Week four of the NHL season has been logged into the
history books and after an outstanding week three, my heater has cooled. Once
again, my imaginary Tailing History portfolio (henceforth referred to as T.H)
created to measure the replicability of history defeated me by a substantial
margin. It felt like week one all over again. But that’s not the only model of
mine to crush my own results. If you
read my Week Five Preview yesterday, you already know that Dr. Frankenstein has given birth to a
new creation, as one tends to do around Halloween.
Before we go any
further, it’s time for my obligatory *DISCLAIMER* it needs be noted that I’m
not betting with real money. These are all fictional wagers in a spreadsheet.
My mission is to engage in a mass betting campaign, picking a winner of every
single game, every over/under, because it provides a complete dataset for
macroeconomic analysis, which can be shared with you, shedding light on what
worked and what failed. I’m also tracking the results of betting every outcome,
to help me (and you) uncover previously unknown or newly emerging profit
vectors. What started as a thought experiment has evolved into much more.
If you’d like to
read more about the first 3 years of this thought experiment, I wrote a
330-page book outlining the results from every angle. What worked, what failed. Lessons learned, market trends,
team-by-team analysis. To read more, visit the Amazon
store. My blog has been moved to Substack this
season and I’ll be repeatedly encouraging everyone to sign-up for a free
subscription to alleviate my dependence on Twitter for traffic. I’m concerned
that Elon will follow through on his threat to charge everyone for Twitter and
ostensibly destroy his own company. Subscribers receive an email notification
each time a new post is published, and even if Twitter stays free, the
algorithm likes to hide Tweets with links so you don’t leave.
My latest creation is currently being called “Betting
Venues” for now (henceforth referred to as B.V) unless I can think of something
better (suggestions are welcome, ideally two words that precisely describe what
it does). This concept had been bouncing around my brain for a couple weeks,
and it only actually took me 30 minutes to build the model. Simply add up all
the games between two teams in any given city since Oct 2019, sum the results
of betting on every outcome, and pick a wager based on what was the most
profitable choice historically.
Frankly this one could also have been called “Tailing
History”, but that name was already taken. All that needed to be done was
adding them up, make a pick for each game, then feed it into my 2023/24
worksheet. It doesn’t care what the line is (except OU total) or what any of
the results from the current season are. What’s the best bet for this match-up
in this city historically? When the picks were fed into the games that had
already happened, the result was astounding. Check out my Sunday preview for a precise explanation of how it makes picks.
Both betting models had great weeks because they
invested a considerable sum in some underdogs -1.5 goals, when this wasn’t even
a particularly profitable week to do so overall. They just picked the right
games. It would have been smart to tail the models more than I did, especially
considering that all their picks are known to me when mine are decided. But
hindsight is 20:20 and my own decision-making process has a longer track record
of success. My trust in my own instincts led me astray in week four, and
looking deeper into the numbers reveals who was responsible for this
performance.
There were two primary culprits who held the
metaphorical murder weapon this week, and they were underdog moneylines and
unders. It’s my own damned fault. My Week 4 Betting Preview showed increasing
profitability of overs at this juncture of the season, hence why Tailing
History bet every single over this week. In doing so, it performed much better
than me. So why didn’t I take my own advice? Well, we finally reached a big
enough sample size for the season to activate my primary over/under algorithm,
which doesn’t account for historical trends. This was among my worst over/under
weeks in 2+ years, mostly from blown unders.
How did this happen? Most of the damage comes down to
a few teams, Chicago shifted from unders to overs, Montreal from overs to
unders, and the Sharks from unders to overs. If you deleted these 3 teams from
my sample, I would have made a profit on over/under. Trend shifting can be a
problem for anyone using over/under algorithms like mine (which have all been
described several times in past reports). Now that every team has played 10
games, all my algorithms are active. I look at all the recommendations and make
a decision, most often following my primary (last 8 games minus highest and
lowest score).
Sometimes I have used 7 games minus highest/lowest,
but empirically there’s not much difference. They’ll make the same pick 95% of
the time. This season I’m keeping a record of how each algorithm has performed,
so that when I have bad weeks like this, I’ll see if any of my alternates
produced better results. The others are just last 5 games and last 10 games;
one averaging goals by the two teams, the other counting how many games went
over or under the total. I’ll flag the games where the primary is indecisive.
Some of those I’ll even bet the other side (looking at goalie rotations in my
Game Summary worksheet (that I affectionately refer to a GS)).
My Team of
the Week: San Jose Sharks, +$725
The San Jose Sharks increased their lead atop my
Profitability Rankings, as they are the worst NHL team by a mile, and I’m all
in. Every game will be my maximum amount on Sharks opponent -1.5 goals until
those start losing money. We could see -400 pucklines in the next couple weeks.
Are they even still available for worst record in the league? I’d probably bet
that -200 right now. This is a trainwreck. My spreadsheet may need to be
modified to allow bets -2.5 goals or even -3.5 goals. They might be bad enough
to justify structural changes to my parameters.
My second-best team of the week snuck into this spot
on Sunday, the New Jersey Devils. Some of you might have noticed that I posted
a Tweet Sunday morning encouraging people to bet the Devils ML and PL (along
with the Anaheim ML at +164 vs Vegas, which also won) then deleted it a few
minutes later after seeing the news that Jack Hughes would miss the game and
was week-to-week. Granted, they only hit that puckline with an empty net goal
in the final second, but still, I deleted a Tweet that 3 for 3 on its picks,
and was embarrassed about it later. Coincidently, I had a good Sunday but
didn’t feel great about it.
My Worst
Team of the Week: Buffalo Sabres, -$726
My ability to bet the incorrect outcome of Buffalo
Sabres games this season has been astonishing. You could not be more wrong if
you were deliberately trying to be wrong, unless you were betting the San Jose
Sharks. I’m strongly considering sending all my Buffalo bets exclusively to
subscribers so they can bet the other side. I was in a similar situation with
the 2021/22 LA Kings, and that continued all season. I tried betting the
opposite of my instincts, even flipping a coin to pick the outcome. It was a
legit statistically verifiable “mush”. My belief in the existence of the
“butterfly effect” was strengthened that season.
Another team that let me down was the Arizona Coyotes,
with my biggest loss coming from betting them to win on the road against
Anaheim. What hurt about that is my presence on the Anaheim bandwagon early in
the season, but we have been on this “betting travel” angle and Anaheim was
returning from a long roadie. The travel angle produced mixed results and I’m
still in the process of quantifying the best versions. There have been some
bumps in the road. We’ll see if that theory can be constructed into a functional
model, but not yet.
My Week 4 Results
*Note* “Overall Market Bets”
based on betting exactly $100 on every outcome.
Do note that 2 of my 4 road favorite pucklines were
against a team who played yesterday, if you’re looking at my category results
wondering how I lost -$1,000. This proved to be
a good week for road teams, which was not the case in past week fours. My
preview 8 days ago pointed to home teams performing better, and Tailing History
indeed lost a considerable sum on home teams, except one specific demographic,
home dogs between +121 and +169 on the moneyline, making max bets on their
pucklines -1.5 goals. That more than made up the difference.
It should be noted that sometimes trends in home-road
performances are often tied to a few good or bad teams having overlapping road
trips or home stands. I did fall into that trap in past seasons, thinking “road
teams are hot” when in reality there were just a few teams driving the bus, and
the whole category cooled once they returned home. This was more pronounced in
2021 with the unique schedule parameters from that season, such as Colorado
playing 4 games in San Jose in a single week. I’ll try to be more mindful of
this phenomenon going forward.
One bandwagon I’m decidedly strapped into is the
Vancouver Canucks (my 3rd best team of the week), which just happens
to be the city in which I’ve lived for the last 20 years. Their games are on my
television every night. It’s by far the team I’ve watched the most in the last
two decades. I even worked at the arena for a few years after first moving out
here from Ontario, and cheer for them whenever they’re in the playoffs (despite
being a life-long Detroit fan). I bet them to win all their games this week and
they went 3-0, but my over/under results were not as joyful.
Me vs Myself
As previously mentioned, my battle vs myself took a
step backwards in week four with Tailing History re-establishing its full
season lead. Though both of us were dwarfed by the accomplishments of the new
kid on the block, Betting Venues. That one logged all its picks for the entire
season without even caring about the lines or previous results from the current
schedule. It does need to know what the over/under total is before making a
pick, but otherwise is completely disconnected from what’s happening this
season.
Perhaps there are improved versions of this that can
add complexity and possibly improve performance, but version 1.0 was such an
outstanding success that I’m happy letting this stretch its legs before any
modifications are required. Those results would have been less spectacular had
it been born last week as 2/3 of its profit for the entire season was generated
in the last 7 days on the backs of underdogs -1.5 goals. The biggest jackpots
came from large wagers on Buffalo +380 vs Toronto, Columbus +300 vs Tampa,
Vancouver +245 vs Dallas, Calgary +260 vs Seattle, and Philly +320 vs Buffalo.
The Graphic below shows the results of me vs my
betting models for the most recent week, and below that are all our picks for
Tuesday.
I never expected Tailing History to be this good
because it pays no attention to the teams involved. It just bets which line had
a better historical return. This week it got crushed on favorites -1.5 goals,
but made up the difference on underdogs -1.5. I know what all its picks are
before mine are logged, and its still kicking my ass. I haven’t been tailing it
much my expectation of its success is low. The purpose was to measure how well
history was repeating from a category point of view, in the even that people
were using the data in my previews to make bets. Then it soundly defeated me in
week one, and this “me vs myself” competition was born.
The Betting Venue model I’m more interested in
tailing, given it has very specific data about the teams involved, and cares
only about their past results, not how the current schedule is unfolding.
BUF @ CAR: The Hurricanes just lost their best goalie indefinitely, and while
this team has recovered from their bad start, Buffalo +170 is a number I
can’t resist. Both my models are taking Carolina (one +1.5 goals, one -1.5),
but I can’t quit my Sabres. Note: my ability to pick the wrong outcome of
Buffalo games this season has been remarkable. They are dead last in my
profitability rankings by a wide margin. The over/under is disputed by my
various models and algorithms. Me and T.H are taking under 6.5 but B.V likes
the over.
TB @ MTL: I’m picking Montreal ML +136 in this one, mostly because I’m
not paying -162 for road Lightning on a back-to-back with Tomkins the likely
starter (assuming Johansson gets the Leafs). B.V is putting a small bet on
Tampa +1.5 goals while T.H is with me on the Habs ML. All three of us are
taking under 6.5 goals (6 of their last 7 meetings in Montreal went
under 6.5, but all of those featured Vasilevskiy in goal (B.V may be misled),
so proceed with caution. Tomkins has an .891 SV% in 2 games played.
DET @ NYR: The Rangers are without Adam Fox and Filip Chytil, possibly also Igor
Shesterkin, who is day-to-day without official word on his status for Tuesday
(at least at the time I’m writing this). My pick is Detroit +160, though
now I’m seeing it has moved to +140 in an hour since the opening line was
released on Draft Kings. T.H has a big bet on Detroit +1.5 goals, while B.V is
taking New York -1.5 goals. There was significant disagreement between my
models and algorithms about the over/under, so I’ll refrain from sharing a
recommendation.
MIN @ NYI: I’m taking the Isles ML at -130 because Minnesota still hasn’t
earned my trust back, and both my models are betting New York (T.H +1.5 goals,
B.V moneyline), and I’m happy taking their side. I’m also betting over 5.5
because my algorithms love that bet (Wild overs are 9-2 this season), though
B.V says they average exactly 5.5 head-to-head in New York.
WPG @ STL: B.V is putting a max bet on Jets -1.5, the problem being they are 3-3
in their last 6 meetings in St. Louis, and two of those had a big payout
because Jets were +170 underdogs and hit -1.5 at +400 (ish). This time they are
favored. So, the payout is smaller than what B.V is accustomed to. I’m going to
put half units on Jets ML -130 (which has already moved to -135 on D.K)
and a half unit -1.5 goals at +185. There is significant disagreement by
my advisory team regarding their over/under.
NSH @ CGY: The Flames have played two good (ish) games in a row, but I’m not
ready to stop shorting. B.V is laying a max bet on the Predators ML +120,
and I’m going to tail, but with one unit instead of two. T.H has a max bet on
Calgary +1.5 goals, so there is not unanimous agreement. Once again, there is
conflict regarding the better over/under choice.
SEA @ ARI: I love getting Arizona +100 in Mullett Arena (the line has
already moved to -110), but be warned that B.V likes Seattle -1.5 goals. Most
of my algorithms like over 6.5 goals (including B.V), so that’s my bet,
but T.H is betting under for every total at 6.5 or 7.
NJD @ COL: No Hughes or Hischier means I’m betting a unit on Avs ML -162 and
half unit PL +150 (otherwise that line would make no sense). B.V has a
minimum wager on Avs -1.5 goals, and T.H has a big bet on Devils +1.5 goals.
The over/under is in dispute.
PIT @ ANA: Many of you already know about my presence on the Ducks bandwagon, and
+136 for Ducks ML vs a shaky Pens team felt like a great price (my line
value algorithm thought Ducks should be -162). But Houston we have a problem.
Both my models have large wagers on Pittsburgh -1.5 goals, and they happen to
be 25-9 on bets they agree. So, my bet is just my minimum, but perhaps it would
wiser to invest in Pittsburgh. There is also significant disagreement on the
over/under.
Team By Team Profitability Rankings
These profitability rankings are based on the sum of all my bets per team,
including where the money was won or lost. Each week my new Profitability
Rankings will be based on all the games in the season, not just what happened
this week.
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