TEs vs Pittsburgh Steelers
As of Week 6 · through 2 games · PPR
Showing every TE who had a role in each game.
| Wk | Team | Player | Slot | Tgt | Rec | ReYd | ReTD | FPts |
|---|---|---|---|---|---|---|---|---|
| 1 |  ATL | Kyle Pitts | TE1 | 1 | 0 | 0 | 0 | 0.0 |
| 2 |  NE | Hunter Henry | TE1 | 5 | 3 | 40 | 0 | 7.0 |
| Eli Raridon | TE2 | 1 | 1 | 30 | 0 | 4.0 | ||
| Average | 2.3 | 1.3 | 23.3 | 0.0 | 3.7 | |||
| Median | 1.0 | 1.0 | 30.0 | 0.0 | 4.0 | |||
| High | 5 | 3 | 40 | 0 | 7.0 | |||
| Low | 1 | 0 | 0 | 0 | 0.0 | |||
Averages are raw, outliers are never removed.
Prediction for Cade Otton
Nobody knows which slot Cade Otton will land in this week, so pick the one you expect. The projection changes because each slot faces a different average from the Steelers.
I predict about 5.7 points for Cade Otton if playing as a TE1. Cade Otton has averaged 7.2 over 2 games, and the Steelers have given up 3.5 to TE1s (-66% vs the league average of 10.3).
How this projection is built
1. The player. Cade Otton scored: Wk 1 5.6, Wk 2 8.8. Average 7.2; weighted toward recent games it is 7.8.
2. The defense. Over 2 games, the Steelers allowed 3.5 points to their opponent's TE1. The league average for that slot is 10.3, so that is -66%.
3. Trusting small samples less. With 2 defensive games, only 40% of the gap is applied, capped at +/-30%. That gives a -26% adjustment (x0.74).
4. Result. 7.8 x 0.74 = 5.7. Switching slots only changes step 2 and 3: the player's own numbers stay the same, but each slot faces a different defensive average.
It is a statistical model, not knowledge of injuries, game script or weather.
Chance to score
Type a point total to see how likely Cade Otton is to reach it as a TE1.
Why this percentage?
The projection (5.7) is the middle of the range. Fantasy scores swing a lot from game to game, so the model spreads possible scores around it by about 3.4 points, leaning on Cade Otton's own game-to-game swings once there are several games (and on typical TE swings before that).
Scores are modelled as right-skewed, since a big game is possible but a very negative one is not. The percentage is the share of that spread at or above 6.
With only 2 games of history, treat this as a rough gauge. It gets sharper as the season goes on. It is never lower than 1% or higher than 99%.
Insights
- all TEs have averaged 1.3 receptions, 23.3 receiving yards and 0.0 receiving TDs per player-game against the Steelers across 2 games.
- The Steelers allow 5.5 PPR points to the position per game, -57% versus the league average of 12.9 (ranked 30 of 32, where 1 is most generous). That is a tough matchup.
- Outlier: Week 1, Kyle Pitts (0.0 pts, 0 yds) sits far below the other games and drags the averages down. Average with it vs without it: pts 3.7 vs 5.5; yds 23.3 vs 35.0. The table above keeps the raw averages.
- Outlier: Week 2, Hunter Henry (7.0 pts) sits far above the other games and pulls the averages up. Average with it vs without it: pts 3.7 vs 2.0. The table above keeps the raw averages.
- 1 of 3 player-games reached 7+ PPR points. Scores range from 0.0 to 7.0 (median 4.0).
- all TEs scored a touchdown in 0 of 3 player-games (0%).
- Small sample: only 2 games so far. Treat these numbers as directional, not settled.
Insights are generated by rules from the table above and recalculate every week.
What do these columns mean?
- Wk
- Week of the season
- Tgt
- Targets (passes thrown to the player)
- Rec
- Receptions
- ReYd
- Receiving yards
- ReTD
- Receiving touchdowns
- Car
- Carries (rushing attempts)
- RuYd
- Rushing yards
- RuTD
- Rushing touchdowns
- Cmp / Att
- Completions and pass attempts
- PaYd / PaTD
- Passing yards and touchdowns
- INT
- Interceptions thrown
- FGM / FGA
- Field goals made and attempted
- FPts
- Fantasy points in your scoring format
- Sck / TO
- Defense: sacks and takeaways
- PA
- Points allowed by a defense
- G
- Games in the sample
- vs Lg
- Percent above or below the league average
Cade Otton usually plays as the team's TE1, so the verdict uses the TE1 numbers, not the slot selected above.
- Cade Otton averages 7.2 PPR points over 2 games (recent-weighted 7.4) and has played as the team's TE1.
- The Steelers have allowed 3.5 points to TE1s over 2 games (-66% vs league). The model applies 40% of that gap because the sample is small, giving a -26% adjustment.
- Last season's average (8.1) is blended in at 50% weight because this season's sample is small.
- Projection: 5.7 points.
- For reference, a typical weekly starter-level TE scores about 13.1 and a top-tier one about 19.6.
- A statistical model from box scores, Vegas lines, weather and injury tags. It cannot see role changes or in-game events.
Cade Otton's stats vs the Steelers
Season so far, compared with what the group you pick below has done against the Steelers, plus a projection for this game.
What is this table?
Rows at the top are every game Cade Otton has played this season, using the same stat columns as the table above.
Compare against picks which group of Steelers opponents to line them up with: the best TE they faced in each game (1), the second best (2), the third best (3), or all of the top three. Nobody knows which slot Cade Otton will land in this week, so this is your call, not a guess.
Steelers avg is the average line of that group. Difference is that minus Cade Otton's own average: green means the defense gives that slot more than Cade Otton usually produces.
Projected is Cade Otton's recent-weighted average scaled by how generous the Steelers were to the slot you picked. Change the slot and the projected points change, because each slot has a different matchup adjustment. Picking Top 3 uses the usual-role projection.
| Wk | Opp | Tgt | Rec | ReYd | ReTD | FPts |
|---|---|---|---|---|---|---|
| 1 | CIN | 5 | 3 | 26 | 0 | 5.6 |
| 2 | CLE | 6 | 5 | 38 | 0 | 8.8 |
| Otton avg | 5.5 | 4.0 | 32.0 | 0.0 | 7.2 | |
| Steelers All avg | 2.3 | 1.3 | 23.3 | 0.0 | 3.7 | |
| Difference | -3.2 | -2.7 | -8.7 | +0.0 | -3.5 | |
| Projected | 4.0 | 2.9 | 23.6 | 0.0 | 5.7 | |
Difference is what the defense allows minus what Otton averages (green = defense gives up more). Projected stats scale the player's averages by the matchup adjustment, so they are a rough guide.