2026 Fantasy Football Rookie Mock Draft 1.0
02/10/2026
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As the 2026 NFL Draft approaches, we are gearing up with our first rookie fantasy football mock draft. In this draft, we
In the 97th installment of our “Does It Matter?” series we’re looking into shuttle times for Tight Ends. For fantasy football purposes, does a faster shuttle actually translate to more NFL fantasy points for Tight Ends?
By crunching the numbers from the last two decades, we’ve identified a specific benchmark that separates the elite producers from the lesser elite. Here are all our findings:
To find a meaningful signal in the noise, we analyzed the top 50 fantasy football finishers at the Tight End position (PPR scoring) every year since 2003.
Why the top 50? We chose this range to establish a “happy medium” for statistical analysis. If we expanded the data set further, we would run into fringe roster players who rarely see the field which would only skew the data and provide little insight. Conversely, to understand what makes a player elite, we need a baseline of “elite” players to compare against. By comparing the “Top 10” finishers to the “Bottom 10” (those finishing 41st–50th), we create a clear comparison to see what makes a player elite.
We began by breaking down finishers into four tiers: The Top 5, the top 10, the 11th–30th range, and the 31st–50th range. While the results were subtle, a trend emerged showing that faster shuttle times generally correlated with higher fantasy scores. The data showed that in 14 out of the 21 seasons studied (66.7%), the Top 5 finishers possessed faster shuttle times than their counterparts in the 31st–50th range.
To further analyze these trends, we charted every Top 10 finisher since 2003 and segmented them into 0.01-second shuttle time buckets. We then compared this to the distribution of the Bottom 10 finishers. By visualizing the weight of these charts side-by-side, it is clear that faster shuttle times are more heavily concentrated within the higher fantasy scores. The Top 10 chart shows a heavier weight amongst faster buckets, whereas the Bottom 10 distribution is more scattered and weighed toward slower times, confirming that lateral quickness appears to be a shared trait among elite producers.
To move beyond broad averages, we wanted to find an applicable threshold that signals top-level consistency while minimizing the “noise” created by unique outliers. To achieve this, we created a differences chart. This involved comparing each shuttle time plus a rolling window of the next 0.05 seconds, then subtracting the Bottom 10 results from the Top 10 results within that specific group. The chart’s desired outcome is a negative number for unique differences (Orange) and a positive number for non-unique differences (Blue). From this chart, a clear performance shelf emerged: 4.19 seconds and below. We will next test this range to verify whether it is the highest producing optimal range or not.
To validate this, we performed iterative range adjustments within our dataset to ensure we identified the highest producing range possible. After testing various thresholds near the mark our differences chart suggested, the results were definitive: Maintaining the benchmark at 4.19 seconds and below produces a 13.0% higher Top 10 appearance rate than what is found in the Bottom 10. When you are evaluating prospects or deciding between two starters, this 4.19 seconds and below threshold is the optimal range you should look for in your favorite athletes and will also be included in our Ultimate Athlete Blueprints which offers an easy-to-read table housing all of our researched metrics combined in one place for you to view as seen here:
Due to these findings, Shuttle Times could play a factor in our Star-Predictor Score (SPS) model. The Star-Predictor Score (SPS) is a scouting tool designed to maximize investment potential and reduce risks when drafting rookies in Fantasy Football. It is proven to have a higher accuracy than draft capital alone to predict fantasy football success. The SPS includes 13 to 17 metrics, with the exact number varying by the player’s position. All these metrics are pre-NFL – some of which are proprietary to BrainyBallers – providing a complete analysis of a player’s analytical profile. The SPS gained widespread notoriety for its high accuracy, having made it on Barstool and The Pat McAfee Show. The SPS can be found here, and future projected SPS grades can be unlocked here.
Don’t just draft for this year. Gain the knowledge needed to position yourself to secure your future with SPS rookie data.
While our range-based analysis shows a clear benefit for elite finishers, it is important to look at the broader statistical picture.
Interestingly, the Pearson Value shows essentially no correlation between shuttle times and future fantasy production when applying standard statistical methods across the entire dataset. We are hoping for a minimum of 0.1, or an inverse correlation maximum of -0.1 when studying world-class athletes as we are. For reference to something that everyone acknowledges matters in prospect scouting, and to show the accompanying Pearson value, QB draft capital prevailed a -0.219 Pearson value – meaning earlier drafted QB’s perform better.
Furthermore, we compared decades to spot evolving trends. When looking at the significance of this optimal shuttle range for the Top 10, 11th–40th, and Bottom 10 finishers, our decadal differences chart reveals that this specific range has actually shown a decreasing trend in the most recent decade. This suggests that the predictive power of shuttle times is narrowing.
| 2004-2013 | |||
| Top 10 | 11 through 40 | Bottom 10 | |
| All count | 53 | 176 | 60 |
| All 4.19 and below | 26 | 63 | 12 |
| % (Optimal Range/all) | 49.1% | 35.8% | 20.0% |
| 2014-2023 | |||
| Top 10 | 11 through 40 | Bottom 10 | |
| All count | 55 | 166 | 55 |
| All 4.19 and below | 10 | 36 | 9 |
| % (Optimal Range/all) | 18.2% | 21.7% | 16.4% |
Does it matter? Not quite. With the low Pearson correlation coefficient, there is not enough evidence to say it matters when using as a standalone predictor. If you do wish to continue using it, you should be looking for players who break that 4.19s barrier.
Our series has always sought to push the boundaries of sports analytics. This latest installment reaffirms our commitment to uncovering the hidden dynamics that define the game. Every Saturday, we’ll dive into intriguing questions, bust myths, and settle debates with thorough analysis. We welcome your input. Therefore, please leave comments or reach out with topics you’re eager to see dissected. All of our research can be found on our Analytics Page. Up next on our agenda for Part 98 of “Does It Matter?” is an examination of Tight End Dominator: Does It Matter? If so, what’s the Dominator threshold necessary for NFL success? Mark your calendars; every Saturday we shed light on the topics that matter to you. All it takes is a quick question being asked and we will go to work for you!
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