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Money Talks: Predicting Fantasy Performance Using Player Contract Data (Fantasy Football)
Jun 9, 2026Samuel DiSorboThe Fantasy Footballers
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Player contracts tell us almost everything a team feels about them – NFL contract data carries implicit information about a player’s expected role, team investment, and organizational confidence that complements raw performance statistics. A player on a high-value extension is more likely to maintain playing time than one in the final year of a cheap deal. A team that spends heavily at the QB or WR position signals a pass-heavy scheme, and these signals are not fully baked into conventional fantasy rankings. I have built a machine learning model that takes in player contract data and uses it to predict their average fantasy points for the next season. In completing this model, the main question is: Can contract data help in predicting year-over-year player performance well enough to provide a meaningful edge? Analysis To read more about the methodology used to build this model, please refer to the ‘Methodology’ section near the bottom of the page. To summarize, this model uses the following data to predict a player’s fantasy points per game for the next year: Fantasy points per game for the three previous seasons EPA and other performance metrics from the previous season $ AAV Total contract value Guaranteed contract value Contract duration Cap hit per year Years remaining on contract Year signed (contract) QB $ AAV OL $ AAV WR $ AAV Average $ AAV of teammates in the same position Age In this, I looked to combine performance metrics with true contract data to capture the true value of each player, in turn highlighting their expected performance for the coming years. To test the hypothesis, I trained three separate models: one using only t-relevant data, one using only player performance-relevant data, and one using both. The results are shown below for all three models, with CV R-squared being the cross-validation R-squared done during training. Stats alone explain 43.6% of the variance in cross-validation. Adding contract data improves this to 47.3% – a meaningful but not transformative lift. However, the contract-only model still explains 41.2% of variance with zero knowledge of how the player actually played, validating the core hypothesis that team investment encodes real information about expected role . It makes sense that using stats and contract together doesn’t provide much uplift – after all, contract value is directly tied to performance, amongst a few other factors. The following chart shows the top model’s predictions against actual 2024 fantasy data for all qualified players, broken down by position. Points above the diagonal (teal-colored) indicate players who outperformed the model’s prediction, and vice versa for the pink. Key observations: QB : Pass throwers are highly predictable by contract value . The R-squared shows that about 57% of the variance in fantasy performance can be explained by the variance in contract data, which is a significant amount for real-world data. RB : Rushers are also highly predictable by contract value . Over 53% of variance is explained by the relationship; however, the 43% of players who outperformed prediction show the high upside of the position. WR : Receivers are the most unpredictable position , with only 30% of variance explained by the relationship with the model parameters. This is also the highest upside group, with 46% of players outperforming expectations. TE : The model is strongly optimistic about TEs – 78% disappointed. A common pattern exists, where the model predicts stab…