Week 11 Rankings & Game Flowbotics

Week 11 Rankings & Game Flowbotics

An uncommon amount of spare time fell into my lap over the weekend. Did I spend it getting a head start on my Week 11 rankings? Of course not. I was busy using most of those idle minutes to construct ineffective DFS lineups (thanks for nothing, Bilal Powell), but the rest did go toward a worthwhile cause. I revamped my Game Flowbotics spreadsheet, slimming down the number of columns for (hopefully) easier consumption on small screens, like the various snooping/tracking devices we carry around to appease Big Brother.

I used to add occasional notes for certain matchups on the spreadsheet to give more context and add my own slant to the numbers presented. In case you haven’t been paying attention, I’ve shifted those notes to my weekly Game Flowbotics A-to-Z articles, leaving the notes columns on the spreadsheet empty and useless for the past few weeks. Those columns were overdue for removal, and while almost everything else is the same, I figured I owed you all a new rundown on how to read the redesigned Game Flowbotics page, as well as some explanation of how I use it week to week. The primer is below, but let’s start with a link to the full spreadsheet itself:

Week 11 Game Flowbotics

The header at the top of the page indicates when it was last updated and provides relevant links for the matchup data shown below:

2017 game flowbotics header

Now let’s go through Thursday night’s matchup of Tennessee at Pittsburgh as it appears on the spreadsheet, from top to bottom, broken up into blocks.

Block 1:  Game & Team Info with Total DVOA

2017 game flowbotics block 1

Let’s skip the easy stuff in the two leftmost sections and skip ahead to the far left where Total DVOA and Total Weighted DVOA are shown. I urge you to check out Football Outsiders for an in-depth explanation of DVOA, but in short, it’s a measurement of team efficiency that adjusts for situation and opponent. Weighted DVOA assigns more weight to recent weeks, whereas normal DVOA weighs the entire season evenly. The “#” columns are the raw DVOA values, with 0.00% being average. The “Rk.” columns show where teams rank among all 32 NFL franchises for that particular metric. In this case, Tennessee is below average in DVOA, while Pittsburgh has performed well and ranks fourth in the league.

With that out of the way, let’s scan to the left and go deeper on the point spread, the over/under (O/U), and how they relate to the implied team total. (All spreads and over/unders are pulled from the Westgate listing on the ESPN page linked in the header.)


The Spread

The spread is the perceived gap in points between the teams in a matchup. In this case, the Westgate has designated the Steelers as seven points “better” than the Titans for Thursday Night Football. The true value of home field is debatable, but to give a point of reference, evenly matched teams traditionally generate a spread with the home team favored by three points. Many other factors can influence the spread, and spreads can change in reaction to news and betting trends during the week.

Ultimately, sportsbooks like the Westgate are trying to get equal betting on both sides of the spread because they typically charge bettors a small fee for most wagers (called the vig, similar to the rake in DFS). With equal action on both sides, the oddsmakers break even on bet payouts while they rack up profit on the fees they charged to all bettors. As you might imagine, this means that spreads are not always an accurate projection of expected outcome. Spreads are also indicative of the gambling public’s perception of teams.

Because some franchises are more popular than others (“public teams”), the spreads will often punish bettors more for picking those teams. The Steelers are a classic public team because they’ve been largely successful in the NFL for a long period of time. Of course, the oddsmakers can’t go overboard with skewing the spreads for public teams. If they do, sharper gamblers can bet heavily on the other side of the line and put oddsmakers at a disadvantage. Setting a spread is a balancing act between projecting the outcome of a game and projecting how people will bet.

The Over/Under (O/U)

The same principles apply to the over/under (or “total”), which represents the combined number of points that could be scored by both teams. Instead of betting on a particular team with plus-or-minus some number of points, bettors simply choose if the combined total of points scored will go over or under a certain number. As with spreads, sportsbooks usually want equal action on both sides of an over/under, so some amount of projection goes into setting the number. As fantasy players, we need to be careful not to read too much into totals, because the they doesn’t discriminate between points from offense, defense, and/or special teams.

The Implied Total

Over/unders don’t discriminate between teams, either, but that doesn’t stop us from using the spread to create implied team totals. Essentially, an implied total assumes both the spread and over/under are accurate projections for a game. In this case of Tennessee at Pittsburgh, if 44 total points are scored and the Steelers win by seven, simple math says their final score will be 25.5 to Tennessee’s 18.5 (25.5 + 18.5 = 44, the total; and 25.5 – 18.5 = 7, the spread). Teams obviously can’t score fractional points, but these implied team totals give a nice frame of reference when comparing different matchups. In theory, the more points a team is implied to score, the more fantasy production we can project for that team’s players.

Block 2:  Overall Offense vs. Overall Defense

2017 game flowbotics block 2

The second block shows overall matchups of offense versus defense in a contest. The “scoring” section shows teams’ points scored, points allowed, and point differentials per game. This is a new addition to Game Flowbotics. I decided to start including actual scoring numbers because they provide a nice sanity check against implied totals and DVOA matchups. In this case, the PPG values don’t line up especially well with the implied totals from the first block. Pittsburgh projects to score over 25 points, despite the fact that they average less than 21 per game, and Tennessee allows less than 24 per game. Perhaps this indicates some public team bias in favor of the Steelers, as discussed above.

The DVOA section on the right side of this block is similar to the one in the first block, except the ratings apply specifically to team offenses and team defenses. Again, the “#” columns contain raw DVOA and Weighted DVOA values, while the “Rk.” columns indicate how those values rank among all NFL teams. This will be a recurring theme going forward, but the matchups are color-coded. In yellow, we see the Titans’ offense matched up diagonally with the Steelers’ defense. In purple, we see the Steelers’ offense matched up diagonally with the TItan’s defense. Note that for defenses, negative DVOA values are a good thing. On offense, negative DVOA values are bad.

Altogether, this block plus the first block should set up a basic expectation for the end result of a game. In this particular matchup, the Steelers are clear favorites. As we progress through the remaining blocks, we can try to estimate how each team will attack the matchup to either reinforce or disrupt the expected result of a convincing Pittsburgh win.

Block 3:  Passing Offense vs. Passing Defense

2017 game flowbotics block 3

The third block focuses only on passing. Again, the matchups are color-coded. Orange plays against orange and blue plays against blue. In addition to DVOA stats and rankings, this block includes Adjusted Sack Rate data to show how well offensive lines block pass rushers and how well defensive lines get after passers.

Against Tennessee, Pittsburgh has advantages in the trenches on both sides of the ball, but especially in pass protection. Ben Roethlisberger should have plenty of time in the pocket to attack Tennessee’s 24th-ranked pass defense thanks to his top-ranked offensive line. Considering how much the Steelers like to throw deep, maybe it isn’t so crazy to think they can hit their implied total of 25+ points in this matchup.

Block 4:  Rushing Offense vs. Rushing Defense

2017 game flowbotics block 4

The fourth block is very similar to the third, except this one applies to rushing, not passing. Red plays against red, green plays against green. In the running game, offensive and defensive lines are measured by Football Outsiders’ Adjusted Line Yards metric. As with all sections, the “Impacted Players” listings are only updated periodically, with the most recent changes date-stamped in the header at the top of the spreadsheet.

On offense, Tennessee rates better than Pittsburgh at running the ball, with the Titans eight spots ahead of Steelers in DVOA and four spots ahead in Adjusted Line Yards. On defense, however, the Steelers have been the better team against the run, especially up front. The Titan’s rank 17th in Adjusted Line Yards, and that small failing along the defensive line could spell doom for them against Le’Veon Bell and Pittsburgh’s 11th-ranked offensive line.

Block 5:  Receiver Types vs. Passing Defense

2017 game flowbotics block 5

In the fifth block (only partially shown above), we finally get to receiver-specific matchups. Football Outsiders gives us DVOA values and rankings for defense against each possible type of pass-catcher: number one wide receivers, number two wide receivers, other wide receivers (WR3+), tight ends, and running backs as receivers. They also give us data on passes per game and yardage per game allowed to each of those receiver types. I’ve added columns to show the differentials in these volume stats between team defenses and NFL averages. There’s no color-coding in this block. The matchups read straight across.

In this game, we can see how Tennessee struggles against No. 2 wideouts. They rank 28th in DVOA against those types of receivers, allowing 2.0 passes per game and 13.4 yards per game above league averages. In other words, JuJu Smith-Schuster has a good shot to stay hot on Thursday night.

On the flip side, the matchup for Rishard Matthews is a good example of DVOA not “agreeing” wholeheartedly with a defense’s volume stats against. By DVOA, Pittsburgh ranks middle of the road against No. 2 wideouts (21st among all teams), but those wide receivers have only averaged 32.5 yards per game against the Steelers, compared to the NFL average of 45.2 yards per game allowed to the position.

At this point, you may be wondering why Rishard Matthews is listed as the Titan’s number two guy, and not their number one guy. On their defensive DVOA page, Football Outsiders admits to the arbitrary nature of their statistical designations with the following disclaimer: “Note that our decision of which receiver is ‘number one’ and which receiver is ‘number two’ is somewhat subjective.” Don’t blame them for what you see on the spreadsheet, though.

Football Outsiders is only responsible for how the different receiving stats are allocated to different receiver types. I personally dictate how the listed players slot into different roles, making educated guesses at FO’s intentions based on snaps played, target share, and general usage philosophy for each player. If you disagree with my choices, use your imagination and pretend the names are switched around to suit your sensibilities.

Go with the Game Flow

That does it for my primer on the new and improved Game Flowbotics spreadsheet. Hopefully, you now have a better understanding of the process that goes into my matchups analysis each week. Without further ado, here are my rankings for Week 11.

Week 11 Rankings

Greg Smith

Greg Smith is an engineer, co-founder of TwoQBs.com, and enthusiast for the strategy and design of variance-based games.  When he started playing fantasy football in 2001, his home league's small number of teams necessitated starting two quarterbacks.  That necessity has since grown into obsession, making Greg one of the preeminent champions of 2QB and Superflex formats.

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