Every NFL play is driven by context. Down, distance, field position, score, and time remaining all influence whether an offense chooses to pass, run, punt, or attempt a field goal. Some teams follow clear situational tendencies, while others are harder to anticipate.
This project uses 2025 NFL play by play data to build a multinomial regression model that predicts play type from game context. The goal is not just to classify plays, but to measure how predictable each team is using an entropy based score derived from model probabilities.
| Rank | Team | Plays | Predictability | Accuracy |
|---|---|---|---|---|
| 1 | MIN | 112 | 0.638 | 0.786 |
| 2 | TEN | 106 | 0.636 | 0.726 |
| 3 | CLE | 136 | 0.636 | 0.772 |
| 4 | DAL | 147 | 0.635 | 0.782 |
| 5 | NE | 128 | 0.635 | 0.820 |
| 6 | PIT | 116 | 0.634 | 0.741 |
| 7 | CIN | 124 | 0.633 | 0.782 |
| 8 | HOU | 136 | 0.633 | 0.846 |
| 9 | TB | 137 | 0.627 | 0.737 |
| 10 | DEN | 154 | 0.625 | 0.812 |
| 11 | LAC | 160 | 0.625 | 0.850 |
| 12 | CHI | 137 | 0.624 | 0.730 |
| 13 | ARI | 119 | 0.622 | 0.773 |
| 14 | JAX | 151 | 0.622 | 0.762 |
| 15 | LV | 110 | 0.613 | 0.727 |
| 16 | MIA | 117 | 0.613 | 0.692 |
| 17 | LA | 146 | 0.607 | 0.685 |
| 18 | PHI | 113 | 0.606 | 0.788 |
| 19 | NO | 121 | 0.606 | 0.760 |
| 20 | NYJ | 121 | 0.605 | 0.744 |
| 21 | SEA | 100 | 0.603 | 0.730 |
| 22 | BUF | 116 | 0.602 | 0.750 |
| 23 | BAL | 118 | 0.602 | 0.686 |
| 24 | SF | 125 | 0.598 | 0.752 |
| 25 | GB | 128 | 0.596 | 0.641 |
| 26 | CAR | 141 | 0.594 | 0.738 |
| 27 | NYG | 141 | 0.591 | 0.730 |
| 28 | DET | 122 | 0.589 | 0.730 |
| 29 | IND | 119 | 0.584 | 0.782 |
| 30 | KC | 145 | 0.583 | 0.710 |
| 31 | ATL | 118 | 0.582 | 0.686 |
| 32 | WAS | 134 | 0.576 | 0.769 |
Teams near the top are easier for the model to read because their decisions align more consistently with game situation. Teams lower on the chart show more uncertainty in predicted probabilities, indicating less predictable behavior.
This does not imply one approach is better. High predictability reflects structure, while lower predictability reflects variation. The value is in measuring how clearly a team’s tendencies show up in the data.
This project moves beyond simple classification by using predicted probabilities to evaluate team behavior. Instead of only asking whether the model can predict the next play, it asks how readable that decision is in the first place. That creates a more meaningful view of offensive tendencies through the lens of data and probability.