Welcome to my First Quarterly Hockey Betting Report of the 2021/22
season. Unlike my weekly reports, the quarterly report will delve deeper into
my team-by-team results and breakdown categories for the entire quarter. 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. Most of my lines were recorded from Draft Kings near
noon Pacific time the day before games, and unlike last season, I’m recording
alt pucklines for underdogs -1.5 goals and favorites +1.5.
If you would like to read a comprehensive analysis of
the last three years of hockey betting, my new book “The Hockey Economist’s
Betting Prospectus” is available in the Amazon store. Just imagine this
quarterly report but nearly 400 pages. You’ll get a profitability breakdown of
the major categories since October 2019, and a chapter detailing the results
for each team. Half of it discusses my own results, while the other half
discusses what I should have been betting. To read more,
visit the Amazon
store.
My 1st Quarter Profit: $5,786
American Thanksgiving receives considerable
publicity as a key juncture of the NHL season because most of the teams sitting
in a playoff spot on that day will go on to qualify for the post-season. But
technically it’s not Thanksgiving that’s the key date, just that the holiday
tends to fall very close to the end of the schedule’s first quarter (henceforth
referred to as Q1). Many pundits will call this “the quarter pole” to borrow
horse racing terminology, except that a real “quarter pole” marks the beginning
of the last quarter of the race. So using it this time of year just makes you
look stupid.
My first quarter last season was my best quarter
since this all began back in October 2019, but the bulk of my outstanding
success came winning the lottery on Arizona being terrible. The books took some
time to adapt as the Yotes lost their first dozen games, with me on the winning
end of all of them. My hope was that everyone’s desire to draft Connor Bedard
would create a perfect storm of tanking that would once again provide me with a lucrative “big short” opportunity. It’s not just one fantastic prospect, it’s a loaded
first round, one of the best in years.
Widespread desire to maximize ping pong balls in
the draft lottery did appear to influence some GM decisions in the summer time,
which only fed my expectation, but no player wants to lose. You can’t make
Jonathan Toews and Patrick Kane tank. You can only build crap around them and
hope they’ll waive their no-trade clauses. My big short of the Arizona Coyotes last October was extraordinarily profitable and allowed me to play with house money for the rest of the season. Following that same blueprint for Tank-A-Palooza 2022/23 burned me in the first 2 weeks.
As this new season began, there were only two
strategies at the top of my mind, exploiting Tank-Fest 2023 and home teams, having observed
their success in past season early in the schedule (more so in 2019 and 2021).
The second strategy proved beneficial as home teams got out to fast start,
dominating the categories for most of the first week (thanks mostly to
favorites), while the expected Tankathon did not materialize as expected. I did
have some early success with road favorites, as home dogs did not start strong.
Home teams did indeed get out to a fast start, winning
60.5% in week 1, 56.6% in week 2, then by week 3 it dropped to a more
normalized 52%. Home teams were also favored in 74% of games in the first 14
days, so oddsmakers were clearly looking at the same data as me, anticipating
some home dominance early in the season. In week 3 home teams were only favored
in 50% of matches, which basically means a few good teams were on the road
concurrently, as it bounced back to 69% in week 4.
Some of those good teams who returned home in week 4 hit concurrent slumps, as Calgary, Washington, St. Louis, Pittsburgh,
Edmonton, and the Rangers went a combined 2 for 15 on home ice. That’s why road
teams won 57% of the week 4 games (their first of the season winning more than
half). Given how often home teams tend to be favored, there’s a strong
correlation between my underdog success and road profitability. Road moneyline
(driven by dogs) finished the quarter as my best category, despite an early emphasis on home squads.
There must have been a full moon for the 10 days
before and after Halloween, as the dogs were howling. We had an extended period
of stunning upsets when underdogs transformed into mighty beasts. Initially the
trend was being driven by longshots of +200 or higher, the big dogs. The
smaller dogs weren’t getting it done early, but did join the party in week 3
(when all underdogs won a combined 51% of moneylines and 31% on the alt
puckline -1.5 goals.
I’m proud to report being among the first to board the
Buffalo bandwagon, but otherwise the first week of the underdog boom hit me
hard with large wagers on some of those heavy favorites, especially -1.5 goals.
Favorites -1.5 goals was supposed to be my primary vehicle to profiteer from
Tank-A-palooza, but led to a large loss in week 2. It was towards the end of
week 2 that I started to really pump the brakes on favorites -1.5 goals and
started experimenting with small wagers on underdogs -1.5 goals.
The art of differentiating an emerging trend from a
random convergence of variance can be a difficult endeavor as both things can
often camouflage as the other. I was ready to declare underdogs officially
trending after 3 weeks of solid gains, but also openly questioned the
sustainability. Oddsmakers made adjustments; in the first two weeks there were
14 teams that opened on the moneyline at +200 or higher. In week 3, there was
2. Somebody took notice.
Regardless of who let the dogs out, their success
proved unsustainable. The trend was being driven by hot or cold streaks from
just a handful of teams, and once their play (or the lines) normalized, the profitability of
underdogs diminished. Any teams that did sustain their hot or cold streaks
would soon be priced appropriately. Teams that shouldn’t be favored stop
getting favored, and visa versa with dogs. Montreal was one example of a
frequent longshot early in the schedule, but the longer they exceeded
expectations, the more line prices adjusted to the new reality (well sort of).
There were losing streaks by Pittsburgh and Calgary
that eventually stabilized, while the Rangers, Blues, Capitals, and Oilers all
started getting mauled by dogs. But most eventually found their form, and the
ones that continued struggling started to get priced accordingly. You need to
ride trends with caution because sometimes it is being driven by small handful
of teams, who can either reverse-course or are subjected to price adjustments.
I’m able to stay on top of trend shifts by doing weekly betting reports tracking all these outcomes.
Underdogs entered week 5 guns blazing, winning 13 of
the first 15 games, coming within 1 match of sweeping Tuesday night. Sadly for all
the dog lovers out there, that proved to be their peak, as it was immediately
followed by 12 days of downward regression. Some of the struggling favorites
settled down, while some of the dogs driving the trend like Chicago and
Philadelphia crashed hard. It took a few games before I was ready to stop
betting them, but eventually found myself staring into a mirror screaming “they
are who we thought they were!”
Week 5 was also when dogs -1.5 goals completely
collapsed, but I still finished with a profit thanks specifically to an
incredible week by Karel Vejmelka. I had been experimenting with small wagers
on these alt pucklines, but didn’t cut myself off until they went 1 for 13 on
the first two nights of week 6. I might have pumped the brakes hard, but didn’t
completely abandon the category, waiting instead for juicy lines against struggling
goalies on good teams. I did manage to finish Q1 with a 10% rate of return on the
category, so it was not a total loss.
Over/Under
My 5 Best Over/Under Bets: Market’s 5
Best Over/Under Bets:
($100
wagers)
1) Columbus overs, (+$890) 1) Columbus
overs, (+$830)
2) Winnipeg unders, (+$797) 2) Toronto
unders, (+$793)
3) Vancouver overs, (+$784) 3) Winnipeg
unders, (+$720)
4) San Jose overs, (+$728) 4) Vancouver
overs, (+$531)
5) Washington unders, (+$665) 5) Rangers
unders, (+$485)
My 5 Worst Over/Under Bets:
1) Rangers overs, (-$700)
2) Boston unders, (-$552)
3) Colorado overs, (-$540)
4) Boston overs, (-$426)
5) Chicago unders, (-$350)
The first couple weeks of the season can be a
tumultuous time to wager on over/unders, when we have very little data on just
how good each team is at scoring and preventing goals. But it’s also a
double-edged sword because oddsmakers also haven’t fully calibrated their own
models, creating an opportunity. There was a big boom in overs last season,
which was widely reported by many betting pundits, surely creating a
perception/appetite for overs among casual bettors. My expectation was that
scoring could regress, and totals might be set too high to offset public hunger
for goals.
Last season I performed very well early in the schedule
on over/unders without using any algorithms (10% return in week 1), but this
time around the start was rougher without my algorithmic safety net that I had
become dependent on in the previous campaign. My primary algorithm looks at the
last 5 games. My auxiliary algorithm looks back 10 games. Because of this rocky
start, I started consulting my primary algorithm in week 2, before most teams
had even played 5 games, but wasn’t always accepting the recommendations.
In week 1, overs did edge out unders, posting a
modest 5% rate of return, then in week 2 the floodgates opened and overs really
caught fire. This only provided a limited window of profitability because
oddsmakers were quick to start raising the totals to offset any scoring
increase. Those floodgates didn’t stay open long, as scoring regressed in week
3 creating value on unders. Despite all the pundit prognostication about
increased scoring, the public started gravitating to unders, which
really became apparent in my line movement tracking.
That short-term scoring boom followed by an
immediate bust created a problem for my algorithm when it did officially come
into use. It was remembering the boom when the bust was taking hold,
recommending too many overs, which oddsmakers had made more expensive. Soon the
newly emerging under boom began to look more like a trend than random variance.
There was also a noticeable trend towards unders in week 3 last season, but
without the surge in overs from day 6 to 13. In 17 games Saturday and Sunday in
week 3, the public bet down the total or payout on the under in 14 of them. A
lot of money was being played on unders.
It’s tough because often I’ll see a trend start to
shift, but don’t want to overreact too soon in the event it’s just variance. My
algorithm can take a few games before properly adapting to a new trend. Just as
it looked like scoring was trending down, week 4 was the highest scoring week
of the season to date with overs going 26-20-3. My algorithm only produced a
small profit, struggling with all that trend shifting. But the data collected
during that erratic week would set the stage for one of my most impressive O/U
performances in my recorded history.
In weeks 5 and 6 combined, I hit on 62% of all my O/U
wagers, with very few manual overrides. Though I was 4-1 on my overrides, all
overs when the back-up was likely or confirmed to be starting. Each of my
betting reports lists my top 3 categories to bet, and each of these two weeks
marked the first and second times that both overs and unders finished in my top
3 (at least that I’m aware of). I managed to post a profit in my overall
account balance both weeks, but would have been in the red had it not been for
over/under.
Overs were one of my best categories, despite being
the 3rd worst overall. Unders did have a higher rate of return and
were my 4th best category, but my algorithm was able to generate a
higher rate of return on overs. I don’t have an official explanation for how
this occurred, but as previously noted, the public was betting unders much more
aggressively than their counterpart. My guess is that this public action helped
me get a better payout on my over selections, except that higher payouts did
not significantly boost over profitability overall.
The New York Rangers were by far the best under
team last season, which makes sense given that their #1 goalie won the Vezina.
That continued this season, but Shesterkin was slightly less dominant. What
really helped New York unders in the first quarter was lower goal scoring. My
algorithm was mostly recommending Ranger unders, and went 0-7 on NYR overs.
Four of those were manual overrides when Jaroslav Halak was the expected
starter, but Halak unders went 5-0 while Shesterkin was 8-7. Halak was worse,
but got much less goal support.
Columbus overs were the best Q1 O/U wager, which
was also the case for most of last season. The addition of Johnny Gaudreau
increased their offensive potency, while the team defense suddenly became
significantly worse. That’s a perfect storm for overs. Meanwhile, the
resurgence of Connor Hellebuyck was a boon for Winnipeg unders, which was one
of my worst bets last season. I had a great first quarter hitting Winnipeg, but
still have some work to do to win back all the money I lost on that bet in the
previous campaign.
My algorithm was terrible at picking the right
outcome of Boston Bruin games, which was entirely the result of which goalie
started. It lost a big sum on Swayman unders and Ullmark overs. Though Ullmark
unders went 7-7, and you would have posted a loss had you bet every over or
every under. I was not expecting Toronto unders to be this profitable when they
lost both their top 2 goalies to injury. It’s a bit of a miracle that the
quarter played out the way it did. Even Kallgren had a low .891 SV% but his unders
went 5-3 because of high totals.
I’ve been using this algorithm for less than one full
season, so the sample is still relatively small, but rarely does it generate
such strong returns from both sides. Its output tends to be lower when there’s
an even balance between overs and unders, but it can cash big dividends when a
trend kicks in one way or the other and sustains for a few weeks. Typically
when it has a fantastic week, either one or the other category tends to be
responsible. Looking back 5 games catches trends quicker, but I do continue
consulting my original 10-game algorithm on each decision.
It’s a remarkably simple formula to use. Seldom does
it lose a substantial sum of money, but some weeks it hits several and
producing a hearty jackpot. Those of you who followed my betting reports last
season know all about its effectiveness. The formula was inspired by the law of
“keep it simple stupid” and basically just takes the average goals per game of
each team’s last 5 matches. If you’d like to know more about my over/under
algorithm and its success last season, click here.
Goalies
Two new columns
that were added to my main worksheet this season were the two starting
goaltenders, which I’m inputting at the end of every night. This makes it easy
to quickly investigate how I’m performing when each goalie starts anytime my
curiosity comes calling, but also when I’m making my bets on each game, I call
up the log. If the back-up starts every fourth game and the #1 just got the nod
3 games in a row, it’s not hard to fill in the blanks. I’m effective at predicting starters from my fantasy hockey experience.
So which goalies were the best to bet on or against
in the first quarter of the season?
Market’s 5 Best Goalies to Bet on: Market’s 5
Best Goalies to Bet Against:
($100 ML + $100 PL+1.5 + $100 PL-1.5) ($100 ML + $100 PL+1.5 + $100 PL-1.5)
1) Linus Ullmark, (+$2,742) 1) Erik
Kallgren, (+$1,808)
2) Karel Vejmelka, (+$2,339) 2) Sergei
Bobrovsky, (+$1,568)
3) Martin Jones, (+$1,538) 3) Jaroslav
Halak, (+$1,547)
4) Ville Husso, (+$1,019) 4) Thatcher
Demko, (+$1,507)
5) Jake Oettinger, (+$1,017) 5) John
Gibson, (+$1,448)
My 5 Best Goalies to Bet on: My 5 Worst
Goalies to Bet on:
(ML +PL) (ML
+ PL)
1) Alexandar Georgiev, (+$1,627) 1) Erik Kallgren,
(-$1,250)
2) Vitek Vanecek, (+$1,163) 2) Jaroslav
Halak, (-$1,075)
3) Karel Vejmelka, (+$1,075) 3) Alex
Nedeljkovic, (-$950)
4) Martin Jones, (+$824) 4) John
Gibson, (-$824)
5) MacKenzie Blackwood, (+$775) 5) Logan Thompson,
(-$749)
My 5 Best Goalies to Bet Against: My 5 Worst
Goalies to Bet Against:
(ML +PL) (ML
+ PL)
1) Thatcher Demko, (+$1,260) 1) Alex
Stalock, (-$1,057)
2) Casey DeSmith, (+$725) 2) Alex
Nedeljkovic, (-$625)
3) Jaroslav Halak, (+$709) 3) Daniil
Tarasov, (-$582)
4) Filip Gustavsson, (+$618) 4) Connor
Hellebuyck, (-$543)
5) Anton Forsberg, (+$588) 5) Jonathan
Quick, (-$490)
My #1 goalie to bet in the first quarter when you
add up the totals from every category was Thatcher Demko, with most of that
money coming from investing in his opponents. When he struggled to start the
season, I was ready. We’ve seen that script play out before, but he has always
followed up those terrible first quarters with a blazing hot streak in the
second quarter. We’ll see, Spencer Martin has moved into an almost equal time
share with slightly better play, but he’s not exactly outstanding either. My wagers
on Vancouver opponents produced a -$408 loss when Martin got the nod. Demko
went 2-11 while Martin went 4-2.
My first quarter was officially a disaster whenever
Alex Nedeljkovic was starting in goal for my favorite team, the Detroit Red Wings.
I posted a profit on the sum of Ville Husso starts, but Big Al was my bane,
losing -$1,808 across all
categories. He went 0-4 when I bet him to win and 2-0 when picking him to lose
(also going 0-3 on his unders and posting a $67 gain on his overs). This wasn’t
even a matter of making my bets early and not knowing who would start. Detroit
had a mostly predictable pattern; I just wasn’t making a big enough effort to
avoid betting Nedeljkovic. The biggest single loss was a failure to beat
Chicago, and after that most of my bets on his starts were small.
Logan Thompson cracked my list of worst goalies to
bet on, even though Vegas was one of the best teams of the quarter. The single
largest reason was a blown -$1,000 bet on the Vegas puckline -1.5 goals vs
Chicago, which is also how Alex Stalock landed as my #1 worst goalie to bet
against. $1,000 is the max amount I’ll allow myself to bet on any single game,
but I did not make any wagers that large after the Chicago game. That wasn’t
even a conscious decision that I made. I just lost my confidence in the
league’s worst teams being that bad.
Linus Ullmark was the best goalie to bet overall,
but he fell $2 short of cracking my top 5 (at +$773). I had an 83% stake in the
Bruins when Linus was in goal, but most of the investment was on the moneyline.
My failure (if you can call it that) was not putting more on the puckline -1.5
goals (where he ranked #2 league-wide behind Karel Vejmelka). Speaking of
Vejmelka, if you bet him to win all his starts, you had a nice quarter, whether
that was moneyline, +1.5 goals or -1.5 goals. Vejmelka on the alt puckline -1.5
goals was one of the sneaky best bets of the first quarter. That one cashed out
far more than the implied probability of the line would have anticipated.
One thing that burned me was Jaroslav Halak starts
when I was expecting Shesterkin. When I’m sure Halak is going to start, my
money is nearly always on New York opponents, and he was my 3rd best
goalie to bet against. But early in the season I had some big bets on the
Rangers expecting Igor, only to get spurned by an out of sequence start. The Rangers
did not have a predictable pattern for goalie starts, not like some teams that
will play the back-up every 3rd or 4th game like
clockwork (with some variability from lining up weaker opponents for the second string keeper).
Live Betting
A new category that I started recording late last
season was live betting, which is done in a separate worksheet and not included
in my weekly total. These are mostly hedges, when the team that I already bet
has a lead and the opponent is offering a high payout on the live line. I began
referring to this worksheet as my “hedge fund”, but it did not lead to profit
last season, and picked up right where it left off in May. Sometimes it works
and you feel really smart when it does, but adding them all up it’s just a big
net loser.
One story that has continued to get media attention
early in the schedule was the number of blown leads and come from behind wins. This
has been a record-breaking year for comeback victories. Intuitively, this would
suggest there was significant value on live betting teams that were trailing.
However, I was recording several live lines throughout this “comeback
revolution”, always for the team trailing, and the sum of my recorded picks
were not leading to profit. I was not recording the live line every single time
a team was behind, but there was a large enough sample that it should have at
least shown some profit.
How is it possible that all these comeback wins did
not translate into profit for my live betting portfolio? My theory is that an
overwhelming majority of the money wagered during games is being laid on the
team that’s losing, not on the team with a 3-0 lead and a -2100 line. If
there’s not an equal balance of wagers coming in on both sides, then the
betting lines are not necessarily going to represent the actual probability of
either team emerging victorious. My hedge fund was a net positive in week one
but was a loser in weeks 2 and 3, leading me to almost abandon them completely
by week 4.
For me to actually prove that these live lines are
unfair, it would require me to assemble a large database of games with the
score at each period’s end. Conversely, it’s much easier to determine if a game
line is fair before puck drop, because you know the winning percentage of each
team involved and can compare it to the implied probability of the moneyline.
But with live lines, it’s much harder for even the most mathematically talented
people to properly determine whether that line being offered is actuarily fair.
The other factor complicating my analysis on this
subject is that many of the live lines recorded were “hedges” on the opposite
side of what I had wagered prior to the game. So if I bet Toronto and they were
up 2-0, I’d hedge the opponent with up to 1/3 of my projected winnings to
guarantee a profit for either outcome. So my database was not a “random
sample”. There was bias in the choices, so the results might have been much
different had I just randomly chosen which lines to write down.
They weren’t all hedges, as I recorded several lines where
the better team was trailing. In total, I logged 43 live lines all for teams
that were trailing and 7 of them came back to win, which is 16%. It was
reported on Spittin Chiclets at the end of November that 46% of wins were come
from behind, which is dramatically higher than my live bet success rate. One
possible explanation is that most of mine tended to be 2 or 3 goal leads,
whereas that 46% surely includes a big number of blown 1-goal leads, which are
not the lines that I was logging.
If a $10 bet was made on each of my recorded live
lines, I would have lost -$45. It was not
catastrophically bad, but not what you’d expect under the circumstances. If a
live line is listed at +400 but should be at +750, most bettors probably
wouldn’t even know the difference, or that they are getting bad value. And if
very few are betting the other side, you could put a line that should be +1000
at +500 instead and I’m assuming people would still bet it. It’s possible to
estimate come from behind probability using my sample, but it would be a flawed
statistic.
Perhaps my best hedge was in week one when Vancouver
was up 2-0 on Philly in the 2nd period but were getting outshot
18-5. I had the Canucks moneyline, so took 1/3 of my projected winnings and put
it on Philly at +550. They won 3-2. It really helped that I was watching the
game that the Flyers were completely dominating play. One of the reasons that I
stopped trying the live hedges was because the lines seemed to be getting less
desirable, requiring me to risk a greater share of my winnings to hit a payout
that would cover both bets with profit.
Right now the observation that the lines became
undesirable is more anecdotal, because I have yet to take the time to dig into
my numbers and start estimating probability. That might be a good project to
tackle in the summer. Right now I’ll just continue recording interesting lines
and tracking profitability.
Back-to-Backs
Last season if
you bet $100 on the moneyline for every rested team to beat every opponent who
played yesterday, you would have banked nearly $4,000. This was a category that
posted an incredible 12% return, whether it was an underdog or a favorite,
didn’t matter. If you’d like to read more about the profitability of this
demographic in the previous 3 seasons, you should check out my new book. To read more, visit the Amazon
store. My theory was that everyone getting infected
with Covid could diminish players ability to recover after games.
My concern over
the summer while writing my book was that oddsmakers also knew exactly how much
they had lost on these games and would make the lines prohibitively expensive
in the new campaign (expressed in my week 1 Betting Report). Towards the end of
last season, there were many games where I noted “this line would make absolutely
no sense if it wasn’t back-to-back”. That was repeated in my game notes often.
So there was absolutely a movement to nerf the lines last season, but the
rested teams continued delivering wins.
Teams with a
rest advantage picked up right where they left off in May, at least early in
the new schedule. They won 78% of games in week 1, 60% in week 2, and 40% in
week 3. We saw diminishing returns, such that by the end of week 5 they had
only won a combined 53% with an average implied probability of 55%, meaning
they weren’t winning often enough to cover the price of their lines. I had a
lot of success initially on the moneyline, but took a giant loss when Vegas
failed to cover -1.5 goals vs Chicago in their second game of the season.
Rested teams vs
tired opponents was a big winner in week 1, but oddsmakers were quick to
compensate the betting lines, making some of them prohibitively expensive (like
the 0-2 Senators closing as -135 favorites to beat the 3-0 Boston Bruins). Despite
these costly prices, the public was still betting down their payouts in a large
majority of their games. Of the first 53 back-to-backs, 40 of them took public
money (meaning the payouts shrank on the closing line) while the tired teams
took money only 12 times.
If you bet $100
on every single back-to-back moneyline on the rested side, you won $400 in weeks
1, 4, and 6, but lost -$666 in weeks 2, 3, and 5. Ergo, these btb moneylines
were a net loser in Q1, but the pucklines produced better results, $189 profit
for +1.5 goals and $407 for -1.5 goals (which includes alt pucklines +1.5 for
favorites and -1.5 for underdogs). I actually finished the quarter down -$1,987 on all my anti
back-to-back bets, though -$1,000 of that came from that Vegas-Chicago game.
Last season I
fell into the habit of unambiguously betting against nearly every rest
disadvantage, but when the price gets too expensive, it can be hard to flip
your mindset and take the other side. Note to self: back-to-backs are a net
loser in the current schedule and it’s worth considering getting on the other
side of some of these wagers. There were 3 games when I bet on the tired
team, going 1-2. So my early attempt at zagging weren't fruitful, but
there’s a lot of games left to play.
My 1st Quarter Results:
*Market Bets calculated by betting
exactly $100 on every outcome this quarter*
My Best Categories: Market’s
Best Categories:
(all wagers) ($100 wagers)
1) Road moneyline, (+$2,969) 1) Longshots
ML (+200 and up), (+$1,140)
2) Over, (+$2,158) 2) Road
dogs moneyline, (+$757)
3) Underdog moneyline, (+$1,759) 3) Shorting
back-to-backs -1.5 goals, (+$407)
My Worst Categories: Market’s
Worst Categories:
(all wagers) ($100
wagers)
1) Shorting back-to-backs, (-$1,987) 1) Favorites moneyline,
(-$2,449)
2) Heavy favorites -1.5 goals, (-$1,803) 2) Favorites -1.5 goals,
(-$2,354)
3) Heavy favorite moneyline, (-$1,009) 3) Overs, (-$1,823)
Market Best Moneyline Bets: Market Best
Teams to Bet Against ML:
($100 wagers) ($100
wagers)
1) New Jersey Devils, (+$1,082) 1) Toronto
Maple Leafs, (+$606)
2) Boston Bruins, (+$946) 2) Florida
Panthers, (+$588)
3) Seattle Kraken, (+$656) 3) Washington
Capitals, (+$480)
Market Best Bets +1.5 Goals: Market Best
Teams to Bet Against +1.5 Goals:
($100 wagers) ($100
wagers)
1) Seattle Kraken, (+$624) 1) Toronto
Maple Leafs, (+$468)
2) Vegas Golden Knights, (+$431) 2) Anaheim Ducks,
(+$459)
3) Boston Bruins, (+$390) 3) Calgary
Flames, (+$437)
Market Best Bets -1.5 Goals: Market Best
Teams to Bet Against -1.5 Goals:
($100 wagers) ($100
wagers)
1) Boston Bruins, (+$1,719) 1) St.
Louis Blues, (+$1,225)
2) Arizona Coyotes, (+$1,590) 2) Florida Panthers,
(+$1,010)
3) Dallas Stars, (+$1,475) 3) Columbus
Blue Jackets, (+$800)
My 5 Best Teams to Bet on: Market’s 5 Best Teams to Bet on:
(ML +PL) ($100 ML + $100 PL+1.5 + $100
PL-1.5)
1) New Jersey Devils, (+$2,333) 1) Boston Bruins,
(+$3,055)
2) Boston Bruins, (+$1,462) 2) New
Jersey Devils, (+$1,885)
3) Colorado Avalanche, (+$1,344) 3) Arizona Coyotes,
(+$1,863)
4) New York Islanders, (+$827) 4) Dallas Stars,
(+$1,760)
5) Seattle Kraken, (+$824) 5) Seattle
Kraken, (+$1,480)
My 5 Worst Teams to Bet on:
(ML +PL)
1) New York Rangers, (-$1,251)
2) Toronto Maple Leafs, (-$1,139)
3) Vegas Golden Knights, (-$856)
4) Chicago Blackhawks, (-$835)
5) Detroit Red Wings, (-$774)
My 5 Best Teams to Bet Against: Market’s 5
Best Teams to Bet Against:
(ML +PL) ($100 ML + $100 PL+1.5 + $100
PL-1.5)
1) Florida Panthers, (+$1,064) 1) Florida
Panthers, (+$2,015)
2) Vancouver Canucks, (+$852) 2) Anaheim
Ducks, (+$1,396)
3) St. Louis Blues, (+$767) 3) Nashville
Predators, (+$1,156)
4) Minnesota Wild, (+$749) 4) Toronto
Maple Leafs, (+$1,129)
5) Pittsburgh Penguins, (+$668) 5) St. Louis
Blues, (+$1,045)
My 5 Worst Teams To Bet Against:
(ML +PL)
1) Los Angeles Kings, (-$858)
2) Seattle Kraken, (-$806)
3) Chicago Blackhawks, (-$736)
4) Vegas Golden Knights, (-$715)
5) Winnipeg Jets, (-$651)
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, over or under 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 each quarter. I’ll also list the results of betting $100 on
every outcome for each team.
1) New Jersey
Devils, ($2,298):
Some of my preseason
team prop bets are looking genius in retrospect (Nashville +170 to miss the
playoffs, Minnesota and Pittsburgh under 101.5 PTS, etc.) but one that’s not
aging well is the New Jersey Devils under 90 PTS. I knew this was a young team
on the rise, but was not expecting the addition of Vitek Vanecek to shore up
their porous goaltending. There’s a reason Washington let him go. We’ve seen
MacKenzie Blackwood be good for stretches in the past, but last season was a
train wreck. The Devils lost their first 2 games against Philly and Detroit by
a combined score of 10-4, and my pessimism looked justified.
But that did not last
long, as they would go on to win 16 of their next 17 games. But it was their
1-0 victory against Colorado that inspired me to start aggressively betting
Jersey to win, with great success. They had won 4 of 5 entering that Avs game,
but all their wins were against teams who missed the playoffs in 2022. Most of
my investment was on the moneyline, totaling $2,198 of profit. They went from
#21 in my week 3 Power Rankings, all the way to the top by the end of week 6. The
thing about doing these weekly betting reports, it doesn’t take long to figure
out when I’m wrong and need to adjust my tactics.
I’m also a big believer in Jack Hughes and drafted him
on all my fantasy teams. Both my fantasy teams drafted too many centers and I
made 30+ trade offers trying to unload my surplus. Hughes was included in
exactly zero of my trade offers. The kid is a super star. Devils over/unders
were 8-8-3, so there was no clear if you always bet one way or the other and my
algorithm posted a small profit on both sides. For me it didn’t matter which
goalie started, as I had a strong rate of return regardless of who got the nod.
2) Montreal
Canadiens, ($1,517):
The Montreal
Canadiens defeated the Toronto Maple Leafs in their first game of the season.
They finished dead last in the previous season, but started this one off in
style. In my game notes for their next match, I wrote “Montreal might be
dangerous” letting myself know that this team might not follow the expected
Tank-A-Palooza 2023 script. So I had already been succeeding with betting
Montreal to win when they were a +250 dog vs St. Louis (including +550 on the
alt puckline -1.5 goals), and struck gold. The Blues had lost 3 consecutive
games by a combined score 13-3, so the bet was really a no-brainer.
Hitting on Habs -1.5
goals at +550 might have been the longest odds that I had ever hit on a hockey wager,
helping lift the team to #2 in my week 3 Power Rankings (I later hit bigger lines on the Yotes). They went 4-5 to close
out the quarter after that Blues win and my money was on Montreal to win 8 of
those (the only game where I bet their opponent in that stretch was New
Jersey), yet they were able to sustain their position in my ranks because they
won some games as a significant underdog with a big payout. My algorithm crushed their overs in weeks 5 and 6. They finished Q1 just a few PTS out of
the wildcard when they were expected to be a lottery team.
My returns were
strong for both Jake Allen and Sam Montembeault, but Sammy was the better
goalie, posting a .915 SV% in the first half while Jake Allen was down at .891.
If you liked betting Montreal opponents -1.5 goals, it was better for you if
Jake Allen was in goal. You also wanted to be betting Allen over and
Montembeault under, though I actually did very well on over/under when
Montembeault started, with equally impressive returns both over and under (in
Monty’s 6 starts, I was 6 for 6 on my O/U bets). Meanwhile, Jake Allen unders
cost me -$218.
3) New York
Islanders, ($1,445):
Looking at my game
notes from the Islanders first 4 matches: “unsure about Isles early” to “not
sure Isles are good yet” (insert blowout win vs Ducks) to “pretty sure Isles
are better”. From that game note on, my money was on New York for 6 of their
next 8 games, paying dividends. Last
season they started with a crazy road schedule while renovations were being
completed to their arena, which played a role derailing their entire season.
With a return to normalcy, they proved the previous campaign was just an
aberration. Ilya Sorokin’s .926 Q1 SV% had a lot to do with their early
success, complemented by reliable scoring.
Though it should be
noted that my proficiency betting their games thus far isn’t just because
they’re a better team and I’m picking them to win. I’ve actually posted a decent
return when betting them to lose. The Isles only lost 8 games in Q1 and 6 of
them were by 2 or more goals, so while their opponent moneyline was a net
loser, you would have turned a profit betting to lose every match on the
puckline -1.5 goals. You might think that the Isles are a tight checking low
scoring team, but their overs went 11-9 thanks to contributions from the
offense. My algorithm generated profit on both sides, but more from overs.
Sorokin was the
better goaltender, but both gatekeepers had a similar win rate. There wasn’t a
lot of difference between the two from a betting standpoint, but it was better
to have Sorokin if you bet the Isles to cover -1.5 goals. Both Sorokin and
Varlamov were net losers on unders with a small gain on overs, so it didn’t
really matter which one was starting when you were making over/under wagers.
4) St. Louis
Blues, ($1,355):
I’ve said it before
and I’ll say it again, one of my big rules is “never trust the St. Louis Blues
to win or lose”. At the core of that decree is Jordan Binnington. When he’s
struggling, which happens every so often with some regularity, you don’t want
your money invested in St. Louis against anyone. But it’s also hard to trust on
the other side, because sometimes he’s brilliant and will steal games. Good
luck figuring out which one will show up. That’s why I’ve struggled myself with
this team at various points in the past 3 seasons, but 2022/23 is off to a good
start.
The Blues on the
second half of a back-to-back with Thomas Greiss in goal got blown out by the
struggling Nashville Predators 6-2. The next game against Montreal, oddsmakers
must have expected them to come out flying to avenge that embarrassing loss
because they were a -300 favorite to beat Montreal. The line made no sense to
me with the Blues losing 3 straight games, so my money was on the Habs, with a
little extra on the alt puckline -1.5 goals at +550. Montreal won 7-5 and I hit
the jackpot. That’s when I smelled blood in the water and my predatory instinct
kicked in.
If you had bet $100
on St. Louis opponents -1.5 goals in all 4 of their games that week of the loss
to Montreal, you walked away with $1,310. Every other week combined their
opponents were a net loser -1.5 goals, as the Blues followed up an 8-game
losing streak with a 6-game winning streak. Ironically as erratic as Jordan
Binnington was, he’s the whole reason St. Louis ranks this high. I’m up
$543 betting J.B to win, $545 betting him to lose, with a small profit on
unders and a strong profit on his overs. Meanwhile, I lost -$58 in the 4 Thomas
Greiss starts.
5) Washington
Capitals, ($1,214):
One month into the
season, Washington was one of two teams generating profit for my portfolio
“from all categories” meaning; betting to win, lose, over, under. Though it was
their very predictable over/unders that was the primary driver of my early
success, especially unders, elevating them into my top 5 teams. There was not a
specific strategy driving my pick selection on moneylines and pucklines, just
that I tend to like betting Washington when they are underdogs, avoiding them
more often when favored. But once they really started to struggle, even betting
them as dogs became unappealing.
They were devastated
by injuries early in the schedule and found themselves 6 PTS out of dead last
at the end of Q1. They had a home game against Arizona as a -280 favorite, and
I had no interest whatsoever in paying that price, so took the Yotes +235,
which was a winner. They made a significant summer upgrade in goal adding Darcy
Kuemper from the Stanley Cup champions (who posted a decent but not impressive
.907 Q1 SV%), but didn’t do much to upgrade their aging forward group. That’s a
partial explanation for why their unders were such a strong first quarter
investment.
All my over/under
profit came when Kuemper was starting, as I was a net loser in Charlie
Lindgren’s 5 starts. There was not a significant drop-off with the back-up
goalie, who posted a .905 SV% and actually had a higher winning percentage.
There was not a lot of public confidence in the Caps, as their projected payout
was only bet down from open to close in 25% of their games (33% of Kuemper’s
starts and 0% of Lindgren’s starts). Nobody was rushing out to bet Washington
when they found out Lindgren would get the net.
6) Vancouver
Canucks, ($1,175):
Living in Vancouver
means that I watch more Canucks games than any other team and also listen to
local sportstalk radio. I was quasi-bullish on the Canucks entering the season,
betting them to win twice in their first 3 games. They went 0-3 and it quickly became
apparent this was another bad start, which we’ve seen each of the last 2 years.
So I started aggressively betting Canuck opponents with fantastic results.
Thatcher Demko struggled early too, mostly thanks to terrible defense in front
of him (especially short handed) making their overs a solid investment early
(which I cashed in on).
The bulk of the
credit for my Q1 Canucks success goes to opponents moneyline and overs (which
went 13-6). The team wasn’t much better at home, winning only 38% of their
games in Vancouver. With J.T Miller, Elias Pettersson, Bo Horvat, Brock Boeser,
Quinn Hughes, and Thatcher Demko, there is no way they should be that bad. At
the end of Q1 they were closer to dead last than a playoff spot, so it’s
looking unlikely that playoffs are probable. There has been a lot of
speculation that the coach is responsible for the terrible defending and that
he could be fired, but at this point, maybe just keep the bad coach and play
for lottery balls.
It should probably be
noted that all my profit betting Vancouver opponents came when Thatcher Demko
was in goal. Spencer Martin was slightly better with an .898 SV% (while Demko
was .883), winning 4 of his 6 starts. My rate of return on overs was similar for
both netminders, but I profited $1,260 betting against Demko while losing -$408 betting against
Martin. Strangely the public seemed enthusiastic betting Vancouver when Martin
was starting, as the team’s projected payout was bet down from open to close in
5 of his 6 starts. This is relatively uncommon with back-up goalies (at least in my short experience tracking closing lines).
7) Minnesota
Wild, ($1,094):
If you follow the
infallible Dom Lyshycycshn at the Athletic, you might have expected Minnesota
to be the best team in the NHL this season. I was less bullish on their outlook
given the salary cap catastrophe they’re dealing with, but certainly wasn’t expecting
them to get outscored 20-12 in their first 3 games. Once that happened, my
investments shifted entirely/quickly to their opponents. I even proposed a same
game parlay Avs -1.5 goals with the over at +475 to one of my Twitter
colleagues, but didn’t actually make the bet. It would have been a winner. You
miss 100% of the shots you don’t take.
They did eventually
settle down and improve, but their lines continued to get priced like that
terrible start never transpired. That’s why I continued betting Wild opponents
after the course correction. They lost 4 of 5 games to start the season, then
won 4 of 5 games, then lost 5 of 7. Bad start, good recovery, then another
slump. The early porous goaltending was a boon for their overs, but eventually
Fleury settled down and their unders starting cashing tickets. I crushed their
overs early in the schedule, then my algorithm switched me to unders almost
exclusively, which is how they became one of my best O/U teams, profiting on
both sides.
Fleury and back-up
Filip Gustavsson posted nearly identical numbers, both with .906 SV% by the end
of the quarter, but Fleury went 6-5 while Gustavsson went 2-5. Fleury started twice as many games, but I actually made more money from Gustavsson’s starts,
mostly from betting him to lose. I lost nearly -$500 betting Fleury to win, and only a small
gain betting him to lose, but my performance betting his over/unders was
outstanding. Despite his early struggles, Fleury’s unders went 7-5.
8) Philadelphia
Flyers, ($1,091):
Most experts seemed
to think the Flyers were going to pick up where they left off last season,
competing for last place. My own expectations were not much higher. What we all
failed to see was Carter Hart being possessed by the spirit of Pelle Lindberg
and carrying the team on his shoulders to win 4 of their first 5 games (I
watched their entire second game vs Vancouver). By their third match vs Tampa,
my game notes said “Flyers are frisky”. This led me to a very nice run betting
Philly to win, but alas that early success would eventually prove to be just an
illusion, or perhaps they were simply derailed by injuries.
I still finished Q1
up more than $300 betting them to win, but that number was much higher before
the clock struck midnight and I was a little too slow to react. The turning
point for me was when Carter Hart began struggling. He still finished the
quarter with a .920 SV%, but that was north on .940 just a few weeks earlier.
He regressed hard in the last 2 weeks of Q1. Philly unders went 10-8-1, but I
somehow managed to post a greater profit on their overs while still performing
strong on the unders. My algorithm had a good handle on which outcome to
select.
The Flyers were one
of 6 teams where I posted a profit betting them to win, lose, over, and under,
which always brings me personal satisfaction. That being said, I’m not
expecting myself to bet them to win very often in the second quarter, it will
mostly be small wagers on longshot moneylines. One interesting Q1 stat is that
the Flyers were in a lot of 1-goal games, whether winning or losing. Betting
either team +1.5 goals would have won your wager in 14 of 19 games, though you
would have generated a higher rate of return betting the road teams +1.5.
9) Dallas
Stars, ($1,053):
The 2022 Stanley Cup
playoffs was a coming out party for Jake Oettinger, as the Stars nearly upset
the heavily favored Calgary Flames in round 1. I wasn’t sure what to make of
this team entering the new season, but it didn’t take long for the Dallas train
to start rolling, winning their first 2 games against Nashville by a combined
score of 9-2. By their third game, the comment in my game notes was “Dallas
might be good”, I just needed to see them beat somebody better than Nashville.
It’s worth noting, they had 11 Q1 wins, but only 3 of them against teams in a
playoff spot by American Thanksgiving.
They finished the
quarter in second place in the western conference, and most of my money was
invested in their victories, but somehow only managed to walk away with $9 of
profit from their 11 wins. Whereas they lost 8 games and I banked over $300
from their losses. Here’s a fun fact, the Stars won 11 games, all of them by at
least two goals; if you bet $100 on them to win every moneyline, you only
banked $135, but if you bet them -1.5 goals every game, you won $1,475. That
was the biggest disparity between any team’s ML and PL -1.5 in Q1, but Arizona
wasn’t far behind.
Oettinger was looking
like a Vezina trophy favorite for the first few weeks, but the train seemed to
come off the tracks when he missed a few games with injury. Upon his return,
there were still some good games, but suddenly he was getting blown up more
often. He still finished the quarter with a .929 SV% but his unders were only
6-5-1. I actually won more money on his overs, but produced a nice profit on
both. Back-up Scott Wedgewood wasn’t bad.
10) Arizona
Coyotes, ($1,033):
I entered this NHL
season fully embracing Tankfest 2023, and Arizona was among my prime
targets. The problem was, oddsmakers were one step ahead of me. The same “Big
Short” of the Coyotes that fueled my success last October was going to be a lot
more expensive in 2023. They played their first 6 games on the road, losing 4.
I managed to win some money betting Arizona opponents for that opening road
trip despite a big loss on their upset win in Toronto. By the time they
returned home, the underdog revolution had started to take hold, and I found
myself betting Yotes to win every match for 11 consecutive games (that streak
was still alive as at the time this was posted).
It’s worth noting
that had I ended the 1st quarter on Wednesday instead of Tuesday,
Arizona would have ranked #2 in my Power Rankings after a 4-0 win against
Carolina. They hit yet another longshot puckline, this time at +750. They were
actually the #1 team in the entire NHL to bet -1.5 goals every game in Q1
(generating $1,590 on $100 wagers) despite only hitting 5 in 17 games. Why? The
lines were +850, +400, +600, +390, and +550. I never would have imagined in
September betting this much on Arizona to win, but they continue cashing big
tickets as extreme longshots.
All my success
betting Arizona to win was thanks to Karel Vejmelka, losing -$550 betting on
Connor Ingram wins. Vejmelka finished Q1 with a respectable .909 SV%, while
Ingram was down at .885. Coyote unders went 10-7, but they were 8-3 with
Vejmelka and 2-4 with Ingram. I was a big winner on Vejmelka unders and Ingram
overs, but a net loser on Vejmelka overs and Ingram unders. My algorithm posted
a small gain on both overs and unders, but I would have been better off waiting
for the starter to be named before making my O/U selection.
11) Pittsburgh
Penguins, ($1,019):
The Penguins were one
of my worst teams to bet last season, but many of those mistakes did not carry
over to the new schedule. The Pens started strong, winning 4 of their first 5
games and I was on the right side for most of those matches. However, when they
started a 7 game losing streak, I was able to quickly adapt thanks to most of
them being road games when I’m already more likely to take their opponent.
October was one of the best months I’ve ever had betting on Pittsburgh games,
mostly thanks to overs and betting them to lose.
The Pens can be a
very public team, so it can be hard to get value on their lines if you’re
betting them to win. When they become extra profitable is when they struggle.
In this case my foot stayed on the gas pedal a little too long, but my course
was corrected once they started winning again. They followed up that 7-game
losing streak by winning 6 of their next 8 games. They went from good, to bad,
then back to good. One of the reasons for the losing streak was Tristan Jarry
struggling, as Casey DeSmith eventually injected some stability.
All my Penguins
profit came with DeSmith in goal. For as erratic as Jarry was, he was
reasonably effective when my money was on Pittsburgh opponents. Whereas DeSmith
had better numbers, but lost more games when I bet Pens to lose. It’s a little
paradoxical. Their overs went 10-8 and my algorithm recommended too many
unders, at least when Jarry was in goal. It posted a profit on Jarry overs and
DeSmith unders, which was the overall strategy you should have been following
in the first quarter.
12) Buffalo
Sabres, ($1,008):
I’ll be the first to
admit that my expectations for the Buffalo Sabres entering the season were not
particularly high. It seems strange to me in retrospect that I was betting them
to win every game right from opening night. Checking my game notes, the first two
bets were just undesirable line price on their opponent. Then by game 3, I
wrote “Sabres are frisky” and then it became a trend, at least until the rest
of the public caught wind of this rising storm and the prices on Buffalo
started becoming prohibitively expensive.
The Sabres would win
7 of their first 10 games, but getting turned off by their line prices proved
to be a gift, as they followed up that impressive start with 8 consecutive
losses (all but one of those were by at least two goals). The collapse wasn’t
something that I anticipated, and had they continued being priced as dogs, I
probably would have continued hitting that far too long. Instead, I finished
the first quarter making a nice profit both when betting them to win and lose,
with a similar amount of money invested in both outcomes.
Buffalo overs went
10-8-1. Most of my money was on that outcome, managing only a small profit. One
of the reasons for their hot start was receiving a few weeks of spectacular
goaltending from both Eric Comrie and Craig Anderson, but it was Comrie
especially who crashed the hardest, as his SV% plummeted all the way to .887 by
the end of the quarter. If you had bet $100 on Comrie to win every game by at
least 2 goals, you banked $735. If you bet his opponents to win every game by
2+, then you also made a profit, $430. Comrie was not in many 1-goal games.
13) Ottawa
Senators, ($753):
One of my favorites
preseason team prop bets was Ottawa over 86.5 PTS after they made some good
moves to strengthen their roster in the summer, but by the end of the first
quarter, they were on pace for 56 PTS. One of the problems with my calculus was
not anticipating how competitive the Atlantic division would be, as both
Montreal, Buffalo, and Detroit all improved over the 2nd half of
last season. While the Sens might have fallen short of my expectations, it
didn’t translate into big betting losses. I wasn’t making big bets on Sens to
win.
After losing their
first 2 games, they went 4 for 4 including wins against Boston, Washington, and
Dallas. In theory that should have boosted my confidence in my preseason
prediction. Instead, my money actually flipped more to the other side, mostly
because of undesirable line prices. Oddsmakers still thought this could be a
playoff team, and they were being priced accordingly. So I had become a
majority shareholder in their losses before even fully realizing that they were
not quite as good as my expectation.
My over/under
algorithm had a strong quarter, pulling a nice profit from both sides of Ottawa. Unders
went 9-8-1, yet I managed a higher rate of return on overs. It was just a
matter of picking the right ones. The expected #1 starter Cam Talbot was
injured heading into the season, and Anton Forsberg seemed to take a step
backwards. Forsberg did the damage to my Sens unders, while my algorithm was
very effective at selecting the right outcome when Cam Talbot was in goal.
14) Florida
Panthers, ($707):
My big criticism of
the Panthers after trading Weegar and Huberdeau for Matthew Tkachuk was that it
made them a worse team, even if it was intelligent roster management (or so
people told me). Early in the season they were clearly not as good, and it was
their loss to Chicago in game 7 that inspired me to take a larger short
position (hitting bets on Philly +175 and Arizona +225 in the games that
followed). They continued struggling to get traction for the rest of Q1,
winning 3 of 4 then losing 3 of 4. They were outside the playoff picture on
American Thanksgiving.
Within 2 games of
that Chicago loss, I was writing in my game notes that the Panthers were in
trouble. Oddsmakers continued charging expensive prices, which was a big factor
driving me to their opponents. This was also right around the time when I was
betting underdogs heavy as a demographic, and Florida was certainly one of the
wounded beasts I was preying on. It cost me some big bets to figure out they
were not the same team, but once my wagers shifted to the other side, I won
back all those losses and more.
A big difference in
Panther play from Q1 last season was a big step backwards for Sergei Bobrovsky,
who produced an .888 SV% versus Spencer Knight’s .918. Their overs went 11-7-1,
with Bobrovsky going 8-4 and Knight going 3-3-1. You definitely wanted to bet
Bobrovsky overs, while Knight overs were a net loser. Bobrovsky lost more
games, but I won more money when betting Spencer Knight to lose, mostly because
he was getting more starts by the time I was aggressively betting Florida to
lose.
15) San Jose
Sharks, ($669):
My expectation
entering the season was that San Jose would be worse after trading away Brent
Burns, leaving their blueline deplorably thin. What I did not foresee was Erik
Karlsson entering the campaign in “beast mode” and producing at a higher rate
than any defenseman in the salary cap era for the first dozen games. Despite
that super-human effort, they were still a bad team. Even during my little
underdog revolution, I wasn’t jumping on Sharks to win terribly often (and was
a net loser when doing so).
They were bad, but I
had difficulty profiteering from that because they were actually pretty good at
covering pucklines +1.5 goals. That’s noteworthy because I had some large
wagers on their opponents -1.5 goals, and was a big loser when doing so,
offsetting my gains on the opponent moneyline. Frankly I was a net loser both
when betting this team to win or lose, but they ranked #15 in my Power Rankings
because my over/under algorithm absolutely loved betting on this team, most especially
overs, which went 11-9-1.
James Reimer was the
better goaltender (.906 Q1 SV% vs Kahkonen’s .894), earning twice as many
starts. Reimer’s over/unders went 7-7, split evenly, but my algorithm went 10-4
in those 14 games, with a good return on both over and under. It was indeed
Reimer who stole all my puckline wagers, though Khakonen was also a net
positive +1.5 goals., while his overs went 4-2-1. Certainly seemed like you
want the over if you think Kahkonen will start, but the sample is still small.
16) Colorado
Avalanche, ($557):
The Stanley Cup
champions lost some big pieces in Darcy Kuemper and Nazem Kadri over the summer
while also losing captain Gabriel Landeskog to long-term injury. This was not
the same Avalanche team that steamrolled the opposition last spring, replacing
Kuemper with Rangers back-up Alexandar Georgiev, whom it was widely reported
seems to be a better goaltender with a bigger workload. That proved to be
correct in Q1, as Georgiev posted an impressive .929 SV%, while Francouz wasn’t
much worse at .925.
The team might have
lost some pieces, but they still had Makar, MacKinnon, Rantanen, and top
quality goaltending; helping them overcome those losses. I won $1,326 on their
first 3 games, then lost -$1,275 on their next 3. In the second and third
weeks, the Avs only won 3 times in 7 games, which might have increased my
pessimism, but it also helped to lower their line prices. Suddenly, we started
getting decent value on Avs to win, and I successfully jumped back on that bet
in time for them to win 6 of 7 in weeks 4, 5, and 6.
All my success
betting Avs to win came with Georgiev in goal, but my algorithm was awful at
betting his over/unders. You’d think with his numbers that unders would have
been a good bet but they only went 6-6 in his 12 starts. My algorithm went 2-10
on those games, losing equally large amounts on overs and unders. Francouz was
the lesser gatekeeper, yet I pulled a nice little profit from his unders. I’m
not precisely sure what happened in those Georgiev starts, but I’m going to
hope it’s just fluke variance.
17) Winnipeg
Jets, ($537):
Add the Winnipeg Jets
to the list of teams who exceeded my expectations early in the schedule. It was
not a deliberate strategy on my part to bet them to lose, but they kicked off
with a very tough schedule vs New York, Dallas, Colorado, Vegas, Toronto, and
St. Louis. My money was on their opponents for 5 of the first 6 games, with the
Jets going 3-3 and me going 4-2. Nikolaj Ehlers was placed on IR, and last time
that happened the Jets really struggled. This time however, they performed
better in his absence, winning 5 of their next 6.
After a few games, I
started taking notice and betting them to win more often, but the damage had
already been done. By the end of the quarter, I won $500 betting the Jets, and
lost -$650 betting their
opponents. The whole reason that I’m here to report a $537 profit is because of
their unders, which went 12-4-1. It’s worth noting that Winnipeg was my #1
worst over/under team last season, and a lot of that red ink came from chasing
unders. It’s satisfying that it’s finally starting to generate positive
returns, but who knows how long that will last.
Of course the reason
that their unders boomed in Q1 is because of the return to form of former
Vezina winner Connor Hellebuyck, posting a .935 SV%. Betting Hellebuyck to lose
was a costly mistake for me early in the schedule, but eventually the correct
adjustment was made. Interestingly, he did post a better return -1.5 goals than
on the moneyline, as the Jets were giving him strong goal support. Back-up Big
Save Dave posted an .890 SV% yet still won 3 of 4 starts (his overs went 2-2).
18) Seattle
Kraken, ($482):
Seattle’s inaugural
season fell far short of the high bar set by the Vegas Golden Knights, as the
Kraken were one of the worst teams in the league, thanks in large part to
porous goaltending. They lost 5 of their first 7 games and it was starting to
look like there was no improvement, at least until Phillip Grubauer was injured
and an unlikely hero stepped into the fold, journeyman Martin Jones. That was a
turning point for me because Grubauer was injured in a victory against Colorado
that cost me a large wager.
Beating the Avalanche
helped gain my confidence, as they would go on to win 8 of their next 12 games
to close out the quarter, and my money was on Seattle to win/cover 9 times in
those 12 games. Martin Jones was even added to one of my fantasy teams. You definitely
didn’t want to bet Seattle to lose in the first quarter, especially on the
road, and especially -1.5 goals. In 18 games, Seattle only lost by 2 or more
goals twice, covering +1.5 goals 16 times (including every single road game).
If you’re wondering, yes that ranks them #1 in the league in that category.
Jones wasn’t
spectacular, just reliable with a .913 SV%. Sometimes reliable is all it takes
for a team to go on a winning streak. That reliable goaltending also helped
juice their unders, which went 10-6-2 (and 9-4-2 when Jones was the starter,
meaning their overs 2-1-1 when someone else was in goal). Certainly all my
success betting Seattle came with Jones in goal, losing -$600 in 3 Grubauer
starts. I spent most of that Jones run waiting for the clock to strike midnight
on Cinderella (which it did at the start of Q2 after Grubauer returned from injury).
19) Los
Angeles Kings, ($339):
Some of you might
recall my uncanny ability to affect the outcome of LA Kings games last season;
whatever my wager, the opposite would happen. It was like I had the power to
make them win or lose based on my choices, but it was more about them beating
good teams and losing to bad teams. Even if there truly was a divine curse on
my Kings bets last season (which is just a joke by the way), it seems to have
lifted. The same perils that befell me last year have mostly been avoided.
Though I’m still a big loser when betting King’s opponents.
Part of my problem
last season was Jonathan Quick outplaying my expectation and Cal Petersen being
entirely erratic. Petersen is equally terrible, posting an .876 Q1 SV%, but
still won 5 of 9 starts thanks to strong goal support. Whereas Quick took a
step backwards and had a losing record in the first quarter, except when my
money was on LA opponents. When I bet Quick to win, his record was 1-5. When I
bet him to lose, his record was 5-2. The explanation is probably similar to
last season, losing to bad teams and beating good teams.
The only reason that
the Kings were not down near the bottom of my Power Rankings yet again was
their over/unders, which my algorithm has been crushing. Having both goalies
struggle meant their overs went 12-9-1, and I pulled a really impressive profit
when invested in that outcome. Quick overs went 6-6-1 (with my algorithm
pulling a nice profit from both sides in his starts) while Petersen went 6-3.
There was definitely a higher rate of return on Petersen overs.
20) Calgary
Flames, ($150):
Many of us expected
the Calgary Flames would either improve with the additions of Kadri, Huberdeau
and Weegar, or at least maintain their status as a playoff contender. Few were
expecting them to get worse, and that’s what happened in the first month of the
season, which even included a 7 game losing streak. They did manage to win 4 of
their first 5 games, where I had initial success betting them to win. Shortly
thereafter the rug got pulled out, and I lost a few big bets before even
realizing what was happening.
Calgary is a team
that I’ve struggled with at various points of the last 3 seasons, especially
2019/20 when they finished dead last in my Power Rankings. I managed to post a
small Q1 profit on their games, thanks mostly to betting their opponents on the
moneyline. The one wager that you really wanted to avoid was Calgary to win by
at least 1.5 goals, as betting $100 on each would have lost you more than -$1,000. That also means
that Calgary opponents +1.5 goals was a good wager to make on a regular basis.
Their overs went 9-9,
and I pulled a small profit on both outcomes. Jacob Markstrom took a step
backwards, posting a SV% of .889, compared to Dan Vladar’s .881. Part of the
reason for the Flames success last season was that Markstrom .922 SV%, but he’s
looking more like 2021 Jake who posted a .904. It’s hard to tell if the change
in personnel is what’s hurt his play, or if he’s simply getting older and
starting to wear down. He also struggled in 2021, badly for some stretches,
when they still had Gaudreau and Tkachuk.
21) Columbus
Blue Jackets, ($140):
One of my bigger
misreads from the NHL preseason was expecting the Columbus Blue Jackets to be
better with Johnny Gaudreau. I encouraged people to draft Elvis Merzlikins in
fantasy, and he was one of the league’s worst goalies in the first quarter. The
BJs ended the quarter closer to getting Connor Bedard than making the playoffs,
leaving me feeling stupid, but I could not have been alone in that assessment.
Fortunately my flawed foresight did not lead to betting losses, as it didn’t
take me long to smell blood in the water and start exploiting the opportunity.
Sadly though,
oddsmakers caught on to the futility pretty quick, so it became expensive to
pick their opponents. The problem for me was that they managed to pull some big
upset victories when I had large wagers on their opponents, so I ended up
losing nearly -$400 when betting BJs to
lose, despite them losing 61% of their games. The problem wasn’t Elvis and his
.864 Q1 SV%, as I pulled a nice profit from his losses. The guy who stole most
of my money was Daniil Tarasov, who was their best goaltender with a .906 SV%
in 5 starts.
The best part of the
collapse defensively was their overs hitting 10 times in their first 14 games,
which was also a very profitable wager for me last season. Déjà vu all over
again. Their overs had a winning record regardless of which goalie was in net.
Though I hit a snag in the form of 3 consecutive unders at the end of the
quarter. They also won a few games after Patrik Laine and Zach Werenski went on
injured reserve, which only encouraged me to lay larger wagers on BJ opponents
with negative consequences.
22) Carolina
Hurricanes, ($122):
My outlook on the
Carolina 2022/23 campaign was very bullish early. One of my favorite team props
in September was Canes +220 to win the division, but some of the shine came off
when they lost 3 of 4 games vs the Oilers, Flames, and Islanders (all of which
I bet Carolina) in week 2. That may have curbed my enthusiasm a little too
much, as it prompted me to bet their opponents in the next 4 games where the
Canes went 4-0. That’s life as a hockey bettor. Sometimes overreacting to a
small sample can backfire.
Digging myself out of
that hole proved more difficult than hoped. It turns out that the team was not
nearly as good as I thought they’d be, and that miscalculation was difficult to
overcome, at least initially. I posted a small loss betting them to win, and a
small gain betting them to lose. But once it became abundantly obvious to me
that their quality was diminished, I was able to post some big gains in the
first few days of the 2nd quarter (which was discussed in my week 7
Betting Report) but won’t be included in my Q1 summary.
One of Carolina’s
issues was not getting the same top quality goaltending from Freddy Andersen
early in the season, posting an .891 SV% in 8 starts before getting injured.
Antti Raanta was an adequate replacement, but he was not stealing games.
Allowing more goals should have juiced their overs, but their unders actually
went 11-7-1, and my algorithm posted a nice little profit on that outcome. Part
of the issue was that they weren’t scoring as many goals, especially in weeks
5, 6, and 7 when they lost 7 of 10 games.
23) Tampa Bay
Lightning, ($52):
Tampa was my 4th
best team to bet last season, whether picking them to win or lose, and they
advanced to their 3rd consecutive Stanley Cup final. Part of the
strategy responsible for my past success involved aggressively betting them to
win at home, while being more skeptical on the road. They started the 2022/23
season with a road heavy schedule, so my action was drifting to their opponents
more than I might have anticipated, which did not generate profit. I posted a
small loss both when betting them to win and lose.
One of the big
glaring standouts was Andrei Vasilevskiy not dominating like we’ve come to
expect, a downward trajectory that began last regular season, but we forget
about because he was great again in the playoffs. Evidence suggests the
workload of the three consecutive trips to the Stanley Cup final is taking a
toll, not only on Vasy but some of his teammates too. He allowed 3 or more
goals 10 times in 14 starts, posting a pedestrian .903 SV% with a GAA of
exactly 3.00. Vasilevskiy unders at least went 8-6, so there was that “good”
news.
What’s interesting is
that I actually posted $250 of profit when betting Vasilevskiy to lose, but
lost -$400 when betting Brian Elliott
to lose. Indeed Elliott was a worse goaltender with an .891 SV%, but the
Lightning outscored those problems, winning 4 of his 5 starts. You definitely
wanted to bet the over with Elliot in goal, as they went 4-1. Scoring goals
wasn’t Tampa’s problem, but rather preventing them. Some of that might be
attributable to unloading Ryan McDonagh to save cap space, though he doesn’t
seem to be helping Juuse Saros much.
24) Boston
Bruins, ($30):
Another one of my
larger misreads entering the season was that the Bruins would struggle without
Marchand and McAvoy, but I quickly jumped on the bandwagon when
they started 3-0. Their 5-3 victory against Florida in the third games was a big
confidence booster and motivated me to start making a large investment in their
victories with great success. Unfortunately, my ability to pick the correct
over/under was abysmal, losing nearly -$1,000 in the first quarter, almost entirely
negating my $1,500 profit from their wins.
Their overs went
10-8-1, so I could have produced a profit by just betting overs every game.
Instead, my algorithm recommended 11 unders and 8 overs, resulting in a big
loss on both sides. My record was 3-8 on unders and 2-6 on overs. Frankly it
didn’t matter how much I was making from their moneylines when I was that
terrible at O/U. They were supposed to have a 1A/1B goalie tandem, but Linus
Ullmark wrestled the reigns early and Swayman was injured after making just 3
starts. Ullmark was outstanding with a .935 Q1 SV% with the 2nd best
Vezina odds.
My money lost on
unders was mostly burned in the few games that Jeremy Swayman was in goal.
Ullmark only went 7-7 on unders despite that outstanding goalkeeping because he
was getting so much goal support. The Bruins were one of the best teams in the
league -1.5 goals, except when I was making that wager. It’s part of the reason I actually lost money
when Ullmark was the starter. Technically my best Boston goalie was Keith
Kinkaid, who only got the nod once while Swayman was on the shelf. Keith beat
the Sabres 3-1.
25) Edmonton
Oilers, (-$225):
While my overall
Oilers outlook was positive heading into the season, when Jack Campbell
struggled right away, my outlook to shift negative very quickly.
One of my rules is “beware betting on good teams with unreliable goaltending”.
That made me more aggressive betting Oiler opponents, but that’s also when
Connor McDavid caught fire and went into beast mode. They started outscoring
their defensive issues. The team hit a major snag when Evander Kane was lost to
a serious injury, forcing him to miss most of the regular season.
Bleak though the
outlook may have been, they still managed to win games here and there. They
struggled stringing together consecutive wins without Kane, but they were
staying in the playoff race. You might recall how badly they were playing
before acquiring Kane last January, then went on a wicked heater that ended at
the conference final. They managed to avoid a major losing streak after losing
Kane, and pulled out wins against Florida, Tampa, and Vegas. I posted a small
gain betting them to lose and a small loss when betting them to win.
My biggest loss
occurred on their unders, which went 7-10-2. A majority of my money was
invested in overs, yet somehow posted a negative balance on those bets. The
problem was the goalies. Stuart Skinner recorded a .921 SV% with Campbell at a
lowly .873. Skinner unders went 5-2-2, while Campbell overs went 8-2. Ergo my bet
selection should have been entirely based on starting goaltender. The
volatility in net made it difficult to reliably predict who would be starting
when my bets were made the day before games near the opening lines.
26) Nashville
Predators, (-$356):
One of my favorite
preseason prop bets was Nashville +170 to miss the playoffs, as they would have
missed last spring if not for a Vegas choke job. Multiple people came at me on
Twitter disagreeing that the acquisition of Ryan McDonagh and Nino Neiderreiter
made them a lock for the post-season. The Preds won their first two games
against the San Jose Sharks, and jumped up to +200 to miss the playoffs. I
should have doubled down as they would lose 7 of their next 8 games, dropping
to -180 to miss the playoffs.
The irony being that
I once again drafted Juuse Saros to both my fantasy teams despite my pessimism,
so that losing skid became painfully apparent to me. I deserved it. Fortunately
for Preds fans and my fantasy teams, the team followed up that futility by
going 6-3 in their next 9. They had stopped the bleeding, but were still
outside the playoff picture on American Thanksgiving (after being first place
in the entire NHL on Canadian Thanksgiving). Juuse Saros stopping more pucks
was a major contributing factor to the turn-around.
Saros finished the
quarter with a .905 SV%, while back-up Kevin Lankinen (who was awful for
Chicago last season) was even better with a .921. There was no consistently
good option on their over/unders, with unders going 9-8-2 and posting a small
gain. My algorithm recommended an even split in my investment and returned a
small loss on both sides. I was able to generate a solid profit when betting
Saros to lose, but was a net loser shorting Kevin Lankinen, who went 2-2 in 4
starts.
27) Anaheim Mighty
Ducks, (-$685):
Some pundits expected
Anaheim to be much improved this season, though I was not quick to buy the
hype. My only expectation for this team was that John Gibson has been red-hot
at the start of the last 3 seasons before collapsing later in the schedule, and
I was ready to jump on Ducks +1.5 goals early. Spoiler alert: they sucked and
and we knew very early. The Ducks gave up 17 goals in Gibson’s first 3
starts, so my Ducks +1.5 goals strategy was abandoned 2 games into the
schedule. My sincerest apologies to anyone who took that advice.
The Ducks were
actually among the worst teams in the league, and were tied for dead last at
Thanksgiving. It wasn’t all John Gibson’s fault, as the defense in front of him
yielded far too many high danger scoring chances. It would have taken a miracle
on his part to perform well with so many shots coming from the slot. Though he
certainly seemed to be excelling in those situations where my money was on
Anaheim opponents, as I was a net loser betting both on or against Gibson. It
shouldn’t have been so hard to profit from Anaheim losses.
Back-up Anthony
Stolarz was slightly better, posting an .894 SV% to Gibson’s .891. While Gibson
went 3-12, Stolarz actually went 2-2 and I pulled a nice little $400 profit from
his 4 starts. Their overs went 9-9-1, but my algorithm recommended a much
larger stake in higher scoring games, producing a small loss on both sides.
Their road games were more likely to go over, while unders were 4-3 on home
ice. The Ducks were also a much better team at home, but only played 7 games in
Anaheim.
28) Toronto
Maple Leafs, (-$846):
Once again the
Toronto Maple Leafs find themselves dwelling in the basement region of my
betting Power Rankings, with most of the damage being done by their failure to
win. Once they started to struggle and Ilya Samsonov was injured, I started
aggressively betting their opponents, and that’s when they decided to get hot
with Erik Kallgren in goal. When they’re hot, the line prices can get very
expensive, which will often nudge me towards their opposition. Unfortunately
that net nudge hasn’t been profitable in recent seasons.
To make matters
worse, they always seem to struggle the worst when I have large wagers on them
to win, making me reluctant to do so, even when the going is good. They had an
awful west coast road trip when I was betting them to win that amplified my
pessimism (and that of Leaf nation), but they peeled off a winning streak on
the other side that cost me dearly. The Samsonov injury came at the start of
that hot streak, when I was motivated to aggressively wager on their opponents.
That damned Erik Kallgren screwed me.
I lost -$1,393 in Kallgren starts
and profited more than $500 with Samsonov and Murray. You could have made a
nice profit betting the Leafs to lose in the first quarter, especially on the
road and earlier in the schedule. They got hot in late November and started the
second quarter with a winning streak. Perhaps the safest Q1 Leafs bet was
unders, which went 13-5-2. I would have expected overs to perform better given
the goaltending injuries, but the betting totals were set really high, never
less than 6.5, and even a few 7s.
29) Vegas
Golden Knights, (-$881):
The Vegas over/under
win total before the season was 97.5, which shows oddsmakers rated them as a
bubble playoff team, right around the range where you’re battling for a
wildcard in the final days of the schedule. But very quickly Vegas established
themselves as much, much more. Surely Robin Lehner being declared out for the
season was a major contributing factor to the discounted pricing, as Logan
Thompson was largely unproven, though solid in replacement of Lehner last
spring. More importantly, Jack Eichel and Mark Stone were healthy.
I might have jumped
on the Vegas bandwagon sooner had they not blown a puckline against a tired
Chicago team in the second game of the season, costing me a max wager. Alex
Stalock decided to be a hero, stopping 36 of 37 shots. This made me pessimistic
about the Knights effectiveness, and I started betting their opponents more
often, which proved to be another costly mistake. Eventually I got on the right
side of the Vegas bets, but the hole that I had dug for myself was too deep to
erase by the end of quarter.
There was an even
10-10 split between overs and unders, but they have very distinct home-road
splits. Unders were 7-2 and home while overs were 8-3 on the road. For whatever
reason, they both scored and allowed significantly more goals on the road,
while home games averaged 3 goals less per game. Logan Thompson posted a .920
SV% while back-up Adin Hill was still decent at .909. My algorithm was
outstanding in Thompson starts, going 10-3 and pulling a nice profit from both
overs and unders.
30) Detroit
Red Wings, (-$1,603):
Before my breakdown,
let me share my obligatory disclaimer: as a Red Wings fan I’m vulnerable to
bipolar behavior when it comes to betting their games, vulnerable to irrational
swings of extreme optimism or pessimism (evidence outlined in my new book). One
of my favorite prop bets was Detroit over 84.5 PTS, and at the end of Q1 they were
pacing for 104. Matt Murley and the Spittin Chiclets crew also pumped up my
Wings before opening night, so I was not alone in my optimism. Unbiased experts
were backing up my belief.
Despite being right
that they were going to be better, my ability to bet the correct outcome of
their games was abysmal, as they dropped to dead last in my Power Rankings by
week 4. I was a big loser whether betting them to win or lose. It wasn’t
exactly irrational of me to make a large wager on Detroit to beat the tanking
Chicago Blackhawks early in the schedule. The indomitable Matt Murley was also
big on the Wings there so I don’t feel too bad, I just wish I had stayed on the
Wings vs Anaheim in their next game (as Murley was).
The only reason the Red Wings ranked this low was
because of Alex Nedeljkovic. I was a net winner when Ville Husso got the nod.
Nedeljkovic went 0-4 when I bet him to win, and 2-0 when I bet him to lose. I
could have improved my Detroit over/under performance by simply betting every
Husso under and Nedeljkovic over, which wouldn’t have been too hard since the
starter pattern was reasonably predictable. Additionally betting Husso to win
and Nedeljkovic to lose is how I could have had a good Q1 betting Detroit
games.
31) New York
Rangers, (-$1,688):
The Rangers turned
heads with a trip to the conference finals last spring on the back of Vezina
winning goaltender Igor Shesterkin. They opened the new season with convincing
victories against Tampa and Minnesota, which helped convince me the Rangers
were officially entrenched in the elite tier of the NHL. I was even taking time
to look up NYR Stanley Cup odds, looking for potential value. Sadly that
illusion of superiority quickly vanished with back-to-back losses against San
Jose and Columbus that cost me more than -$1,000 combined.
Losing to two
struggling teams put my guard up, and diminished how much I was willing to
spend for their wins. At least until they played a home game against Detroit on
a back-to-back and lost 3-2, costing me nearly -$500. Those 3 games account for the bulk of
my Rangers losses, as they were choking away some very winnable games. Once
again they were one of the best under bets in the league, going 13-7 in the
first quarter. What’s crazy is that Shesterkin unders went 8-7 while Jaroslav
Halak unders went a perfect 5-0.
There were a few
Rangers games where I executed a manual override of an under wager because
Halak was the likely starter. I bet the over in every single Halak start, going
0-5. What’s strange about that is that Halak was bad, posting an .883 SV% and a
3.22 GAA. That should have translated to better overs, but the inferior goalie
did not get as much goal support with the Rangers losing all 5 of his starts. I
did well when betting Halak to lose, but the problem was that he had a few
unexpected starts when I was betting them to win thinking it would be Igor.
32) Chicago
Blackhawks, (-$1,984):
There was a point in
the offseason when the thought crossed my mind that the Chicago Blackhawks
might go 0-82, making them a big target of mine to short early in the schedule.
They lost their first 2 games against Colorado and Vegas before pealing off a run
of 4-straight victories (which cost me a lot of money). That’s when I started
betting them to win more often as part of my longshot revolution, but they
decided to follow up that mini-win streak by losing 5 of their next 6. That’s
how they fell to last in my Power Rankings by the end of week 3.
There was a moment
very early in the schedule when they sat second place in the division, defying
all our expectations, but eventually they devolved into who we thought them to
be. Soon the losses started pilling up, and the catalyst was an injury to Seth
Jones. That was the missing piece that brought it all down. But even if you
were aggressively betting them to lose throughout that period of futility,
there was not a lot of profit to be had because the lines on their opponents
were absurdly expensive. So I was still picking them to win long after it was
profitable.
The goalie who was
responsible for stealing all of my wagers on Hawk opponents was Alex Stalock,
who had a respectable .913 SV% in 6 starts. Meanwhile Arvid Soderblom had a
.909 SV% while Petr Mrazek (the expected #1 guy heading into the season) was
all the way down at .889. I performed well when betting Soderblom and Mrazek to
lose. Their overs went 10-8 while my algorithm was recommending unders far too
often. The issue was a lack of consistency, as they’d have low scoring games
followed by a string of high scoring games.
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