For the 25th installment of our “Does It Matter?” series, we dive into the world of NFL Running Backs to understand if Rushing Yards Over Expected (RYOE) can accurately predict future success. By analyzing our data we collected, we discovered an interesting pattern. All of our findings are laid out here.
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To form our analysis, we focused on the top 50 fantasy football finishers each season since 2008, since that is the first season RYOE data is available for. We specifically attached each player’s Rookie RYOE to their yearly performances. Our Fantasy points system was PPR (points per reception) fantasy scores. Throughout this study, you will hear the term “bottom 10”. This is defined as those ranked 41st-50th in fantasy finishes each season. All numbers within this article are rounded to the nearest tenth (0.1). All of our data was sourced from mfb analytics, put together by Tej Seth at PFF.
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Our first goal was to compare different groups in the top 50 with one another to see if there are any noticeable trends. We categorized the top 50 finishers into four distinct groups based on their placement:
The results were obvious. In 12 of the 16 seasons, the top 10 finishers had higher Rookie RYOE than the 31st-50th place finishers. This pattern suggests that RBs with higher Rookie RYOE scores perform better in fantasy football, consistently ranking among the top producers.
To continue diving deeper, we created a chart specifically for the top and bottom 10 finishers since 2008, once again using only Rookie RYOE data. We segmented this data into increments of 0.1 RYOE. The chart revealed a subtle trend: as the RYOE score increases, the prevalence of finishes appeared to occur more frequently in the top 10 than in the bottom 10. These two charts can be seen below, with the top 10 first:
In our quest to identify the optimal range for Rushing Yards Over Expected (RYOE), we created a differences chart. This compares each RYOE increment plus the next 0.3 RYOE, subtracting the bottom 10 results from the top 10 results. The chart’s desired outcome is a negative number for unique differences (Orange) and a positive number for non-unique differences (Blue). This would indicate the desired consistent top level performances we are hoping for. This analysis revealed that a threshold of 0.3 RYOE and above appeared to be the the most effective range for future performances. This approach got us closer to identifying the optimal range for top-performing RBs, which we will verify next.
To verify our findings, we looked at how many RBs were above this RYOE threshold amongst those who placed in the bottom 10 since 2008. Out of a dataset including 98 Running Backs, 27 achieved RYOE of 0.3 and above, representing 27.6%. This serves as our baseline to distinguish whether we found an average performing Rookie RB’s RYOE, or an average top-performing Rookie RB RYOE.
If more than 27.6% occurs within the top 10, then we found a top-performing RB Rookie RYOE range. Among 111 top 10 finishers, 53 fell into the 0.3 RYOE threshold and above, accounting for 47.7%. This signifies a 20.2% increase, indicating that this threshold could indeed be the top-performing range.
We now wanted to manually fine-tune this range to see if we can find the actual optimal range, or to verify 0.3 and above as the optimal range. Through numerous tests, the range of 0.2 to 1.3 RYOE yielded a 24.3% higher appearance rate in the top 10 than the bottom 10. Therefore, this is our optimal range, and will be the subject of this research going forward.
The critical value is simply the threshold at which an outcome change should be expected. We broke this down into different finisher thresholds to find what the bare minimum Rookie RYOE you should hope for.
Due to this, we are setting -2.32 Rookie RYOE as a critical value for our StarPredictor Score (SPS) model, which will attempt to predict successes and busts. Essentially, Running Backs must have a -2.32 and above Rookie RYOE in order to not be considered a future bust by our model. We are hoping to have this model fully functional by the beginning of the 2025 NFL season. You can subscribe to our mailing list to get updates on this model when it begins coming out here:
Our statistical analysis reveals a near correlation between RYOE and fantasy production. These results are as follows:
For an Rsquared value, the desired threshold is 0.01 (1.0%) and above since we are studying world-class athletes. Therefore, regression testing failed for rookie RB RYOE. For reference to data which prevailed correlation, part 22 of our “Does It Matter?” series found that Rookie WR Receiving Yards predicts 5.6% of the changes in WR fantasy points. The RB RYOE regression chart can be seen below:
For our decades breakdown, the key trend is the 14.2% increase in the occurrence of our identified optimal RYOE range among the top 10 finishers when compared to the bottom 10 finishers in the most recent decade. This jump indicates a stronger presence of efficient runners who exceed expectations in their performance. This decadal breakdown can be seen below:
| 2004-2013 | |||
| Top 10 | 11 through 40 | Bottom 10 | |
| All count | 29 | 83 | 29 |
| All 0.2 to 1.3 | 11 | 21 | 7 |
| % (Optimal Range/all) | 37.9% | 25.3% | 24.1% |
| 2014-2023 | |||
| Top 10 | 11 through 40 | Bottom 10 | |
| All count | 82 | 233 | 69 |
| All 0.2 to 1.3 | 42 | 83 | 16 |
| % (Optimal Range/all) | 51.2% | 35.6% | 23.2% |
Since 2022 is the most recent Rushing Yards Over Expected data available per mfbanalytics, we looked at the rookie Running Backs that year and how they shaped out in comparison to our optimal range. All of the Rookie RB’s who finished in our optimal range is detailed below:
| Jaylen Warren | 0.8 |
| Travis Etienne | 0.7 |
| Breece Hall | 0.6 |
| Tyler Allgeier | 0.2 |
| Josh Jacobs | 0.2 |
| Isiah Pacheco | 0.2 |
For those who wish to use RYOE data religiously, we created a detailed graphical guide to help you track and analyze career success probabilities for rookie running backs. Using historical data, this guide features easy-to-read charts and graphs that visually represent various RYOE values needed to achieve various future performance probabilities. This allows you to follow your favorite rookie RBs after their rookie season (if RYOE data becomes available after the season), keeping an eye on their potential for long-term success. Additionally, the guide is a valuable tool for identifying sleeper RBs—those less obvious players who have a higher chance of achieving 2-1,400 All Purpose yard seasons in their career. By leveraging this graphical guide, you can make more informed decisions in fantasy leagues or just enjoy a deeper understanding of the game.
Our number-crunching for part 25 of the “Does It Matter?” series found that the optimal range for Rookie RB RYOE is 0.2 to 1.3 RYOE (rounded). The decade comparison of RYOE reveals significant trends and their rising presence in top RBs, although these findings were a near miss on regression testing.
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 26 of “Does It Matter?” is an examination of TE 40: Does it matter? If so, what’s the 40-time threshold necessary for 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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