2023/24 Week 10 Betting Report

Week ten of the NHL season has been logged into the history books with Christmas just 7 days away. That’s typically when I’m busy making my July free agency predictions, but surely that will also be a fertile few days to create more betting models. Many that I’m creating in preparation for my 2nd half “Tournament of Models” won’t have their picks shared in my game-by-game graphics, but may get mentioned in the write-up. Some need a little trial and error to figure out if that’s the optimal bet selection matrix, others simply need to prove their effectiveness before I want people seeing all their picks.
 
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.
 

This was my second consecutive winning week following an extended slump, thanks to effective over/under picks and staying away from favorites -1.5 goals. This week my sum wagered -1.5 goals was exactly zero (the best two teams to bet against -1.5 were Edmonton and Ottawa). While some of my models continued to seriously struggle (at least with moneylines and pucklines), the Megatron model was they only one managing to beat me. It just steals from the other models, summing their profit/loss on each potential wager with these teams last 30 days. I even let it steal from me (which technically isn’t stealing, but I’m trying to anthropomorphize a simple formula with a villainous personality for the sake of entertainment).
 
My new “Expected Goals Last 30 Days” model has me very excited after its first week in action. Granted, all its profit came from over/under, losing a small amount on moneylines and pucklines, but its bet selection might be far too conservative, needing re-calibration. The potential is there. It took a significant loss on favorites +1.5 goals, and lost both fave -1.5 bets, but produced a respectable profit on moneylines (fave, dog, home, road). Road moneylines were a terrible bet to make this week, but xGL30 still turned a nice profit (Betting Venues did too in an otherwise terrible week for that model).
 
This was the best week for home teams since week one, winning an astonishing 65% of the games (10 teams won 2 home games, the Avs won 3). That was a 50-50 just one week ago. Hosts won 51.3% of home games last season 2nd quarter, so it’s not like this should have been expected. Well, there were a higher-than-normal assembly of teams who play better in front of their own fans having 2-3 opportunities (like Vancouver, Minnesota, Arizona, Colorado, etc). The decline of -1.5 pucklines continued, but nearly broke even on the home side. The visitors bore the brunt of the difficulty this week.
 
The other category that came creeping back this week was overs, as the early December unders run has dissipated. Thankfully my team of algorithms handled this nicely and a majority posted a strong profit for the week. It was a little disappointing to see Game Sum and Megatron near the bottom of the list, with both getting a tire pump here last week for their strong performance. Both make decisions based on which pick was more profitable in the last 30 days, and overs haven’t been particularly fruitful. Whereas those who make decisions independent of recent profitability all performed admirably.
 
Overs went 26-23-2 this week as there was a significant increase in goal scoring by nearly a half goal per game, making it the highest scoring week since early November. My algorithms tend to thrive in a steady-state environment, so it’s a little confusing how well they performed this week, but variance does the most damage at the team level. As long as the individual teams are behaving in a predictable manner, they tend to do well. Below is the graphic with all their performances, and even “Full Season Average” pulled a profit. The lesser performers have been dropped from my picks graphics. The “OU Council” will be a meritocracy going forward. You need to earn and sustain your spot, but I’ll continue tracking every model.
 

While most of my models betting wins and losses struggled, it was a different story for my harem of over/under algorithms. I’m on fire with my over/under picks in the 2nd quarter of the season, getting closer to wiping out that embarrassing deficit from Q1. I’m following my primary algorithm (aka OU Prime) in at least 90% of games, but am lagging behind because of poor performance when betting double (which Optimus doesn’t do) and when disagreeing. But the big winner in week 10 was “Betting Goalies” which is only as good as my prediction of each goalie’s starting probability. It had a bad first week, but is now running a profit for its short lifespan.
 
Another new model was born yesterday (discussed in my Week 11 Preview) called “Goalies Last 30 Days” who is also going to make over/under picks, and should pick the same outcome as Betting Goalies more often than not. The two will have substantial covariance, but whichever proves to better will be my lead advisor on goalie-based over/under picks. My old primary algorithm (avg goals last 5 games) had a good week after a bad first quarter, as it tends to cash in whenever there is a scoring surge (which did happen in December 2021 the same month it was born).
 
 
My Team of the Week: New York Islanders, +$1,016
 
I’ll admit to having some difficulty getting a read on the New York Islanders this season but my foresight was effectively tuned in week 10, or at least my over/under algorithms had them figured out (generating 70% of my Isles profit). Their overs went 4-0, which is not a natural state for this Islander team whose unders went 47-38-3 last season. They still get good goaltending but suddenly the power play is burying a few more tucks, and their overs are on a 13-3 run. For whatever reason the Sportsbooks have been either too slow to react or don’t believe it’s real.
 
My second-best team of the week was the Anaheim Ducks, which is a bandwagon I boarded early but departed once the nosedive began. Looking at the game log yielded an interesting result, as I’ve hit all my Anaheim bets for their last 5 games, which includes betting them to win and lose, over and under. The was a push in there. That Ducks streak extends into last week, and possibly tonight if they cover +1.5 against Detroit (they’re up 4-2 in the 3rd period).
 
 
My Worst Team of the Week: Chicago Blackhawks, -$630
 
The Chicago Blackhawks were my single worst over/under team in a week that produced an impressive bounty of OU winnings. Betting their wins and losses wasn’t a major problem, given that I completely stayed away from pucklines -1.5 goals (and they would have been my number one target after the Sharks resurgence). The Hawks have lost 8 of their last 10 games and haven’t been covering +1.5 goals all that often. It might be getting safer to bet their opponents -1.5 goals, and I’m hoping that only improves as we get closer to the trade deadline and they start shipping out pieces. Post-deadline doesn’t always boost the favorites though.
 
The Winnipeg Jets were my second worst team of the week. I was aggressively shorting both Detroit and Winnipeg after losing Dylan Larkin and Kyle Connor last weekend, two of the most valuable players in the league. Both teams exceeded my expectations, but the Jets did more damage to my bottom line by defeating Colorado and Los Angeles (after losing to San Jose). Not to mention that their unders had been on fire before their offensive explosion against the Kings and Avs. It’s hard for me to believe that collapse is not imminent given the situation, but Hellebuyck might be making his case for the Vezina and one of those solves a lot of problems.
 
 

My Week 10 Results

*Note* “Overall Market Bets” based on betting exactly $100 on every outcome.
 

A new feature in my category results is making a distinction between “mild” or “big” for either dog or favorite. The border between the two is +150 and -150 on the moneyline (also two teams at -110 are both considered mildly favored). You can see from the above that dogs +1.5 and over/under carried me into profitability this week. My moneyline performance is somewhat unnerving, as is taking a substantial loss shorting back-to-backs ML. Though Carolina deserves a majority blame for that one.

For curiosity’s sake tonight I decided to check how the 2nd quarter results are looking (since Nov 27), and the Washington Capitals are my #1 worst team to bet on and my #1 worst team to bet against, dropping to #27 in my full season profit ranks. Vancouver and Nashville were two of top 3 teams to bet on, and they play tomorrow. Scroll down for my pick…
 

 

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 rankings will be based on all the games in the season, not just what happened this week.
 

Me vs Myself

 
For those of you who are new here, the “Me vs Myself” section outlines my competition against my betting models, in my vain attempt to prove my own decision making is superior to the models that I’ve created. Me vs my creations. But rather than explain myself every week, a new post was published outlining how all these models make their decisions. For the full breakdown, click here. Sorry that there have been a few new models added recently that have not yet been added to my “model explainer”, but it’s on my to-do list. Megatron is just a villain who steals from other models based on profitability last 30 days.
 
Some news of note in my model week 10 results, this was an awful week for my Shorting Travel model, which was previously among the best performing by rate of return (it only bets 14% of games). The entire problem was it only bet pucklines -1.5 goals, which had an awful week as a category, bringing down a few of my models in the process. “Shorty” only made 7 bets this week and went 0-7, and 6 of those were -1.5 goals. The big problem here is these crashing pucklines are not normal. History is not repeating in a predictable manner. The issue here might be parity. So many teams tanked for Bedard, that they don’t want to tank again or the attendance might tank with them.
 
I could re-program all my models to tune out pucklines -1.5 goals, but these are not using real money. Even though I’m sharing their puckline picks with you, I’m also telling you that the category is crashing. If you are skipping my analysis and going right to the picks graphics to tail my models, sorry if you’ve had a rough December. I tried to warn you. Instead of changing all to avoid pucklines, I installed safety protocols to limit bet sizes on bets that aren’t working, and may expand those from teams to entire categories if the bleeding doesn’t stop. The safety protocols were engaged starting with my Sunday picks email sent exclusively to my subscribers Saturday.
 

Above is the category chart for me, betting $100 on everything, and my most interesting models. There are too many models now to show them all (these reports are already too long), so I’ll share the most relevant or interesting. Game Sum and Megatron look at a lot of the same data, and for as bad as pucklines -1.5 goals was this week, both these models did a respectable job betting them. Game Sum took a much bigger loss on road moneylines, but otherwise would have had a decent week. My belief that Game Sum should eventually work has not wavered. It’s up to me to find the optimal bet selection. The data is strong.