VALD NHL Report: Key Testing Insights for Ice Hockey Practitioners
- Jo Clubb
- May 6
- 5 min read
This post explores VALD's new 2024/25 NHL report, with key insights for those testing ice hockey athletes.
VALD’s newly released 2024/25 NHL Report offers one of the most comprehensive benchmarking datasets currently available in elite ice hockey. Built from five seasons of aggregated testing data across more than 2,000 athletes from NHL and European teams, the report provides practitioners with valuable insight into how elite hockey athletes are being assessed and where they sit relative to their peers.
But as with any benchmarking data, the real value is not in the numbers alone.
Benchmarking only becomes useful when it is interpreted within context.
In this article, I highlight several of the key findings from the report and discuss how practitioners might use this information to inform testing and decision-making in their own environments.
Why This Report Matters
Historically, access to large-scale sports science benchmarking data in ice hockey has been relatively limited. Many practitioners have relied on internal datasets, anecdotal norms, or extrapolation from other sports when interpreting athlete testing results. Reports such as this help bridge that gap by providing population-level context across a broad elite sample.
That said, normative data should not be treated as target values to blindly chase.
Instead, benchmarking data should help practitioners ask better questions:
Where do our athletes sit relative to this population?
Are we observing similar trends within our own dataset?
If not, are those differences meaningful, or simply contextual?
The goal is not to replicate the NHL average. It is to better understand your own athletes in relation to an appropriate reference population.
For a demonstration of where to find the report on your VALD Hub to download your copy, jump to 00:49 in the video below:
Countermovement Jump Strategy Appears Sport-Specific in Ice Hockey
The countermovement jump (CMJ) was the most commonly performed ForceDecks assessment across NHL organisations, and one of the most interesting findings from the report related to CMJ depth (one of the foundational metrics in my CMJ analysis framework).
VALD found that NHL athletes demonstrated:
Greater countermovement jump depth
Longer contraction times
compared to athletes in European hockey leagues.
This suggests that differences may exist not only in jump output, but in jump strategy.
Given the flexed, squat-like posture inherent to skating mechanics, this is perhaps unsurprising. Ice hockey athletes may simply be more comfortable and mechanically accustomed to operating from deeper positions than athletes in many other team sports.
For practitioners, this serves as an important reminder that:
Population-specific benchmarking matters.
Comparing hockey athletes against generic team sport normative data may lead to misleading conclusions if sport-specific movement strategies are not considered.
Ice hockey athletes may simply be more comfortable and mechanically accustomed to operating from deeper positions
Single-Leg Jump Performance Differentiates Higher-Level Athletes
Another notable finding was that single-leg countermovement jump performance tended to differentiate higher-level athletes, with NHL players producing greater outputs than their European counterparts.
Again, this aligns with the biomechanical demands of the sport.
Ice hockey is highly unilateral in nature. Each skating stride, acceleration, and directional change is largely produced off one limb at a time. This finding reinforces the likely importance of unilateral strength and power qualities in the sport.
However, two caveats are important.
First, this is cross-sectional data. We cannot conclude that improving single-leg jump performance will cause improvements in hockey performance.
Second, these findings still require contextualisation within your own environment and athlete population.
Nonetheless, the data supports the idea that standalone single-leg testing should be considered a key component of athlete profiling in ice hockey, rather than simply analysing asymmetries within bilateral tasks. For more on that difference, see my video diving into the Asymmetry Calculation Conundrum.
Reframing the Adductor:Abductor Ratio
The report also provides valuable insight into hip and groin profiling, particularly around the adductor:abductor ratio. This ratio has long been of interest in ice hockey due to its association with groin injury risk, with much of the foundational research in this area originating from NHL cohorts.
Early work by Tyler et al. (2001) suggested that athletes who remain uninjured demonstrated higher adductor:abductor ratios than those who sustained groin injuries, leading to commonly cited thresholds of approximately 0.8–1.2.
However, the data presented in this report adds useful nuance.
Specifically, athletes with lower adductor force outputs appeared less likely to achieve ratios above 0.8. This suggests that in many cases, a poor ratio may reflect not merely imbalance, but insufficient adductor strength capacity.
For practitioners, this is an important distinction. Rather than simply “chasing the ratio,” it may be more appropriate to consider both absolute strength of the adductors and the abductors, alongside the ratio.
This reinforces the need to always explore the underlying components of a ratio, as I've previously discussed with RSI, for example.
a poor ratio may reflect not merely imbalance, but insufficient adductor strength capacity.
A balanced ratio with poor absolute force production may still represent an underprepared athlete.
For more on the adductor: abductor ratio, and how we can measure it with VALD's ForceFrame, take a look at the video below from the GPI YouTube channel:
Testing Protocol Standardisation Remains Critical
The report also highlights considerable variation in how hip strength testing is performed across organisations.
Testing positions included:
45° hip flexion
60° hip flexion
Seated positions
Supine long-lever positions
Each of these positions changes joint angle, lever arm, and likely force output, as shown in the scatter plots (right) taken from the report.
This reinforces an important practical point:
Benchmarking is only useful when testing protocols are standardised.
Comparing your data against normative values is only meaningful if:
The same testing position is used
The same setup is employed
The data is correctly tagged and categorised
Otherwise, practitioners risk comparing fundamentally different assessments, so always be careful and critical of what positions are used.
How Practitioners Should Use This Report
The key value of this report, like all benchmarking resources, lies in the context it provides.
It should not be used to set arbitrary targets or to define what every athlete “should” look like.
Instead, it should help guide interpretation and questioning.
For example:
Are your athletes substantially below normative ranges?
Are they simply different due to positional or contextual demands?
Are your testing protocols aligned with those used in the benchmark dataset?
Do observed deficits reflect weakness, imbalance, or merely different movement strategies?
Used appropriately, benchmarking data can improve decision-making.
Used poorly, it can create false expectations and misleading comparisons.
Final Thoughts
VALD’s 2024/25 NHL Report represents one of the most valuable benchmarking resources currently available for practitioners working in elite ice hockey.
Its greatest strength is not that it provides answers.
It is that it provides context.
And when combined with sound practitioner judgement and sport-specific understanding, that context can support more informed testing interpretation and athlete management.
You can download it now on the VALD Hub using the instructions here, or follow along with the video on the Global Performance Insights channel, here.
Frequently Asked Questions
Should benchmarking data be used as target values?
No. Benchmarking data should provide context rather than fixed performance targets. Normative values can inform interpretation, but must always be contextualised to your sport, level, environment, and athlete population.
Why might ice hockey athletes use deeper depth in a countermovement jump?
A likely explanation is the sport-specific adaptation to skating mechanics, where athletes spend large amounts of time in flexed, squat-like positions.
Does better single-leg jump performance cause better hockey performance?
Not necessarily. While the data presented in the report found higher single leg jump performance at higher levels of the sport, the data remains cross-sectional, meaning it shows association rather than causation.
What is a good adductor:abductor strength ratio?
Common recommendations in ice hockey often cite approximately 0.8–1.2, but this should be interpreted alongside absolute force values and within the context of the individual athlete.
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