2023/24 Week 16 Betting Report

Week sixteen of the NHL season has been logged into the history books, noting that I’m including the last few games before the break as part of that week. That’s why there was no report last week, because otherwise there would be nothing to discuss today. By pushing it back 7 days, I can give picks for a busier slate of games tomorrow. I did not watch or care about the All-Star game. Every season this period has become refuge for me to dig deeper into my numbers, this year studying and improving on my models. The first order of business (before the break had even begun) was building a new version of Betting Venues fit to a larger sample (discussed in my Sunday Preview).
 
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.
 
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. You’ll also receive weekly exclusive picks emails that are not posted on my blog.
 

My Second Quarter Report was published last week, and a disturbing amount of the information is already obsolete. There are certainly a few observations that can help going forward, but a few teams suddenly reversed course as the second half began. That’s the biggest difficulty doing what I do, because successful strategies don’t just change from year-to-year, they can shift drastically from week-to-week. I published a betting book last year but have already stopped promoting it because too many of the strategies don’t work anymore. They did at the time, but not anymore. That realization became clear as the book was being finished, so the final and most important rule was checking my weekly reports to make sure everything is still valid. This is a moving target.
 
My primary focus was getting to know my betting models better, as some are still very young, so exploring strengths and weaknesses will help me make better picks and give better advice. This started with a simple “how often does each model bet the same side as each of the other models, how often do they make the exact same pick, and what is the rate of return when doing so” investigation which was compared to how all the models perform when disagreeing with each of the others. From that data it would be easy to extrapolate which models work best as a team. One of the first things that jumped up off the screen is how well Maximus works with the Hedge Fund.
 
The “co-profit” sample included 407 games, and a majority of the models investigated made a pick on every single game in the sample. Granted, handful of these models were only fit to a specific 230 game sample (running automated for 177 games since). Goalies vs Teams appears to be in the process of completely collapsing, which I mused was likely when it finished #1 in my 2nd Quarter Rankings. I’m expecting the same fate to befall “Goalies Last 30 Days” which is hanging on a little longer, but it’s betting the opposite of the value it was designed to find, and I’d prefer the original version work as planned. Maybe the reason that’s still working is goalies are predictably unpredictable. Like a majority of hot goalies inevitably cool within 30 days, and visa versa for those struggling.
 
Is goalie performance predictably cyclical? It’s a hypothesis worth testing, and the longer GL30 sustains respectable performance, the more likely it is to be at least partially true (at the macro level, if not the micro). I’ll certainly need more evidence before struggling goalies start getting deliberately targeted to join my fantasy teams, but I’m leaving my brain open to the concept. GL30 did perform substantially better when betting the same side as Max Profit, but that seems to be true for several models. Maximus has developed the most influence over my own wagers of all the models, so the best strategy should be pairing Max with teammates who elicit the highest return and make them a voting block.
 
In other model news, this was a rough week for a few Hedge Fund members, most especially Goalies vs Teams, but that was actually expected. Maybe not this soon, but this was predicted in past posts. It was #1 among my models in the second quarter, but only because there were several goalies performing the opposite of their past selves. It was not fit to a big sample, just 230 games in December (which was opposite month for several netminders). The original version was looking for goalies being themselves, it was terrible, so I flipped the picks and it was awesome. That’s why it’s in the Hedge Fund that bets negative value.
 
The rest of the Fund had mixed results. The Composite got dragged to Hell by Goalies vs Teams, Weighted Wins, and Shorting Value. However, Shorting Goalies which also looks for negative value based on my estimated starter probability, actually turned a profit. That’s a big reason why I created 3 different models doing different versions of the same thing, such that the cream would rise to the top and the crap would sink to the bottom. The Composite model dropped Goalies vs Teams from the decision matrix, but I’m also not going to turf the Q2 winner after a bad 10 days.
 
Yesterday was eventful despite a lack of hockey on my television (which stayed off) with yet another new model coming off the factory floor. This one was quite different from all the others, originally intended to a quasi-simple “zig zag” model (betting the loser of the previous game) with a special interest in playoffs, but by the end it placed a wager on nearly every game that has been played in the last 3 seasons producing a 17% return. The inputs were pretty simple: did each team win or lose their previous game, how many goals did they win or lose by, how many consecutive games has either team won or lost.
 
The finishing touches were applied this morning and I’ll be keeping the cerebral cortex a little closer to the vest because it was sensational at betting playoff games. The model construction started with the playoffs, producing a remarkable 40% return. Granted, that’s just 3 playoffs, so not a comfortably large sample. It’s trained to the small data it bet, and there’s no guarantee those parameters will carry over to future playoffs. After the playoff portion was completed, it’s focus was turned on 3.5 regular seasons, simply looking at whether the home or road team was streaking (bad or good), if they had won or lost the previous game, and by how much. Nearly every permutation revealed profitable wagers, though many had tiny margins.
 
It wagered nearly $1,000,000 fake dollars for a return of nearly $170,000. I’m very excited for this model, as it is already one of my best performers on the current schedule as well. I’m a little concerned that there are 60 sub-compartments to the decision matrix, but most of those have large samples inside because its spread over 4500 games. The automation is pretty complex, so there is still some error checking on the fly (I’d like to see the automated version sustain before trusting it too much). This model is being called “Betting Loss Trends” because that’s mostly what it does (though it does make picks based on teams winning their previous game or being on a winning streak, so it’s more than just Betting Losses).
 
 
My Team of the Week: Edmonton Oilers, +$1,452
 
In one week the Edmonton Oilers jumped from #31 in my profitability rankings up to #19 as they’re on the verge of possibly setting a new NHL record for longest winning streak. It’s a little embarrassing that it took me that long into the streak before they rose from the rubble. It was a deep hole that I had dug, but have picked the Oilers in 12 of their last 14 games. Part of the issue is their lines have been painfully expensive since before the streak began. The books never bought their struggles. This week I went 3 for 3 betting their wins/losses, and 3 for 3 betting their unders. Half the profit you see up there came from big bets on their unders. That’s why they moved so far up the ranks.
 
My second best team of the week was the Washington Capitals, but for entirely different reasons. They are descending into rock-bottom territory, as the goaltending isn’t stealing as many games as in December. Evidence suggests that Father Time’s may have delivered a knockout blow to Alex Ovechkin’s goal chase, which is rippling through the rest of the roster, especially on the power play. I’ll be mostly be betting them to lose for the rest of the season, unless one of the goalies gets hot, or Ovechkin returns from the break rejuvenated. Capitals overs also went 3 for 3 and I went 3 for 3 betting them. Please forget what I just wrote when we get to tomorrow’s games…
 
My Worst Team of the Week: Ottawa Senators, -$1,033
 
The Ottawa Senators went 3-2 this week, and the reason that was problematic for me is because they went 2-0 on the road and 1-2 at home. They had previously been a bad road team and better on home ice. That’s how I’ve been betting them. The script flipped, but I’ll need to see that persist a little longer before jumping enthusiastically on board the revolution. Their unders went 3-2 and I had the over in all 5 games. The goaltending appears to be settling down, and a hot streak could be brewing. Just as Sens fans were starting to get exciting about the draft lottery, they’ll catch fire and pick 12th.
 
My second worst team of the week was the Los Angeles Kings, and the biggest reason why is going 1-11 on their last 12 over/unders. Their unders had been on a 10-1 run, then their overs went 4-2, then their unders went 4-2. That kind of flip flopping can even tip up a team of algorithms who are all looking at different angles. They fired the coach during the break, though I haven’t been hearing any discourse that the coach was the reason for their current skid, but that’s outside my purview to measure. The General Manager handing the coach Cam Talbot, Pheonix Copley, and Dave Rittich to mind the net surely bore more responsibility for their struggles than coaching.
 
 

My Week 16 Results

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

Favorites started the week on fire, but dogs gained ground as the all-star break grew closer. Some of my initial observations required revision because they crashed as the week closed. Favorites +1.5 goals cracked the leaderboard, but that category often requires uncomfortably large wagers to ensure a nice return. It’s not a category that I’m selecting very often, but some of my betting models certainly do, and a few are good when following that path. Unders were my second best category, but they only won the week by a small margin. My team did a good job at identifying the right ones. However, overs tend to be better following the all-star break, so keep that in mind (that may not be playing out tonight).
 

I finished slightly above zero on my non-OU wagers for the week, with most of the credit going to Colorado and Edmonton being good, plus Washington and Chicago being bad. I’m also profiting from the downfall of the Philadelphia Flyers, who may have been torpedoed by Carter Hart’s sudden departure and subsequent bad mojo. Samuel Ersson was already starting to struggle before that happened, and once the entire responsibility was dropped on his shoulders, it might have been more than he can carry. We’ll return to that subject in my picks below.
 

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.
 

 

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. That does need to be updated for some of my upgrades completed over the all-star break. I also found an error in the Grand Aggregator automation (the macro was pasting a formula instead of the value), in the process of carrying out repairs, I changed its old picks to also account for the newer models. Maybe that’s like cheating, but it’s pretend money anyway.
 

 
As you can see from the evidence above, it was maybe sub-optimal to include the algorithm formerly known as Prime on the newly formed “Small Council” (for my fellow Game of Thrones fans), but it felt wrong to exclude my ex-primary. That might be why they collectively lost money on 3-2 decisions this past week, but had a very high success rate on 4-1 and 5-0 votes. All the other voting members are turning a 3rd quarter profit. I’m currently on top of the leaderboard with my own picks, but should probably note that my max bet sizes are larger. All the others just bet the same amount for every game. It might be worth investigating if scaling up their picks leads to greater profit.