Welcome to my Third Quarterly Hockey Betting Report of the 2021/22
season. 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 second
quarterly report, click
here.
My 1st Quarter Profit: $8,927
My 2nd Quarter Profit: $7,206
My 3rd Quarter Profit: $3,714
They say all good things come to an end, and that best
encapsulates my “big short” of the Arizona Coyotes. 55% of all my first half
profit came from betting on Arizona to lose, which was inflated by a
historically bad start to the season. At the end of the first half, they sat
atop my Power Rankings with a giant lead on 2nd place. Two months
later, that lead has almost entirely evaporated after losing a pile of money
betting against the Yotes. (Note: Columbus did overtake Arizona in the first week of the 4th quarter)
In the third quarter (spanning from Jan 22 to Mar 13, henceforth
referred to as Q3), my two most profitable bets of Q2 reversed course, Montreal
and Arizona to lose (which produced nearly $5,000 in Q2 profit). The league’s
two basement dwellers who were believed to be tanking for draft status began
winning hockey games. My gravy train picking the biggest losers to lose came
off the tracks in Q3, generating nearly a -$4,000 loss. Where’s a proper tank when you need one?
The only reason that my portfolio was able to endure
those devastating reversals of misfortune by Montreal and Arizona was because
of all the goals being scored. This is my first season doing over/under
betting, and 3rd quarter overs netted me a whopping +$5,601 for a
17% rate of return. There were 5.9 goals per game in Q1, 6.3 in Q2, and 6.4 in
Q3. It was 3 weeks into Q2 before I started using an algorithm to make my
decisions for me, but it wasn’t until creating my 2nd algorithm during the
All-Star break that my performance really exploded (more on over/under below).
The Calgary Flames were the NHL’s hottest team this
past quarter, winning more games than any other team, including a streak of 10
in a row. I’ll admit that I could have done a better job profiteering off
Calgary’s success. Sure, the $1,306 that I won betting Flames games was
respectable, but they were only my 10th best team to bet in the third
quarter. The biggest flaw in my approach was investing too much in the Flames
moneyline, when their puckline produced a much higher rate of return. Calgary
won 13 home games, 11 of them by at least 2 goals.
Betting favorites was the key to my 2nd quarter success, but that flipped in Q3, losing money on faves but profiting
from underdogs. From Dec 20 to Jan 30, favorites were red-hot, both moneyline
and puckline (betting $100 on each during that window would have produced $5,445
in profit, a 12% rate of return). The team that was “favored” on the betting
lines won 72% of January games, and I was pounding them hard, producing
outstanding results. Unfortunately, favorites only won 59% in February,
which was insufficient to generate profit given the expensive line offerings.
Underdogs started to strike back, which is something
we also saw in the second half of 2019/20. Road underdogs moneyline was my 2nd best category of the third quarter.
My 3rd Quarter Results:
*Market Bets calculated by betting
exactly $100 on every outcome*
*L2S = Last 2 Seasons*
*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”
*
It wasn’t just favorites moneyline that declined. By the end of January,
we were starting to see some regression in favorites -1.5 goals on the
puckline, as a disturbing number failed to cover in some presumably easy games.
Faves -1.5 had been vastly outperforming
their results from the previous 2 seasons, but produced significant negative
returns as a category every week from Jan 17 to March 13 (if you bet $100 on
every favorite -1.5 goals in that span, you lost -$4,689). The gap between underdogs and favorites shrunk
substantially, which you’d never know by looking at the betting lines.
Visitors were favored in just 38% of games from Jan
1 to Feb 11, but won 48%. Home teams dominated the first half of the season, but
from Jan 26 to Feb 20, road teams won 60% of games and if you bet $100 on each,
you banked more than $3,000. However, this was not the start of a permanent
trend in road winning, as the results would shift back to home dominance in
late February. Basically, 5 teams got hot on the road at the same time, with
Montreal and Ottawa playing the most home games, losing 14 of 19.
That trend of hot road teams came to an abrupt end in the last week of February, winning just 43%. When that shift
occurred, I got hammered on visitor moneylines. The previous trend did give me
more confidence laying money on road warriors, but it was some bad beats that
did most of the damage: Toronto losing to Montreal, Washington losing to
Philadelphia, Minnesota losing to Ottawa, Vegas losing to Arizona, and to a
lesser extent, Colorado losing against Boston.
The decreasing profitability of pucklines -1.5 goals led me to shift a greater stake into favorites moneyline, but in February favorites hit a brick wall, and underdogs were cashing tickets. By March, I had virtually stopped laying any money on pucklines -1.5 goals, unless it was Florida or Colorado, and even those teams made me nervous. The Avs whiffed on some pucklines against inferior opponents that cost me some large wagers (If you bet the Avalanche -1.5 goals every game from Jan 24 to Mar 13, you lost -$329), but the sportsbooks continued offering lower and lower payouts on heavy favorites. It’s hard to get a fair line on Colorado.
In March, the underdog revolution started gaining
momentum. Sportsbooks have been pushing the betting lines further and further
towards the heavy favorites, helping drive up the profitability of underdogs
(that has also been observed in past seasons). In week 18 alone, Toronto -425
vs Buffalo, Colorado -380 vs Arizona, and Calgary -400 vs Montreal were all losers.
For those of you who may not know, a -400 betting line implies an 80%
probability of victory. There is an argument to be made that no team should
ever be an 80% favorite in this league. 7 teams have been favored by at least
-400 this season, and 4 (57%) have won the game.
Over/Under
Goal scoring dramatically increased in the 2nd quarter of the season, and the boom spilled over into Q3. Near the end of the 2nd quarter, my first over/under algorithm had one catastrophic weekend that
inspired me to create a new version during the all-star break using data from
the first half. The original was not based on any specific data, because my
sample size was still relatively small. It was simply the % of each team’s 10
previous games that went over the total, then subtracted implied probability of
the betting line, selecting the side offering the most value.
Scoring was on the decline at the end of the second
quarter as nearly all primary starting goaltenders had emerged from Covid
protocol. However, the 5.9 goals per game in week 13 (Jan 10-16) was more of a
blip in the radar, as week 14 was the 3rd highest scoring week of the season. While
a few more goalies entered Covid protocol in week 14, the re-emerging scoring
increase was more explainable by good goalies playing badly.
Week 16 in early February brought the All-Star
break, and gave me free time to do some over/under modelling based on first
half results. I wrote and empirically tested a new algorithm that produced
superior results than the previous version. The original had a 2.5% rate of
return through 268 games. The biggest flaw was that it produced a strong
negative return on the games it liked the most (where I wager $200 instead of
$100). That paradox gave me concerns about sustainability.
The new algorithm took average goals per game over
each team’s previous 5 games; then subtracts the betting total. Simply bet over
for >=0 and under for <0. If it’s more than half a goal, bet double.
Ignoring the odds offering and focusing solely on the total produced a better return.
Coming out of the All-Star break, I had one of my best O/U weeks of the whole
season, but it was driven almost entirely by overs.
There was an increase in scoring coming out of the
all-star break, just as there was a scoring bump after Christmas. Inactivity
appears to have a negative impact on goal prevention, which surely has more to
do with goaltending than shooting. The re-emerging scoring trend was more
sustainable than the weeks after Christmas. The new algorithm proved very
efficient at picking overs and mildly bad at picking unders. Given that goal
scoring continued to prevail, it produced a profit ($1,500 in first 2 weeks).
Both algorithms have produced positive profit since
their creation, but I can already see that both are vulnerable to shifting
trends. If defense were to tighten up at any point in the future with even a
moderate decrease in scoring, I’m going to get hit hard for 1-2 weeks before
the numbers can compensate (which seems to be happening in the first week of
the 4th quarter). As the rate of return on favorites dropped towards
the end of Q3, over/under wagers were the only thing preventing me from getting
absolutely crushed.
I continued tracking the recommendations of the
original algorithm after the new one came into use. For the first 100 games,
they agreed on the same outcome in 77% of games, which proved to be the winning
bet 54% of the time. The newer version with the shorter memory was prevailing
when they disagreed entirely because of overs; meaning, this discrepancy is
entirely because of the scoring boom.
Line Movement
The other addition to my betting spreadsheet over
All-Star weekend were columns to keep a record of closing lines. I had been
noticing a substantial amount of line movement on game day, and decided to
quantify the value of shifting prices. When a line moves from X to Y, what is
the rate of return for both X and Y? When lines move after the initial public
offering, it’s a reaction to the action coming in on one side or the other. The
sportsbooks track the betting habits of their most successful accounts, and use
them as barometers to determine which direction a line needs to move.
My bets are logged the day before games, before
most books have even posted their lines. I’ll often default to Draft Kings
simply because they publish early. The line prices I’m recording tends to be
very close to the “opening” number. When you look at the whole sample with every game, the rate of return on the closing line is zero, but it gets more interesting when you look at underdogs vs favorites. For 222 games, when a line “moved towards”
a favorite (meaning their implied probability of victory increases), the rate
of return on the closing line is less than zero ($0.96 per $1 wagered).
However, when the line moves towards the underdog, the rate of return on the
closing line is 12%.
Before we jump to conclusions, it’s important to
point out that underdogs produced a good rate of return in general over this
sample, so had I been tracking line movement back in January when favorites
were killing it, the results would be different. But for the post-All-Star
break sample, if you went to Draft Kings 1-3 hours before puck drop and bet
$100 on every underdog who had a lower payout than the opening line, you made
$1,163. This may just be a residual effect of underdogs in general becoming
more profitable.
Market Best Bets +1.5 Goals: Market Worst
Bets +1.5 Goals:
1) Arizona Coyotes, (+$411) 1) Detroit Red Wings, (-$574)
2) Los Angeles Kings, (+$348) 2) Buffalo Sabres, (-$396)
3) Columbus Blue Jackets, (+$337) 3) New York Islanders, (-$309)
Market Best Bets -1.5 Goals: Market Worst Bets -1.5 Goals:
1) Calgary Flames, (+$1,143) 1) Minnesota Wild, (-$988)
2) Chicago Blackhawks, (+$365) 2) Carolina Hurricanes, (-$976)
3) St. Louis Blues, (+$310) 3) Boston Bruins, (-$920)
My 5 Best Q3 Over/Under Bets: Market’s 5
Best Q3 Over/Under Bets:
1) Toronto over, (+$1,359) 1) Toronto over, (+$1,046)
2) Columbus over, (+$1,257) 2) Ottawa under, (+$969)
3) Florida over, (+$1,203) 3) Columbus over, (+$923)
4) Minnesota over, (+$1,190) 4) Florida over, (+$879)
5) Ottawa under, (+$1,128) 5) Montreal over, (+$673)
My 5 Worst 3rd Quarter Over/Under Bets:
1) Los Angeles under, (-$641)
2) Pittsburgh over, (-$512)
3) Arizona under, (-$476)
4) Winnipeg under, (-$467)
5) Tampa Bay over, (-$452)
My 5 Best Q3 Teams To Bet
On: Market’s 5 Best Q3 Teams To Bet On:
*over/under not included
1) Carolina Hurricanes, (+$1,658) 1) Calgary Flames, (+$1,600)
2) Columbus Blue Jackets, (+$1,186) 2) Arizona
Coyotes, (+$1,137)
3) New York Rangers, (+$975) 3) Columbus Blue Jackets,
(+$1,070)
4) Calgary Flames, (+$850) 4) Los Angeles Kings, (+$861)
5) Vancouver Canucks, (+$845) 5) Vancouver Canucks, (+$710)
My 5 Worst 3rd Quarter Teams To
Bet On:
*over/under not included
1) Colorado Avalanche, (-$1,401)
2) St. Louis Blues, (-$916)
3) Boston Bruins, (-$794)
4) Pittsburgh Penguins, (-$725)
5) New York Islanders, (-$682)
My 5 Best Q3 Teams To Bet
Against: Market’s 5
Best Q3 Teams To Bet Against:
*over/under not included
1) Buffalo Sabres, (+$1,850) 1) Minnesota
Wild, (+$889)
2) Vegas Golden Knights, (+$1,529) 2) Vegas
Golden Knights, (+$862)
3) Chicago Blackhawks, (+$1,183) 3) Philadelphia
Flyers, (+$608)
4) Philadelphia Flyers, (+$832) 4) Detroit
Red Wings, (+$585)
5) Detroit Red Wings, (+$803) 5) Toronto
Maple Leafs, (+$573)
My 5 Worst 3rd Quarter Teams To
Bet Against:
*over/under not included
1) Arizona Coyotes, (-$2,608)
2) Los Angeles Kings, (-$1,840)
3) Winnipeg Jets, (-$1,438)
4) Montreal Canadiens, (-$1,397)
5) Nashville Predators, (-$951)
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, ($5,666):
Last Quarter Rank: 1
1st Quarter Profit: $6,333
2nd Quarter Profit: $2,526
3rd Quarter Profit: -$3,194
Q3 Win-Loss Record: 8-11
Q3 % Money Bet On: 4%
(-$175)
If you bet on them
every game ML+PL: $1,137 (LR: 2)
Q3 % Money Bet Against: 96%
(-$2,608)
If
you bet against them every game ML+PL: -$767 (LR: 27)
Q3 % Bet Over: 50% ($65), Market Return
on $1: $1.08
Q3 % Bet Under: 50% (-$476), Market Return
on $1: $0.79
The Coyotes jumped
out to a massive early lead in my power rankings after an atrocious start to
their season, but the gap between them and 2nd place shrunk
considerably in Q3, posting negative returns nearly every week. The main driver
behind their turn-around has been goal scoring, and the man behind the wheel is
Clayton Keller. The team still lost 11 of 19 games, but were the 2nd best team in the entire league to bet to win every game because the high payout
on their betting lines. They had victories against Colorado, Toronto, and
Colorado again where they were +360, +310, and +290. That just about pays for
their 11 losses right there, and the rest is gravy. They were also the #1 best
puckline bet +1.5 goals.
For as much as I’ve
praised my over/under algorithm in the preceding paragraphs, it did a poor job
recommending Coyote outcomes. My investment was split almost exactly 50-50
between over and under, when I would have been better off betting overs in
every game. In my defense, their unders were more profitable in the first half,
and those incorrect recommendations came mostly at the beginning of Q3. A big
chunk of those misses only went over by 1 goal. The old algorithm that looked
back 10 games was too slow to react when they started scoring more goals.
2) Columbus
Blue Jackets, ($5,257):
Last Quarter Rank: 4
1st Quarter Profit: $1,483
2nd Quarter Profit: $1,414
3rd Quarter Profit: $2,360
Q3 Win-Loss Record: 12-10
Q3 % Money Bet On: 68%
($1,186)
If you bet on them
every game ML+PL: $1,070 (LR: 3)
Q3 % Money Bet Against: 32%
($17)
If you bet against
them every game ML+PL: -$1,311 (LR: 31)
Q3 % Bet Over: 97% ($1,257), Market Return
on $1: $1.42
Q3 % Bet Under: 3% (-$100), Market Return
on $1: $0.47
Heading into this
season, I never would have expected to have this much success betting on the
Columbus Jackets to win hockey games. They’ve only been slightly better than
.500 but their lines get so heavily discounted by sportsbooks that it has proven
to be profitable wager. This wasn’t just a Q3 phenomenon, as I’ve been sustaining
this success since the first week of the season. In the recent episode of the
PDOcast where gambling was discussed, there was some disdain expressed by the
guests at Columbus for ruining their bets. I’ve seen other sharps on Twitter
complaining about the Blue Jackets. It suggests this team has been defying the
modelling expectations.
It's possible that
“expected goals” modelling has discounted this team’s probability of victory,
but one bet that surely every model can agree on is the awesomeness of Columbus
overs. For the first ¾ of the season, I banked $2,757 on BJ overs. They are the
perfect confluence of solid goal scoring and porous defense that you want to
see in a quality “over” investment. My algorithms have recommended the Columbus
over 21 times in 22 third quarter games, which put them just behind Toronto as
my best over team this quarter.
3) Florida
Panthers, ($5,077):
Last Quarter Rank: 2
1st Quarter Profit: $1,437
2nd Quarter Profit: $2,146
3rd Quarter Profit: $1,494
Q3 Win-Loss Record: 12-6
Q3 % Money Bet On: 87%
($716)
If you bet on them
every game ML+PL: $344 (LR: 7)
Q3 % Money Bet Against: 13%
(-$113)
If you bet against
them every game ML+PL: -$664 (LR: 26)
Q3 % Bet Over: 80% ($1,203), Market Return
on $1: $1.49
Q3 % Bet Under: 20% (-$313), Market Return
on $1: $0.43
The Florida Panthers
have been among the most reliable teams to ride this season, aside from maybe 2
or 3 bad weeks. It’s been “time to hunt” since October. They played a home
heavy schedule in the first half, and struggled as visitors. But when they
started playing more road games in the 3rd quarter, their
proficiency improved, winning 7 of 10. This was key to my own success as well,
because their previous struggles away from home helped produce some Q3 road line
value. Meanwhile at home, their moneyline produced negative returns despite
winning 63% because the line prices demanded at least 65% to break even.
While I may have
benefited from some bargain shopping on road lines, my biggest gains came from
overs. This team can score, and their aggressive offense can lead to defensive
liabilities, which is a perfect storm for betting overs (although their unders
managed to outperform overs in Q1). The Panthers were also my best team to bet
-1.5 goals, which was a category that I was actively avoiding towards the end
of Q3 and into the fourth quarter. Having lost confidence in the Avalanche
pucklines, Florida is the one team left that I have any confidence picking
-1.5.
4) New York
Islanders, ($4,401):
Last Quarter Rank: 3
1st Quarter Profit: $1,513
2nd Quarter Profit: $1,918
3rd Quarter Profit: $970
Q3 Win-Loss Record: 10-13
Q3 % Money Bet On: 67%
(-$682)
If you bet on them
every game ML+PL: -$648
(LR: 25)
Q3 % Money Bet Against: 33%
($434)
If you bet against
them every game ML+PL: $465 (LR: 6)
Q3 % Bet Over: 68% ($1,128), Market Return
on $1: $1.20
Q3 % Bet Under: 32% ($90), Market Return
on $1: $0.73
The Islanders have
had a bipolar season to date, showing flashes of both good and bad play, but
more bad than good. They had a winning record in Q2, and I bet them to
win/cover far too often in Q3 (when they were actually one of the best teams to
short). Although it’s worth pointing out that a majority of that lost money
came in one game against Montreal when the Habs flipped from a 10-game losing
streak and started winning. That was more of a miscalculation on Montreal than
New York, and if you deleted that large wager from the sample, I actually did a
decent job picking their wins and losses.
Strangely enough,
they would have fallen further in my Power Rankings if not for overs. They were
a strong under team throughout the first half of the season, but that trend
dramatically reversed course in the 3rd quarter, as both their goals
scored and allowed went up. This change would have cost me dearly if not for my
algorithm. It caught the shift early enough to profit. Otherwise, I would have
continued hammering those unders for the rest of the season, and didn’t even
realize how much profit I was generating from their overs until I started
compiling this report.
5) Vancouver
Canucks, ($4,152):
Last Quarter Rank: 6
1st Quarter Profit: $40
2nd Quarter Profit: $2,317
3rd Quarter Profit: $1,795
Q3 Win-Loss Record: 11-9
Q3 % Money Bet On: 50%
($845)
If you bet on them
every game ML+PL: $710 (LR: 5)
Q3 % Money Bet Against: 50%
(-$43)
If you bet against
them every game ML+PL: -$994 (LR: 29)
Q3 % Bet Over: 69% ($1,119), Market Return
on $1: $1.32
Q3 % Bet Under: 31% (-$126), Market Return
on $1: $0.62
The Canucks were
among the hottest teams in the NHL when Covid shut them down for an extended
absence in December, and they struggled to find their footing in the weeks that
followed. My money was evenly split between Vancouver and their opponents, and
that would have produced a good return on both sides if I had not incorrectly
bet $500 on the Leafs to beat them. In February the Canucks started to
recapture the magic. For whatever reason, in 16 of their 20 third quarter
games, I bet the road team. This was not a conscious strategy on my part, it
just materialized on its own. Reviewing my notes, it was entirely about line price.
Home teams tended to be overvalued in their games. Vancouver won 6 of 9 on the
road, but only 5 of 11 at home, which helped me.
The Canucks were
similar to the Islanders in that they were a strong under team in the first
half, then floodgates opened in Q3 at both ends of the ice. I was losing money
on Vancouver Q3 over/unders until the introduction of my new algorithm that
only looked at the last 5 games. This led to an immediate shift to overs. I
went from 6 of 8 bets being on the under, to 11 of the next 12 bets on the over
(with 9 of 11 cashing in).
6) Chicago
Blackhawks, ($3,781):
Last Quarter Rank: 10
1st Quarter Profit: $1,632
2nd Quarter Profit: $84
3rd Quarter Profit: $2,065
Q3 Win-Loss Record: 7-13
Q3 % Money Bet On: 9%
($190)
If you bet on them
every game ML+PL: -$382
(LR: 19)
Q3 % Money Bet Against: 91%
($1,183)
If you bet against
them every game ML+PL: -$63 (LR: 15)
Q3 % Bet Over: 76% ($725), Market Return
on $1: $1.21
Q3 % Bet Under: 24% (-$33), Market Return
on $1: $0.72
The Blackhawks had a
6-week stretch in November-December when Marc-Andre Fleury got hot and they won
9 of 15 games, but that proved to be woefully unsustainable. They were a
reliably bad team in Q3, which was exploitable. You would have lost money
betting them to lose every game (moneyline + puckline) because they were good
at covering pucklines +1.5 goals as underdogs. The key for me was that they
upset very few good teams when I had large bets on their opponents.
One important fact
that you need to consider when betting this team is that they’ve been better on
the road than at home. In the last 2 quarters, they won 9 of 20 road games, but
only 7 of 21 home games. Although I was just as effective betting Chicago on
the road. The reason being, they were consistently losing to good teams
regardless of venue, and that’s when I was laying down the most action. Another
key home-road split was that they gave up significantly more goals in Chicago. Chicago
overs have been trending upwards all season, as their goal scoring has improved
considerably each quarter. My algorithm handled this team very well, but the
return on overs was better at home.
7) New York
Rangers, ($3,594):
Last Quarter Rank: 5
1st Quarter Profit: $901
2nd Quarter Profit: $1,610
3rd Quarter Profit: $1,084
Q3 Win-Loss Record: 11-7
Q3 % Money Bet On: 91%
($975)
If you bet on them
every game ML+PL: -$58
(LR: 14)
Q3 % Money Bet Against: 9%
($63)
If you bet against
them every game ML+PL: -$536 (LR: 22)
Q3 % Bet Over: 46% (-$152), Market Return
on $1: $0.85
Q3 % Bet Under: 54% ($198), Market Return
on $1: $1.05
The New York Rangers
have climbed this high in my rankings on the back of Igor Shesterkin. Picking
the Rangers to win and betting the under have been the winning formula,
although the profitability of their unders declined in the 3rd quarter thanks to a small increase in both goals scored and goals allowed. I
was surprised to see my algorithm recommended a 46% stake in overs, which
produced a loss. Half the formula is based on the opponent’s last 5 games,
where they probably weren’t facing a goaltender anywhere near as good as
Shesterkin. Granted, ignoring the algorithm and betting $100 on the under for
all of their 18 games, I would only have won $90 combined.
If you bet $100 on
the favorite -1.5 goals in every Rangers game, you lost -$535. New York was bad at
covering when they were favored, and their opponents struggled to cover when
the Rangers were underdogs. I have not bet even $1 on a favorite -1.5 goals in
any Ranger game all season, but have consistently performed very well on the
New York moneyline, especially at home. They won 60% of their road games in the
first half, but that dropped to 43% in Q3.
8) Minnesota
Wild, ($3,552):
Last Quarter Rank: 9
1st Quarter Profit: $1,658
2nd Quarter Profit: $253
3rd Quarter Profit: $1,640
Q3 Win-Loss Record: 11-11
Q3 % Money Bet On: 80%
($139)
If you bet on them
every game ML+PL: -$1,543
(LR: 32)
Q3 % Money Bet Against: 20%
($329)
If you bet against
them every game ML+PL: $889 (LR: 1)
Q3 % Bet Over: 88% ($1,190), Market Return
on $1: $1.30
Q3 % Bet Under: 12% (-$18), Market Return
on $1: $0.61
The Minnesota Wild
won 7 of their first 8 games to start Q3, generating more than $1,000 of profit
for my portfolio. It was a wild ride, but they did eventually cool off when the
goaltending started to struggle (their goals against average jumped from 2.9 to
3.8; Q2 to Q3). It did take me a few weeks to adapt to this changing
reality, but I still managed a small profit when betting Minnesota to win. However, if you laid $100 on every Wild moneyline and $100 on every Wild puckline, you
lost -$1,543 (ranking them dead
last in the league). The primary reason was that they were terrible at covering
-1.5 goals. In their 22 Q3 games, they were favored 19 times, and covered the
puckline in just 4 of them.
One good thing about
the collapse of their goaltending was that it was good for their overs, which
was where most of my Wild profit was generated. Although, there was a slight
drop in the rate of return on overs from Q2 because they also scored fewer
goals per game. That may have been attributable to playing a road heavy
schedule in the third quarter, as they scored more goals per game on home ice.
9) Carolina
Hurricanes, ($2,844):
Last Quarter Rank: 22
1st Quarter Profit: $1,181
2nd Quarter Profit: -$821
3rd Quarter Profit: $2,484
Q3 Win-Loss Record: 14-8
Q3 % Money Bet On: 94%
($1,658)
If you bet on them
every game ML+PL: -$853
(LR: 26)
Q3 % Money Bet Against: 6%
($502)
If you bet against
them every game ML+PL: $12 (LR: 14)
Q3 % Bet Over: 56% (-$70), Market Return
on $1: $0.76
Q3 % Bet Under: 44% ($393), Market Return
on $1: $1.13
One of the best bets
this season has been Carolina to win, aside from a few scattered weeks when
they have struggled. Most of my Q3 Cane bets were on Carolina to win, yielding
substantial profit, however if you blindly bet them to win every single
moneyline and puckline, you had a bad month. Carolina won 14 of 22, but 10-1 at
home and 4-7 on the road. The rate of return on their home wins was low due to
over-priced lines, so looking only at moneyline, their 8 losses cost you more
than their 14 wins banked. Where I unwittingly managed to avoid that trap was betting
them +1.5 goals as underdogs when they were on the road against good teams, and
they covered some spreads despite losing the actual game. The second component
of that -$853 above was from
pucklines -1.5 goals. They were favored 16 times and covered -1.5 goals only 3
times.
The whole explanation
above was the result of me doing an investigation to ensure there was not an
error in my spreadsheet. How could I have such a great return on Carolina wins
when they were a bad investment overall? How is it possible to lose -$853 betting a 14-8 team
to win? You have your answer.
10) Buffalo
Sabres, ($2,471):
Last Quarter Rank: 26
1st Quarter Profit: $575
2nd Quarter Profit: -$976
3rd Quarter Profit: $2,872
Q3 Win-Loss Record: 8-12
Q3 % Money Bet On: 21%
($535)
If you bet on them
every game ML+PL: -$395
(LR: 20)
Q3 % Money Bet Against: 79%
($1,850)
If you bet against
them every game ML+PL: $112 (LR: 12)
Q3 % Bet Over: 63% ($647), Market Return
on $1: $1.17
Q3 % Bet Under: 37% (-$159), Market Return
on $1: $0.76
I made more money in
the 3rd quarter betting on Buffalo Sabres games than any other team.
I was stunningly good at picking the winner, after struggling with this team in
the first half. It might have taken me 15 weeks to get a handle on them,
but my performance betting their games took a giant leap forward, both when
picking them to win and lose. Though rarely did I lay money on Buffalo to win/cover
because I actually expected them to win the game. Almost all of it was a result
of disliking the line on their opponents (like Toronto -425 for example).
The Sabres had lost 10
of their last 14 games entering March, then won 4 of their next 6. Usually a
turn-around like that would cost me a lot of money, but that Toronto -425 game
really turned me off. Then they beat Toronto 5-1, and that inspired me to bet
on Buffalo in 4 of their next 5 games, winning 3 of them. One thing that helped
make this the winningest quarter of their season to date was an increase in
goal scoring. Alex Tuch may not be as good as Jack Eichel, but he injected some
talent into that top line.
11) Vegas
Golden Knights, ($2,436):
Last Quarter Rank: 20
1st Quarter Profit: -$353
2nd Quarter Profit: $1,100
3rd Quarter Profit: $1,689
Q3 Win-Loss Record: 8-12
Q3 % Money Bet On: 66%
(-$636)
If you bet on them
every game ML+PL: -$1,241
(LR: 30)
Q3 % Money Bet Against: 34%
($1,529)
If you bet against
them every game ML+PL: $862 (LR: 2)
Q3 % Bet Over: 26% ($41), Market Return
on $1: $0.71
Q3 % Bet Under: 74% ($754), Market Return
on $1: $1.21
It’s been a rough
ride for the Vegas Golden Knights, falling short of expectations and
languishing in the middle of the pack with ¾ of the schedule in the books.
Injuries have been a big problem, but even when Jack Eichel got healthy, the
team did not significantly improve. They had to put Mark Stone on IR in order
to have the cap space to activate Eichel, so perhaps Stone was the more
important piece. If Stone needs to stay off the ice to stay cap compliant, this
team might not even make the playoffs.
Vegas only won 8 of
20 games in Q3, and it took me a little time before I was ready to accept that
they were not as good as previously thought. Considering that only 34% of my
stake was on Vegas opponents, that $1,529 return is impressive. I bet Vegas 12
times in 20 third quarter games, but the 8 times my money was on the opposition
were all winners. My over/under algorithm also helped boost my output, as they
were one of the few teams with profitable unders during this scoring boom. The
new algorithm picked the correct outcome 12 times in 14 games.
12) Anaheim Mighty
Ducks, ($2,127):
Last Quarter Rank: 15
1st Quarter Profit: $1,167
2nd Quarter Profit: $212
3rd Quarter Profit: $748
Q3 Win-Loss Record: 7-12
Q3 % Money Bet On: 48%
(-$37)
If you bet on them
every game ML+PL: -$980
(LR: 28)
Q3 % Money Bet Against: 52%
($151)
If you bet against
them every game ML+PL: $293 (LR: 9)
Q3 % Bet Over: 81% ($729), Market Return
on $1: $1.23
Q3 % Bet Under: 19% (-$95), Market Return
on $1: $0.71
While the Ducks
defied most people’s expectations early this season, that proved unsustainable.
They are who we thought they were, but can still be a tricky team to bet,
capable of beating good teams and losing to bad teams. They lost 63% of their Q3 games, so if you were investing regularly in Duck opponents, you likely
generated positive returns. I only took a 52% stake and under-performed a
potential opportunity. As a bettor, I’m always hunting for nosedives (insert
Duck Hunt joke).
Anaheim still climbed
my Power Rankings thanks to their overs. The goal scored per game increased
marginally from 2.6 to 2.9, while goals against jumped from 2.9 to 3.9. One of
the contributors to their strong start was a home heavy schedule and good
goaltending. In the third quarter, 12 of their 19 games were on the road, and from
Feb 11 to Mar 15, John Gibson posted an abysmal .846 SV% and won 2 games in 11
starts. I was expecting Gibson to fall apart in the 2nd quarter as
usual, but he maintained his quality start longer than recent season. He’s a goalie who
can get hot at any moment, but he’s ice cold right now, which is fueling Duck
overs.
13) Boston
Bruins, ($1,982):
Last Quarter Rank: 11
1st Quarter Profit: $1,620
2nd Quarter Profit: $49
3rd Quarter Profit: $312
Q3 Win-Loss Record: 13-9
Q3 % Money Bet On: 56%
(-$794)
If you bet on them
every game ML+PL: -$884
(LR: 27)
Q3 % Money Bet Against: 44%
(-$94)
If you bet against
them every game ML+PL: $248 (LR: 10)
Q3 % Bet Over: 54% ($331), Market Return
on $1: $0.83
Q3 % Bet Under: 46% ($868), Market Return
on $1: $1.07
The Bruins went 13-9
in the 3rd quarter, but you would have lost -$884 by betting $100 on every Boston moneyline and puckline. They were legitimately bad covering -1.5
goals as favorites and only won 5 of 9 home games. The Bruins did win 62% of
their road games and played a road heavy schedule, but their average road
moneylines was -150, and you need to win 60% for that to be profitable. It
wasn’t road games that hurt me, but rather 2 home losses versus Anaheim and Los
Angeles that cost me -$1,345. If you deleted
those from the sample, I would have produced a decent profit on Bruin wins and
losses.
The only reason that
I was able to manufacture a positive return on all Boston bets was
extraordinarily predictable over/under results. My algorithm went 14-5-3 with
its O/U recommendations, with a 54-46 split between overs and unders (2nd best OU team overall). No one side was dominant, yet it was very easy to crack
their code. The Bruins goal scoring did diminish slightly, but that might be
attributable to playing 6 games without Brad Marchand. Tuukka Rask retired, but
Jeremy Swayman was more than capable of filling that void.
14) Washington
Capitals, ($1,961):
Last Quarter Rank: 12
1st Quarter Profit: $2,294
2nd Quarter Profit: -$661
3rd Quarter Profit: $327
Q3 Win-Loss Record: 10-9
Q3 % Money Bet On: 83%
($78)
If you bet on them
every game ML+PL: -$216
(LR: 17)
Q3 % Money Bet Against: 17%
(-$68)
If you bet against
them every game ML+PL: $169 (LR: 11)
Q3 % Bet Over: 78% ($359), Market Return
on $1: $1.00
Q3 % Bet Under: 22% (-$41), Market Return
on $1: $0.91
For the first three
quarters of the NHL season, Washington won 60% of their road games and 47% of
their home games. The Caps being a strong road team is not a new concept.
That’s a horse I’ve ridden in past seasons and continued to do in 2021/22. Even
in the 3rd quarter, I banked more than $700 on Washington road
games. Where I got hammered was them only winning 3 of 9 home games (including
losses to Ottawa, Columbus, and San Jose). The Capitals peaked at #2 in my Power
Rankings on Nov 28 by winning 9 of 11 games with me moderately invested in
their victories. That gluttonous stretch was followed by gloom, losing 10 of their
next 17, and plunging them to the bottom half of my ranks.
Most of my money was
on Washington to win (83%), producing $78 of profit. That’s not terrible
considering you would have lost money if you bet them to win every game. One loss to the
slumping Philadelphia Flyers cost me -$700, so if you deleted that one game from
the sample, then I would have had a strong quarter. Washington overs went 10-9,
and my algorithm was effective at forecasting that outcome (whereas if you
simply bet every Caps over, you broke even).
15) Philadelphia
Flyers, ($1,779):
Last Quarter Rank: 18
1st Quarter Profit: $595
2nd Quarter Profit: $378
3rd Quarter Profit: $805
Q3 Win-Loss Record: 5-14
Q3 % Money Bet On: 3%
(-$47)
If you bet on them
every game ML+PL: -$1,450
(LR: 31)
Q3 % Money Bet Against: 97%
($832)
If you bet against
them every game ML+PL: $608 (LR: 3)
Q3 % Bet Over: 59% ($397), Market Return
on $1: $1.15
Q3 % Bet Under: 41% (-$376), Market Return
on $1: $0.74
Betting the Flyers to
lose has been a profitable wager for most of the season, and that’s largely
where my money has been invested. The team defense has been mostly terrible,
but every so often Carter Hart plays well in goal and steals a game. They only
won 5 games in Q3, but the ones against Vegas, LA, and Washington cost me
nearly -$1,500. Those upsets
prevented me from maximizing my profit, which tends to be one of my super
powers when it comes to really bad teams. When laying dough of Flyer opponents,
the moneyline had a much better rate of return than the puckline -1.5 goals, at
home anyway. On the road they covered the spread less frequently.
The other thing
stalling Philadelphia’s upward growth in my Power Rankings has been
over/unders. My algorithm is struggling with their unders, recommending them
too often. That could be attributable to Hart’s good games. The Flyers played a
home heavy Q3 schedule and went winless on the road. They’ll play a
disproportionate number of road games in the 4th quarter, and might
be tanking post-trade deadline. I’m hoping that my profits on this team will be
flying high in Q4.
16) New
Jersey Devils, ($1,607):
Last Quarter Rank: 19
1st Quarter Profit: $877
2nd Quarter Profit: -$125
3rd Quarter Profit: $856
Q3 Win-Loss Record: 8-13
Q3 % Money Bet On: 6%
(-$190)
If you bet on them
every game ML+PL: $47 (LR: 13)
Q3 % Money Bet Against: 94%
($393)
If you bet against
them every game ML+PL: -$132 (LR: 17)
Q3 % Bet Over: 87% ($442), Market Return
on $1: $1.09
Q3 % Bet Under: 13% ($210), Market Return
on $1: $0.82
Injuries and
inconsistency in goal have plagued New Jersey for most of the season, but
that’s a problem you can exploit as a bettor. By week 9, if you had bet every
over or every under, you lost money. Neither side was profitable for the first
2 months. Then in December when the goal scoring boom began, their overs
started cashing tickets. My algorithm produced outstanding results on Devils
games, even posting profit on NJD unders when overall that was a bad bet to
make.
When it came to
wagering on wins and losses, I took a 94% stake in Devil opponents, which was
too much. Yes, it did net a positive return, but they won nearly 40% of their
games (including wins against Carolina, Colorado, and St. Louis). They notched
some big upsets, so I probably should have given them more credit against lower
tier competition. Part of the issue is that they play in a very competitive
division, so they’re often facing tough opponents. If you bet them to lose
every game (moneyline or puckline), you were net loser. In February rookie
Nico Daws took control of the starting job in goal, and finally injected some
stability into their back-end.
17) Tampa Bay
Lightning, ($1,411):
Last Quarter Rank: 7
1st Quarter Profit: $1,400
2nd Quarter Profit: $536
3rd Quarter Profit: -$526
Q3 Win-Loss Record: 11-6
Q3 % Money Bet On: 83%
(-$282)
If you bet on them
every game ML+PL: -$582
(LR: 23)
Q3 % Money Bet Against: 17%
($330)
If you bet against
them every game ML+PL: -$233 (LR: 19)
Q3 % Bet Over: 68% (-$452), Market Return
on $1: $0.85
Q3 % Bet Under: 32% (-$122), Market Return
on $1: $1.06
It’s getting harder
and harder to make a profit on Tampa Bay wins due to stingy line offerings by
the sportsbooks (which is hardly a new development). They are the only team to
lift the Stanley Cup this decade, and attract too much public money. How was an
11-6 team -$582 if you bet them to
win/cover every game? They might have won 65% of their games, but their average
moneyline was an expensive -220, which implies a 69% probability of victory. So
basically, they need to win at least 70% of their games to produce a profit.
Though a majority of your losses if you bet them every game came on the
puckline -1.5 goals, where they were not good at covering (unlike past seasons, especially at home).
83% of my Tampa money
was on them to win, which led to a -$282 loss. But the 17% invested in Tampa
opponents netted $330, so I managed a small gain on their wins and losses. What
really hurt my Lightning performance was over/unders. My algorithm really
struggled with this team, recommending a 68% stake in overs when unders were a
more profitable bet (their average goals per game barely changed from Q2 to
Q3).
18) Seattle
Kraken, ($1,376):
Last Quarter Rank: 16
1st Quarter Profit: $1,343
2nd Quarter Profit: -$14
3rd Quarter Profit: $46
Q3 Win-Loss Record: 6-15
Q3 % Money Bet On: 2%
(-$200)
If you bet on them
every game ML+PL: -$597
(LR: 24)
Q3 % Money Bet Against: 98%
(-$538)
If you bet against
them every game ML+PL: $373 (LR: 8)
Q3 % Bet Over: 50% ($704),
Market Return on $1: $1.19
Q3 % Bet Under: 50%
($80), Market Return on $1: $0.72
Betting the Kraken
has been a roller-coaster ride. The expansion team was within a few points of
dead last by the end of the 3rd quarter, and I had some monster
weeks betting them to lose. But then there were also weeks when they won 2-3
games, and I incurred heavy losses. 98%
of my money was laid on Kraken opponents, leading to a -$538 loss (-$500 of which came from
pucklines -1.5 goals). If you laid $100 on every Seattle opponent moneyline,
you banked $370, but if you made the same wager on every opposition puckline,
you netted $3. They lost a lot of 1-goal games.
The only thing that
has kept me above zero was their overs. They were the best team in the league
to bet over in the first quarter, dropping to #21 in Q2, then bouncing back to
#11 in Q3. Half my O/U investment was laid on the unders, which still produced
a small profit despite being a bad bet overall. The key was 80% accuracy
selecting overs. The Kraken had a fire-sale leading up to the trade deadline,
so they should be an even worse team in the 4th quarter.
19) San Jose
Sharks, ($1,238):
Last Quarter Rank: 25
1st Quarter Profit: -$537
2nd Quarter Profit: $242
3rd Quarter Profit: $1,534
Q3 Win-Loss Record: 5-12
Q3 % Money Bet On: 23%
($87)
If you bet on them
every game ML+PL: -$329
(LR: 18)
Q3 % Money Bet Against: 77%
($587)
If you bet against
them every game ML+PL: -$67 (LR: 16)
Q3 % Bet Over: 55% ($196), Market Return
on $1: $0.82
Q3 % Bet Under: 45% ($663), Market Return
on $1: $1.11
The Sharks climbed my
Power Rankings in the 3rd quarter, as I turned a profit whether
betting on a win, loss, over, or under. The biggest contributor to my success
was the unders, though I was surprised to see that my algorithm only
recommended a 45% stake in that category. Their unders went 10-7 and my
algorithm went 12-5 at picking the correct outcome. When betting on hockey,
predictable = profitable, whether a team is good or bad. They had injury issues
in goal, which should have made them less predictable.
If you bet San Jose
to win every game (ML+PL) or lose every night, you lost money. Opponent
moneylines generated $183 in profit while opponent pucklines lost -$250. My strong
performance betting Shark opponents was mostly attributable to avoiding
pucklines. Reviewing my notes, many of my decisions were based on line value.
This was a bad team, but their opponents tended to be heavily favored. Most of
my 23% stake in San Jose wins/covers was because the line was off, and they
tended to be small wagers +1.5 goals on the puckline. If you bet the Sharks ML
to win every game, you lost -$565; but if you bet $100 on every
puckline, you actually turned a $236 profit from a 5-12 team.
20) Dallas
Stars, ($1,098):
Last Quarter Rank: 17
1st Quarter Profit: $597
2nd Quarter Profit: $509
3rd Quarter Profit: -$9
Q3 Win-Loss Record: 12-7
Q3 % Money Bet On: 67%
($99)
If you bet on them
every game ML+PL: $96 (LR: 12)
Q3 % Money Bet Against: 33%
(-$69)
If you bet against
them every game ML+PL: -$630 (LR: 23)
Q3 % Bet Over: 76% ($12), Market Return
on $1: $0.99
Q3 % Bet Under: 24% (-$50), Market Return
on $1: $0.91
In the first half of
the season, Dallas won 73% of their home games and 32% of their road games, and
if you bet those splits, you did well. What tripped me up on Dallas in the 3rd quarter was their improvement on the road, where they went 6-3. Not including
over/under, I won $1,284 betting on games in Dallas, and lost -$1,255 betting their road
games. The caveat being that half my profit on Dallas home games came from
laying money on their opponents. They were slightly worse at home in Q3
compared to Q2. Star defenseman Miro Heiskanen was diagnosed with mono near the
end of Q3, and I’ll be extremely reluctant to bet this team to win without that
player, arguably the most important on their roster.
They’re another
franchise who has confused my O/U algorithm, as they’re prone to runs of both
high and low scoring games. Inconsistency in goal has been among the reasons
for these fluctuations, but they’re a good team to bet on when the goalies get
hot. Jake Oettinger, their goalie of the future, had some brilliant hot
streaks, but also struggled at times. Still, they averaged 3.2 goals against in
Q2, and 2.6 in Q3, so overall the goaltending did improve.
21) Edmonton
Oilers, ($538):
Last Quarter Rank: 13
1st Quarter Profit: $1,572
2nd Quarter Profit: -$84
3rd Quarter Profit: -$950
Q3 Win-Loss Record: 14-9
Q3 % Money Bet On: 42%
(-$420)
If you bet on them
every game ML+PL: $663 (LR: 6)
Q3 % Money Bet Against: 58%
(-$429)
If you bet against
them every game ML+PL: -$790 (LR: 28)
Q3 % Bet Over: 53% ($146), Market Return
on $1: $0.95
Q3 % Bet Under: 47% (-$248), Market Return
on $1: $0.96
It feels like the
2021/22 Edmonton Oilers have 2 gears, awesome or awful. It’s one or the other
with no middle ground. Navigating this bipolar minefield has been a challenge,
with Edmonton dropping from #5 to #19 in my rankings from week 12 to week 17.
They went 14-9 in Q3 and if you bet them to win every game, you had a strong
quarter. Not me. I failed to strike oil when laying money on Edmonton. I was a
large net loser on both sides of the Oilers bet. They had wins against Florida,
Tampa, Washington, Calgary, and losses to Ottawa, Montreal, Chicago. They can
beat any team any given night, but can also lose to any team any night. That’s
a dangerous recipe (see St. Louis).
There was very little
difference between their overs and unders, as Mikko Koskinen was quasi-decent
in goal, bringing their GAA down while their goals scored inched upwards. My
two algorithms disagreed often on the best Oilers bet, as unders started hot,
then overs went on a run, alternating back-and-forth to end the third quarter.
More trend shifting issues, hence the appeal of algorithms with shorter
memories.
.
22) Montreal
Canadiens, ($375):
Last Quarter Rank: 8
1st Quarter Profit: $7
2nd Quarter Profit: $1,926
3rd Quarter Profit: -$1,559
Q3 Win-Loss Record: 8-12
Q3 % Money Bet On: 11%
(-$278)
If you bet on them
every game ML+PL: $315 (LR: 8)
Q3 % Money Bet Against: 89%
(-$1,397)
If you bet against
them every game ML+PL: -$256 (LR: 21)
Q3 % Bet Over: 81% ($343), Market Return
on $1: $1.34
Q3 % Bet Under: 19% (-$226), Market Return
on $1: $0.56
From Jan 20 to Feb
13, the Montreal Canadiens lost 10 games in a row, leading to the firing of
their head coach. During that span, I was laying heavy money on the Habs to
lose, and had some outstanding weeks doing so, propelling them all the way up to #4
in my week 16 Power Rankings. That was the week when they fired their head
coach. At that point, their chances of making the playoffs were zero, so why
not keep the coach who was giving you the most ping pong balls in the draft
lottery?
When they hired Marty
St. Louis as their new coach, I was selfishly hoping he would suck at coaching,
or at least suffer some growing pains early in his tenure. Nope. Instead, they
completely reversed course, winning 7 of 8 games following that streak of
futility. That stretch cost me more than -$3,000, dropping them to #23 in my
ranks in just 3 weeks. Dramatic reversals of fortune like that can be
devastating to my betting portfolio. I thrive when bad teams are reliably
awful.
My over/under
algorithm did struggle at times with this team, recommending unders too often
for a team where you would have exceeded by just betting over in every single
match. This was among the teams were my old algorithm outperformed the new one,
as it pumped the over 19 times in 20 games.
23) Calgary
Flames, ($242):
Last Quarter Rank: 28
1st Quarter Profit: -$1,398
2nd Quarter Profit: $335
3rd Quarter Profit: $1,306
Q3 Win-Loss Record: 18-6
Q3 % Money Bet On: 65%
($850)
If you bet on them
every game ML+PL: $1,600 (LR: 1)
Q3 % Money Bet Against: 35%
($58)
If you bet against
them every game ML+PL: -$1,560 (LR: 32)
Q3 % Bet Over: 98% ($398), Market Return
on $1: $1.16
Q3 % Bet Under: 2% ($0), Market Return
on $1: $0.76
The Flames have
exceeded expectations as much as any team in the league this season, but they
performed poorly when I bet them to win in the first half, dropping them into
the basement region of my Power Rankings for several weeks (including dead last
at the end of week 7). In the 3rd quarter they went on a 10-game
winning streak, which allowed me to reverse my misfortune. Though I’ll admit, I
could have done a better job manufacturing profit from their wins. They won 18
games, with 14 of those being by at least 2 goals. As a result, Calgary
pucklines -1.5 goals had double the rate of return of their moneylines.
Calgary was the worst
team to bet against in Q3, but I managed to produce a small profit when doing
so (thanks to 2 large wagers on St. Louis and Washington to win when the Flames
were on the 2nd half of a back-to-back). My over/under algorithm
recommended overs in 23 of their 24 games, and my only under bet was a push.
Their goals scored per game climbed from 2.8 in Q2 to 3.9 in Q3, but they’re
goals against decreased from 3.3 to 2.3. The two sides cancelled each other
out, and there was not a big difference in their O/U results quarter to
quarter.
24) Colorado
Avalanche, (-$96):
Last Quarter Rank: 14
1st Quarter Profit: -$486
2nd Quarter Profit: $1,925
3rd Quarter Profit: -$1,535
Q3 Win-Loss Record: 15-7
Q3 % Money Bet On: 93%
(-$1,401)
If you bet on them
every game ML+PL: -$182
(LR: 16)
Q3 % Money Bet Against: 7%
(-$117)
If you bet against
them every game ML+PL: -$185 (LR: 18)
Q3 % Bet Over: 48% (-$153), Market Return
on $1: $0.84
Q3 % Bet Under: 52% ($136), Market Return
on $1: $1.10
The Avalanche are
considered by many to be the best team in the league, and you’ll get better
value betting them to win the Stanley Cup than defeating any given team any
given night. They won 68% of their Q3 games, and 93% of my money was invested
in that outcome, yet I lost -$1,401 doing so. How was that possible?
Pretty simple actually, 3 games they lost to Montreal, Arizona and Arizona
again cost me -$3,026. If not for those
wickedly unlikely abominations, I performed well betting Avalanche to win. If
you deleted Colorado’s season series against Arizona from existence, the Avs
would have finished the 3rd quarter #13 in my Power
Rankings.
One Avalanche trend
that shifted dramatically in the third quarter was overs, after being among the
league’s best over teams in the first half. The goaltending improved, and their
goals scored per game dropped by 1.2 tucks. My O/U algorithm encountered difficulty
predicting outcomes due to abrupt trend shifting. Overs were hot, then unders
hit in 6 straight games, then overs hit in 7 of the next 10. Frankly I’m
fortunate to have only lost -$17 on Colorado over/under (the old
algorithm would have lost slightly more).
25) Toronto
Maple Leafs, (-$432):
Last Quarter Rank: 27
1st Quarter Profit: -$1,120
2nd Quarter Profit: $234
3rd Quarter Profit: $454
Q3 Win-Loss Record: 13-9
Q3 % Money Bet On: 78%
(-$660)
If you bet on them
every game ML+PL: -$100
(LR: 15)
Q3 % Money Bet Against: 22%
($54)
If you bet against
them every game ML+PL: $573 (LR: 5)
Q3 % Bet Over: 91% ($1,359), Market Return
on $1: $1.48
Q3 % Bet Under: 9% (-$300), Market Return
on $1: $0.44
The Leafs were among
the league’s worst teams at keeping the puck out of their net in the 3rd quarter, though proved capable of outscoring their problems on most nights
until hitting a wall in March. The porous goaltending became the main headline
engulfing the franchise approaching the trade deadline. They won 59% of their Q3
games, but the average implied probability of their moneylines was 67%, so they
did not win often enough to cover the cost of their losses. The Leafs are
always a very profitable team to bet against when they’re struggling, but hard
to profit from when they’re winning because they attract too much public money
thanks to their large fan base. I was mostly betting them to win until their
5-2 loss against Montreal on Feb 21, then shifted to their opponents in 9 of
their next 10 games.
The one wager you
really needed to make on Toronto games in the 3rd quarter was overs.
High powered offense and struggling goalies is the perfect storm for over
betting, and my algorithm crushed the Leafs. It was my #1 most profitable O/U
wager of Q3. They even had a game against Detroit where 17 goals were scored.
26) Detroit
Red Wings, (-$1,168):
Last Quarter Rank: 30
1st Quarter Profit: -$1,752
2nd Quarter Profit: -$101
3rd Quarter Profit: $686
Q3 Win-Loss Record: 6-12
Q3 % Money Bet On: 20%
(-$375)
If you bet on them
every game ML+PL: -$1,133
(LR: 29)
Q3 % Money Bet Against: 80%
($803)
If you bet against
them every game ML+PL: $585 (LR: 4)
Q3 % Bet Over: 83% ($558), Market Return
on $1: $1.22
Q3 % Bet Under: 17% (-$300), Market Return
on $1: $0.68
This was my best
quarter betting on Detroit games dating back to before Covid. After a decent
start to the season, the team began to struggle in the new year, especially
when it came to keeping pucks out of their net. This allowed me to grind out a
small profit when betting them to lose. In the first half the Wings won 59% at
home and 26% on the road, but that normalized in Q3 when they were equally bad
in all venues. They were actually the best team in the NHL to bet against -1.5
goals, as all but one of their 12 losses was by at least 2 goals.
Goaltender Alex
Nedeljkovic was getting Calder trophy buzz early in the schedule, but he
definitively took his name out of the race in the third quarter. The team’s
average goals against rose from 3.3 to 4.8; Q2 to Q3. This was accompanied
with a 19% increase in goals scored, making their overs an increasingly
lucrative proposition. My algorithm incorrectly recommended a few unders, and
would have produced a higher rate of return by just betting every over (which
is not necessarily replicable going forward).
27) Nashville
Predators, (-$1,516):
Last Quarter Rank: 21
1st Quarter Profit: $199
2nd Quarter Profit: $321
3rd Quarter Profit: -$2,037
Q3 Win-Loss Record: 9-8
Q3 % Money Bet On: 59%
(-$533)
If you bet on them
every game ML+PL: $190 (LR: 10)
Q3 % Money Bet Against: 41%
(-$951)
If you bet against
them every game ML+PL: -$644 (LR: 24)
Q3 % Bet Over: 74% (-$153), Market Return
on $1: $1.07
Q3 % Bet Under: 26% (-$400), Market Return
on $1: $0.84
For the last 2
seasons, the Nashville Predators have fallen into that category of “dangerous
to bet against” but “dangerous to bet on”. That’s what they continue to be for
me. The team has flaws, but players who can score and a goalie that can steal
games. My performance betting Nashville games was abysmal, posting
significantly large losses in all categories. Laying money on the Predators to
lose was my most common mistake, though did include opponents like Florida,
Minnesota, and Dallas. I would have swung a nice profit betting Preds to win if
you deleted 2 games from the sample (failing to beat Seattle and Winnipeg who
were on the 2nd half of a back-to-back).
Juuse Saros was
frustrating to own in fantasy hockey for most of the 3rd quarter, as
he allowed at least 4 goals in more than 1/3 of his starts, not all against
high scoring teams; but on the flip side he allowed 1 or less goal in nearly
the same number of starts, not all against low scoring teams. In other words,
his quality of play was not strong correlated to quality of opponent, making
him difficult to predict reliably.
28) St. Louis
Blues, (-$1,583):
Last Quarter Rank: 23
1st Quarter Profit: -$824
2nd Quarter Profit: $771
3rd Quarter Profit: -$1,530
Q3 Win-Loss Record: 10-9
Q3 % Money Bet On: 68%
(-$916)
If you bet on them
every game ML+PL: $115 (LR: 11)
Q3 % Money Bet Against: 32%
(-$328)
If you bet against
them every game ML+PL: $111 (LR: 13)
Q3 % Bet Over: 76% (-$172), Market Return
on $1: $1.02
Q3 % Bet Under: 24% (-$114), Market Return
on $1: $0.91
St. Louis was among
the league’s best teams in the 2nd quarter winning 67% of their
games, but cooled off in Q3, winning 53%. From week 8 to 15, I banked $1,374
betting on Blues games. From week 16 to 19, I lost -$2,344. The “TSN Turning
Point” was a pair of home losses against Winnipeg and New Jersey that cost me -$1,000 combined, and a loss
to a previously terrible Montreal team that burned -$1,200. Delete those 3
games from the sample, and I would not be singing the blues…
Betting their
over/under totals also proved to be enigmatic, as overs went 9-8-2, often
alternating back-and-forth. Sometimes their goalie is good, sometimes awful.
Jordan Binnington has been wildly erratic, which is problematic if you’re
trying to decipher scoring patterns. Ville Husso had been the better goalie for
most of the season, but that started to flip in March, as Binnington settled
down and Husso diminished.
St. Louis won 55% of
their Q3 road games, except the one in Montreal. Two nights later they visited
Toronto, and I made a large wager on the Leafs, despite Tweeting a warning
about betting against the Blues in that position. I ignored my own advice. Shit
happens.
29) Ottawa
Senators, (-$1,973):
Last Quarter Rank: 31
1st Quarter Profit: $347
2nd Quarter Profit: -$2,733
3rd Quarter Profit: $413
Q3 Win-Loss Record: 10-15
Q3 % Money Bet On: 32%
(-$7)
If you bet on them
every game ML+PL: $237 (LR: 9)
Q3 % Money Bet Against: 68%
(-$681)
If you bet against
them every game ML+PL: -$653 (LR: 25)
Q3 % Bet Over: 22% (-$28), Market Return
on $1: $0.54
Q3 % Bet Under: 78% ($1,128), Market Return
on $1: $1.39
My performance
betting on the Ottawa Senators improved in Q3 after a disastrous 2nd quarter. They lost 60% of their games, yet paradoxically were a bad team to bet
against, which was attributable to line value. They won 40% of their road
games, but with an average betting line of +200 (with a 33% implied
probability). Their home moneyline was a net loser, but they were good at
covering home pucklines. The Sens were the 3rd worst team to bet
against -1.5 goals, which also implies that they were good at covering +1.5.
I lost -$688 betting ML and PL in
Ottawa games, but thankfully was able to make substantial profit betting their
unders as their goaltending dramatically improved in February. Matt Murray was
among the best goalies in the NHL for one month of the season, with Anton
Forsberg also excelling. Though Matt Murray was injured at the end of the 3rd quarter and there have been rumours that Forsberg might be moved at the trade
deadline as a pending UFA. If the Sens decide to tank for draft status, the Q3
trends might not be transferable to the 4th quarter. Buyer beware.
Thomas Chabot is out for the season. This team looks ripe for a nosedive.
30) Winnipeg
Jets, (-$2,492):
Last Quarter Rank: 24
1st Quarter Profit: -$274
2nd Quarter Profit: $101
3rd Quarter Profit: -$2,319
Q3 Win-Loss Record: 10-14
Q3 % Money Bet On: 13%
(-$344)
If you bet on them
every game ML+PL: -$413
(LR: 21)
Q3 % Money Bet Against: 87%
(-$1,438)
If you bet against
them every game ML+PL: -$254 (LR: 20)
Q3 % Bet Over: 68% (-$71), Market Return
on $1: $1.09
Q3 % Bet Under: 32% (-$467), Market Return
on $1: $0.84
The Winnipeg Jets
were my 3rd worst team to bet in Q3, whether betting them to win or
lose, over or under. They lost 58% of their games, yet I somehow burned -$1,438 on that outcome. The
main contributor was upset road wins against St. Louis and Nashville, plus a
pair of home wins against Minnesota cost me a combined -$2,250. The problem was
making some large wagers on the wrong games. If you bet $100 on all Winnipeg Q3
opponents (both moneyline and puckline), you were a net loser thanks mainly to
Jets covering pucklines. They were a bad team to bet against -1.5 goals.
My over/under
algorithm really struggled with Winnipeg, especially when recommending unders.
Granted, I’ve been struggling with Jet unders the whole season, both pre and
post algorithm (totalling -$1,248 in the first 3 quarters). The primary
driver has been inconsistency at goal prevention, as for Vezina winning
goaltender Connor Hellebuyck continued to struggle. He has been capable of bad
games against bad teams and good games against good teams. Unpredictable =
unprofitable. If I were betting real money, I wouldn’t lay another dime on
Winnipeg over/unders. But since these are all fake bets, I’ll give my algorithm
the opportunity to win some of that money back in the final quarter.
31) Pittsburgh
Penguins, (-$3,044):
Last Quarter Rank: 29
1st Quarter Profit: -$2,233
2nd Quarter Profit: $720
3rd Quarter Profit: -$1,531
Q3 Win-Loss Record: 11-9
Q3 % Money Bet On: 78%
(-$725)
If you bet on them
every game ML+PL: -$574
(LR: 22)
Q3 % Money Bet Against: 22%
(-$203)
If you bet against
them every game ML+PL: $400 (LR: 7)
Q3 % Bet Over: 41% (-$512), Market Return
on $1: $0.92
Q3 % Bet Under: 59% (-$92), Market Return
on $1: $0.99
For the first 20
weeks of the NHL season, the highest position the Pittsburgh Penguins occupied
in my Power Rankings was 27. Betting them to win, betting them to lose, betting
over, betting under have all been net losers for me personally. It feels like
no matter which outcome I bet, it’s a failure (I should copy/paste the last 2
sentences when discussing the LA Kings). My O/U algorithm really struggled to
figure them out, posting big losses on overs. The erratic play of Tristan Jarry surely contributed to that
volatility.
One of the major
issues for anyone betting Pittsburgh games in Q3 is that they won 5 of 7 road
games but only 6 of 13 home games. The Penguins losing on home ice (even to bad
road teams like Detroit and Seattle) was responsible for 81% of the money I
lost on this squad. For one of those home losses, I laid $500 on the Pens to
beat LA but wrote in my notes “these are the games where LA kills me”. The
Kings won 5-3. I tried to warn myself, but refused to listen.
32) Los
Angeles Kings, (-$6,968):
Last Quarter Rank: 32
1st Quarter Profit: -$1,943
2nd Quarter Profit: -$1,702
3rd Quarter Profit: -$3,322
Q3 Win-Loss Record: 13-7
Q3 % Money Bet On: 43%
(-$457)
If you bet on them
every game ML+PL: $861 (LR: 4)
Q3 % Money Bet Against: 57%
(-$1,840)
If you bet against
them every game ML+PL: -$1,136 (LR: 30)
Q3 % Bet Over: 59% (-$385), Market Return
on $1: $1.05
Q3 % Bet Under: 41% (-$641), Market Return
on $1: $0.85
The LA Kings sat dead
last in my Power Rankings from week 10 to week 20, and built up a giant lead
over Pittsburgh in the race to the bottom. Every category was a significantly
large net loser. The Kings have greatly exceeded my expectations this season,
and my inability to adapt is the biggest black eye on my betting portfolio. If
you bet them to win every game, you had a good third quarter. Meanwhile, 57% of
my money was on the other side, and I got destroyed. How did that happen? They
won 11 of 15 road games and 2 of 5 home games. Playing a successful road heavy
schedule when they were a bad road team in the first half was the largest
contributor to my failure, but defensible.
Not only did my brain
struggle picking their wins and losses, but my algorithm also got hammered by
both their overs and unders. Most of my blown unders came from the old
algorithm pre-All-Star break when there was an increase in goals scored, then
the new algorithm shifted mostly to recommending overs, and goal scoring
decreased. Shifting trends are problematic and can really throw a monkey wrench
into a formula that makes trend-based recommendations.
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