Andrew Mack
@Gingfacekillah
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Sports Bettor & Retail Trader. JD/MSc Grad. Statistical Sports Models with R & Excel. Bayesian Enthusiast. Pricing Uncertainty.
Calgary, Alberta
Joined November 2018
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After a very long writing hiatus, I am pleased to announce the release of a new book, "Bayesian Sports Models in R".
355 pages. Fully colorized R & Stan code. Full R code download package.
Now if you'll excuse me, I've got some bets/trades to make 🫡
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Good places to look for +EV:
1. The idea is hard to backtest
2. The data is hard to acquire
3. A known input can be transformed into something novel or hard to compute.
5. The idea is known, but has only been applied to one specific area or time horizon.
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A few suggested online courses for taking your sports modelling to the next level. [Available to take for free]
1. Excel: Wayne Winston "The Math Behind Moneyball"
2. Basic Statistics With R
3. Bayesian Statistics in R
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After a long day of buying at the ask and selling at the bid
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In law school, we were taught to prepare case briefs/ exam answers using a structure called FIRAC. Facts, Issue, Ratio, Analysis, Conclusion. After spending some time this weekend going over some of
@therobotjames
trading threads, I sketched out a structure for trading edges.
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We're almost finished. Hoping to release around the end of October / start of November.
Chapter on exactly how to look for inefficiencies.
Followed by 100 pages of examples.
Don't say we didn't tell you.
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I sketched this out a while ago when working on (the exceedingly difficult problem of) options forecasting models. I've made some tweaks and changes since - but maybe some of my old basic framework stuff will help you get where you're trying to go.
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Sports bet tracker spreadsheet. Fully customizable for your own bet types & model/strategy tags. You might find it useful for segmenting your bets and identifying key sources of profit and loss.
P.S. It's free 🫡
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This is a very interesting paper. Some game theory gems here.
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@AlexHormozi
To quote Hemingway, gradually - then suddenly - it all starts to click. Somewhere around this time people start calling you “lucky”.
Will becomes skill with persistence 💯
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Sure. A selection of books I've read in the last few years. You don't have time to make every mistake yourself - reading keeps the idea engine running 🫡
Options Trading
Options Trading // Sinclair
Volatility Trading // Sinclair
Positional Options Trading // Sinclair
Trading
@Gingfacekillah
I really enjoy your contributions. Do you have a reading list you'd recommend? Cheers
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The importance of cogent first principles: the tool is not the goal. From "Volatility Trading" by
@SinclairEuan
. A mentality that is highly applicable to sports betting/modelling as well.
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DM's flooded with people asking me about the book I didn't publish after the
@UnabatedSports
podcast.
I'm going to share a shortened chapter on market efficiency to get your mind thinking in the right direction. The rest of it stays in the vault.
Part 1 📖
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When you're about to make a yard but people don't like how you did it
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Some of the most effective strategies I've found have ended up being the simplest - and least intellectually satisfying from a purely technical perspective.
At some point you have to decide if you *actually* want to make money -- don't overlook simple. Execution counts. 🫡
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If you only ever look for edges within the parameter space others have defined as reasonable, you'll never find the really good ideas. Question what others have defined as impossible or unviable.
More fucking around and finding out. Less parroting established "known knowns".
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In this YT vid,
@therobotjames
presents a simple way to quickly look for potentially tradable effects in market data.
One of my favourite applications has been to apply this methodology to sports betting ideas. You may find it useful. Happy hunting.
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In sports betting, there are situations where using a known market expectation with a slightly better distributional assumption can locate small edges in various markets. 2 years ago, I thought a similar approach might yield modest results in the options trading space. 1/n
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"Seasonality" in the NBA 🧵
Despite the fact that most sports data is relatively stationary, most professional bettors know there is a certain seasonality to major sports.
As an example, the NBA betting season can be broadly broken down into 4 components...
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I know it can boring & tedious to log bets (trades), but you have to do it if you're planning on winning.
- What's your best performance ticker/team?
- Day of week/month?
- Delta/Odds?
- Median winner/loser %?
- Bet/trade structure?
- Strategy/system?
If you can't answer any of
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Finding an edge is actually the fun part, as challenging as it can be.
The real work is pressing your edge every day with consistency. Sounds fun in theory, but as the weeks become months/years you'll realize there is no escaping the grind.
You just get to pick your grind.
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I found this paper quite interesting - Using futures basis spread, ATM IV skew and other factors to forecast stock price jumps:
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Happy Friday 🫡
Here's a basic NBA player points model using RStan MCMC that can kickstart your learning in R.
Let's suppose we have a player's average points per minute, minutes played and the stdev for both. First we load library(rstan) and define the parameters:
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10. The idea uses information from highly liquid markets to attack smaller liquidity markets.
11. The idea takes advantage of more or less granularity that the aggregate market.
12. The idea identifies areas where unsophisticated market participants are particularly active.
/fin
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Parrondo's Paradox in Sports Betting 🧵
Parrondo's Paradox refers to the concept that 2 losing games can sometimes be combined to producing a winning result. This strategy might not be a perfect example, but I think you'll see what I mean.
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I recently had the opportunity to give a presentation at Balyasny Asset Management as part of their Quant Seminar series. Thank you to Sarah, Andrew and the rest of the BAM team for having me.
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Have you ever noticed that the best TA is usually a proxy for realized volatility? And that volatility has stylized facts that lead to repeatable effects? And that effects are where edges can be found?
Focus on effects. It will shave some time off your learning curve.
@__paleologo
The issue with a lot of the r/daytrading et al material is it tends to be focused on TA methods and other ideas that aren't getting at the root of what traders need: effects that can lead to edge.
Here are some books that get closer:
1. Stocks on the Move by
@clenow
2.
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"Statistical Sports Models in Excel Volume 2" is now live and available in paperback on Amazon. The Kindle edition has been uploaded as well and will be live on Saturday Feb. 29th.
I hope you find it helpful for your sports modelling efforts.
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Back in 2017, I was still on construction sites working as an electrician. Our leadhand Jim was in charge of construction progress.
Jim was a bit of a dickhead. A tough guy to work for, but I learned a lot from him. The most important thing I learned?
Keep going.
One day,
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6. The idea is known, but only applied in a different field
7. The idea attacks a market with low liquidity, which dissuades larger market participants from competing.
8. The idea attacks an excess of liquidity.
9. The idea attacks a behavioural glitch/ logical fallacy.
2/n
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Nobody cares if you "called it".
What's the trade (bet)? Did you take it?
Playing the "I was right" game is very different from the "Making money" game. Optimize for what you actually want.
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"The best models are not predictive models per se, but ‘perturbation’ models that start with the assumption that the public is right and then work out what small errors they might make. The public odds are not just a signal – they are a remarkably efficient signal." - David Walsh
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🧵Spread/ML conversion for lesser known sports:
Let's say you want to look for potential value between the spread and the moneyline in a new sport like NRL. First you'll need a conversion formula. Here's how to do it:
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My fiancé surprised me today with a statistical sports models in excel cake 🥳
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New Release: NBA Player Prop Bet Scanner in R
It's an R script that helps you scan for structural market inefficiencies across NBA prop betting markets. Full code & tutorial included.
Check it out below if you're interested ⬇️
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Statistical Sports Models in Excel Volume 2 is now available for preorder [Kindle edition]. Scheduled for release May 1st, although I hope to have it ready before that [I'm aiming for April 1st]. Paperback will be available upon release.
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Using machine learning in R w/ technical indicators to time going long TQQQ. Simple example. Feedback appreciated.
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Random thoughts from today's touch grass / 6k walk:
Most procrastination is a disguised form of fear. Fear of failure, fear of feedback, fear of what that feedback might say about you.
But feedback is just the outcome of an experiment where your thoughts and the
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What Pinnacle looks for when hiring for a Data Scientist position. There are a few hints here for those looking to sharpen up their SB game... *R* *SQL* *Bayes*
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Sports bettors: This is one of the most powerful concepts you can learn. After that, watch
@rabaath
's YT series to get an intuition for approximate Bayesian computation. From there MCMC is only a few steps away and you can do a tremendous amount of useful Bayesian modelling.
Implemented a simple beta-binomial model today for an analysis where the payoffs are fixed within conditions (you can only buy a thing once and its either discounted or not) and there something very elegant about the simplicity of conjugacy.
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The optimal time to bet is based on:
1) How much you're trying to get down; and
2) How much information you need to detect value
@Quantum_Sport
talks about this at length in his book "Winning Sports Betting".
Have a great weekend 🫡
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Thank you for the support, I really appreciate it. I think this is the best thing I’ve ever written, and I hope it helps you get where you want to go 🫡
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Someone else got dealt the same hand you did (or worse) and found a way to win.
Focus.
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One tip that will do wonders for your trading:
Freeing yourself from the idea that markets, at least in the short term, need to “make sense”.
Humans interacting in large groups aren’t known for producing rational or mechanically logical results.
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I've finally had some time to update Statistical Sports Models in Excel volumes 1 & 2:
➡️Fixed up a few errors in the text and model glitches. ➡️Download section for vol.2 is now at start of book.
➡️Improved spreadsheet download process.
🧵[1/3]
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Math is very useful. Coding is very useful.
But this isn't the math Olympics & it's not a Kaggle competition.
"The tool is not the insight" -
@SinclairEuan
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Happy Friday.
With the NFL season almost back on the horizon, here are some resources to check out before the season starts:
1. TD prop model in Python:
2. Season win totals by
@PlusEVAnalytics
:
3. QB Passing yards model in
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Important ideas I learned from Bayesian statistics:
1. The full posterior distribution tells us almost everything we could want to know about a data problem. It’s worth the effort to produce.
2. It’s important to model parameter uncertainty as well as process uncertainty.
3.
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how does it go perfectly to an extreme where it looks unsinkable, then proceeds to sink
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3 books all bettors serious about winning need to read:
@truepokerjoe
"Sharper"
@Quantum_Sport
"Winning Sports Betting"
@EdMillerPoker
" The Logic of Sports Betting"
Start with these three. Seriously.
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You need one (or a small handful) of "good enough" edges and then you need to mechanically execute.
The paradox of choice will mess with you otherwise.
Strategy hoppers get their accounts blown out. You do want to make money, don't you?
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Actuarial science....a low-key sports modelling treasure trove.
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One of the most interesting finance books I read in 2022 was "The Speculator's Edge: Strategies for Profit in the Futures Markets" By Albert Peter Pacelli.
A really great presentation of market efficiency and where it can break down. Entertaining style.
Would recommend.
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"Analyzing more than one 100k bets over 30 years across the NBA, NFL, MLB, and NHL, there is strong evidence of momentum and weaker evidence of value effects that move prices from the open to the close..."
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If you were completely sure, there would be no risk.
If there was no risk, why would there be +EV?
If there was no +EV, why would you bet?
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Fantastic (+free!) video lecture series by
@rlmcelreath
working through his book "Statistical Rethinking: A Bayesian Course with R and Stan".
Grab the book and watch the videos if you're down with
#bayes
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1). One of the hardest parts is saving up enough of a starting bankroll to give yourself a realistic shot.
Undercapitalized starting position means you need a minor miracle. Save your ass off to get to the starting blocks in half-decent shape.
2). Learn bayesian statistics
I have a simple question to ask everyone. Given what you know now after years of investing, trading, or simply making money, what would you do differently if you had to do it all over again?
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🤫🫡
Took 5 years but I finally got around to collaborating with
@Gingfacekillah
Our book on retail option trading will be out sometime before the end of this year.
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SSRN: Can Day Trading Really Be Profitable?
"...The resulting portfolio would have earned an outstanding return of 1,484% during the same period of 2016 to 2023, while an investment in the QQQ ETF would have earned only 169%."
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Handy R packages for quant trading development:
- blotter
- quantmod
- quantstrat
- parallel
- TTR
- rugarch
- PerformanceAnalytics
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This remains one of the best (5-part) articles on the differences between the Bayesian vs Frequentist approaches. Some very important implications for those betting on uncertain events.
Includes code examples. Required reading imho
H/T
@jakevdp
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I get a lot of questions related to "why R vs Python?".
1. R is a statistical programming language
R was designed with statistical analysis & modeling in mind. It's more beginner friendly to learn if you don't come from a CS background - and it comes with a large number of
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Imagine for a minute that instead of arguing with strangers on the interwebs you put your head down and got to work on your goals.
How many extra hours would you have? What would one path look like vs the other 5yrs from now?
You don’t have to take my advice, but this is alpha
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I try to help people in the DMs as much as possible, but when I see code I've given them get presented as their own work publicly, I think:
1) Anyone doing that is well behind, so nbd; but
2) I don't think very highly of that; and
3) Maybe I should stop helping people
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Learning about volatility trading changed the way I view sports betting markets.
If you’re a sports bettor looking for some tangentially related ideas, I’d suggest you look there.
Pick up
@SinclairEuan
‘s book trilogy.
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So much of success boils down to whether you can follow a strategy through the ups & downs of variance.
Fitness, betting, trading, education - edge means nothing if you can't ride out the path dependency.
Happy Friday - Canada Day 🇨🇦/July 4th 🇺🇸weekend on deck. Enjoy 🫡🥳
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If you've read "An Introduction to Empirical Bayes", you've already seen some simple ways to incorporate Bayesian updating into your sports modelling process. It's not just for player stats though - you can use a simple beta-binomial conjugate prior to do all kinds of things...
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Finding an edge and grinding out that edge are 2 completely different things. One requires creativity, the other requires consistency.
Most people seem to be naturally better at one or the other - but you need to both to succeed long term.
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"Statistical Sports Models in Excel" eBook version is now live . If you've pre-ordered it, you should have it available to you now. I have submitted updates immediately to fix a few minor issues, should be updated by Amazon within 24 hours. Thank you to everyone that purchased!
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How to Download Excel Model Sheets for Statistical Sports Models in Excel Books 1 & 2:
Step 1: Buy SSME 1, 2 or both on Amazon.
Step 2: Locate the download chapter in the table of contents.
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ARCTIC: Anomaly, Rationale, Counterparty, Threats, Issues & Considerations.
Let's explore:
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To do something others can’t you have to see something others can’t see.
The first step is to learn what others see by understanding how the consensus is formed. The second step is to look for places, times or situations where the consensus breaks down.
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With March Madness approaching here's one of the better papers on pool betting strategy. It explains the essence of differentiation very well (IE maximizing the value of a win rather than a raw win). DFS implications in there too 🤫
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It turns out that this was a good learning opportunity, but not exactly alpha lol. Laplace assumptions tend to tell you that everything is underpriced because of tail risk. Insightful in a way, but not overly helpful.
Sharing this so you can learn from my mistakes.
/fin
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Started working on SSME Volume 2 ...initial stages of planning. More details to come.
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R has developed into a cutting edge statistical modeling platform. Tidyverse and TidyModels changed the game.
Areas that Python really shines imo:
- interfacing with w/ live data feeds
- projects requiring tricky scraping
- cloud deployment.
Use the right tool for the job 🫡
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I used to read a lot of market related news every morning. These days, the only essential reading prior to market open for me are:
-
@BlacklionCTA
's market notes
-
@KrisAbdelmessih
's Moontower
Here's an MT quote from this morning that belongs in your trading notes
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You walk into a boxing gym. You learn basic footwork. You learn the jab, cross, hook & uppercut. You practice until you can get your shots off with ease.
Then you spar with an opponent - and promptly get your ass handed to you.
Every time you throw your jab, it gets slipped.
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> Go to YT
> Find day trading strategies with highest views
> Watch videos, figure out what they're doing
> Test strategy, find the points of failure
> Uninformed counterparty signal triggers identified
> Shake & bake
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Sports bettors could learn a lot from traders & the finance community. NBBO is a newish thing in the sports betting world but has been around for a long time in finance. Your models are likely betting on mean reversion or momentum - finance has ideas there for you too. Explore.
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Let's have some fun with Bayesian statistics by modeling the returns of the SPX using RStan - R's interface for the Stan programming language.
As always, we start by gathering some data. Let's next calculate the log returns and plot the kernel density.
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It seems a lot of people think trading/betting is this magical realm where hard work can be short-circuited.
Not true.
It's more akin to that quote about working 70 hrs/ week for yourself to avoid working 40 hours for someone else. Autonomy has a cost, but imo it's worth it.
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