Bad Betting Tips: FBS College Football Season Sneak Peek
So far this summer, I have calculated the numbers and run the simulations to estimate the odds that each FBS team has of winning their division and conference, as well as making the college football qualifiers and winning the title. national. I also calculated the expected number of wins for each team, as well as the odds of each team being winless, undefeated and everything in between.
While these calculations are interesting, it does raise the question of whether all of this data that I have produced might have some value in the betting arena. If you, dear reader, have an interest in using this data to place a bet or two, then I have a little advice.
But before we begin, I will offer the following set of caveats. While I find the mathematics of the betting market fascinating, I do not personally use my own data to bet on sports (other than an NCAA tournament pool or bowling pool).
The retrospective analyzes I performed on the data previously showed that my methods are promising. However, I have never tested a large-scale analysis like the one I will present today. Also, I think my numbers are solid, but they’re only as good as the source data used to generate them, which is a consensus of the preseason rankings. In other words, “beware bettor” and note that I call this column “Bad Betting Tips” for a reason.
That said, let’s dig into the numbers
The ten big odds
In order to make a comparison between my odds and Vegas odds, I had to extract all relevant futures betting data from a consistent and reliable source. I decided to use the Draft Kings website and a cursory cross-check of a few other sites suggests that this provided a reasonable and representative dataset. The Big Ten lines, as of mid-August, are shown below in Table 1:
In order to compare these odds to the probabilities generated by my simulations, I converted the bet lines to implied probabilities using standard formulas which are well summarized here. Table 2 compares the implied probabilities of Vegas with my numbers for the Big Ten.
Note that the odds calculated from the simulations for wins over or under regular season wins are relative to the over / under set by Vegas. For example, Ohio State’s over / under for regular season wins is 11 wins, according to Vegas. My sims for the season calculate an expected win total for the Buckeyes at just 9.7 wins. My data also only gives OSU a 16% chance of winning 12 games and a 61% chance of winning 10 games or less.
In order to place a “smart” bet (assuming my data is correct, or rather the underlying assumptions are correct), the trick is to look for situations where the odds I’m calculating are higher than the implied odds. for the same bet.
To use the same example, the money line for the plus / minus suggests, in effect, that there is a 56 percent chance that the Buckeyes will win 12 games and a 50 percent chance that they will win 10 games or less. My data suggests that getting the upper hand over OSU is a terrible bet (as I have these odds only 16%, 40 percentage points lower), while taking the under one might be a good bet (as I set these odds at 61%, 11 percentage points more).
The way to quantify these differences is to calculate the return on investment (ROI) of each bet, the formula of which is explained here. Basically I am calculating the average expected return if placing a large number of bets of $ 100 using the given Vegas money line, but assuming my odds are correct.
Table 3 below shows the 20 total bets involving Big Ten teams where the return on investment is positive (i.e. a “good bet”) based on this analysis.
Several of the recommended bets in Table 3 boil down to one idea, which may or may not be correct: My simulation projects that the state of Ohio is overvalued. As I explained in my pre-season analysis, my simulation treats all teams equally. The computer views Ohio State not as Ohio State, but as a historically average No.4 pre-season ranked team.
Over the past decade or so, teams like Ohio State, Clemson, Alabama, and to some extent Oklahoma have all consistently exceeded their goals. It’s certainly possible (and possibly likely) that my simulation underestimates them, as the normal rules of averages don’t seem to apply to these teams in the current place in history.
In the Big Ten in 2021, the implication of my betting analysis is that betting on a team other than the Buckeyes to win the Big Ten East and possibly the Big Ten is considered a bet with a positive ROI. Bets on Penn State to win the East Division or Wisconsin to win the Big Ten look pretty good.
To a lesser extent, bets on Indiana, Iowa, and the Northwest to get to or win Indianapolis (or both) look promising. But not all bets are equally good. For example, Northwestern’s chances of winning the West Division have a positive ROI, but the Wildcats’ chances of winning the Big Ten do not. For Iowa, the situation is reversed.
It’s also noteworthy that placing bets on teams like Wisconsin, Penn State or even Iowa to win the national title also looks like good bets, although they are clearly from a distance. If one was interested in taking a flyer about a team that isn’t one of the Big Ten’s usual suspects, those would be the teams to invest in.
The other notable bets in Table 3 are the seven over / under bets that rank with a positive ROI. These include betting the least on Michigan, Ohio State, Iowa, and (unfortunately) MSU, and gaining the upper hand over Northwestern, Wisconsin, and Illinois.
In general, betting on the over / under is a safer bet. If I sum the probabilities shown in Table 3, the calculations suggest that only five or six of those 20 choices in total will end up being correct. Of these, four of the correct bets are likely to come from the over / under category.
Calculations suggest that only one or two of the other bets are likely to hit, and many of them are mutually exclusive. If Ohio State defeats Wisconsin in the Big Ten Championship game (which is the most likely scenario), then all remaining bets (no more / less) would lose. So, I consider these suggestions to be the most likely set of longer strokes for the more adventurous bettor.
The same logic and process that I used above for the Big Ten can easily be applied to the rest of the country. For those truly adventurous souls, my full data table for the 130 FBS teams can be viewed here. Table 4 below summarizes the top division, conference, playoff and national title win bets, based on ROI.
Interestingly, the team that appears to be the best to bet on in 2021 is MSU … Mississippi State, that is. My calculations suggest the Bulldogs are the most undervalued team in the country. That said, the odds of MSU South winning the SEC and / or the SEC West are both +10,000 and +5,000.
My math also favors betting on the state of Arizona, Notre Dame, Oregon and Wisconsin as possible candidates for the Black Horse National Title, again with very long odds. When it comes to bets that seem a little more reasonable, a bet on North Carolina to win the ACC is intriguing, as are bets on Texas A&M and Florida in the SEC and Notre Dame race to make the playoffs. college football.
That said, the calculations also indicate that only two or three of these bets are likely to be correct, and several are again mutually exclusive.
Table 5 summarizes the best bets in the over / under categories for the 2021 season, based on my analysis.
If I was actually inclined to place a bet or two myself, this is where I would focus. That said, there are a few things to note before calling your bookmaker. A lot of Table 5 bets fall into two categories: betting the least on teams that should be really good (like Alabama, Clemson, Georgia, and Ohio State) and betting the most on teams that should. be really bad (like UNLV, Bowling Green, ULM, Akron and UTEP).
As I explained earlier, betting less on “good” teams can be precarious. But, betting on the “bad” teams could be a good strategy. There is no real reason to believe that any of these teams are a regular loser. In addition, Table 5 contains several other intriguing bets, including gaining the upper hand over Louisiana Tech, Texas Tech, Duke, Nevada and USC, and trailing Michigan, Rice, Kansas State and Army.
In total, my calculations suggest that 19 of the 30 bets listed above are likely to be winners. Even though I’m missing out on a few games, it feels like a winning strategy for me.
So that’s what the data is telling me. As we head towards the start of the 2021 season, I will once again provide a weekly set of best bet predictions for the week ahead. Then I will check my work and report on what I have done. Until next time, enjoy it, stay tuned and go green.