $1,299.
My wagers for St.
Louis games tended to be small for most of the first half. This is not a team
that I like to play, because in the past they’ve burned me by losing games to
bad teams, but can also punch above their weight and beat any team on any given
night. By the end of Q2, I on-board as they climbed my
power rankings, laying more money on them to win. The goal scoring picked up in
Q2, helping the overs become more profitable. My algorithm had 78% accuracy
picking Blues O/U from Dec 15 to Jan 21.
24) Winnipeg
Jets, (-$172):
Last Quarter Rank: 23
1st Quarter Profit: -$274
2nd Quarter Profit: $101
Q2 Win-Loss Record: 8-9
Q2 % Money Bet On: 27%
($295)
If you bet on them
every game ML+PL: $242 (LR: 12)
Q2 % Money Bet Against: 73%
(-$165)
If you bet against
them every game ML+PL: $62 (LR: 12)
Q2 % Bet Over: 70% ($488), Market Return
on $1: $1.44
Q2 % Bet Under: 30% (-$517), Market Return
on $1: $0.45
The Jets started the
season strong, but really struggled to keep pace in the playoff race during
December and January. Head coach Paul Maurice quit, and his replacement did not
provide any kind of bump to their win totals. You might say the team failed to
take-off. Boarding is delayed while they wait for the pilot to show up. They
spent almost the entire first half stuck between #22 and #26 in my power
rankings. They can win any given game or lose any given game, and teams like
that are hard to trust. They won 47% of their games in Q1 and 47% in Q2.
Winnipeg’s Vezina
caliber goaltender was pretty average, posting a .912 SV% for the first half. Goals
allowed did increase significantly in Q2, which helped flip them from a good
“under” team to a solid “over” bet. I burned some money while making that
transition. One of the stranger things about their quarterly performances, they
became a better team on the road in Q2, and a worse team at home. In past
season I’ve had success betting the Jets +1.5 goals as road dogs. Betting them
to win on home ice was a big loser.
25) San Jose
Sharks, (-$296):
Last Quarter Rank: 26
1st Quarter Profit: -$537
2nd Quarter Profit: $242
Q2 Win-Loss Record: 11-11
Q2 % Money Bet On: 56%
($41)
If you bet on them
every game ML+PL: -$969
(LR: 26)
Q2 % Money Bet Against: 44%
($288)
If you bet against
them every game ML+PL: $297 (LR: 11)
Q2 % Bet Over: 38% (-$109), Market Return
on $1: $0.79
Q2 % Bet Under: 62% ($22), Market Return
on $1: $1.14
There were not many
experts proclaiming the San Jose Sharks would be sitting in a playoff spot
approaching the halfway point of the season. The unexpected resurgence of James
Reimer helped the team steal a few extra games, but eventually his play did
start to slide and Adin Hill became the primary starter. They’re winning
percentage went largely unchanged from Q1 to Q2, and they were equally good on
the road as they were on home ice. My Sharks performance was mediocre, much
like the team. I was rarely confident placing a big bet on them to win or lose
(except when they played Arizona).
One thing that really
tailed off was my performance betting San Jose over/unders. They were a strong “under”
team for the first quarter, but when James Reimer started to struggle, the
overs went on a small run. It took a few games before my algorithm fully
appreciated the gravity of that situation, but eventually Adin Hill returned
the unders to normalcy.
26) Buffalo
Sabres, (-$401):
Last Quarter Rank: 18
1st Quarter Profit: $575
2nd Quarter Profit: -$976
Q2 Win-Loss Record: 5-16
Q2 % Money Bet On: 20%
(-$229)
If you bet on them every
game ML+PL: -$388
(LR: 18)
Q2 % Money Bet Against: 80%
(-$756)
If you bet against
them every game ML+PL: -$119 (LR: 14)
Q2 % Bet Over: 43% (-$236), Market Return
on $1: $0.72
Q2 % Bet Under: 57% ($244), Market Return
on $1: $1.19
I never would have
envisioned heading into this season having this much difficulty profiting from
the 2021/22 Buffalo Sabres. They were my #1 team to short last season, and I
was expecting even greater returns after their uninspiring offseason. The
emergence of Ukko-Pekka Luukkonen did drain more than -$1,500 my portfolio early
in his run. My two single biggest financial losses came when the Sabres upset
Nashville and Minnesota. Most of my money was on Buffalo to lose and they lost
most of their games, yet somehow I finished Q2 down -$756 when making that
wager. All of it came in shortly after UPL arrival (who posted a .939 SV% in
his first 5 GP).
My Sabres Q2 O/U was
revenue neutral. Their overs produced profit in Q1 and the unders produced
profit in Q2 as the scoring diminished. I invested to heavily in the overs
after UPL was recalled from the minors, and a week later my algorithm was born,
leading me to a solid run of success on the unders.
27) Toronto
Maple Leafs, (-$886):
Last Quarter Rank: 28
1st Quarter Profit: -$1,120
2nd Quarter Profit: $234
Q2 Win-Loss Record: 10-6
Q2 % Money Bet On: 64%
(-$294)
If you bet on them
every game ML+PL: -$14
(LR: 14)
Q2 % Money Bet Against: 36%
($410)
If you bet against
them every game ML+PL: -$379 (LR: 20)
Q2 % Bet Over: 55% ($452), Market Return
on $1: $1.39
Q2 % Bet Under: 45% (-$334), Market Return
on $1: $0.54
In late November, the
Leafs caught fire and I was late to the party. But once I started logging more
bets on Toronto to win, they started climbing my power rankings. The single
largest contributor to that reversal of my misfortune was a big win against Edmonton
when I got them +180 on the puckline, plus Colorado’s come from behind victory
3 games later. It can be difficult to profit from wins when the Leafs are good.
The lines are always off, as there is a tax on Leafs to win. The fan base is
too big. Where you can really make good money is betting them to lose when they
struggle.
The ship was starting
to find its proper course towards prosperity, until the devastating loss to the
Arizona Coyotes on Jan 12. The super-human goaltending display by Karel Vejmelka,
stealing a victory, drove me deep into the red for Leafs Q2. Toronto was the #1
team to bet under in the first quarter, but in Q2 their average goals scored
climbed from 2.7 to 4.3, while goals against went from 2.2 to 3.3. Better
scoring, worse goaltending, the perfect storm to become a reliable “over” bet. Once again, I lost some money while making the transition.
28) Calgary
Flames, (-$1,064):
Last Quarter Rank: 29
1st Quarter Profit: -$1,398
2nd Quarter Profit: $335
Q2 Win-Loss Record: 6-9
Q2 % Money Bet On: 72%
(-$23)
If you bet on them every
game ML+PL: -$1,119
(LR: 27)
Q2 % Money Bet Against: 28%
($645)
If you bet against
them every game ML+PL: $1,001 (LR: 5)
Q2 % Bet Over: 50% (-$34), Market Return
on $1: $1.09
Q2 % Bet Under: 50% (-$253), Market Return
on $1: $0.81
The Calgary Flames can
be a tricky team to bet, as they are better on the road than they are at home. Maybe
it has something to do with the old disgusting arena in which they play, or
perhaps it is a function of the player’s collective psychology. I’m at a loss
to explain why, but early in the schedule most of my Flames losses came from
betting them to win at home. They have also been very inconsistent, running hot
and cold. They went 2-8 from Dec 5 to Jan 13 which included a prolonged period
of inactivity due to a Covid outbreak.
They’ll have weeks
when they go 4-0, then 1 for 3, seemingly alternating back and forth between
really good and mediocre. My results were better in Q2, as they delivered some
big road wins for me. They did not play any games between Dec 11 and Dec 30 due
to Covid. They returned from that absence with two big road wins (albeit
against teams on the 2nd half of back-to-backs), but afterwards
kicked off a losing streak.
29) Pittsburgh
Penguins, (-$1,513):
Last Quarter Rank: 32
1st Quarter Profit: -$2,233
2nd Quarter Profit: $720
Q2 Win-Loss Record: 16-5
Q2 % Money Bet On: 84%
($774)
If you bet on them
every game ML+PL: $1,087 (LR: 3)
Q2 % Money Bet Against: 16%
(-$380)
If you bet against
them every game ML+PL: -$1,698 (LR: 32)
Q2 % Bet Over: 27% ($324), Market Return
on $1: $1.04
Q2 % Bet Under: 73% ($2), Market Return
on $1: $0.86
The Penguins finished
dead last in my Q1 power rankings, but have climbed a few spots thanks to their
winning ways. They had a 10-game winning streak where I failed to significantly
profit, but mostly because there was heavy juice on the betting lines (and they
whiffed on a few key pucklines). The Pens can be a very public team and everyone
loves betting Pittsburgh when they’re winning. Some of the line prices scared
me away, and at one point Tristan Jarry went into Covid protocol. It wasn’t
until they went on a road trip that the lines became more reasonably priced,
soliciting my investment. 84% of my stake was on Pit, producing +$774.
Jarry was a Vezina
contender from October to December, then his play started to slip. Looking at
my halftime goalie results, Jarry cost me a ton of money in the first half,
with some of that coming from failed overs, and most of it in the first quarter
when he was exceeding my expectations. Evgeni Malkin returned to the line-up on
Jan 11, which no doubt helped the team solicit even more public money. Even when this team
is good, it can be hard to produce a high rate of return on their wins (like Toronto); they’re more
profitable when they struggle.
30) Detroit
Red Wings, (-$1,853):
Last Quarter Rank: 30
1st Quarter Profit: -$1,752
2nd Quarter Profit: -$101
Q2 Win-Loss Record: 9-11
Q2 % Money Bet On: 40%
(-$644)
If you bet on them
every game ML+PL: -$718
(LR: 24)
Q2 % Money Bet Against: 60%
($675)
If you bet against
them every game ML+PL: $440 (LR: 8)
Q2 % Bet Over: 68% ($79), Market Return
on $1: $1.09
Q2 % Bet Under: 32% (-$211), Market Return
on $1: $0.87
Weeks 5-8, I lost -$2,000 betting Detroit
games, then weeks 9-12, profited +$746 (over/under not included). They exceeded
my expectations early in the season, but once their home-road splits veered off
in opposite directions, it became easier to pick the outcomes. Although, there
were some instances when I got too confident, betting Detroit to win at home
against good teams, and it cost me. More than anything, they stayed near the
bottom of my power rankings because of over/under. Disclaimer: I’ve been a Red
Wings fan since the 1980s. It can be difficult to remove bias from my bets on
this team. No franchise cost me more money in the calendar year of 2021 than my
favorite team.
My first half
over/under results were atrocious, mostly from lost unders. Their winning
percentage and average goals scored/allowed per game barely changed from Q1 to
Q2. They were the same team in both quarters. Nearly all of my first half
Detroit losses occurred in the first quarter, with an improved performance in
Q2, mostly due to betting Wings to lose on the road. The problem was that they
burned me when betting them to win at home (the costliest of which was against
Winnipeg on Jan 13). Rewarded for my pessimism, punished for my faith. Alex
Nedeljkovic had a decent first half with a .916 SV%, but he was my #1 worst
goalie to bet to win (keeping in mind that Detroit had not once confirmed their
starting goalie when all my Wings bets were logged).
31) Ottawa
Senators, (-$2,386):
Last Quarter Rank: 19
1st Quarter Profit: $347
2nd Quarter Profit: -$2,733
Q2 Win-Loss Record: 7-9
Q2 % Money Bet On: 18%
(-$36)
If you bet on them
every game ML+PL: $515 (LR: 9)
Q2 % Money Bet Against: 82%
(-$2,154)
If you bet against
them every game ML+PL: -$209 (LR: 16)
Q2 % Bet Over: 68% (-$133), Market Return
on $1: $1.01
Q2 % Bet Under: 32% (-$409), Market Return
on $1: $0.90
From November 1st
to December 1st, the Ottawa Senators played 13 games and only
managed a single win. The final 10 games of that stretch was bountiful for me
personally, banking $1,092. They were in a full nosedive, which is generally
where I’m at my best, profiting from badness. In my Week 7 Betting Report I
wrote: “they may have captured the title of my favorite team to bet against.
They may very well be worse than Arizona at this moment in time, losing 9 of
their last 10 games.”
This stretch of
ineptitude was followed by a 6-game span where they won 5 times (including against
Carolina, Colorado, Florida, and Tampa) costing me -$2,571 and plunging them
down my power rankings from #19 to #26. I’d be curious to see how many times in
modern history a team went on a 1-12 run, and then upset 4 of
the league’s best teams in the next 10 days. Sadly, that stunning sample helped
convince me that the team was good, soliciting my investments, only to lose 5
of their next 7. The Sens only played 1 game between Dec 18 and Jan 13, nearly
a month of inactivity due to Covid (the one game they did play was a massacre
at the hands of Toronto).
32) Los
Angeles Kings, (-$3,645):
Last Quarter Rank: 31
1st Quarter Profit: -$1,943
2nd Quarter Profit: -$1,702
Q2 Win-Loss Record: 12-10
Q2 % Money Bet On: 38%
($338)
If you bet on them
every game ML+PL: $631 (LR: 7)
Q2 % Money Bet Against: 62%
(-$1,328)
If you bet against
them every game ML+PL: -$702 (LR: 24)
Q2 % Bet Over: 43% (-$533), Market Return
on $1: $0.82
Q2 % Bet Under: 57% (-$179), Market Return
on $1: $1.08
The Kings were dead
last in my power rankings from Dec 13 to the end of the first half. Most of the
damage was done by LA pulling off upset victories against Minnesota, Florida,
Pittsburgh, Washington, and the Rangers that all cost me large wagers. 14 of
their 22 Q2 games were played at home, featuring some difficult opponents, whom
I laid money on far too often. Though I did burn a greater sum of money when
betting their opponents on the road (most notably vs Florida and Washington).
Aside from
incorrectly picking the Kings to lose, the next biggest factor in my LA betting
struggle was awful over/under decisions. They were my #1 under bet in the first
quarter, and continued returning a majority unders in Q2. I was turning a small
Q2 profit on Kings O/U at the inception of my algorithm. Once the decision
making was taken out of my hands, my performance plummeted, losing -$755 in the next 14
games. The algorithm recommended the over 5 times (all against high scoring
teams) and lost 4 of them. It picked the under 9 times and only won 3. Most of
my lost O/U bets came with Jonathan Quick in net, who posted a .952 SV% when I
bet the over, and .880 when I bet the under. Meaning he performed much better
against higher scoring teams. I’m not ashamed to take the “L” on that one.
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