Just above these words, it says “posted by Chase.” And it was literally posted by Chase, but the words below the line belong to Steve Buzzard, who has agreed to write this guest post for us. And I thank him for it. Steve is a lifelong Colts fan and long time fantasy football aficionado. He spends most of his free time applying advanced statistical techniques to football to better understand the game he loves and improve his prediction models.
The way to win fantasy football games is to have players that score a lot of points. Players tend to score more points when they get more touches. One of the most important factors in determining how many touches each player is going to have is to determine the Game Script ahead of time. As we all know positive game scripts result in more passing attempts and negative Game Scripts result in more rushing attempts. But I am going to try to project the pass ratio using two key stats, Pass Identity rating and the Vegas spreads. We can use these projected pass ratios to build our own projections or at least look for outliers and figure out how to adjust players from their year to date averages.
Regular readers know what Pass Identity means. For newer readers, you can read here to see how Pass Identities are calculated. But the quick summary is that Pass Identity grades allow us to predict the pass ratio of any game where the point spread is zero. This is because Pass Identity tries to eliminate the Game Script from the pass ratios. For example since the Bears/Cowboys game is a pick’em this week, we can predict the pass ratio of the Bears by using the following formula. League average pass ratio + (A + B) *C, where
- (A) = number of standard deviations above/below average the Bears are in Game Script (-0.49);
- (B) = number of standard deviations above/below average the Bears are in Pass Ratio (+0.53); and
(C) = the standard deviation among the thirty-two teams with respect to Pass Ratio (5.3%)
Of course, the product of (A) and (B) is the Pass Identity grade for each team; once we determine that, we multiply that number by the standard deviation of the pass ratios of all teams to get us a prediction for the pass ratio in a game with a Game Script of 0.0. Since the Bears have a Pass Identity of basically 100, the projected Pass Ratio for Chicago against Dallas is 58.9%.
We can then compare this projection to Chicago’s year-to-date pass ratio of 61.5% and predict that all else equal Jay Cutler and the passing game should score about 4% [1]Since 58.9% is 96% of 61.5%. less this week than their average week where as Matt Forte and the run game would score about 4% more.
Of course, most game scripts aren’t zero so we can’t use this simple process for many games. However, there is a very good estimate for game scripts readily available, the Vegas point spread. The approximate game script of any Vegas point spread is half of the original spread. For example, a game with a point spread of 10 would generally produce a game script of about 5. If we calculate how many standard deviations above/below average each teams point spread is in regards to the average game script and add it to the formula above we can project the teams pass ratio.
For example, Arizona is a 6-point favorite today, which converts to Game Script of 3.0, which would be 0.87 standard deviations below average. By plugging this into the formula above we get the following formula.
58.7%+(0.22+0.43-0.87)*5.3%=57.5%, which is 6% less than their year to date average of 61%.
Here is a full list of projections for this week. Here’s how to read the table. The team projected to be the most pass-happy this week is Cleveland. The Browns have an average Game Script of -2.44, which is 0.71 standard deviations below average. Cleveland also has an average Pass Ratio of 68.4%, which is 1.82 standard deviations above average. That means the Browns have a Pass Identity that is 1.11 standard deviations above average, giving them a Pass Identity of 116.7. Cleveland is an 11.5-point underdog this week against New England, which translates to a projected Game Script of -5.75. That means the Browns are projected to be 1.68 standard deviations below average against the Patriots, which means we would project Cleveland (based on having a Pass Identity that is 1.11 standard deviations above average) to be 2.79 standard deviations above average in terms of pass ratio this week. That gives Cleveland a projected pass ratio of 73.5%, which is 7.4% higher than normal.
| Proj PR Rk | Team | GS | StD_GS | Pass Ratio | StD_PR | PI (StD) | Pass Identity | Point Spread | Proj GS | Proj Std_GS | Proj Std_PR | Proj PR | % Change |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | CLE | -2.44 | -0.71 | 68.4% | 1.82 | 1.11 | 116.7 | 11.5 | -5.75 | 1.68 | 2.79 | 73.5% | 7.4% |
| 2 | DAL | 2.7 | 0.79 | 66.3% | 1.43 | 2.22 | 133.3 | 0 | 0 | 0 | 2.22 | 70.5% | 6.3% |
| 3 | MIA | -0.89 | -0.26 | 66.6% | 1.49 | 1.23 | 118.4 | 3 | -1.5 | 0.44 | 1.67 | 67.5% | 1.4% |
| 4 | NOR | 3.97 | 1.16 | 62.5% | 0.72 | 1.88 | 128.1 | -3 | 1.5 | -0.44 | 1.44 | 66.3% | 6.2% |
| 5 | DET | 0.18 | 0.05 | 63.2% | 0.84 | 0.89 | 113.4 | 2.5 | -1.25 | 0.36 | 1.25 | 65.3% | 3.4% |
| 6 | TEN | -0.68 | -0.2 | 56.7% | -0.38 | -0.58 | 91.3 | 11 | -5.5 | 1.6 | 1.02 | 64.1% | 13.1% |
| 7 | ATL | -2.28 | -0.67 | 68.7% | 1.88 | 1.21 | 118.2 | -3 | 1.5 | -0.44 | 0.77 | 62.8% | -8.6% |
| 8 | CAR | 5.05 | 1.47 | 51.4% | -1.39 | 0.08 | 101.2 | 3.5 | -1.75 | 0.51 | 0.59 | 61.8% | 20.3% |
| 9 | KAN | 3.01 | 0.88 | 59.2% | 0.08 | 0.96 | 114.4 | -3 | 1.5 | -0.44 | 0.52 | 61.5% | 3.8% |
| 10 | STL | 0.14 | 0.04 | 56.6% | -0.41 | -0.37 | 94.5 | 6 | -3 | 0.87 | 0.5 | 61.4% | 8.4% |
| 11 | IND | -3.41 | -1 | 60.7% | 0.37 | -0.63 | 90.7 | 6 | -3 | 0.87 | 0.24 | 60% | -1.2% |
| 12 | DEN | 6.85 | 2 | 58.6% | -0.03 | 1.97 | 129.6 | -12 | 6 | -1.75 | 0.22 | 59.9% | 2.2% |
| 13 | NYG | -2.58 | -0.75 | 61.5% | 0.52 | -0.23 | 96.5 | 3 | -1.5 | 0.44 | 0.21 | 59.8% | -2.8% |
| 14 | MIN | -2.93 | -0.86 | 58.2% | -0.1 | -0.96 | 85.7 | 7 | -3.5 | 1.02 | 0.06 | 59% | 1.4% |
| 15 | CHI | -1.67 | -0.49 | 61.5% | 0.52 | 0.03 | 100.4 | 0 | 0 | 0 | 0.03 | 58.9% | -4.3% |
| 16 | GNB | 2.33 | 0.68 | 56.8% | -0.36 | 0.32 | 104.8 | -3 | 1.5 | -0.44 | -0.12 | 58.1% | 2.3% |
| 17 | PIT | -2 | -0.58 | 63.5% | 0.89 | 0.31 | 104.7 | -3.5 | 1.75 | -0.51 | -0.2 | 57.6% | -9.2% |
| 18 | ARI | 0.74 | 0.22 | 61% | 0.43 | 0.65 | 109.7 | -6 | 3 | -0.87 | -0.22 | 57.5% | -5.7% |
| 19 | CIN | 2.77 | 0.81 | 57.6% | -0.22 | 0.59 | 108.9 | -6.5 | 3.25 | -0.95 | -0.36 | 56.8% | -1.4% |
| 20 | SEA | 4.68 | 1.37 | 47.6% | -2.1 | -0.73 | 89 | 2.5 | -1.25 | 0.36 | -0.37 | 56.8% | 19.2% |
| 21 | OAK | 0.11 | 0.03 | 54.1% | -0.88 | -0.85 | 87.3 | 3 | -1.5 | 0.44 | -0.41 | 56.5% | 4.5% |
| 22 | SDG | 0.56 | 0.16 | 58% | -0.14 | 0.02 | 100.3 | -3.5 | 1.75 | -0.51 | -0.49 | 56.1% | -3.3% |
| 23 | BUF | -0.46 | -0.13 | 53.2% | -1.04 | -1.17 | 82.3 | 2.5 | -1.25 | 0.36 | -0.81 | 54.4% | 2.3% |
| 24 | SFO | 6.09 | 1.78 | 46.5% | -2.3 | -0.52 | 92.2 | -2.5 | 1.25 | -0.36 | -0.88 | 54% | 16.2% |
| 25 | PHI | 2.02 | 0.59 | 53% | -1.08 | -0.49 | 92.7 | -3 | 1.5 | -0.44 | -0.93 | 53.8% | 1.5% |
| 26 | BAL | -0.18 | -0.05 | 58.7% | -0.01 | -0.06 | 99.1 | -6 | 3 | -0.87 | -0.93 | 53.7% | -8.4% |
| 27 | TAM | 0.06 | 0.02 | 55.4% | -0.63 | -0.61 | 90.9 | -2.5 | 1.25 | -0.36 | -0.97 | 53.5% | -3.4% |
| 28 | HOU | -3.86 | -1.13 | 61.7% | 0.56 | -0.57 | 91.5 | -3 | 1.5 | -0.44 | -1.01 | 53.4% | -13.5% |
| 29 | NWE | 1.47 | 0.43 | 58.7% | -0.02 | 0.41 | 106.2 | -10 | 5 | -1.46 | -1.05 | 53.1% | -9.5% |
| 30 | JAX | -8.38 | -2.45 | 63.8% | 0.95 | -1.5 | 77.5 | 3 | -1.5 | 0.44 | -1.06 | 53.1% | -16.8% |
| 31 | WAS | -5.89 | -1.72 | 56.9% | -0.35 | -2.07 | 69.1 | 3 | -1.5 | 0.44 | -1.63 | 50% | -12% |
| 32 | NYJ | -5.06 | -1.48 | 53.1% | -1.06 | -2.54 | 61.9 | -2.5 | 1.25 | -0.36 | -2.9 | 43.3% | -18.4% |
Let’s see how we can best utilize this information for this week’s fantasy playoffs. Unfortunately Maurice Jones-Drew would have been one of the highest upside plays of the week, but since that doesn’t do you any good here are some other players to consider:
Possible upside plays:
Cam Newton and the Carolina passing game – The Panthers rank 30th in the league in pass ratio at 51.4% but they do so mainly because their defense is so stout producing a 5.05 game script. This leaves their Passer Identity at basically a league average 14th. This week Vegas thinks that the Panthers defense is going to have a little more problems than normal against the strong Saints offense and have actually put the Saints as a 3 point favorite. This means the Panthers are going to have to pass a lot more than they have throughout the season, and that results in this formula projecting a pass ratio of 61.8% or a 20% increase from their year to date pass ratio. This is actually the 8th highest projected pass ratio for the week which is quite a big difference from their season to date 30th rank. Add in the fact that this game is being played inside when a lot of the league is playing in cold weather and I would start Newton with confidence this week. Note that the higher pass ratio may actually lead to more Newton scrambles too. Unfortunately, the Panthers don’t have a lot of great passing threats and the Saints are pretty good at covering wide receiver ones and tight ends but I would still feel comfortable starting Greg Olsen and might even consider starting Steve Smith or Brandon LaFell as upside WR3 or flex plays depending on my other options.
Russell Wilson and Doug Baldwin – Similar to the Panthers, the Seahawks rank 31st in pass ratio this year due to their strong game script of 4.68; the team’s Pass Identity isn’t much better, ranking 26th. However, with a tough divisional matchup with the 49ers this week where they are listed as 2.5-point underdogs, I expect them to rank 20th in pass ratio at 56.8% which is 19% better than their year to date average. Unfortunately, the 49ers pass defense is very good so Wilson’s YPA will likely be down about 10-15% offsetting a lot of this increase in attempts. But Wilson’s matchup is probably not as bad as many would have you think and I would expect him to exceed his YTD passing and rushing yard averages. On a similar note, I would expect Doug Baldwin who has been playing a lot more since Sidney Rice’s injury to be a solid WR3 start with upside as a WR2 that you might be able to find on your leagues waiver wire.
Watch out for:
Marshawn Lynch – Any time one side of the ball gets a big boost the other side is probably going to be negatively affected and since Lynch isn’t typically very heavily involved in the passing game it will probably take some early touchdowns for Lynch to score anywhere near his normal value. That doesn’t sound like a formula for success in the fantasy playoffs. While it is hard to bench Lynch in the playoffs if you have similar options I would certainly consider it as I consider Lynch only an average RB2 this week vs his normal strong RB1 rank and is a definite avoid in daily games.
References
| ↑1 | Since 58.9% is 96% of 61.5%. |
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