2023/24 Week 13 Betting Report

Week thirteen of the NHL season has been logged into the history books and it felt like an unusually lucky week for my picks, most especially Thursday, going 10-2 on my over/under picks (mostly tailing Prime). There were a few come from behind victories that helped me along the way and the picks I shared on Twitter weren’t great (a Jets empty net goal could have helped that), so I’m not going to brag about my genius. This felt like luck and this week was named after Pavel Datsyuk, but looking at past week 13s in my historical database didn’t yield similar good fortune.
 
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 time this week was disproportionately spent preparing for the beginning of the 2nd half, when my “Tournament of Models” is set to begin (when everyone starts at $0). My “Dog Lover” model has already been put down, sent to the pet cemetery before most of you were even formally introduced. That’s one where the bet selection was completely arbitrary and could only be computed in my Game Summary worksheet, meaning I couldn’t retroactively make its picks for older games. Granted, the “Dog Lover” concept itself isn’t dead. There are a few possible alternative methods that can be entirely based on other model picks. My “Fave Lover” survived the audit and had a good week.
 

 
This turned out to be a big week for road teams more than anything, winning 65%, but that certainly wasn’t the case last season when home teams won a majority at this period of the schedule. That was good for me, who ended up betting road teams disproportionately even though it wasn’t a deliberate strategy. I just liked the teams on road trips better than the teams on homestands, and sometimes that’s all home-road splits come down to, who is at home or on the road any given week. This was also a good week to bet against back-to-backs, or at least home teams who played yesterday, especially for me. New Jersey vs Washington, St. Louis vs Carolina, and Vancouver vs New Jersey all won.
 
My preview yesterday mentioned my project to build some new models and recalibrate existing ones (which is still ongoing, so structural changes may be coming soon). I accumulated 230 games worth of input data to assist in the model building process. One of the new models was a version of the “fair line estimator” (aka FLE) model that uses the last 10 road games of the road team and last 10 home games of the home team to estimate what the line should be. Then bet the so-called “value”. When applied to the sample data, it was a giant loser in every possible category. It could not have been more wrong if I were deliberately trying to build a model that picks wrong outcomes.
 
This then begged the question, if it’s THAT good at being wrong, what about a model that bets the opposite? So, I flipped the picks, didn’t even change the bet sizes, and voila, big profit. As the other one was awful at everything, this one was good at nearly everything. A few weeks ago I built a model that uses the full season FLE to bet “line value”, which had a great first week and is still above zero, but slowly declining. That’s why I built the new version for “last 10 home or road games” to get a more recent sample that is larger than “last 30 days”. The full season had a great first week, but that could have been a fluke. Broken clocks are right twice a day.
 
Once the full season model was applied to the last 230 games instead of the last couple weeks, it was bad too. Not quite as profitable to bet against, but still profitable (7% vs 12%). On one hand, it should have been exciting to stumble upon a winning betting formula, but this had implications beyond giving out better picks in the future. Some of you may already be thinking it. I have been giving people betting advice based on this so-called “line value”, when it might actually be better to bet the opposite. This moral quandary forced me on a long walk engaged in deep introspection. What exactly does this mean?  How can betting against line value be so effective?
 
If a line is +160 and the records of the two teams dictates that the line SHOULD be +120, I’ve always looked at that as getting $40 of value on $100 bet. But perhaps the I should have been dwelling more on why is that line off? What do the sportsbooks know that I don’t? I’m a pretty smart guy with an IQ in the 130s, but there’s a lot of people who are smarter than me, several of which probably work for oddsmakers. They have more money, resources, man-power, etc, etc, etc, and I’m just a one man show (flying too close to the sun at times). There is also an expression among seasoned bettors called “a rat line” where a line is suspiciously off. Almost like the oddsmakers want you to bet that.
 
One of my summer projects was sorting thousands of my “game notes” into categories then calculating rate of return for each. There is a column in my spreadsheet where I write why I made each pick, which was examined for profitable patterns (there’s a lot of unfinished work in that project). The biggest takeaway was a 20% return when I mentioned anything to do with goalies. There was also a paradox that I did not give much thought; a higher rate of return when complaining about line price but betting it anyway versus making a bet based on line value. That wasn’t expected, but it was an unscientific study so I moved on. Perhaps I should have dug a little deeper.
 
On one hand it’s embarrassing that I’ve been pumping this fair line estimator when it should have been a tool for sniffing out rats, but now that we’re here, this is good news.  Live and learn. The fair line estimator was a new invention, but I was using a version of that for all my picks dating back to 2019. Maybe that’s why my historical return is only 2.2%, with the bulk of my profit coming from betting against terrible teams and goalies. My best skill is exploiting bad teams and bad goalies while my line value-based picks have been a hinderance. This new model is being called “Shorting Value” because it was designed to find value, but now wants to bet against it.
 
 
My Team of the Week: Tampa Bay Lightning, +$1,107
 
I wasn’t even aware of my success betting Tampa Bay Lightning games this week until Saturday night when I first looked at my weekly team results. They played 3 times losing twice, with me picking the correct outcome in each game (including the correct over/under). It kicked off with a winning bet on Jets -1.5 goals on Tuesday (a pick shared in my report last week), then betting Tampa to beat Minnesota without Kaprizov, then picking the Bruins on Saturday. We’re nearly at half-time on the season, and the Lightning are not currently occupying a playoff spot. Now they’re running Vasilevskiy into the ground trying to close the gap. Sell any Tampa stock you have.
 
My second best team of the week was the Dallas Stars, who went 0-3 missing Jake Oettinger and Miro Heiskanen due to injury. I kept betting them to lose because Scott Wedgewood was starting all their games and Matt Murray 2.0 was a risk to start any given night. Dallas went 0-3 this week but their overs went 3-0 (hint: I bet all those because of Wedge). This team still has plenty of good players and Oettinger should be back any day now, which is why I picked them to win Monday. Well, also because they’re playing Minnesota who are missing some stars of their own. I’ll mostly be shorting Dallas without Miro.
 

My Worst Team of the Week: Los Angeles Kings, -$357
 
This was quite a good week for me, so there were not many contenders in my worst team of the week category. The LA Kings sank to the bottom after losing to Washington on Sunday, with most of my red ink coming from that one game. Still, this team has not been performing as well as that beast from the first quarter, which might be partially because Cam Talbot is slowly transforming back into a pumpkin, which would make me very concerned about betting them in the 2nd half (and very concerned about Talbot being on both my fantasy teams). LA has now lost 5 consecutive games, which does raise a few alarm bells.
 
The Leafs were occupying last place in my weekly ranks as of Saturday night, despite winning their game against San Jose. I told subscribers in my picks email that it was one of my largest wagers, but wasn’t sharing it as an “official” pick because this team has a long history of choking as giant favorites when I make big bets on them to win (more on that below). They managed to pull out a 4-1 victory, which can’t be said for the Rangers (who were my other big unofficial bet that actually did choke as big favorites). As long as Martin Jones is playing well, this team should win more than they lose, but as we saw last year, Marty can flip the switch from good to bad very quickly.
 
 

My Week 13 Results

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

My solid week was being driven largely be visitors, favorites -1.5 goals, and underdog moneylines. I was mostly avoiding teams -1.5 goals in December (with a few exceptions) but have slowly been getting sucked back in. Faves -1.5 produced a net profit this week, while favorites ML did not (mostly because of dogs cashing over the weekend). The recent success of road teams was partially reflected in my preview yesterday, and we have a few good teams departing for road trips, so I’m anticipating another strong performance for the road warriors.
 

There was an underdog -1.5 goals in my winnings this week when Buffalo upset the Penguins in Pittsburgh. My models put me on that pick, which hit at +315. I also hit Buffalo -1.5 at +180 vs Montreal, which was also strongly recommended by my models (Betting Venues is skilled with Sabres pucklines). Now that I’m building up a harem of trusted advisors and better understand their strengths and weaknesses, I’m hoping this will lead to a fantastic second half. My faith in the Kings and Coyotes created the most red ink for my portfolio this week, but I’m going to double down and pick both those teams again tomorrow.
 

If you’d like to know how my Tailing History model did so well with over/unders this week, if you bet $100 on over 6 and under 6.5 whenever one of those was the total (which was 96% of games), you banked more than $1,300. That tends to be what Tails does more often than not. Over 6, Under 6.5. That has produced a better return than all my over/under picks this season.
 

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. Below is the graphic with everyone’s performance this week, including betting $100 on every outcome.
 

The Shorting Value model might have been born yesterday, but it had the data to make picks all the way back to Dec 3. I could go back even further and also run the same model for past seasons looking to short value. That will probably need to be done eventually, but I’m focusing right now on my second quarter report and getting all these models upgraded and running by the start of Q3. This was one of my best weeks of the season, and Max Profit more than doubled my winnings (hint, I’ve been tailing Maximus often, so he’s partially responsible for my success too). I do have some sad news to share, my Seasonal Goalies model has been permanently deleted. The raw data is still in my spreadsheet, but is no longer making active picks.
 
I’m busy preparing a “Tournament of Models” to commence at the beginning of the second half, which beings on January 15th. Some of my 15 existing models have been performing so poorly they were scheduled for deletion, but I decided to go back and review their input data from the last month and see if there was a more optimal bet selection algorithm, specifically regarding bet sizes. Some of these models were created using current data, but they did not receive a proper “bet size” optimization calibration. That was my big job on Friday and Saturday, checking to make sure there wasn’t a better way to use the data before abandoning the concept. Seasonal Goalies failed.
 

More models were created in this process, but there will be more information on that soon. Looking at my team of over/under algorithms (aka the OU Council), most of them had a good week. The games were predictable from a few different angles, with avg goals last 5 games being the biggest winner (that was my previous primary). Prime did have a good week and I mostly followed the picks, but went 2-5 when disagreeing. Game Sum was deleted as a betting model but still makes OU picks. Prime Line Value has a seat on the OUC, but is facing demotion for bad performance. There isn’t an obvious replacement among the existing algorithms, so it might be one of the newbies.