Survival Manual: Week 1 and Beyond
A few people have asked me about the Survivor article I used to write for RotoWire, whether I’ll be doing it again this year. The answer is probably no. My aim in life is only to do and write about the things I want to do and write about. I don’t want a scheduled column wherein I have to list 10 different options because various readers might have used up nine of them. I am no longer in the business of making product for customers. I am now in the business of expressing what interests me for those who are interested.
That said, I bought into the Circa Survivor contest, and I’ll likely be in a few home pools too. I’m still playing the game, and I’ll discuss it as long as I have surviving entries. But if you were looking for a formal article with my chart and rankings, I probably won’t be doing it on a regular basis.
I will, however, do the chart for Week 1, as an instruction manual of sorts. With it, you don’t need me to analyze the math part of it for you. Beyond the chart, my picks are speculation just as yours will be, and ultimately the math is necessary but not sufficient. There’s always an element of vision involved — seeing where the puck is going, so to speak, rather than just taking the market numbers as gospel. But it’s usually good to have a baseline understanding too, so without further ado, here’s how I build the chart.
Let’s start with the full chart from Week 1 last year:
The third column (percent taken) is the distribution of teams taken in pools on Officefootballpools.com. I have an account there from other pools, and I click on “picks”, “search picks” and check off “search all survivor pools.” From there it gives you a page that looks like this:
(I wouldn’t use the August 7 numbers for Week 1 of 2022 yet as the sample of people who are locked into their picks is probably small.)
I use OfficeFootballPools instead of say Yahoo or ESPN because I imagine there are more paid pools there. Free pools (which people can half-ass or try poor strategies like choosing bad teams to use them up without penalty) add noise. In any event, we’ll come back to why this information is important, but this is where it comes from. If you don’t have access to OFP, you can use Yahoo or ESPN (or perhaps average the two) and it’ll probably be close enough.
The fourth column (Vegas Moneyline) is how I generate the fifth column (Vegas Odds). Vegas moneylines are usually presented as follows:
Note the “Moneyline” column above. In the first game (Bills at Rams) the column has -135 for the Bills and 115 for the Rams. That means to win $100 on the Bills to win outright, you’d have to risk $135. It also means if you bet $100 on the Rams to win outright, you’d win $115. Hence -135/+115.
You might wonder why it’s asymmetrical, i.e, that you risk $135 on the favorite for that $100, but only get $115 if you bet the underdog. That’s the rake, how the sportsbook earns its profit. It lets you choose the side, but pays out less on the underdog than it charges on the favorite.
The problem with this is neither number represents the market’s real estimate of each team’s odds. The estimate is distorted by the rake. In order to get a sense of the real odds, I average the two numbers. (This isn’t perfect, but it’s close enough.) So -135/+115 averages to 125. (Don’t worry about the pluses and minuses, think absolute value.)
Once we have the more accurate estimate, we can generate column 5 which is the favorite’s percentage chance to win (Vegas odds). If the rake-free odds for the Bills winning this game are -125 (or 1.25 to 1), then that means the Bills are expected to win 1.25 times out of 2.25. That’s because x to 1 odds just means x out of (x+1). (In moneyline terms it’s x plus 100, since 100 is the standard unit.)
I realize some math-phobic people can get alarmed when they see variables, but I assure you this is something you can grasp. If something has 3:1 odds, that means 3 out of (3+1), or 3 out of 4. If something has 1:1 odds or “even money,” that means it’s 1 out of (1+1) or 1 out of 2 — 50 percent. So in this case, the Bills being -125 (averaged) means risk $125 to win $100, i.e., 1.25 to 1, or 1.25 out of 2.25. How do you find out the percentage of 1.25 out of 2.25? Easy, just divide the former by the latter. 1.25/2.25 = 55.55555 or 55.56%. (Again, in moneyline terms, it’s 125/(125+100)= 55.56%.)
In other words, the market (as of August 7) gives the Bills a 55.56 chance to beat the Rams in the Thursday night opener.
By doing this same process:
Find out the moneyline odds for the favorite and underdog in a game.
Average the two to get a rake-free estimate of the market’s odds for the game.
Take the rake-free estimate and divide it by itself plus 100 to generate the market’s implied percent-chance to win.
You can generate the percent-chance to win for each team in each game.
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