Today I am excited to finally announce my plans for the MLB model I’ve built for the 2019 season. Many of you have been asking about the MLB model for months now, especially following the success of the NFL and NCAAM models I’ve shared. I thought it’d be appropriate to recap those performances for those who may not be familiar.
Starting last October, I began sharing NFL model plays for free. The model ended up going 75-47-6 (a 60.94% hit rate) and yielded 89.3 units of profit (an 18.10% ROI). A $100 bettor would have made nearly $9,000 over the course of the season. Given that the NFL is considered the sharpest sports market, I thought I’d try my hand at NCAAM.
In mid-February, I began providing NCAAM model plays to those who contributed $20+ to Doctors Without Borders. Across two donation periods, there were 995 contributors for $25,379. The model as of this writing is 71-46-2 (a 60.50% hit rate) and has yielded 29.3 units of profit (a 12.78% ROI). A $100 bettor would currently be up $1600 in four weeks if they donated during the first period, or up $1000 in two weeks if they donated during the second period. Sharing model plays for the NCAAM was a learning experience, as I have faced several issues:
There could be as many games in one Saturday as an entire NFL season. In just over a month of NCAAM model plays, there have already been as many model plays as there were in the entire five month NFL season.
Given the volume that comes with a sport that has a good chunk of games daily and given that I still have a full-time job, the size of the workload and the time I had available was not the most ideal. I firmly believe the NCAAM model plays would be performing better on some level if I had more time available.
With nearly a thousand people having access to the model plays, I received many comments (and complaints) that lines would move extremely quickly after I would share a model play. This made getting the same model play lines an extremely difficult task for many.
MLB Model Play Packages
Given that providing MLB model plays will require a similar workload to NCAAM, I thought it would be within reason to charge for MLB model plays. As a good faith measure and as a proof-of-concept of the MLB model’s ability, I will provide MLB model plays for free through April. After that period, I will be closing off access to two packages:
Includes model-recommended futures
Includes all postseason plays
Receive the free plays 30 minutes in advance
Purchases of 30 day packages prior to Opening Day (March 28th) will include model-recommended futures
With the Super Bowl behind us, the 2018 NFL season comes to an end. This season definitely was quite the ride, and the debut of my NFL model concluded with a 75-47-6 record (good for a 60.94% hit rate) and a +89.30 unit yield and 18.10% ROI. Before we say goodbye to the NFL season, I thought it’d be interesting to do a model recap and take a deeper look at how the model performed across various splits and to highlight some things I’ve eyed as potential areas of improvement for the 2019 season. Let’s get started.
Did higher unit plays generate higher profit?
One of the biggest ways to measure the strength of a model is to see how it performs as its disagreement levels increase. I first took a look at model plays of 3.5 units or more, as there doesn’t exist a spread that doesn’t move off of or through a key number or zero when adding 3.5 points. I then went up by two units from there. Here’s a look at those splits:
All plays: 75-47-6 (60.94%) for +89.30 units (18.10 % ROI)
3.5+ unit plays: 34-21-0 (61.82%) for +52.94 units (19.04% ROI)
5.5+ unit plays: 10-6-0 (62.5%) for +22.19 units (21.36% ROI)
7.5+ unit plays: 3-2-0 (60.0%) for +9.53 units (23.59% ROI)
As you can see, the ROI steadily increases as the unit split increases. This is very reassuring for the model as it demonstrates the model’s ability to scale unit disagreement with profit expectation. On top of that, the fifteen largest plays of the season had a 10-5-0 record with a 27.82 unit yield, which was good for a 28.32% ROI. This obviously includes a win on the model’s largest play of the season when the Panthers miraculously covered a 8.8 unit play on a +6 spread in their Week 15 matchup against the Saints. That also marked the model’s peak profit point of the season at +102.27 units. Furthermore, the top 15 model plays were all on underdogs, and 37 of the top 38 model plays were on underdogs. 26 of the top 29 model plays were also on home teams. Here’s a look at the overall home / road and favorite / underdog splits:
How did the model do by team?
Speaking of the Panthers, they happened to be the most profitable team to bet on for the model this season and the third-most profitable team to bet against. Across those two sets of plays for the Panthers, the model went 7-0 for +28.52 and an insane 98.32% ROI. Below is a look at the earnings betting on each team, betting against each team, and total earnings on model plays relating to each team.
Looking at the last chart, there are only 11 teams the model had overall losses on (only 9 were of >1 unit) while it managed to profit on the remaining 21. Outside of that, there are definitely some other key takeaways you can pull from this. Looking at the “Earnings Betting Against Each Team” chart, you’ll see that 11 of the 17 teams that the model profited on were teams with winning records. Out of the other six teams, three made the playoffs last year (CAR, ATL, JAX) and one is a historical public favorite (GB). This demonstrates the typical overvaluing of good teams by the public and the consequent overpricing of those teams in the market to capitalize on that perception. When looking at the “Earnings Betting On Each Team” chart, you’ll see that 8 of the 11 most profitable teams had losing records. The counterpoint would be that 7 of the 8 teams that are net negative in that chart had losing records as well, but the losses of those seven teams are offset by the earnings generated by just the top five profit-generating teams with losing records.
Was your low juice tolerance +EV?
Many, especially those who read these newsletters, have noticed that I try my best to keep the juice low on model plays (-105 or lower). I succeded in that regard as 80.5% of all model plays had a juice of -105 or lower. A common misconception was that I was often selling a half point, which was definitely not the case. This is because the book I used for this experiment had a reduced juice of -105 on equal lines as opposed to the “standard” -110. However, there are 33 model plays out of the total 128 plays (25.6%) that had a juice of -102 or lower. Of those 33, two plays lost by a half point and one play pushed. They were:
Loss on TB -3.5 -102 (2.254 for 2.2u) Week 7 vs. CLE
Loss on NYJ +6.5 -101 (5.781 for 5.7u) Week 15 vs. HOU
Push on SEA +2 +106 (2.1 for 2.218u) Wild Card round @ DAL
The first play wasn’t really selling a half point since you pay way more juice to move to a key number. TB -3 would have likely been around -120, which is just way too much juice. The same applies to the second play, as a NYJ +7 line would have similarly been around -120. On top of that, I removed spreads of +7 and higher from model consideration by that point so NYJ +7 wouldn’t have been a play for that reason as well. However, the last play I did indeed sell a half point and it was very lucky to push but I still stand firmly behind the proposition of selling points, especially around non-key numbers. Speaking of key numbers…
Are “key numbers” really that important?
These splits were actually the most eye-opening to me, as I really didn’t take a good look at it until the season was over:
Plays on +3 and +3.5: 20-7-1 (70.69%) for +37.05 units (38.07% ROI)
Plays on +2.5: 2-5-0 (40%) for -6.79 units (-25.90% ROI)
Plays on -2.5 and -3: 5-3-1 (61.11%) for +6.07 units (25.47% ROI)
Plays on -3.5: 1-3-0 (25.00%) for -4.74 units (-52.56% ROI)
I didn’t include plays on or around -7 and +7 since there weren’t many plays at all on those numbers, especially with the model exclusion that was established Week 10 onward.
What are you looking to change for 2019?
As great as the model’s performance was both on a macro and micro level, there are certainly many improvements I am looking to make in preparation for next season. The most obvious areas of improvement would be taking a look at the two things that caused enough trouble to warrant on-the-fly model exclusions: Thursday Night Football games and spreads of +7 and higher.
For Thursday Night Football, I don’t believe there is a mathematically derivable solution or adjustment that I can make that would provide a consistent positive performance. The game of football is just played so entirely different when you have just three days to rest and prepare, and for that reason I fully anticipate removing Thursday Night Football games entirely from model consideration from the start.
As for spreads of +7 and higher, I will have to take a more thorough and refined look at the win probability data at the more extreme handicap levels and see what adjustments I can make to more accurately capture edges in those ranges. I think the relatively poor performance on such spreads may have been tied to the league-wide jump in scoring, but that will obviously take some time to investigate and even if my assumption is true, using a single season of data would be a dangerous proposition.
Another thing I’ve mentioned previously as something I’d like to take a deeper look at in the offseason is the entire “not every point is created equal” reality that comes with football spreads. This applies in several ways, whether it be points moving on/off key numbers being worth more or points being worth more in low-scoring environments and less in high-scoring environments.
I’ve also considered backtesting how the model would have performed if all model plays were done on the moneyline as opposed to against the spread. This could potentially be beneficial as it avoids the “not every point is created equal” dilemma while also aligning our desired outcome (a win) with the team’s desired outcome (a win).
On the complete opposite end of the spectrum, I want to see if there is a way to backtest how the model would have performed if model plays consisted of playing alternate spreads that matched the model’s spread. This would be interesting as the disagreement level between the model expectation and the Vegas implied probability would be captured in the odds of the alternative spread, and the staking method would just have to be a flat unit on each wager.
All in all it was a great season, whether it was the model’s performance or the amazing response I’ve gotten from all of you. I’d like to thank you all again for your support and interest in my work. I am more than excited to see how my work with other sports compares, especially with the MLB. Beginning next week I’ll be pivoting my weekly write-ups to the MLB, covering several higher level analytical topics as we inch closer and closer to Opening Day.
And if you need just a little more NFL content to hold you over, I did join Whale Capper and Andy from the Deep Dive podcast to discuss some of the lessons we’ve learned from our 2018 NFL seasons. I think we had some pretty insightful discussion on quite a few topics, so make sure to check it out! And as always, don’t hesitate to reach out to me via Twitter.
It’s been quite a while since we last spoke. Both of the conference championships had some unfortunate events not fall our way, namely a missed pass interference / helmet-to-helmet call in the NFC Championship game that compromised our NO -3 play, and then an unfortunate neutral zone infraction by Dee Ford that negated a game-sealing interception that would have won us our KC -3.5 model play. Despite those worst-case scenarios, playoff model plays sit at 3-3-1 for basically a wash at +0.03 units. More interestingly, those two losses on home favorites moves that split to a negative ROI on the season – the only split to do so across home / road and favorite / underdog splits:
But enough about that, there is now just one game remaining in the 2018 NFL season. It’s been a long journey to get to this point for both the Patriots and Rams, and it would only be right for us to take a deeper look at both of these teams and this monstrous matchup ahead of the biggest sports game of the year. There’s quite a lot to unpack, so let’s get right into it.
#2 Patriots (13-5) vs. #2 Rams (15-3)
I’m going to skip right past all of the narrative stuff that I’m sure you’ve been bombarded with the last two weeks (SB 36 rematch, young vs. old coach/QB, etc.) and dive head-first into the numbers. Before the season began, the Rams were projected to be the second-strongest team at 10.68 expected wins and the Patriots were projected to be the seventh-strongest team at 9.66 expected wins. As the season progressed, the Rams continuously outperformed their expectation while the Patriots somewhat tread water in the eyes of the model. The culmination of this was a gap between the two that peaked at a 2.91 expected win difference. The Rams then spent the remainder of the season tumbling back down to Earth, landing at 10.53 expected wins and within just 0.15 expected wins of their preseason projection. Meanwhile, the Patriots continued to tread water and would go on to finish the season with 9.86 expected wins, which was just 0.20 expected wins off from their preseason projection.
Last write-up I spent a lot of time talking about Rams defensive coordinator and his history of devising defensive gameplans to stifle elite quarterbacks and passing attacks. This week, he of course has the task of slowing down Tom Brady. Not enough can be said about Brady, especially in regards to his efficiency against blitzes even in his “old age”. Since 2016, the Patriots are 29-2 when brady has been pressured less than 27% against four or less pass rushers. The quick answer a lot of defensive coordinators come up with when faced with a statistic like that is “blitz more then”. But during that same stretch, the Patriots are 15-1 when Brady is blitzed 30% or more on dropbacks (via NextGenStats). So what is the appropriate gameplan?
I think Wade Phillips knows that answer. The last time Phillips faced off against Brady and Belicheck in the playoffs (2016 AFC Championship), he crafted a defensive gameplan that limited Brady to a 48% completion rate while forcing two interceptions and generating four sacks. He did so by utilizing the gameplan I highlighted two weeks ago in my newsletter: utilizing line-of-scrimmage disruption on the pass catchers to delay route development in order to generate an effective four-man pass rush. We’ve seen throughout these playoffs that Brady has been able to completely neuter solid pass rushes by getting the ball out quickly. Forcing Brady to hold the ball for an extra second makes a world of difference, as it gives Aaron Donald (who has the fastest time-to-sack average in the league) and Ndamukong Suh more time to penetrate the pocket. Only sending four to generate pressure gives the Rams more resources to dedicate in coverage, making Brady’s job even harder.
Despite all of that, Brady will not be the only quarterback who could have a hard time come Sunday. In the Patriots two playoff games thus far, they’ve generated two of their three highest pressure rates of the season (45.3% against the Chargers and 44.4% against the Chiefs). Unlike Brady, Goff isn’t quick to get rid of the ball. In fact Goff holds the ball on average for 2.96 seconds, the fourth-longest of all starting quarterbacks this year. That characteristic of Goff’s is blood in the water for the Patriots pass rush, as the Patriots have secured a perfect 10-0 record this year against quarterbacks holding the ball 2.8 seconds or longer (via ESPN Stats & Info). The one caveat to all of this is that this tendency of Goff’s is a product of the Rams’ reliance on play-action passes, which only exists due to the quality and outstanding performance of their offensive line. It obviously remains to be seen who will win that battle in particular.
If the Rams’ offensive line happens to hold up against the Patriots pass rush, then the Patriots coverage scheme become arguably the single-most important element to this game. As mentioned in a previous newsletter, the Patriots utilized man coverage more than any other team in the NFL this season. It would not surprise me one bit if Patriots Defensive Coordinator Brian Flores and of course the defensive-minded Bill Belicheck opt to rely less on man coverage and utilize more zone coverage. The reason being is that Jared Goff has the highest yard per attempt average in the league against man coverage this year, and has thrown 16 TDs and just 2 INTs against man. The magnitude of that efficiency is more apparent when compared to his 9 TD / 9 INT split against zone coverage.
The model has an easier time projecting the Super Bowl than it does any other game because the game is played on a neutral field, requiring no travel or homefield adjustments. This particular Super Bowl also has an extremely clean injury report, meaning the data is even more pure than usual. As for the actual numbers, the model actually projects the Rams as the favorite with a 51.64% chance of winning the game which converts to a LAR -1 model spread. The spread has been stuck in the mud at NE +2.5 for the past ten or so days and I don’t anticipate a move to LAR +3 that comes with juice of -110 or lower. LAR +2.5 would be an official model play for 3.5 units but the juice unfortunately moved from -102 to -109 between the time I started this write-up and the time I submitted it to be published. I do think I can get better than the current -109 juice and will wait to do so, and as always you can expect a tweet from me with the official model play when it is made.
And with that, the NFL 2018 season as well as the accompanying model plays come to a close. I hope my write-ups this season provided some insight into modeling the NFL and how to assess matchups. I want to thank all of you who have reached out via Twitter with words of encouragement, it certainly means a lot. Next week, I’ll return with a full recap and in-depth look at the model and its performance. This will include week-by-week splits, team splits, and areas of potential improvement for the 2019 season. If you have any questions or particular things you would like me to cover, make sure to give me a shout on Twitter.
And with the NFL season coming to a close, I now have my eyes set on the 2019 MLB season and the model I’ve been building in preparation for it. Following next week’s NFL model recap write-up, I plan to do a weekly MLB series that will cover higher-level MLB topics and model principals, so stay tuned for that. The plan as of right now is to provide MLB model plays for free through April, and to utilize a publicly available Google Sheet that allows you guys to see what the model line is and at what prices each team begins to show enough value to be a model play. I have some other tools in the works as well, so make sure to keep your eyes open for all of that.
The Divisional round saw a split between the two model plays of KC -4 and LAC +4.5, bringing the model to 75-44-6 for a +93.67 unit yield and a 19.98% ROI while playoff model plays move to 3-1-1 for +4.41 units. In last week’s pair of newsletters, I wrote about the numerical value of byes and the numerical effect of weather to help give us a better grip on what was driving last week’s spread numbers. This week, I’d like to discuss a game between the Green Bay Packers and Denver Broncos from 2015 and the significance it holds on the two Conference Championship games being played this week. No I am not kidding, but I am aware that sounds strange so I’ll jump right into it and explain its significance.
November 1, 2015 is a night I’ll remember for as long as I’m a football fan. It was Week 8 of the NFL season, and the 6-0 Packers who had just come off the choke job of the century against the Seahawks in the NFC Championship the previous season were travelling to Sports Authority Field to take on the 6-0 Denver Broncos on Sunday Night Football. As a Packer fan, the 6-0 start was reassuring with a win at Soldier Field, a revenge win against the Seahawks, and a win on the road against Colin Kaepernick and the 49ers who had bounced the Packers in the 2012 and 2013 playoffs. Aaron Rodgers was essentially untouchable, boasting a 76-37 (67.3%) record as a starter heading into that game. I was feeling great about the Packers.
And then we got absolutely romped. The Broncos went up 17-0 by the middle of the second quarter and I knew it was over. I didn’t care that we had Aaron Rodgers, who seemingly could make a game out of any of them no matter what the deficit. This game just felt different; it felt like Broncos Defensive Coordinator Wade Phillips had figured out the formula on how to beat Aaron Rodgers and the Packers offense. That offense had beaten teams for years as many defenses relied on soft man and zone coverage with plenty of help over the top to defend the deep ball, or with over-committed blitzes to try and generate meaningful pressure. Wade Phillips on the other hand had the talent and gameplan that finally did what so many had failed to do until then.
The Broncos’ gameplan involved generating an effective pass rush with just four men while playing press man coverage. With Jordy Nelson going down with a season-ending injury in the preseason, the Packers’ really lacked a pass catcher with the skill set to beat disruption at the line of scrimmage. The delay in route development caused by that disruption was long enough to give the Broncos’ pass rushers just enough time to generate meaningful pressure, wreaking absolute havoc on Rodgers’ ability to find an open man. Rodgers would finish the game 14 of 22 for 77 yards and a 65.8 rating as the Packers lost 10-29. The Packers would finish that season 9-7 and with an exit in the Divisional round of the playoffs. Rodgers since that game has gone 24-23-1 as a starter (51.0%). Wade Phillips and the Broncos defense would go on to become the centerpiece of a masterful season that culminated in a Super Bowl win. So what does this have to do with this week’s games? Let’s get right to that.
#2 Rams (14-3) @ #1 Saints (14-3)
Wade Phillips, now the defensive coordinator of the Rams, will have a similarly tall task to the one he faced for the November 1st, 2015 game against Aaron Rodgers. This time the stakes are quite a bit higher as he will be going up against the Saints in the NFC Conference Championship, a team that ranks third in pass offense and eigth in run offense. In their first meeting, it’s safe to say that Wade Phillips and the Rams’ defense utterly failed, allowing 45 points, 487 total yards, and a 346 yard – 4 TD – 0 INT line from Drew Brees while totalling zero sacks. Although they picked up a decisive win last week, the Rams only sack came on a strange play where forward progress was deemed stopped as Prescott was being held up and pushed forward by his own lineman. To add to the disappointment, the Cowboys offensive line ranked 27th in adjusted sack rate this season. To even further add to that disappointment, the Rams played that game at home in LA which means crowd noise was at its peak as the Cowboy’s o-line was waiting for the ball to snap. The tables could not be turned any more for this week as the Saints rank third in adjusted snap rate and this game will of course be played in the raucous Superdome.
To draw another parallel to that aforementioned Packers-Broncos game, C.J. Anderson lit the Packers defense on fire in that 2015 game with 101 yards on 14 carries and a touchdown (while having two goal-line TDs sniped by Ronnie Hillman). Last week, CJA continued his recent renaissance with the Rams with a 23 att / 123 yds / 2 TD line while teammate Todd Gurly added on a 16 att / 115 yds / 1 TD performance. However, the Saints’ third-ranked rush defense comes more than equipped to slow down the two backs as it held Eagles’ running backs to 37 yards on 13 carries last week. Even if we ignore the Saints’ ability to defend the run, I think the Rams’ backfield will be plenty busy in pass protection anyways as the Saints rank fifth in pressures generated in home games (via Sports Info Solutions).
In their first matchup, Sean McVay found only 13 opportunities to hand off the ball to his backfield and I expect much of that to hold this time around. However, passing more for the Rams could actually play to their favor, as the Saints are allowing a 47% success rate on pass attempts against 11 personnel (which the Rams run a ton of), a number that ranks bottom 27th in the league (via Sharp Football Stats). On top of their defeciencies defending the pass against 11 personnel, the Saints also struggle incredibly against deep passes. In fact, the Saints are the worst team in the league at defending deep passes (>15 yards) according to Football Outsiders’ DVOA metric.
As for the model, this game is made to be NO -5.1 which means a model play on the Saints would begin at -3 whereas the Rams would become a model play at +7.5 (which we have no chance of seeing). I do see NO -3 currently but it is priced at -114, which would be a good amount higher than I could tolerate. If and when the juice comes within an acceptable range, I’ll be sure to lock in the Saints as a model play on Twitter.
Much like the 2015 Broncos, the Patriots utilize a lot of man coverage. In fact, they led the league this year in man coverage usage with 56.8% of their snaps being in man (via Sports Info Solutions). But unlike the Broncos, the Patriots may need to shy away from man coverage in their defensive game-planning if they want to succeed. Patrick Mahomes has played five games against teams in the top twelve of man coverage usage and in those games he has averaged 328 passing yards, 3.8 TDs, and a 69.2% completion rate, which are all above his already absurd season averages. Sammy Watkins, who just returned last week from a lengthy injury, has averaged 4 rec / 74.8 yds / 0.5 TDs in those particular games. Watkins first season with the Cheifs has proven to be a success when he has been able to play, as he has averaged 5 rec / 64.1 yds while generating a 120.0 passer rating when targeted and a 72.7% catch rate on a 80% catchable target rate, all of which are either career highs or second to his career high (via Graham Barfield).
Although the Patriots have a run defense that otherwise grades as average overall, they do have one glaring weakness that went unchallenged in last week’s game. The Patriots are horrid at defending runs out of 11 personnel, allowing a league-worst 61% success rate and 6.8 yards per carry. The Chargers, who have never really done much to show that they care about or gameplan around analytics, gave Melvin Gordon just four carries last week from 11 personnel. On the other hand, Chiefs running back Damien Williams runs from 11 personnel on 64% of his attempts while averaging a 67% success rate and 5.2 yards per carry.
It’d be cheeky to talk about the Patriots’ defensive shortcomings given the defensive unit they’re going up against this week. To say that the Chiefs have a poor defensive reputation would be an understatatement. On the surface, they allow the ninth-most points per game and the second-most yards per game. That is certainly not good, but I do think there are certain elements of their defense that could pose some trouble for the Patriots. The most notable is that their front seven has generated the most quarterback pressures in home games this year. Granted the strength of the opponents they’ve played at home isn’t the greatest, but when looking at pass defense DVOA (which is opponent-adjusted) the Chiefs rank twelvth in the league. They’ve also played three playoff teams in their last four home games, and in those games they’ve allowed an average of just 221 passing yards while surrendering a 59.2% completion rate. Those games included games against the second-ranked Chargers’ pass offense and tenth-ranked Colts’ pass offense. They also generated ten sacks against those offensive lines, which ranked 2nd, 8th, and 13th in adjusted sack rate. To give you an idea of how impressive that 3.33 sacks per game average is, the league leader in sacks per game was the Steelers with 3.2 sacks per game.
Speaking of home and away splits, those get even uglier the deeper you dive into the game. The Chiefs allowed the second-most points per game on the road (34.6) while allowing the third-least at home (17.4). Of those three aforementioned recent home games against playoff teams (BAL, LAC, IND), they allowed an average of 22 points. The Patriots splits are equally massive: their net yards per play was +0.9 (2nd) at home and -0.6 (26th) on the road, and their average margin was +15.9 (1st) at home and -2.4 (17th) on the road. And I’m sure many of you have heard many times this week that the Brady/Belichick era Patriots are 3-4 on the road in the playoffs (which accounts for half of their total playoff losses), but that can be sliced and framed so many different ways to play for or against them. Three of the losses are in Denver, a notoriously tough place to play with the included altitude element. Three of the losses are to some guy named Peyton Manning. The three wins are against the Ben Roethlisberger Steelers and the Philip Rivers Chargers, both of which have been manhandled particularly egregiously by Brady and Belichick.
But I don’t want to make it sound entirely doom and gloom for the Patriots. They obviously stand a reasonable chance to win this game and possess advantageous matchups that can help push the game more in their favor. The Patriots rely heavily on 21 personnel (two back sets), ranking second in the league in play frequency on the year at 29% and only trailing the 49ers who use it on 41% of plays and are the only team who use it as their most common personnel package. However, the Patriots have used 21 personnel even more since their bye and since their committee of running backs have returned to full health. Since Week 12, the Patriots have used 21 personnel on 35% of all of their plays. This could spell disaster for the Chiefs’ defense as they are the worst in the league at defending 21 personnel, allowing a 65% success rate overall and a 98.2 passer rating on pass plays and 6.2 yards per carry on run plays.
As for the model, the game is made to be KC -5.7 which means there would be a model play on the Chiefs beginning at -3 and a play on the Patriots at +8.KC is currently -3 -116 which is too much juice, but just like the Saints play I will make sure to share if and when that comes within an acceptable range.
In yesterday’s write-up, I covered the playoff bye dynamic and what historical effects it has had on team performance. Today, I’d like to take a look at another game element that has a measurable effect on games and is also prevalent in this week’s slate of games. That effect is weather. Each of the three games being played outdoors this weekend have a weather element to them, and everyone from bettors to players to analysts to talking heads have chipped in with their thoughts on how each condition will effect each team and game. But as always, I like to slice right through the noise and use a data-driven approach.
Whether Weather Has An Effect
Let’s first take a look at temperature. In a study conducted by advancedfootballstats.com, teams were separated out by four climate types: warm, moderate, cold, and domed. Those teams’ performances as a road team were then analyzed in temperature increments to see the correlation between temperature and win percentage, and below are the results of that analysis. As you can see, the most discernible effect temperature has is on domed teams playing in cold weather. The rest of the results are what I would consider to be inconclusive, given that warm teams had the highest win percentage at extreme lows (11-20 °F) and cold teams had the best win percentage at extreme highs (81-90 °F). That would obviously go against the what you typically hear from just about everyone.
Next up is precipitation, which is obviously split into games with rain and games with snow. The former typically has a negligible effect on games, which may go against common narratives you hear. A fantastic analysis done by Chris Allen from 4for4 showed that when compared to a “clear” game (60-75 °F, <9 mph wind), rain games have just a 2.4% decrease in total pass attempts, a 2.0% decrease in air yards, and a 0.5% decrease in deep pass attempts. Even target distributions by position don’t see a noteworthy enough change, which you can see below. Before tackling snow’s effect, I thought I’d quickly mention that games with significant wind see a negligible 2.4% decrease in total pass attempts and a 0.8% decrease in air yards, but a most-certainly noteworthy 6.2% decrease in deep pass attempts and a 4-8 point decrease in actual game totals when reaching 15 mph.
Snow is a weird weather element when looking at the data. The common narrative with snow is that passing volume decreases and rushing volume increases, which is generally true with a 6% swing in pass / run ratio. But what about the effect on efficiency of those types of plays? In the aforementioned 4for4 analysis, there was an 8.3% drop-off in total pass attempts, yet just a 1.4% decrease is deep pass percentage and a 5.3% increase in air yards. But not all snow is the same. In a study done by Pro Football Focus, it was found that “light” snow has a very negligible effect on passing efficiency (1.8% increase in completion percentage) and rushing efficiency (0.27 more yards per carry). It isn’t until snow is “heavy” that the there begins to be a more profound effect, with a 8.4% dip in completion percentage being the most notable change. I believe that public bettors tend to overreact at just the very sight of snow, and you see this with the light snow expected to be on the field for the Colts-Chiefs game despite the fact that it isn’t expected to snow at all during the actual game.
#5 Chargers (13-4) @ #2 Patriots (11-5)
The Patriots are a team the model can channel their Dennis Green with and say, “They are who we thought they were”. Coming into the season, the Patriots were expected to regress just a tad. They finished the 2017 season as the #3 team according to the model and they were projected to finish the 2018 season sixth-best with 9.66 expected wins. They ended up finishing the 2018 regular season with the fifth-best expected wins total, with 9.86. The Patriots never dipped lower than 9.21 expected wins and peaked at 10.03, showing that their performance has been consistent. Known to draw a lot of public money, their prices are often inflated and overvalued and the model finding zero times throughout the season to back them supports that. As for betting against the Patriots, the model found plenty of opportunities and went 5-2 for +13.36 units and a 45.01% ROI if we ignore the Week 5 model play backing the Colts against the Patriots on Thursday Night Football which remains the only model play I’ve personally advised against backing.
So what reasons would there be to back the Patriots this week against the Chargers? The model doesn’t really find any across the board. The Patriots rank behind the Chargers in every model category except special teams, which accounts for roughly just 6% of total team strength. Granted, the Patriots trail the Chargers in pass offense, rush offense, and pass defense by just a combined nine spots across all three categories, but it’s the Patriots 18th ranked rush defense going up against the Chargers’ sixth-best rush offense that will pose a problem in this game.
If there’s one thing the Patriots do have going for them, it’s that their defensive personnel and typical gameplan fits the mold of what brings down Philip Rivers’ effectiveness. The Patriots certainly have the ability to play man coverage, which yields 1.55 less yards per attempt from Rivers compared to zone coverage. The Patriots also pressure opposing quarterbacks at the fifth-highest rate in the league, which limits Rivers’ ability to wait in the pocket and fire deep to Mike Williams who may be drawing favorable coverage in this matchup if the Patriots’ top corner Stephon Gilmore is tasked with covering Keenan Allen.
Hopefully most of you have been following along on Twitter and received the play as soon as it was made, as I am only seeing LAC +4 available now, which would reduce the points of disagreement to 1.7 and no longer be enough for a model play.
#6 Eagles (10-7) @ #1 Saints (13-3)
The Eagles and Saints are two teams I’ve covered in detail in past newsletters, so I’ll just quickly recap their model numbers. The Eagles stand 15th overall in the model with 8.26 expected wins whereas the Saints sit fourth overall with 10.29 expected wins. An argument can be made that the Saints are actually undervalued in the model, as their adjusted Pythagorean win total for this year is 11.45 and of the remaining playoff teams, only the Colts possess a larger gap between the two numbers. Despite this game featuring the largest spread of the playoffs thus far, there is quite a bit to unpack in this game. Yes the Saints absolutely throttled the Eagles in their regular season matchup, but the Eagles are certainly a much different team this time around and they come into this game with a few things that could work their way.
The Eagles are of course coming off a win against the top-ranked defense of the Chicago bears. In particular, they’ll be able to take what helped beat the top-ranked rush defense in the conference and apply that to the Saints rush defense which trailed the Bears’ unit by one spot. In addition, the Saints defense possesses a weakness against the running back position in the passing game as they have the fourth-worst pass defense DVOA against RBs. That means ex-Saint Darren Sproles may find himself in the mix quite a bit this time around, as he was yet to return from his near season-long injury by the time of the first matchup.
Establishing their running backs as a receiving threat will help keep the Saints defense modest, and should help open up the deep ball which has killed the Saints this season who rank as the worst in the league against passes of 15 yards or more. The problem is that Nick Foles has not always been a great candidate to throw deep. Last week Foles completed just two of his seven attempts at that range, including an interception (although he did have two 14 yard completions, one of which went for a TD). Whether the Eagles will get the Foles who averaged 7.2 and 5.3 yards per attempt in the last two regular seasons or the Foles that averaged 9.2 YPA in the 2017 playoffs will probably determine how successful the Eagles’ passing attack will be come Sunday night.
As for the model, the Saints are made to be three point favorites against the Eagles on a neutral field when coming off a bye. The remaining game-specific factors then bring the spread up to NO -7.1, which is just 0.9 points off the current line of NO -8. That of course means that the Saints would become a model play beginning at -5, whereas the Eagles would become one at +9.5 (but the model removes spreads of +7 or higher from consideration).
That will wrap it up for the Divisional round, but I will see you all next weekend for the conference championships. Don’t forget to follow me on Twitter, where you can catch model plays the second they are made.
What a great way to start the playoffs! A nice 2-0-1 Wild Card weekend for +4.6 units also brings highlighted newsletter plays to 5-1-1 for +15.2 units. As a whole, the model moves to 74-43-6 for +93.69 units and a 20.18% ROI. The Wild Card round is always intriguing since division winners get home field advantage over Wild Card teams, creating situations where sometimes the better team is forced to play on the road which then in turn creates super close coin-flip matchups. The Divisional round is also interesting, as every year talking heads and fans alike weigh in on the debate of whether a bye or the momentum from winning a Wild Card game is more valuable.
The Value of a First Round Bye
Teams with a first-round bye are 44-21 in divisional round games since 2002, when the current playoff format was adopted. That’s good for a 67.7% win percentage for those teams, which is pretty significant at first glance. But how do we discern how much that advantage is from playing at home and how much is from coming off a bye? How do we separate that distinction from the fact that teams receiving byes are generally stronger teams to begin with and should be winning games at a higher clip? Looking at just playoff splits can be a bit deceiving due to small sample size, as a 65 game sample across 15 years is laughably small. As a result, I’ll look at splits using regular season games to capture a larger sample.
Since 2002, home teams have won 57.6% of games in the regular season and teams coming off of a bye (home or away) have won 55.2% of their games. If we look at a split combining the two, home teams coming off a bye have a 62.8% win percentage. Given that most first round bye teams are favorites for their Divisional round game, I thought it’d be worth mentioning that home favorites have a 73.9% win percentage coming off a bye since 2002. But these are all just in terms of wins and losses. The question for our purposes,should shift to how effectively Vegas has accounted for bye week value. Home teams off a bye are 53.6% ATS; home favorites off a bye are 55.0% ATS. Although those splits are “winning”, Vegas has been able to take advantage of bettors trying to take advantage of the bye week angle as of late. Since 2010, home teams off of a bye are 43.3% ATS and were 3-10-1 this year alone. With this in mind, it is very likely that Vegas has in recent years intentionally baked in more value than necessary on teams coming off a bye week in order to capitalize on public bettors trying to take advantage of the angle.
In my opinion, I think the real-world value of a playoff bye is from just being able to play less games in order to reach and win a Super Bowl. Without a bye, winning four straight games against above average to elite teams is a big mountain to climb considering winning four straight games in the NFL is tough enough as-is. Reducing that to three games with at least one game guaranteed to be at home and another on a neutral site is a much more manageable task. But this is all a digression and the matter of the fact is that I do account for the additional edge of a team coming off of a first round bye in the playoffs, but it is likely not as large of an edge as most bettors make it out to be.
#6 Colts (11-6) vs. #1 Chiefs (12-4)
Last week I called T.Y. Hilton the key to the Colts-Texans game, and surely enough he delivered. A nice 5 rec / 85 yard game helped extend key Colts drives while also spreading the field to make everyone else’s job on offense easier. Eric Ebron, the other candidate I called out as a potential producer, also contributed by securing a touchdown. Marlon Mack also joined in on the fun, tearing apart the highest-rated rush defense for 148 yards and a touchdown. But enough about last week, let’s get into the Colts’ matchup this week against the awaiting top-seeded Kansas City Chiefs.
The Chiefs’ journey in the model has been a very interesting one, to say the least. Prior to the season I had them Chiefs as the ninth-best team overall and fifth-best AFC team with 9.10 expected wins, and projected them as a fifth seed for the playoffs. My “low” evaluation didn’t come from me not being a believer in Patrick Mahomes, as I had the Chiefs improving their pass offense up to fourth-best heading into the season. Obviously what he and that offense has been able to achieve this season has blown anyone’s projections out of the water, and it should come as no surprise that the Chiefs’ pass offense ranks first by quite the margin. In fact, the gap between them and the second-ranked Chargers’ pass offense is as large as the gap between the Chargers and the twelfth-ranked Eagles unit.
One thing I have talked about on multiple occasions is the model’s ability to identify incorrect evaluations and adapt. Despite starting the season as the ninth-best team, the model had already jumped the Chiefs to the #2 spot after just three games and improved their expected wins total by 1.71, which is a massive jump for that time frame. The Chiefs’ peak this season came after Week 10, at which point they had 14.27 expected wins – a mark that no other team has come close to touching this season. The Chiefs of today aren’t as impressive in the model as they were at their peak, but they still rank first with 11.75 expected wins. They also possess the third-best improvement in expected wins over the course of the season, a mark that the Colts coincidentally bested as covered in last week’s newsletter.
As for this week’s matchup, I think we will see Andy Reid rely a lot on 12 personnel (two tight end sets). The Chiefs ranked third in the league in 12 personnel play frequency (26%) and the Colts ranked third-worst in defensive success rate against that package (59%) while allowing a 112.0 passer rating in those situations. They also surrender the most yards per game to tight ends and rank 29th in pass defense DVOA to the position. In last week’s newsletter I mentioned All-Pro rookie linebacker Darius Leonard who is certainly the anchor of the Colts’ front seven, but his ability (PFF coverage grade: 78.9) has not been enough to hide Anthony Walker (55.7), Matthew Adams (41.6), and Zaire Franklin (41.4) when they are called upon to cover two tight end sets. With this in mind, second-string Chiefs tight end Demetrius Harris is perfectly poised to have a career performance and doing so may help his agent fool a team into paying him the big bucks this offseason.
As for the model, the Chiefs are made to be 6.2 point favorites. The current line shows KC -5, which isn’t a large enough disagreement to make a model play on either side. If the line were to move to KC -4, then the Chiefs would become a model play at 2.2 units with each additional half point on the spread away from 6.2 being another half unit added to the play. The Colts would in theory become a model play at +8.5 or higher, but the model excludes spreads of +7 or higher from consideration and I have yet to make a decision on whether that exclusion should be lifted for the playoffs and will put off that decision until need be.
#4 Cowboys (11-6) vs. #2 Rams (13-3)
The Rams are kind of an unexciting team to discuss as they’re one that many pegged as being a top team heading into the season and they’ve of course finished as such. The model is no different, as the Rams entered the season as the #2 with 10.69 expected wins and finished the regular season as the third-best team with 10.53 expected wins. That may seem low given that the Rams finished with 13 wins, but their adjusted Pythagorean win total was 11.15 for this year. The one area in which the Rams did surprise many was their awful run defense, which finished 28th in the model despite the presence of Aaron Donald and Ndamukong Suh up front. This soft spot will certainly be one that Jason Garrett can send Ezekiel Elliott at, who just tore the Seahawks up for 127 yards on 16 carries (7.94 yds/att).
Outside of that, Jason Garrett will have to come up with a gameplan in the passing game that vastly differs than the one executed last Saturday, as a lot of what yielded success for them in that game will be unlikely to bear fruit again this Saturday. Of Dak Prescott’s 33 attempts, 27 of them were for 15 yards or less and only eight of those were thrown to the left of the left hash mark. If this trend continues, the Rams are going to absolutely feast on Dak. The Rams defense ranks sixth overall in DVOA against short passes as a whole, and ranks second in pass DVOA against short passes (15 yards or less) to the right. Going the complete opposite – deep and to the left and/or down the middle – would be much more beneficial for the Cowboys offense, as the Rams defense ranks 23rd and 21st in those directions. Given that Prescott has only attempted 8% of his total passes this year to deep left and deep middle and a whopping 85% 15 yards or shorter, the Cowboys offense might find themselves in a lot of trouble early and often.
What about the Rams? Concerns on the offensive side of the ball started to surface after putting up six points against the Bears and 23 against the ravaged Eagles secondary in back-to-back weeks. Those concerns quickly went away as the Rams seemingly returned to form, putting up 31 and 48 in the final two weeks. Sean McVay has received endless praise this season for the success of the Rams’ offense, who has kept it simple and have run 96% of their plays from the same formation. Although McVay is certainly a great coach, I think the praise has been a bit exaggerated. If you exclude their Week 1 cupcake matchup against the Raiders, the Rams have faced the eight-toughest schedule of pass defenses. That may make their offensive performance this year look even more impressive, but if you look at the Rams’ schedule from Week 7 onwards, the Rams have faced eighth-easiest schedule of pass defenses. The problem is that the Cowboys don’t exactly possess the talent to adequately challenge the Rams, as they rank league-average in pass defense anyways.
As for the model, the Rams are made -5.5 favorites for this game. With the Vegas spread set currently at LAR -7, there is no model play currently but the Rams would become a play starting at -3.5 (don’t expect to see this line) and the Cowboys would technically become a play at +7.5, which comes with the same caveat the Colts did in the other game’s write-up. If potential model plays on IND or DAL show, I will be sure to make a decision and communicate it via Twitter. As of now I am siding with not lifting the exception in order to maintain the status quo, but I admittedly can’t think of any other reason not to. We shall see.
That’s going to wrap it up for today. Don’t forget to check your inboxes again tomorrow for a write-up covering the Sunday games. As per my Twitter, there is already a locked in model play on LAC +4.5 which I will be covering in-depth for that write-up. Below you can find a schedule for the remaining NFL Playoffs write-up schedule.
Today we will be finishing out the Wild Card round by taking a look at the two Sunday games. To begin, lets take a look at the noon matchup.
#5 Chargers (12-4) @ #4 Ravens (10-6)
Every NFL season has at least one or two teams that make me go, “Wow, that team definitely deserved to be in the playoffs”. This game happens to have the two teams I thought were most undeserving of missing out on last year’s playoffs. For those who don’t remember, the Chargers started 0-4 last year which was a stretch that was full of incredibly unlucky circumstances (and that has been the Chargers’ “thing” for quite some time now). They then went on to go 9-3 for the remainder of the season, with their losses coming all on the road against eventual playoff teams (Patriots, Jaguars, and Chiefs). They finished with 10.46 adjusted Pythagorean wins, good for fifth in the conference and ahead of the Chiefs (9.99), Titans (7.38), and Bills (6.35) – all of who made the playoffs last year. The Chargers differential between actual wins and adjusted Pythagorean wins (-1.46) was the sixth largest in the league in 2017, which was a sign that they were potentially due to put together a better campaign in 2018.
The model seemed to agree. Heading into the 2018 season, the Chargers were the highest ranked team in the model, ranking first in pass offense and third in pass defense. The Chargers ended up finishing second overall in the model with 10.53 expected wins and within 0.35 expected wins of their preseason projection. They also finished top ten in every model category except special teams, finishing second in pass offense, sixth in rush offense, tenth in pass defense, and tenth in rush defense. On top of their excellent finishes, the Chargers have shown incredible consistency in the model having never ranked lower than third in the model at any point this season. Some of you may be asking, “If the Chargers are so good according to the model, why did they get hosed by the Ravens in Week 16?”. It’s a valid question, for sure.
In my Week 17 write-up, I highlighted the Ravens’ ability to grind clock through their relentless running which in turn vastly reduces the amount of offensive opportunities opposing offense have. The Chargers actually did not fall victim to that trend in the Week 16 matchup as they ran only two fewer plays than their season average and had 28:35 in time of possession to the Ravens’ 31:25. And it wasn’t Lamar Jackson’s running that decidedly beat the Chargers – he turned in a career-low 39 rushing yards while passing for a career-high 204 yards. So how did the Ravens pull of the Week 16 upset?
Although a 22-10 score may tell a different story at surface level, this game was very close to going the Chargers way. With 5:29 remaining in the 4th quarter, the Chargers were down 10-16 and had a 3rd and 5 on the Ravens’ 29. Philip Rivers on this drive had already converted three straight third downs and was putting together the Chargers’ largest drive of the game. He was then sacked for an 11 yard loss, pushing the Chargers out of field goal range. Luckily, they were able to pin the Ravens on their own 2 on the ensuing punt and held the Ravens’ offense to a three-and-out. The Chargers got the ball back on the Ravens 39 yard line and just 39 yards sat between the Chargers and a likely number one seed for the playoffs. On first down, Melvin Gordon ripped off an eight yard run which was called back by holding. Then on the next play, Antonio Gates fumbled the ball which the Ravens returned for a 62 yard touchdown.
On top of that ending sequence to the game, this game had a lot of unfortunate and uncharacteristic things go against the Chargers. On offense, their first offensive play led to an interception and then they also had three early third down conversions negated by penalties which killed drives. On defense, the Chargers allowed a 68 yard touchdown to rookie tight end Mark Andrews. This was particularly egregious given that the Chargers are the top-ranked defense against tight ends according to DVOA. These kind of things are not the type that I would expect to happen regularly. Conversely, the Chargers are likely the most injured team coming into the playoffs and that could most certainly work to their disadvantage for this go-around.
Ultimately, this week’s rematch will likely come down to who commands the lead when the fourth quarter begins. The Ravens are the only playoff team to not have a comeback win when trailing in the fourth quarter. And although Lamar Jackson has a superb 6-1 record to start his career, every one of his starts have featured a one-score game in the fourth quarter. That becomes a bit scarier when you consider some of the teams the Ravens have played during that stretch (CIN, OAK, ATL, and TB). It would be wise of the Ravens to try to get a lead early on the west coast Chargers who will be travelling across the country for an early game in which their body clocks will be set to 10 AM. Either way, this game is certainly my favorite of the opening round. On one hand we have a very experienced quarterback with a coach making his playoff debut going up against the youngest starting playoff quarterback ever with an experienced and successful playoff coach (Harbaugh is 10-5 all-time in the playoffs).
The model gives the Chargers a 53.64% chance of winning this game on a neutral field, which would be good for a LAC -2 spread in such scenario. Even though the Chargers don’t really have a “home field” and essentially have played every game of this season as the away team (and are 7-1 in actual away games this season), the model does make this game BAL -0.9 after those adjustments. That means that LAC +3 -110 is a model play risking 2.300 units to win 2.1 units. That makes it the first official model play of the NFL playoffs. Exciting stuff.
#6 Eagles (9-7) @ #3 Bears (12-4)
In yesterday’s newsletter, I mentioned that the Dallas Cowboys were the most undeserving team to make the playoffs based on expected wins given their 23rd rank in that regard. The Philadelphia Eagles would be the second-most undeserving team to make the playoffs, as they rank as just an average team in the model at 15th with 8.26 expected wins for the season. Granted, the Eagles do rank near the top ten in pass offense which is and has been the most important model category of the season by a wide margin. The problem is that the Bears rank first overall in pass defense, and given that the Eagles have one of the league’s worst rushing attacks and are going up against the second-best rush defense, the Eagles will certainly have to try to make the passing game work in whatever ways they can. But they will have to do so on early downs as the Bears have the best third-and-long defense ever recorded according to Football Outsider’s DVOA metric.
The good news is that although this match up certainly favors the Bears, the task may not be as large as Vegas is making it out to be. The Bears themselves aren’t too far off in the model from where the Eagles stand, as they currently sit as the tenth best team with 8.90 expected wins. This may seem odd to some given the fact that they have twelve wins on the season and given how absolutely dominant their defense has been. A lot of that actually lies on the strength of their opponents, as the Bears have faced the easiest schedule of opposing defenses and the thirteenth-easiest schedule of opposing offenses according to Sharp Football Stats.
But as some of you may point out, I have already shared that the Bears have the top-ranked pass defense and second-best rush defense in my model, which adjusts for opponent strength. So why aren’t they higher in the model? For those of you that have joined the Bet It Up newsletter recently, my initial introduction to the model highlighted that the model dynamically weighs five categories (pass offense, rush offense, pass defense, run defense, and special teams) based on their calculated correlation to team strength across the league. Those weightings are exactly why the Bears may not be as terrifying as their defense is. Pass defense and rush defense combined are not weighted as much as even just rush offense alone. And pass offense, if you’ve been following along, has significantly more weight than rush offense.
It’s because of those weightings the Bears’ strength in the model and the dominance of their defense is certainly dragged down by their below average pass and rush offenses. And that may be exactly why the model makes this game CHI -4.2 which is a much shorter spread than the current spread of CHI -6.5. Short enough that PHI +6.5 -104 qualifies as a model play risking 2.403 units to win 2.3 units. That will wrap it up for today’s write-up as well as this week’s round of playoff games. Don’t forget to tune in for next week’s matchups as well as the remainder of the playoffs (write-up schedule below). As always, you can catch model plays the second they’re made by following me on Twitter.
Last week’s highlighted newsletter play of CLE +6 pulled through for the readers here bringing highlighted plays to 3-1-1 for +10.60 units, but the rest of the model’s Week 17 plays weren’t as hot. The week finished 2-2 for -4.51 units, bringing the model’s record for the regular season to 72-43-5 for +89.27u and a 19.51% ROI. With the regular season wrapped up, it’s time to shift our focus to the playoffs. I will be covering every playoff game for the readers here, providing the model’s spread as well as analytical insight on the matchup and the underlying model data. To start, lets take a look at the Saturday set of Wild Card games.
#6 Colts (10-6) @ #3 Texans (11-5)
The first playoff game of the season will pit two teams that the model would have never predicted to be in this position before the season began. The Colts’ 2017 season was of course hampered by the entire Andrew Luck shoulder fiasco, but a lot of the team’s performance despite that was very concerning. The offensive line allowed a league-worst 56 sacks and on the other side of the ball the Indianapolis secondary ranked dead last against the pass. The Luck shoulder fiasco then bled into the 2018 offseason, camp, and preseason as concerns of his ability to throw at even moderate lengths were in question. Heading into the season, the Colts ranked 27th in the model and were only expected to win 6.49 games. After a very flat and uninspiring 1-5 start, the Colts looked to be trending to be exactly who the model thought they were.
What Frank Reich and the talent on that team has been able to do since then has been nothing short of impressive. In their 1-5 start, the Colts averaged 25.3 points scored and 30.0 points allowed. Since then, the Colts have averaged just 16.4 points allowed and have averaged 31.2 points scored if you remove their Week 10 6-0 dud against the Jaguars. A lot of the team’s success has come on the back of some new faces. Sixth overall pick RG Quenton Nelson not only finished as the highest-rated rookie offensive lineman by Pro Football Focus, he also finished sixth at the position overall. Nelson’s performance helped turn the league-worst offensive line unit into the league-best, with the Colts finishing with the least amount of sacks allowed (18) as well as finishing second in adjusted sack rate. On defense, second-round pick Darius Leonard also turned in a Pro Bowl-caliber season (but was unfortunately snubbed of the honor), and finished second for rookie linebackers and sixth overall at his position. Interesting enough the model caught on to the Colts’ turnaround pretty quickly, upgrading them following their Week 8 win against the Raiders to 8.42 expected wins, which ranked 15th at the time. The Colts have since climbed even further, finishing the regular season ranked ninth in expected wins with 9.07 expected wins. The Colts’ expected wins increase represents the second-largest in the model for this season.
The Texans have had a similar trajectory as the Colts, having started the season as the 28th ranked team in the model and only expected to win 6.37 games. The Texans had a poor start as well, dropping its first three games. However, the model actually saw in the underlying data that the 0-3 start was somewhat deceiving and even bumped them to 7.16 expected wins after that stretch. That 0.79 increase in expected wins intrigued me as the ensuing nine game win streak that followed led to a 1.43 expected win increase. In other words, the Texans’ performance during their nine game win streak was just short of being twice as impressive to the model as their performance during their 0-3 start. Today the Texans sit in the model as a team expected to have won 8.74 games, good for 12th best in the league. Their expected wins increase represents the fourth-largest in the model this season.
As for their matchup on Saturday, you can infer from their close current expected wins numbers that these two teams are matched pretty evenly. The Colts are given a 50.9% chance of winning on a neutral field, good for a IND -0.5 spread. After including game-specific adjustments, the model’s line for the game is made to be HOU -2.6. The Vegas line as of the time of this writing is HOU -1.5, and at that line there is no suggested model play since model plays are when the disagreement between the model line and Vegas line is by two or more points. However, there would be value on the Texans starting at HOU -0.5 (2.1 units) and there would be value on IND starting at IND +5 (2.4 units). Obviously each half point further from each of those spreads would then be matched with an additional half unit on the play (example: HOU +1 would be a 3.6 unit play).
As for the game itself, I believe the key to this game will be Colts wide receiver T.Y. Hilton. Hilton has been on an absolute heater as of late: in the last seven weeks he has led the league in receiving with 840 yards (or 120 yards per game), which is 163 yards more than the next best receiver during that span and all coming despite battling an ankle injury. Secondly, he has torched the Texans’ secondary for quite a while now. In his last five games against the Texans, T.Y. Hilton has averaged a 6 rec / 107.6 yds / 0.6 TD line which includes a 9 rec / 199 yds line in the most recent meeting. Hilton burns the Texans’ secondary for good reason: Houston ranks 29th in yards allowed to WR1s and they rank 31st in DVOA pass defense against WR1s. Eric Ebron is another candidate to tear the Texans’ pass defense apart given that they rank 31st in yards allowed to tight ends and 23rd in DVOA pass defense against the position. In his two games against HOU this year, Ebron has accumulated 9 rec / 105 yds / 2 TDs.
#5 Seahawks (10-6) @ #4 Cowboys (10-6)
Saturday’s NFC Wild Card game will feature another team that has greatly outperformed the model’s preseason expectations: the Seattle Seahawks. I already covered their journey in some depth in my Week 14 write-up, and for those of you who weren’t subscribed to Bet It Up back then (shame on you), here is what I said then:
“The Seahawks on the other hand were a very unimpressive 9-7 team in 2017 that performed well above their 6.57 adjusted Pythagorean expected win total. The team then proceeded to lose Richard Sherman, Michael Bennett, Paul Richardson, Sheldon Richardson, Jimmy Graham, and more in the offseason, and were set to be without their top wideout Doug Baldwin for an indeterminate amount of time. They also burned their first round pick on San Diego State running back Rashaad Penny, which I thought was an awful use of the pick given that they already had the serviceable Chris Carson and had more glaring team needs. With all of this in mind, I’m not ashamed to share that by the end of the preseason the Seahawks were the 29th best team in the model, expected to win only 6.31 games in 2018.“
I then went on to share that the Seahawks had gradually improved up to sixth in the model ahead of their Week 14 game against the Vikings. The Seahawks ended up finishing sixth with 9.50 expected wins, which was a 3.19 expected wins improvement – the largest of any team in the model this year. This is largely in part due to the well-roundedness of their team, with the only model category they failed to land inside the top ten in being run defense. A lightbulb may suddenly go off in your head as you envision league-leading rusher and Cowboys franchise running back Ezekiel Elliott on the other side of this contest. However, Zeke has had somewhat of a deceiving season. From Football Outsiders Quick Reads: 2018 in Review article, “Elliott led the league in rushing, but was just ninth in DYAR and 18th in both DVOA and success rate. He led all running backs with six fumbles on running plays — or, one for each rushing touchdown he scored”. Elliott’s more meaningful contributions may come in the passing game, as the Seahawks rank 26th in running back receiving yards allowed. Or potentially the threat of Elliott running could force the Seahawks to stack the box and allow trade deadline acquisition Amari Cooper or rookie Michael Gallup to exploit the Seahawk’s 28th-ranked defense against explosive passing (courtesy of Sharp Football Stats).
Either way, the Cowboys will have to find some avenue of success to exploit early and often as they are by far the most undeserving team to make the playoffs according to the model. The model has them as a 7.539 expected wins team, which puts them 23rd overall. With this in mind the model makes this game on a neutral field SEA -2.5 but home field factors bring this game to a near pick ’em, with the model spread being DAL +0.1. The Vegas spread for this game at the time of this writing is DAL -2, which would qualify SEA +2 as a model play for 2.1 units. However, I am anticipating a move to at least SEA +2.5 juiced at -105 or lower. As a result, I will not make the play on SEA +2 an official model play as of right now but I will be tweeting out the model play when I do officially make it. If by some magic the spread hits DAL +2.5, that would become a model play on DAL for 2.4 units.
That’s going to wrap it up for today’s write-up. I hope you guys enjoyed it, and don’t forget to check your inboxes for another write-up tomorrow as I will be covering the Sunday Wild Card games as well. As a reminder, here is a schedule for my playoff write-ups:
Last week was admittedly not the brightest of spots for the model, going 1-3-1 for -8.495u and generating the first losing week since Week 9. A performance like that is bound to pop up every so often given the size a one week sample is in the grand scheme of things. The highlighted newsletter play of HOU +2 -104 (2.403 for 2.3 units) pushed, with Nick Foles turning in his best regular season performances by most metrics since 2015. Highlighted plays move to 2-1-1 for +8.50u since the model’s introduction to the readers here and the model overall moves to 70-41-5 for +93.78u and a 21.15% ROI. Week 17 (luckily) has a lot at stake for a handful of teams, and should make for an entertaining week of football.
As I’ve mentioned before, spreads of +7 or higher have been removed from model consideration since Week 9. With so many of those spreads on the board this week, there are slim pickings when it comes to playable games. Luckily for us, there is one model play that is not only worth making, but one that includes two teams that I’ve been itching to talk about in detail for a while now.
There may not be two hotter teams in the NFL than the Browns and the Ravens, both 5-1 in their last six games. The Ravens’ run has come on the back of their incredible defense and the change at quarterback to Lamar Jackson, who with the help of John Harbaugh has completely changed the offensive identity of the now AFC East-leading Baltimore Ravens. A win on Sunday would secure an AFC East title and a four seed in the AFC, likely pairing them in a rematch with the Chargers (or a less-than-likely rematch with the Chiefs) during the Wild Card round. A loss would have them watching the Wild Card games from home instead.
As for the Browns, their run has come on the back of firing Hue Jackson. A win on Sunday doesn’t do much for the Browns as they are mathematically eliminated from playoff contention, very largely in part due to Hue’s incompetence which costed them potential wins earlier in the season. The common angle public bettors take year-after-year in Week 17 is that teams with something to play for (like the Ravens) should easily win and cover against teams that have nothing to play for (like the Browns). But do the Browns truly have “nothing” to play for?
Cleveland Browns quarterback Baker Mayfield has not been shy when it comes to sharing what he believes he and the rest of team is capable of (and presumably indirectly, what Hue Jackson prevented them from accomplishing). This week is no different, as Baker Mayfield made it very clear what’s at stake for both teams: “They’re fighting for a playoff spot, and we’re fighting to prove who we are” (via ESPN). So now that we can clear this game of any motivational questions, we can take a higher-level look at the matchup.
The Ravens finished the 2017 season with a 9-7 record and without a playoff berth. Joe Flacco’s play and contract continued to draw criticism from Ravens fans and the front office made an attempt to give Flacco a better chance at succeeding (signing Willie Snead and Michael Crabtree, drafting Mark Andrews and Hayden Hurst) while also warming up his seat a bit (drafting Lamar Jackson) to signal the urgency to perform in 2018.
Everything coming out of camp from Ravens beat writers signaled an invigorated Flacco and Baltimore offense, and pairing that with an already elite defense convinced me enough to project Baltimore in the model as the fifth-best team heading into the season. They would hang around that range during their 3-1 start until the Ravens stumbled through a 1-4 stretch during which Flacco would sustain a hip injury, and Lamar Jackson would be named the starter following their Week 10 bye.
Even before Flacco’s injury, you can see above that from Week 7 to Week 9 the Ravens pass offense had already began to decline whereas its run game began to bounce back. The shift from Flacco to the run-first, pass-shy Jackson gave John Harbaugh a chance to expand on these trends and completely shift the identity of the offense. The change to a run-first-and-keep-running offense also played into the Ravens ultimate strength: its defense. Running the ball at absurd rates meant the clock was moving more on offense compared to the previous iteration of the Flacco-ran offense, which works twofold in their favor. Opposing offenses are having to do more on offense with less opportunities. Per Evan Silva, teams are averaging 10.9 less offensive snaps per game against the Lamar Jackson Ravens compared to their season average. That is a 17% decrease in offensive snaps on average, meaning teams are losing almost a fifth of their offensive opportunities. Secondly, less opposing offensive snaps obviously means less defensive snaps for the Baltimore defense, which keeps their defense fresher and sharper which makes defending those limited opportunities easier. The spike in pass defense above illustrates this rather clearly.
Now despite this massive change and a 5-1 run, the Ravens’ expected wins number has only moved by 0.028 since the change. But sometimes staying afloat while others unravel is all you need in this league to survive. Before naming Lamar Jackson the starter, the Steelers were a surefire bet to win the AFC East and in the model there were four teams with two or more expected wins than the Ravens. Today, the Ravens lead the division and in the model there is only one team with two or more expected wins than the Ravens.
Shifting to Cleveland, the Browns were a team during my offseason deep dive and projection process that had a lot of talent I loved. Jarvis Landry was a great talent to add, Josh Gordon was seemingly going to turn it around (again), David Njoku was a developing freak at tight end, Nick Chubb was a draft prospect that really popped off the page, and Myles Garrett and Denzel Ward were incredible young defensive talents who I trusted to anchor their respective segments on defense. On the other hand, I admittedly was not the highest on Baker Mayfield and thought he would be above average at best. I did think Mayfield was better than then-starter Tyrod Taylor, but I also expected Hue Jackson to botch the handling of that situation (as well as the rest of the team). Two years of laughably bad coaching was enough to convince me that this team was doomed as long as that man was in town, and I projected them as the third worst team in the model.
The Browns season has been covered in much detail, so I don’t feel the need to recap how their season has gone. The above chart is a look at how the team has fared before and after Baker Mayfield becoming the starter and Hue Jackson getting the boot. Note that Cleveland’s pass offense under Hue Jackson peaked in the Jets game in Week 3 when Mayfield had to come in for the injured Tyrod Taylor.Also note that the pass offense got progressively worse from that point on until Jackson’s firing. Then note the absolutely incredible improvement to the Browns’ pass offense following his firing. Hue Jackson is a cancer to every talent, team, and organization he finds himself meddling with and I am absolutely bewildered that he was able to find legitimate work in the NFL so quickly (but am not surprised it was with the Bengals, of all teams).
For their Week 17 matchup, the Ravens and Browns currently rank 9th (9.000) and 16th (8.018) in expected wins in the model. This gives the Ravens a 52.88% chance of winning on a neutral field, good for a -1.5 spread under those conditions. After factoring in the various game-specific elements the model makes this game BAL -4.4. With the line set at BAL -6.5, there are 2.1 points of disagreement between the model and Vegas lines which makes CLE +6.5 -105 a model play risking 2.215 units to win 2.1 units.
Last week was quite the week for the model, to say the least. It all began with an atrocious 0-4 start for -17.163 units before rebounding in the Sunday afternoon games with two wins. This then set us up for the Monday Night Football game, which was covered in-depth in last week’s newsletter. When it was all said and done and the dust settled, the model’s largest play of the year, CAR +6 for 8.8 units, cashed and brought Week 15 in the green with a 3-4 finish for +0.84u. For the year, the model is now 69-38-4 for +102.27 units and a 23.93% ROI. That includes an 11-6 record for +20.30 units since its introduction to the Bet It Up newsletter.
Before proceeding, I just wanted to highlight a few things from the 0-4 start to Week 15. Of those four losses, three were by a combined five points and two of them (NYJ and GB) were by a point or less. For reference, there were a total of two model losses by a point or less in the first fourteen weeks and of the season (104 plays). But even if we ignore that (which we probably should) the most important thing to remember about betting a model is that method of profiting is to accumulate edges over a relatively large sample. If we believe the model does have an edge, then even losing a bet or a stretch of bets should be okay in our frame of mind since we are aware that each placed bet accumulates positive expected value.
Through Week 15, the model has accumulated 415.8 points of disagreement across 111 plays (or 3.7 points per play). From that accumulated disagreement, the model has generated 102.27 units of profit. That means that simply the action of placing a bet on a model play has generated 0.92 units of expected profit this season, regardless of the outcome of the bet. Another way of looking at it is that every time someone chooses to not “tail” a model play due to their own disagreement or hesitation, they are losing 0.25 units of expected profit per point/unit of the play. Last week, I got plenty of people telling me they were passing on the CAR +6 play because “It’s the Saints” or something similarly arbitrary. Using the information I just highlighted, those people passed on nearly 2.2 units of expected profit on the spot by simply choosing to not “tail” and place a bet on CAR +6, regardless of the outcome of the game. If that doesn’t highlight the inefficiency of choosing to not follow any given model play, I’m not sure what can.
For this week I wanted to highlight a play that I’ve already shared on Twitter that has been met with some hesitation, which is obviously fitting with what we just discussed in regards to the value of model betting. This week we will focus on the Week 16 matchup between the Houston Texans (10-4) and the Philadelphia Eagles (7-7), and take a deeper look at the entire Wentz vs. Foles debate, which has now reared its head for the second consecutive season. In particular, I’d like to refute the idea that the Eagles are better with Foles at quarterback. I’ll do this by using actual comments I’ve received this week.
“I don’t know. Nick Foles > Carson Wentz.”
The two things that drive this sentiment are that the two most memorable things about Foles for public bettors and fans are A) his postseason run and Super Bowl win last year and B) the Eagles win this past Sunday against the Rams. It’s easy to forget that Foles was near irreprehensible in the first two games of this season and that he was very mediocre in the three starts he had in 2017 leading into that postseason. Below is a chart showing various stats and metrics comparing Wentz and Foles, using data only from their starts and I even included Foles’ postseason data to try and tip the scale in his favor. Note that Foles edges Wentz in only seven out of forty total splits shown here. All data was obtained from Pro Football Reference, ProFootballFocus, and Football Outsiders.
“Talent-wise I believe the change with Foles stepping in at QB is important to note. He throws to the receivers much more often than Wentz did, who gave a lot of attention to the TEs.”
“I think the fact that he focuses on different receivers throws out some defending [preparation] for the majority of Eagles’ footage. [There is] different play-calling with a less mobile QB, which also changes gameplans.”
Both of these comments came from the same comment thread, so I’m combining them here. The overarching argument here is that the Eagles with Foles offer something different (and better) due to a supposed change in target share. But this is simply untrue as Alshon Jeffery is the only pass catcher who has seen a >5% change in their target share when going from Wentz to Foles in the last two seasons. But that’s nullified by the fact that Jeffery’s splits shows a whopping three game sample size with Foles, with one of those games (W17 2017) having Jeffery only in on 31% of offensive snaps when he is usually in on ~93-95%. The Eagles opted to rest many of their starters during that game as they had nothing to play for, having locked up the 1 seed regardless of the outcome of that game. Agholor’s smaller jump in target share (+3.7%) is largely driven by from Week 1 and 2 of this year in which he had 22 targets, which is an astounding 26.2% of his total targets on the season. His volume from those two games can be explained by Alshon Jeffery’s absence as well as having Mike Wallace as their WR2 that game, who has zero receptions on three targets this season.
Lets step away from the whole Wentz vs. Foles debate for a second and just do a quick thought experiment. Just last week, the Eagles were 14 point underdogs for their game in Los Angeles against the Rams. This week, they are now -2 favorites at home against the Houston Texans. Nothing talent-wise has changed across those three teams. If we use a flat 3 point home field factor for this thought experiment, that means the Eagles would be +4 if this week’s game was in Houston (-2 -> +1 when moved to a neutral field, +1 -> +4 when moved to Houston). Comparing that to last week’s +14 spread @ LAR, these lines are implying that the Rams are approximately ten points stronger than the Texans. That means that in a game between the two, the Texans would either be 13 point underdogs in Los Angeles or 7 point underdogs at home. I don’t know about you, but I would take either of those lines in a heartbeat. So that should say to you that either A) last week’s line was an overreaction, B) this week’s line is an overreaction, or C) both are an overreaction.
What did the model think? Last week, the model made the PHI @ LAR game PHI +7.1 after Foles was declared the starting quarterback. This was obviously a pretty sizeable 6.9 point disagreement from the PHI +14 line that moved from +9.5 as a result of the news (Reminder: spreads of +7 or higher have been removed from model consideration as of Week 10 which is why PHI +14 was not a play). So if the public and the market saw Foles as having a significantly negative impact on the Eagles strength, why is it so high on him this week? The obvious answer is that beating the Rams on the road in primetime has drawn a lot of attention. And what have we learned in the past? Recency. Bias. Generates. Value. And the model thinks that that is the case this week. For the Texans / Eagles game, the model makes the game PHI +0.3, so a line of PHI -2 means there is 2.3 points of disagreement. This makes HOU +2 -104 a model play, risking 2.403 to win 2.3 units. This is obviously not a massive disagreement, but value is value and we’re here to accumulate any and all significant model edges.
The model continues to roll with another 4-1 week, this time good for +9.455 units. We even picked up a win on the SEA -3 play highlighted in last week’s newsletter, which was a nice touch. After it was all said and done, the model moved to 66-34-4 for the season with a +101.44 unit yield and a 25.89% ROI.
This week, I wanted to cover a play that will be the model’s largest of the season thus far. We’re going to take a look at another Monday Night Football game and take a deeper look at the Week 15 matchup between the New Orleans Saints (11-2) and the Carolina Panthers (6-7). On the surface we have a Saints team who rallied last week from a first half 14 point deficit to clinch the NFC South and a Panthers team who has now lost five straight games by a combined 48 points. Looks like the perfect spot to take the Superbowl contender Saints against the faultering Panthers, right?
Yes, the Panthers have fallen quite a bit as of late. But sometimes it’s not about if you fall, but rather where you fall from and where you end up landing. Prior to their skid, the Panthers hit their peak in the model following their Week 9 win against Tampa Bay. Their expected wins total at the time was 10.64, which had them as the fifth best team in the league. That same week, the Saints dismantled the unbeaten Los Angeles Rams which brought their expected wins total to 10.79, making them the fourth best team in the model at the time. At that point in the season, a neutral field game between the two would have given the Saints a 50.4% chance of winning and the Panthers a 49.6% chance of winning.
But a lot has happened since. The Saints in the five weeks since have seen a marginal drop in the model. In Weeks 10-12, the Saints whooped up on three teams (Bengals, Eagles, and Falcons) who have lost a combined 5.27 expected wins since their games against the Saints. In other words, the Saints’ wins against these teams haven’t been retroactively all too impressive in the model and they did not add much to the Saints strength as a result. In Week 13 the Saints lost a Thursday Night Football game against the Cowboys, which stunned many including the overwhelming majority of public bettors who sided with the Saints in what was the largest bet game for many sportsbooks. This result didn’t surprise the model nearly as much, however, as it had the game as DAL +0.6 before kickoff. And whatever “harm” was done to the Saints in the model from that game was mostly made up by their performance in the following week in which they pulled out an impressive victory against the Buccaneers, who the model has repeatedly viewed as being undervalued, backing them in 8 out of their 13 games for a 5-2-1 record and a +12.34 unit yield. In summation, the Saints haven’t changed much since their Week 9 win over the Rams despite a 4-1 record during that stretch, having kept them within 0.15 total expected wins over that span.
On the other side, we have the Panthers and their five game losing streak. Obviously losing five in a row is never good, but sometimes losses and losing streaks are more than what they look like on the surface. Across those five losses:
Four were on the road
Two were on tail ends of a back-to-back road spot
Two were against playoff-bound teams
One was as the road TNF team against PIT
One was against SEA, who was coming off extended rest
Mind you, all of these games also came at a time when Cam Newton was clearly playing through an injury, which has been documented wide and far by various reporters. But that’s not to be excused, and any dips in the underlying data that came from suboptimal play stemming from Cam’s injury or elswhere would show in the model. And it does, as the Panthers expected win total has decreased by 1.53 over the course of this losing streak.
Bringing it all together, the model currently gives the Saints a 53.9% chance of winning on a neutral field and the Panthers a 46.1% chance. This makes the neutral field spread CAR +2. Now usually I blow through the game-specific factors but this week’s game actually has a little extra sauce on it. First off, we have the Saints on their third straight road game, which is a rare scheduling stretch to have (there are only two other instances this year). Granted, this stretch includes an extended rest period between the DAL and TB games, as well as an extra day between the TB game and this Monday Night Football game.
But even so, there is one more situational element to this game: the Panthers are seeking revenge for their playoff loss in last year’s postseason in which the Saints beat them 31-26 in the Wild Card round. This year alone, teams seeking playoff revenge are 5-2 straight up with all five teams being underdogs on the opening line. Against the spread, those teams are 6-1 and the six teams who covered did so by an average of 10.83 points. The model has backed three teams seeking playoff revenge this season and went 3-0 for +11.8 units in those games. Needless to say, playoff revenge spots are noteworthy and worth accounting for.
When it’s all said and done, the model makes the Panthers -2.8 for their Monday Night Football showdown with the New Orleans Saints. With the line currently CAR +6, there is an astounding 8.8 points of difference between the model and the Vegas spread. This disagreement is the model’s largest of the season, making a wager on CAR +6 -101 risking 8.924 to win 8.8 units the model’s largest play of the season. Now I know the vast majority of you are sensible, responsible bettors. But I think it’s my responsibility to tell you that this is not a “MEGA LOCK OF THE YEAR” or an “IF IT LOSES, YOU’LL BE REFUNDED” type play. This is just one numbers-driven play that happens to be larger than any before it. At the end of the day, the point of the model is to take every quantifiable edge in order to generate the most positive EV over the course of the sample. Let me repeat: It is NEVER about the result of any singular play, it’s about the accumulation of value.
Thanks for reading! As always, you can find the rest of the model plays by following me on Twitter. I hope the model can turn in another great week, and even if it doesn’t, we’ll stay the course and keep finding the edge.
Last week’s introduction to my NFL model proved to be fruitful for those that followed along, going 4-1 and yielding +10.01 units. This brought the model to a 61-34-4 record that has yielded 86.45 units and a 22.94% ROI (Note: the model record in last week’s newsletter had a few miscounted games, and has since been corrected). Yes that means the one play I chose to highlight for last week’s newsletter was the only losing play, which is a bit unfortunate. But enough about that, this week I wanted to dive into another model play and highlight some key model concepts relevant to the game.
For Week 14, we will be looking at the Monday Night Football game between the 6-5-1 Minnesota Vikings and the 7-5 Seattle Seahawks. Before we dive into this game, I would actually like to rewind quite a bit to demonstrate what I believe to be one of the model’s strengths. As I was building the model this offseason, I colllected various data from the 2017 season and used hand-made adjustments for each team based on coaching changes, player movement, scheme changes, talent development trajectories, and more. The Vikings were coming off a strong 2017 campaign in which they finished one win away from a Superbowl appearance. On top of that, they added Kirk Cousins as their long-term solution at quarterback, were getting a returning Dalvin Cook, and had a plethora of young talent on both sides of the ball that were set to take the “next step” in 2018. Following the preseason, the model had the Vikings as the fourth best team, expected to win 10.40 games.
The Seahawks on the other hand were a very unimpressive 9-7 team in 2017 that performed well above their 6.57 adjusted Pythagorean expected win total. The team then proceeded to lose Richard Sherman, Michael Bennett, Paul Richardson, Sheldon Richardson, Jimmy Graham, and more in the offseason, and were set to be without their top wideout Doug Baldwin for an indeterminate amount of time. They also burned their first round pick on USC running back Rashaad Penny, which I thought was an awful use of the pick given that they already had the serviceable Chris Carson and had more glaring team needs. With all of this in mind, I’m not ashamed to share that by the end of the preseason the Seahawks were the 29th best team in the model, expected to win only 6.31 games in 2018.
As I highlighted last week, the model is good at dynamically identifying what parts of the game teams are good at and what parts of the game being good at matters. After just a month into the season, the model had already downgraded the Vikings to a league average team (16th) and adjusted their expected wins to 8.36 The Vikings have since floated around those numbers. Seattle’s progression has been a bit more gradual as the model gave more credence to their performance week after week (29th – 28th – 27th – 24th – 23rd – 22nd – 18th – 14th – 12th – 11th – 9th – 6th – 6th). It was these two massive corrections and swings that made this game so interesting to me, because it is certainly outside of the norm for the model.
As of the conclusion of Week 13, the model has only changed 18/32 teams’ expected win total from the preseason by more than one game. That may sound like a lot at first, but a deeper dive into those eighteen teams paints a different picture. Eleven teams have lost an expected win or more over the course of the season, but seven of those teams are either currently starting (or have started for a significant portion of games) a backup quarterback and have lost expected wins in the process. On top of that, we have the Browns who ditched Hue Jackson (the model doesn’t project coaching changes) and have since crossed that one-game threshold in the positive direction. So if we take out the teams that have been subjected to backup quarterbacks or head coaching changes, the model has been within one expected win for 22 of the 32 teams. That’s not too bad if you ask me.
Back to the actual game at hand, the model has a lot to like about the Seahawks this week. Looking at the model’s five factors, the Seahawks possess a significant advantage in pass offense, rush offense (MIN has the league-worst), and special teams (shoutout to Michael Dickson). On the other side of the ball, the Vikings have a very slight edge in pass defense and a significant advantage in rush defense. Circling back to the model’s dynamic correlation coefficients discussed in last week’s newsletter, Seattle’s advantages amount to much more with offense contributing to league-wide success three times as much as total defense. As a result, the model gives the Seahawks a 56.25% probability of winning on a neutral field, and after factoring in the various game-specific factors which include Seattle’s second-best home field factor and the Vikings being in a back-to-back and three-in-four travel spot, the model makes the game SEA -6.5 which puts us in a position to lock in a SEA -3 -100 wager risking 3.514 units to win 3.5 units.
Thanks again for reading. The response and feedback I received for last week’s write-up was overwhelmingly positive and encouraging, so thank you for that. If you would like to follow the rest of the model’s plays, make sure to follow me on Twitter.