League Overview
See all 32 teams ranked by estimated win impact in 2026 and jump to a highlighted overview metric.
NFL Analysis
Front Office in a Browser
Estimates how trades and free-agent signings affect team win probability - seasons 2017-2026.
Data range: 2017-2026; sources: NFL play-by-play, team transactions, public salary databases.
Explore the 2026 Season →What This Dashboard Shows
Every NFL trade and free agent signing changes a team's roster. But by how much? This dashboard uses a statistical model to estimate the win probability impact of each player move - ranking all 32 teams by how much their offseason activity is predicted to help or hurt them.
The model covers ten seasons (2017–2026), 643 player moves, and three outcome metrics: win percentage, point differential per game, and offensive EPA per play.
The Research Behind It
This project is grounded in behavioral economics research on how disruption and roster change affect performance. The primary inspiration is Hengchen Dai's research on the reset effect in Major League Baseball, featured in the Freakonomics Radio episode Are You Ready for a Fresh Start?
Dai found that when a struggling player is traded — triggering a statistical reset — their performance improves significantly. The implication for NFL analysis: player movement is not a neutral event. A trade or signing carries a measurable signal about expected performance change.
How to Use the Dashboard
Team Detail
Jump to a Jacksonville 2022 walkthrough and highlight the first movement card used in analysis.
What-If Simulator
Open a prefiltered scenario page and highlight the side-by-side comparison chart section.
Browse All Moves
Open the movement explorer with a spend-versus-impact focus and highlight the key timeline area.
Key Terms
MIS - Movement Impact Score
How much this move is estimated to change a team's chance of winning, expressed in percentage points.
The estimated change in win probability from a single player move. A value of +0.020 means the move is estimated to add approximately 2 percentage points to the team's win rate - roughly one additional win over a 50-game stretch.
MISz - Standardized Score
A normalized MIS that lets you compare moves across seasons and teams.
MIS scaled to compare across all moves in the dataset. Above +1.0 is a strong positive signal. Below -1.0 is a strong negative signal. Near zero is neutral or uncertain.
AAV - Average Annual Value
The average yearly salary for a player's contract. Used to compute contract efficiency — how much win impact a team got per dollar spent.
EPA - Expected Points Added
A measure of offensive efficiency used widely in modern NFL analytics. Positive means the offense is adding value above what an average NFL play would produce.
Confidence flag
When the model has limited data to estimate a move's impact reliably, it flags the estimate as low confidence. Treat flagged values as directional guidance, not precise predictions.