DadBodFootball Week 1 NFL Picks Against the Spread, Totals, and Implied Totals
DadBodFootball Week 1 NFL Picks Against the Spread, Totals, and Implied Totals
Let's F****** Go.... Baby!
We’re so back - football is on TV in less than 24 hours, fantasy drafts are all but complete, and Florida State continues to show why it’s a… Women’s Soccer Powerhouse (nothing against women’s soccer, everything against FSU’s football team). If there’s one thing I learned during the off-season, it’s that outside of March Madness, there is no greater period of unadulterated excitement than the week before the NFL season kicks off in earnest.
Before we get in to the content, I do need to take a “hand-up, accountability” moment. I went in to the offseason with grand plans of pouring over the model, weekly trends, and deeper efficiency stats to better understand gaps that may exist within the process. Sadly, most of these stones either remained un-turned or proved of little to no value in explaining the weekly variance in the model.
To illustrate a point - here is DBF’s performance across every game of the 2023 season. REMINDER - The model is run for every game’s spread, total, and implied total - I do not pick favorites, though same games get a “neutral” rating if the DBF spread/total are within 0.5 of the vegas spread/total.
Spread: 124-95-8 (57% Win Rate), 5 total weeks below .500 - steady improvement as the season went on.
Totals: 121-101-2 (54.5% Win Rate), 5 total weeks below .500 - slight downward trend.
This means that if you were to bet every spread and total for every game, according to the model, you’d end up winning on both Spreads and Totals (roughly 50 net wins).
My goal to start the year was to try to get that as close to 60% as we could, because that would mean that we’re predicting every NFL game as well as the average professional gambler (who hits ~60% of their bets).
I spent the better part of a month analyzing un-used efficiency and scoring metrics to try and move the needle. This means re-running the model for match-ups and trying to identify gaps (i.e. Thursday Night Football). My most effective remodeling got this to roughly 53 net wins, but usually at the expense of either Spreads or Totals (both seldom increased together).
To finish belaboring a point, while the model has been honed - I wouldn’t say that we’ve found a “golden ticket” just yet, but that doesn’t stop us from trying.
DBF 2.0
If you’re wondering what the future has in store for DBF - as of right now, my intent is to continue the formula we had towards the end of last season. The new concept I want to try is a 3 pack - 3 spreads, lines, or totals that get me excited (if I had to bet 3 games…), and what in the model or historical data is making me feel a certain way (in other words, how to apply the model).
This is a great chance for me to look stupid, and for you all to point and laugh like Nelson in the Simpsons. That’s honestly great - what else would be the point of a football spreads, totals, and implied totals substack without looking like a complete fool to your closest family and friends (and any strangers on the internet who actually read this).
NFL Win Totals Projections for the 2024 Season
If you read no further, I’d suggest stopping AFTER this section as you have to get your season wins total bet in before kick off on week 1.
I regretted not publishing this list last year as it’s actually where I started my effort - the thought was, could I use objective data points to assess how teams might perform in a given season. This includes multiple previous seasons’ efficiency and scoring data, as well as variables such as New Coaching Staff (generally a good thing), Rookie QB (generally a bad thing), Injuries, and teams with an X-Factor (i.e. the Steelers never being below .500). Yes, an X-Factor is objective, but sue me… we all know certain teams generally get the lucky tuck, bounce, PSI, etc. (go shit in a hat, Pats fans).
Wins Projected: This is how the model feels a team will perform across a 17 game slate of a neutral schedule. I didn’t want to plug in every matchup for a season as there is so much variability week to week.
Vegas: Vegas Win Total
Delta: Does the model feel the team will win MORE or FEWER games.
Confidence: This is largely a factor of 1. Were there substantial changes from the previous season (i.e. Star QB returned from injury this year - See: Cardinals), 2. Is there a new Coaching Staff or Rookie Signal Caller that may reduce their offensive efficiency.
Spreads, Totals
V Note - More on this below, but I expect the UNDER to be a major factor this week
Implied Totals For Week 1
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, the Seahawks and Broncos generally lose above scoring average games (last year, anyhow) - meaning somewhat effective offense and a bad defense. Their TOTAL this week is 42 (below the 2023 game scoring average of 43.5 points), so there is nothing in previous years’ data to tell us that the game will be a low scoring affair - consider the OVER
NFL Week 1 - 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.
UNDER 46.5 - Ravens vs. Chiefs on TNF
DBF projects 38.2 points, Vegas spread is 46.5. Both teams boasted top 5 defensive units, and while the Ravens were also a top 5 offensive team, the Chiefs were average to below average. They found a new way to grind their way to a Super Bowl win, but it wasn’t by scoring 40+ points.
Knowing the above, and that the 3 year game scoring average is right around 44 points, expecting two defensive juggernauts to score 2.5 points more than an average game feels like a sucker bet (to me, at least).
UNDER, well EVERYTHING
DBF projects a total of 680 points to be scored across ALL 16 games this week. Vegas is projecting 712. This would be an “above average” week by 2023 (and 2022) standards in terms of scoring by nearly 1 PPG.
More importantly, who remembers this crazy graphic from last year? It feels like that meme of Charlie from IASIP where he’s connected all the pictures with a crazy amount of string, but bear with me explaining it….
This Chart shows how many times the OVER hit in Weeks 1-6 of the 2023, 2022, and 2021 NFL Season. The Lines represent Vegas total points (dashed lines) and actual points scored (solid lines).
In 2022 and 2023, Week 1 represented one of the lowest total point outputs of the first 6 weeks, and even in 2021 when more points were scored than Vegas expected, the OVER carried 6 of 16 games. This means that in the past 3 years, the Week 1 NFL Games have seen 15 OVERS and 33 UNDERS in Week 1. Bet.The.Under.
WHY? I wrote about this last year and I have two theories -
Defenses are generally further ahead of offenses (particularly offensive lines) this early in the season; meaning games are lower scoring.
Vegas knows everyone loves an OVER. You love it, she loves it, her sister loves it… yes, everyone is so hopped up on NFL Football that they go buck wild expecting every game to be a barn burner, and they’re just not…
Tampa Bay -3.5 vs. Washington Commanders
Why is Tampa basically getting the home field advantage points (for the unaware, the home team generally gets 2-3 points for playing at home - this is statistically proven using historical scoring averages - closer to 1.87 points but who is counting), against a Commanders team with a new head coach, rookie QB, playing on the road in their first NFL game.
To make matters worse, the Commander’s defense was exceptionally bad in 2023. They surrendered 518 points - the average team gave up right around 370 - and the next “worst” defense gave us 455. This defense gave up a score on 47% of their opponent’s drives.
Even if they’re not historically bad, I can’t look at that roster and make an argument they’re that much better. So in order for this game to be a 3.5 point spread - you’re asking them to beat their previous season’s offensive scoring average on the road while finding a way to keep Tampa from scoring more than their average output at home. That’s asking… a lot.
Long and sweet this week, but I wanted to include the season WIN total projections (one time thing) as well as ensure that we were all locked and loaded for Week 1 of the NFL Season. As always, questions, comments, general musings - drop me a note.