Tight Ends: Can NFL Combine Vertical Jumps Help Predict NFL Success?

For the 83rd part in our “Does It Matter?” series where we subject College and NFL Combine metrics to statistical testing to find true predictive indicators of future fantasy success, we next wanted to focus on the Tight End Vertical Jump — a metric often cited as a proxy for explosion and red-zone ability. Can a single measurement taken in February help predict years of consistent production? We wanted to find whether it does matter, how much it matters, and a benchmark for identifying success. Here are all of our findings.

Methodology

Our basis for this study consists of the Top 50 fantasy football Tight Ends every season since 2003. We utilized PPR (Point Per Reception) scoring as the data as it best reflects overall offensive usage, which is key to understanding a Tight End’s value.

Why only the top 50? Throughout this analysis, we will frequently compare the elite performers to the low-end contributors. The “bottom 10,” which will be referenced, refers specifically to players finishing 41st through 50th among TEs in fantasy scoring each season. This comparison allows us to maintain a “happy medium”, since comparing to players further down the rankings would be comparing to players who are fringe roster players and are almost guaranteed to not score in the top 10, therefore simply skewing the data. Although, we still needed a lesser grouping to compare to, which we identified as the 41st-50th place finishers as that “happy medium” threshold.

TE Averages and Trends

Our first step was creating an averages chart which segments the players into four groups: Top 5, Top 10, 11th-30th, and 31st-50th. The goal was to see if a linear trend emerged. For instance, if the Top 5 consistently registered a significantly higher average Vertical Jump than the 31st-50th group.

Interestingly, when looking purely at the average Vertical Jump across all groups, no obvious linear trend emerged that suggested higher or lower vertical jumps directly correlated with a higher fantasy score. We found that in 12/21 seasons (57.1%), the Top 5 finishers had a higher vertical jumps than the 31st−50th place finishers. This is a lack of findings, as this averages chart his historically proven to be an early indicator of correlation in metrics.

Segmented Top & Bottom 10 TE Finishers Since 2003

Moving beyond simple averages, we charted every Top 10 finisher since 2003 and segmented their Vertical Jump measurements into 0.25′′ rounded buckets (e.g., 34.00′′−34.24′′, 34.25′′−34.49′′, etc.). This granular look allowed us to visualize the distribution of elite performers. From the resulting distribution chart, no trends appeared. This confirmed the instinct that Vertical Jump might not be relevant, but we still needed to find the critical performance window and statistically test with the Pearson value.

Tight End Vertical Jump Differences Chart Analysis

Our next goal was to find an applicable threshold that historically signaled top-level consistency and minimized the appearance of unique, outlier players, therefore showcasing consistent performers across the entire time frame. To achieve this, we developed a differences chart. This chart compares each vertical jump, plus the next 1.0′′, subtracting the Bottom 10 appearance rate from the Top 10 appearance rate within that one-inch group. This methodology highlights where the density of elite performers most consistently separates from the lower performers. The chart’s desired outcome is a negative number for unique differences (Orange) and a positive number for non-unique differences (Blue). From this differences chart, it became difficult to pinpoint one threshold since there are multiple increases followed by immediate decreases. Therefore, we have to rely on our iterative analysis next to locate a top performing threshold.

Verifying the Optimal TE Draft Age Range

We next performed iterative analysis in our spreadsheet to identify the highest producing range with the smallest possible differential needed to maintain strong predictability.bAfter testing various numbers, we found that setting this range at 30.5′′ and above produces an 8.7% higher Top 10 appearance rate than in the Bottom 10. Therefore, this is the optimal age threshold that you should hope for in your favorite athletes and will be the subject of our study going forward. This range 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 seen here:

Star-Predictor Score (SPS) Predictive Model

Due to these findings, Vertical Jumps 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, and some are invented by us, 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.

Standard Statistical Findings

For comparison, it is important to note what standard statistical methods show when looking for linear correlation across all values:

• Pearson Value: 0.056

Interpretation: This shows no linear correlation between vertical jumps and future fantasy production when looking at standard statistical methods. When studying world-class athletes as we are, a Pearson value greater than 0.1 (or less than -0.1) is considered significant. For context, the accompanying Pearson value for QB draft capital – a metric everyone acknowledges matters in prospect scouting – prevailed a -0.219 Pearson value.

Decadal Trends

Next, we compared results across different decades to spot recent trends and to see how significant our TE vertical jump optimal range (30.5′′+) remains. The decadal differences chart shows the percentage point difference between the Top 10 and Bottom 10 falling within this optimal range. You can see from this chart that this optimal range is a slightly decreasing trend in the most recent decade, suggesting that while still relevant, its predictive power may be slightly softening over time.

	2004-2013
	Top 10	11 through 40	Bottom 10
All count	68	207	71
All 30.5” and above	64	186	58
% (Optimal Range/all)	94.1%	89.9%	81.7%
	2014-2023
	Top 10	11 through 40	Bottom 10
All count	65	187	59
All 30.5” and above	64	172	55
% (Optimal Range/all)	98.5%	92.0%	93.2%

Conclusion

The Tight End Vertical Jump is not a strong linear predictor, but it offers threshold-based insight. The goal is not to find the highest possible jump, but to identify a prospect who has crossed the minimum desired athletic threshold for consistent fantasy production.

The ideal, most widely applicable benchmark is the 30.5′′ and above Vertical Jump range.

More Data Next Week!

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 84 of “Does It Matter?” is an examination of Tight End Broad Jumps: Do They Matter? If so, what’s the draft age 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!

Related Content:

BrainyBallers Buy-Hold-Sell Chart (All Players)

Get Your Products 100% Refunded By Predicting The Next SuperBowl Winner!

Gunnar Helm Stats