DadBodFootball Week 2 NFL Picks Against the Spread, Totals, and Implied Totals
Walk In Your Trap, Take Over Your Trap
Week 2 feels like the right time for the return of one of my favorite breakdowns from last season… The Road Dog ML.
For the uninitiated, a buddy of mine made the ludicrous statement - that if you’re going to take an underdog against the spread, you might as well ALWAYS take the money line (and pick the underdog to win straight up).
Why? Well, in 2023 underdogs covered the spread 47.7% of the time and won 32.2% of the time. While most of us would react and say they’d rather win 47.7% of bets than 32.2%…. the improved odds on MLs put the break even point around +150. What we uncovered in the above post is that certain games fit a profile that gives us a slight edge against the house in betting the underdog ML. These are:
Road Teams - this is kind of a no brainer - home teams are favored in 60% of games, so road games give you more opportunities to identify UNDERDOG MLs
Non-Divisional Games - Divisional matchups are the least likely to see the underdog win; Conference games and non-Conference games (AFC vs. NFC) represent your best bet
+150 to +200 odds - Anything > +200 isn’t hitting frequently enough to justify the longer odds.
What games fit the profile this week?
The Browns +135 against the Jaguars is the only one that fits the three criteria. The model doesn’t love that matchup - for what it’s worth. If we expand betting odds to anything below +225, we could consider the Saints (+225) against the Cowboys or the Bengals (+205) against the Chiefs. That said, I’d consider taking the points in those games as the model doesn’t like any of those teams.
Chasing Zebras - For Spreads less than 3.5 points, are you better off taking the ML or the Points
In healthcare, the idiom “Chasing Zebras” applies with a clinician starts with the least likely or plausible cause of a patient’s symptoms/disease rather than the most obvious. This can cause the clinician to ignore even the most obvious evidence/conclusions while they are laser focused on pursuing what they feel is the actual cause.
In investing/gambling, it actually has the opposite meaning - in many cases, low-risk investments (zebras) are a major hedge against losing significant capital in high risk investments (unicorns).
The title of this section is where I seek to better understand how common NFL game betting trends can be used to help us make better informed, safer decisions when applied across an entire season of betting. This week’s question was - For “Pick-Em” games, are you better off taking the Spread/Points or taking your preferred teams Money Line.
Let’s start with the method:
Definition of a Pick-Em Game: Any game with a spread of less than 3.5 points; effectively any game expected to be within a field goal, most bettors will “pick” whichever team they feel will win because there are not significant points in play via the spread.
Data Selected: 2023 NFL Regular Season
Count of spreads less than 3.5 points: 119
Count of Favorites Winning: 77
Count of Favorites Winning, but not covering: 70
Count of Underdogs Winning (and covering): 42
Count of Underdogs Covering: 49
Average Odds (blended across every one of these matchups)
Favorite: -150
Spread Bet/Even Odds: -110
Underdog: +125
What we found is that across 272 NFL games in 2023, 119 games or 44% of games had a spread less than 3.5 points. In these games, the favored team won 65% of the matchups while the underdog won 35% of the matchups.
Takeaway 1: If you’re not sure, bet the favorite
Across ALL NFL games in 2023, the favorite won 68% of their matchups and covered 52% of spreads. If we start to evaluate this by home and road - the home team is slightly more likely to win (69% home vs. 66% road), but slightly less likely to cover (51% home vs. 53% away). Again, this is for ALL games, but it’s helpful context when we look at splits for Pick Em games.
Takeaway 2: If you’re REALLY not sure, root… root for the home team
However, while the favored team won 65% of the time, they also covered 59% of the time. This means that there were only 7 total instances across all of 2023 where the favored team WON but did not cover the ~3 point or less spread (in other words, they did not win by more than 3 points).
When we run the numbers for the favored team for both spread bets and money line bets, we see that:
Favored Team Spread Bets
70 Wins * 10 unit bets with -110 odds = $1336 in winnings, $636 net profit
Favored Team ML Bets
77 Wins * 10 unit bets with -125 odds = $1213 in winnings, $513 net profit
Takeaway 3: If you’re betting on a favorite, always bet the spread - don’t reduce your odds by taking the Money Line UNLESS you’re getting cute with a parlay… and even then, I wouldn’t do it.
If we examine the underdog’s performance, we found that they win 35% of the time and though they cover 41% of the time… we have to ask the question, do the improved odds of the ML bet outweigh the downside of missing a few bets because the team covers in a loss…
Underdog Team Spread Bets
49 Wins * 10 unit bets with -110 odds = $1145 in winnings, $445 net profit
42 Wins * 10 unit bets with +125 odds = $1225 in winnings, $525 net profit
Takeaway 4: If you’re betting on an underdog in a Pick-em game, always bet the ML - the improved odds will more than cover your losses when the underdog covers in a loss.
Takeaway 5 that will interest no one: I also ran a similar model against totals in these games, and did not find any strong indication either way that Pick-Em games fit a higher or lower scoring trend.
Spreads, Totals
Implied Totals
For those new to implied totals (or wondering what the heck I’m talking about) - an Implied Total is the expected points for ONE team in a matchup (with a TOTAL being both teams).
DBF Data points - Interpreting Implied Totals: In the DBF Tracker below, I include the Implied Total and delta to two other important data points
Average Points Scored: Informs how many points a team scores on average against a neutral opponent
DBF Points Expected: The Model’s output for expected points including adjustments for opponent defense, weather, home/road performance, injuries
This allows us to calculate the DOUBLE DELTA - how far off both average points and DBF Expected Points an implied total is. The more negative or positive, the more… theoretically… likely it is to be OVER or UNDER.
Winning Quadrants - Do Teams Win And How?
This is a mainstay of the weekly post - visit Week 10 of the 2023 season for a longer description Winning Quadrants graphic from Week 10. I’ll be adjusting to 2024 data right around Week 6.
Y-AXIS Net Points: The “higher” a team is on the Y Axis - the more team “Wins” by, and the “lower” - the more a team loses by.
X-AXIS Average Total Points (net 2023 NFL Scoring average of ~43.8 PPG) -The left-most teams participate in games with the lowest TOTAL points - the right-most teams participate in the highest scoring games. I netted (subtracted) season scoring average to show if they are above or below the average game.
How can you use this? Well - it’s an interesting way of aligning our “feel” about certain teams - the Dolphins tend to win and score a lot of points - and identify how they generally align against their spreads/totals.
For instance, while the Lions historically win in high scoring games - they have made significant improvements on the defensive side of the ball in 2024 and are playing the Buccaneers who historically have had a stingy defense. As such, the Over - 51 points - looks high as the teams are modeled to score around 47 points.
NFL Week 2 - 3 Pack
This new section is where I go a bit deeper on how the data and analytics in this post can be applied to a given week’s matchups. I wouldn’t say that these are picks - more how I’m feeling about a week’s set of lines, totals, and implied totals.
*** NOTE *** - I generally write these on Thursday/Friday before game day, so the lines may NOT be exactly as they are below, but the notes won’t change substantially.
San Francisco 49ers -5 at Minnesota Vikings
The model loves the 49ers this week as they have the second largest delta to spread (with the largest being the matchup below). The Vikings beat up on a bad (efficiency-wise) Giants team, but one game does not define a season. The model also likes the 49ers Implied Total (25.5 points) as both their 2023 scoring average and Efficiency Metrics put them safely above this against the Vikings defense.
Houston Texans -6.5 vs. Chicago Bears
Another 6 point spread where the model feels Vegas is trying to suck gamblers in to overreacting to week 1. Caleb Williams passed for 93 yards against a depleted Titans secondary (at home), and history has shown that teams with new quarterbacks take time to get going/show improvement in efficiency. I don’t expect this to be a close game, and also like the UNDER (45.5) and UNDER for the Bear’s Implied Total - 19.5 points.
The Over…
Words I never thought I’d write, but Vegas moved the expected points to 701 this week. The Model (based on 2022 and 2023 scoring averages) is predicting 694.5 points - so I’d feel more confident letting OVER bets fly on games where the model (or our gut feelings) are aligned.
Model Performance Week 1 Recap
For those of us who are new - I model EVERY game’s spread and total, I do not make picks. Below I show both overall model performance (every pick ATS and Total) as well as call out those that are “High Confidence”
High Confidence represents the largest DBF Model deviation from the actual spread… for example, if Vegas has Team A as a 3 point favorite (-3), and DBF Model has Team A as a 10 point favorite (-10), our confidence score/delta is a 7. This also works the other way where Vegas has Team A as a 3 point favorite (-3), but DBF Model says the team should be 5 point underdogs (+5)…. that gives us a confidence interval of 8 (to bet on Team B).
I, personally, do not make every bet the model gives us - instead, I focus on 80% high confidence bets and 20% parlays or “feel” bets as a way to still enjoy the process (and lose money).
Spreads: 7 -7 on spreads with all 5 of the high confidence spreads hitting (and 4 losses being by 1 point or less).
Totals: 8 - 4 and going 4-0-1 on the high confidence totals; despite being weighted toward the UNDER (check out last week’s post for WHY), we hit all 3 of our OVER bets.
With our confidence based betting (bet more on games you’re more confident in), we netted a return of 60 units using 10 unit wagers for high confidence.