This is a guest post from Colin Davy, a talented data scientist that works at Facebook and has founded betscope.io.

Think of a bet you recently liked. Maybe it was a recommendation from someone you trust, maybe it was a system you think works, maybe it was based off The Power Rank’s analytics.

Think about why exactly you liked the bet. What about the game did you think would play out differently than the way the market implied it would? And most importantly, what particular market did you enter to act on that belief?

Chances are it was one of the standard bets (spread, moneyline, total), since those are the ones we’re most familiar with. But there are a lot of other markets available to act on your belief that the game will play out differently than the market believes it will.

Let’s take the Bills-Patriots game for tonight as an example, where the Bills are favored by 3. Let’s say you do your research, and you like the Bills to cover, so you bet on Bills -3. In a world where you are correct and the Bills do cover, all else being equal, all of the following are true: 

• The Bills are more likely to cover their first half line of -2 and second half line of -1.
• The Bills are more likely to go over their team total of 21.5 points, and the Patriots are more likely to go under their team total of 19.5.
• Josh Allen is more likely to go over his passing yards prop line of 234.5 yards, and Mac Jones is more likely to go under his passing yards prop line of 200.5 yards. 
• Stefon Diggs is more likely to go over his receptions prop line of 4.5, and Hunter Henry is more likely to go under his receptions prop line of 2.5. 

You might have some intuition around this and might not even realize it. If you’ve played fantasy football, DFS, or any other contest where you’re constructing lineups of players, you have probably heard of strategies like stacking, where you might roster a QB/WR combo to potentially double up on big games from both players.

This taps into a powerful concept in sports betting: the idea of correlated outcomes. The same principles of fantasy apply to sports betting as well, where outcomes in one market are correlated to the outcomes in other markets.

I like to sum this concept for sports betting in a single sentence: if you believe one market is different, you have to believe every market is different. 

Why is this so powerful in sports betting? Because knowing your full range of beliefs and correlated outcomes allows you to search for the best possible price across every market and book to act on your beliefs and get the best return possible.

There are price discrepancies across sportsbooks for every kind of bet imaginable, and being able to attack the market at its weakest possible points provides an actionable avenue for maximizing your long-term returns on your bets. 

This sounds nice in theory, but it’s very difficult to do in practice.

• Scanning every price at every book is time consuming when done manually.
• Quantifying the correlated outcomes is difficult without some complex math.
• Matching correlated beliefs against all markets is hard to execute, especially when price discrepancies can be fleeting as books adjust their numbers.

Fortunately, we’re developing some tools that will help bettors address all of these issues. Sports betting is a growing industry with all sorts of new betting products, and with that growth comes opportunities to take advantage of these products and boost your bankroll.

The Software Tools

Hey, this is Ed again. I’ve known Colin for a long time, and I wrote about his predictive golf analytics over this past year.

Colin is a very talented data scientist, and I’ve had a chance to take a look at the software tools he is developing to put these ideas into action.

To make sure you don’t miss out on his content and software updates, sign up for his free email newsletter at Substack.

Welcome to the Saturday pour over, a collection of sports betting insights written by Edward Egros.

Homefield Advantage. Dr. Eric Eager has pointed out on his podcast PFF Forecast the difficulty predicting homefield advantage early in the season.  In 2019, homefield advantage was almost non-existent. Then last year, empty stadiums and COVID concerns also wiped away that edge. 

Will homefield advantage in 2021 mean almost nothing (e.g. a 0.5-point boost) or something closer to normal (e.g. 2-2.5 points)?  Dr. Ed Feng is currently using 1.84 points for NFL homefield advantage based upon 2015-2019 regular-season data (he discusses this number on his podcast “Covering the Spread”). 

Betting on Week 1 in the NFL. Drew Dinsick mentioned on his podcast Deep Dive w/ Andy and the Whale that he believes Week 1 lines have been driven into the ground, because of the lengthy offseason, so much that it is near impossible to find market efficiencies. 

Drew added it would be better to look at Week 2 in college football to find where the market has not caught up to reality.  However, late movement may still happen (e.g. Washington going for a 1-point dog to a 1.5-point favorite against the Los Angeles Chargers).

Is the Pac-12 THAT Bad? With nearly every team in the Pac-12 North losing, the reputation for the entire conference has been tarnished.  But, if you break up conferences into divisions and rank those, the Pac-12 South has a lot of value. 

Jeff Sagarin did this exercise and has the Pac-12 South as the 4th toughest in the country.  UCLA’s win over LSU helps, USC’s offense is off to a solid start and Arizona State currently ranks 3rd in America in EPA/Play (granted, with one data point). 

Heisman Futures. As I pointed out on BetQL Daily, because overall attention for Group of Five teams has lessened over time, it has become more difficult for some elite players to get a proper shake for the Heisman trophy.  On cfb-graphs.com, the top two offenses belong to Cincinnati and Coastal Carolina, each led by a quarterback with proven track records. 

However, Desmond Ridder is 6th in Heisman odds per FanDuel Sportsbook and Grayson McCall is near the bottom at +20000.  As great as these quarterbacks are, the national college football landscape revolves around playoff teams and seemingly nothing else.

Valuable Resource. Dr. Ben Baldwin has created an incredible resource in rbsdm.com, a site that has advanced NFL QB statistics, 4th down decision making and pass percentage in neutral game situations. Ben talked about his work on The Football Analytics Show.

Ed Feng, host of the podcast, didn’t understand and couldn’t remember the name of the site. Dr. Baldwin had to educate him, a light hearted moment that you can catch on this minute long Twitter video.

September 11th. As we observe the 20^th anniversary of one of the more devastating events in American history, the PBS series Frontline detailed how that day has affected how we live and how we interact with each other as fellow Americans.

This is a new experiment at The Power Rank. Please let us know what you think in the comments.

Suppose you bet Tampa Bay +3 in an NFL game against New Orleans. In the game, Tampa Bay wins outright by 10 points over New Orleans.

You’re a genius, right? You take the winnings over the bar and do your best Beth Harmon imitation.

Well, you might be a genius, but not because you won. And no, this isn’t a lecture on small sample size. While winning money is great, there’s a better way to evaluate your bets: closing line value (CLV).

In this game, the markets closed with New Orleans as a 2.5 point favorite. Since the point spread decreased from 3 to 2.5 and you liked the underdog Tampa Bay, you got closing line value.

To understand the importance of CLV, I’ll use an explanation from Spanky, a professional sports bettor. In an episode of his podcast Be Better Bettors, he explains that sportsbooks move the spread based on sharp players, not the size of the bet.

Floyd Mayweather could put $300k on a spread, and a bookmaker won’t flinch. A quant in the basement of his mother’s home in Pittsburgh puts $5k on the other side, and the spread moves in that direction.

When the markets move in the direction of your bet, this means the sharp players in the market agree with you. This means long term winning.

Spanky suggests getting closing line value on 80% or more of your bets. A professional bettor I asked said the market moved his direction on about 90% of college basketball totals during the 2020-21 season.

Looking at CLV also makes sense from a mathematical perspective. Assuming the outcome of games are independent, the variance in outcomes is smaller with a rate close to 1 than near a half.

For example, suppose you make 50 bets. If you hit a win rate of 53%, the uncertainty in this win rate is 7.1%. If you get CLV on 90% of your bets, the uncertainty in CLV percentage is 4.2%. There is less uncertainty in CLV rate than win rate.

There’s a ton of nuance to this idea that closing line value leads to long term profits. It depends on factors such as:

• market strength – CLV is more important in a strong market like NFL spreads than a weaker market like golf
• kind of bettor – some bettors make plays based on information not available to everyone, which potentially implies a lack of CLV
• timing of bet – the market might go too far right before a game starts, you bet the other side, and the market doesn’t react

But let’s suppose you’re a newcomer with no inside information. For saturated markets like NFL and college football, closing line value is a better metric than winning percentage to evaluate your bets.

As Spanky says about CLV:

“And when you beat the line, that means you won. That’s better than winning money. That’s better than sex.”

Whoa Spanky, let’s not go too far on that last one.

For more resources on CLV:

• Podcast episode of Be Better Bettors. Listen at 5:26.
• Podcast episode of Bet the Process with professional bettors Rufus Peabody and Rob Pizzola. Listen from 13:37 to 28:58.
• Podcast episode of The Football Analytics Show with Spanky and how he bets: Listen at 3:08.

Data driven betting information

This article was featured in The Power Rank’s email newsletter. In a recent revamping of the newsletter, I’m striving for content that is:

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