Fantasy Football Projections Keep Human Touch In An AI World

As fantasy football drafts get under way from sea to shining sea, many managers are looking to built-in fantasy football projections to guide their draft day decision-making. Projections inform everything from early-round picks to late-round sleepers. In today’s world where artificial intelligence (AI) powers everything from cars to watches, it would be natural to assume that these projections are the result of amorphous algorithms or black box models. But the reality is more nuanced. Despite the growing influence of AI in sports analytics, fantasy football projections remain a hybrid process: part statistical learning, part human judgment. Understanding that blend of art and science reveals how savvy managers can use projections more effectively.

What Are Fantasy Football Projections?

Fantasy football projections are estimates of a player’s future performance. These projections are tailored to specific timeframes, such as a single game or a full season. Generally, they are customized to reflect each league’s scoring format and serve as a foundational tool for ranking and evaluating players ahead of drafts, providing a baseline for comparing players across positions and tiers. By translating real-world football metrics into projected fantasy points, these models help managers identify value, assess risk, and construct balanced rosters. While the core function is predictive, projections also play a strategic role in shaping draft strategy, trade evaluations, and weekly lineup decisions.

How Are Fantasy Football Projections Created?

Yahoo Fantasy Football

Different fantasy football leagues compute their projections in different ways. Yahoo Fantasy Football’s projection system is a strong example of how modern fantasy platforms blend automation with human expertise. They are built from a combination of historical player data, machine learning, statistical models, matchup analysis, and contextual factors such as weather, depth chart changes, and coaching tendencies. Rather than relying on a single model, Yahoo incorporates inputs from multiple trusted sources, including The BLITZ, Rotowire, and FTN Fantasy. Each offers a distinct methodology, emphasizing different elements like advanced efficiency metrics or manual adjustments based on breaking news. What makes this approach notable is its flexibility. Yahoo provides a consensus projection but also gives users the ability to compare data from different sources, offering a more detailed view of player value.

ESPN Fantasy Football

ESPN’s projections are built similarly through a structured but manual process. ESPN begins by analyzing team- and player-level trends, then assigns predicted dropback, carry, and target shares to each player. These usage assumptions form the basis of the projections, which are refined using statistical regression, efficiency metrics, and context such as coaching changes, injury histories, and age curves.

BetIQ/Team Rankings Fantasy Football

BetIQ and TeamRankings offer the most detailed look into how they compute their fantasy football projections. They start their fantasy player projections by first building team-level forecasts. Their model is rooted in a similarity-score approach: each team’s recent statistics, Vegas win totals, roster characteristics, and coaching situations are compared against two decades of historical data. Each current team is then matched to the 25 most similar past teams, and its baseline projection is generated as a weighted average of how those comparable teams performed the following season. From there, the model produces expectations for points scored, passing and rushing volume, touchdowns, and other categories.

Those team-level totals are then fed into player-level projections using three separate models. The Similar Player Model identifies historical players that are comparable to the current player and projects forward based on how those historical players performed. The Average Draft Position (ADP) Model incorporates market expectations by translating draft position into expected fantasy points. The Team Usage Model applies franchise-specific trends to distribute touches and opportunities by position. The three models are blended, with the Similar Player Model weighted most heavily, followed by ADP and team usage. Unlike many sites, BetIQ explicitly factors in injury risk and missed games, producing more conservative numbers that often fall below other platforms’ projections.

Formulas Behind Fantasy Football Projections Explained

Regression

Regression is one of the most fundamental statistical methods used in any fantasy football projection engine. A regression model takes in many different variables and makes a prediction on a certain future statistic, such as yards per carry. Comparing actual recorded statistics to these regression predictions can help identify when a player’s performance is likely to be unsustainable. In fantasy football, regression is often applied to efficiency stats like touchdowns per target, catch rate, or yards per attempt. For example, if a tight end scores 12 touchdowns on only 60 catches, regression analysis will reveal that performance to be an outlier and likely to decrease next season. Regressions help set realistic expectations for fantasy football projections and guard against overvaluing a player coming off a statistical outlier game or season.

Similarity Scores

Similarity scores are a way of finding “historical twins” for today’s players or teams. A model looks at a wide set of factors, such as stats, age, role, team style, and even context like a coaching change, and then searches through decades of NFL history to find past players or teams with the most similar profiles. By seeing how those players performed the following season, we get a window into what might happen next. The power of similarity scores is that they enable predictions on the future using a weighted average of the past.

Weighted Average

Weighted averages are a way of combining multiple sources of information into one projection, while giving more influence to the sources considered most reliable. Rather than simply averaging everything together, models assign “weights.” Weights could be based on everything from similarity scores to historical accuracy. For example, if one method has historically been very accurate, it might count for 60% of the final projection, while two other methods make up the remaining 40%. Importantly, weights provide an easy mechanism for human expert analysts to “tune” their models based on intuition, unstructured data, and experience.

The Art Behind The Science Of Fantasy Football Projections

Behind all the statistics and modeling lies the literal beating heart of fantasy football projections: the human analyst. It is the human analyst who decides what data to gather and how to clean and structure it for maximum utility. It is the analyst who interprets the signals from the model, distinguishing between noise and meaning. It is the analyst who fine-tunes ensembles and validates projections against both historical benchmarks and real-world logic. Ultimately, it is the analyst who is responsible for understanding the art of the data science effort: knowing when to trust the math, when to question it, and how to reconcile it with real-world context that no model can fully capture.

So, this season, you should treat fantasy football projections as sage advice rather than a guarantee of success. Take a risk on a rookie, trust your instincts when you see an emerging role before the models do, and swing for upside late in the draft. It might just be your personal human touch that transforms a good team into a championship team.