The 2025-26 NCAA hockey season marked an important shift in the landscape. With the lifting of eligibility restrictions on players from the Canadian Hockey League (OHL, WHL, and QMJHL), the talent pool for college hockey expanded overnight.
This “Great Migration” — for lack of a better phrase — provided a built-in experiment for hockey analytics and answered a lot of questions. Namely, how will CHL scoring rates translate to the NCAA game? And how do they compare to the USHL and BCHL?
This report provides an in-depth deep-dive into the inaugural CHL translation data, utilizes a tiered production model to account for role compression, and contextualizes these findings against the established three-year weighted averages of the USHL and BCHL.
(Note: All data was derived from InStat and the point averages may differ slightly from other media outlets)
Junior Hockey Translation
Comparing the 2024-25 junior production of more than 150 CHL players to their 2025-26 NCAA freshman totals reveals a distinct hierarchy of league strength. The data shows that the jump to the NCAA carries a significant “production tax” or drop-off in scoring that fluctuates depending on which league a player is coming from.
The 2025-26 data reveals a clear divide in how junior production carries over to the NCAA, with the OHL and USHL emerging as the most stable pathways for offensive transition. These two leagues lead the group with median point factors of 0.58 and 0.56, respectively, suggesting that players retained over 55% of their junior scoring pace upon entering the college game. In contrast, a notable gap exists within junior hockey itself. While the OHL shows high stability, the WHL and QMJHL faced a much steeper drop-off, where freshman typically saw their scoring totals cut by more than half.
Specific metrics further highlight the OHL’s unique position, particularly in goal-scoring retention, where its 0.61 factor significantly outperformed the QMJHL’s 0.40. On the playmaking side, the USHL leads the field with an assist translation factor of 0.59, indicating that its distributors find the most consistent success in the collegiate environment. Meanwhile, the BCHL remains the most difficult league to project, carrying the highest drop-off in the study with a median point factor of 0.35.
The Tiered Approach
The tiered analysis of the OHL uncovers a non-linear relationship between a player’s junior role and their eventual collegiate output. Rather than a flat conversion rate across the board, the data indicates that scoring retention is heavily influenced by the specific “tier” of production a player occupied before entering the NCAA. This breakdown suggests that the jump to college hockey affects high-volume scorers and bottom-six contributors in fundamentally different ways, challenging the assumption that elite junior numbers automatically lead to elite freshman totals.
The data highlights a unique statistical trend where depth producers from the OHL maintained the highest level of scoring stability. Players averaging under 0.6 points per game in junior hockey retained 73% of their production at the NCAA level, likely because the physical, defensive-minded roles they played in the OHL naturally carried over into the same style of play at the college level.
In contrast, elite producers faced a steeper drop-off — 54.0% retention rate — largely due to role compression; these high-volume scorers couldn’t replicate the same success against bigger and older competition, creating a temporary ceiling on their freshman stat lines. Despite this compression at the top, the OHL remains a premier developmental path, with even its highest-tier talent retaining more than half of its statistical value during the 2025-26 NCAA season.
OHL Case Studies
Michigan State’s Porter Martone stands out as a clear outlier, as his prolific 1.65 PPG junior pace translated to a 1.42 PPG freshman campaign for a remarkable 86.0% retention rate. Michigan’s Malcolm Spence provided a more representative sample of the top tier, seeing his 1.09 PPG transition into 0.63 PPG and aligning almost perfectly with the OHL elite median at 58.0%. Conversely, Kristian Epperson experienced a steeper developmental curve in his first season at Denver, with a 1.33 PPG junior season resulting in 0.55 PPG at the collegiate level, or a 41.4% retention rate.
What about the WHL and QMJHL?
A tiered examination of the WHL and QMJHL reveals how league-specific scoring environments and role compression dictated the recalibration of points in the NCAA. These findings suggest that the transition from Western and Atlantic Canadian junior systems is not uniform, as different levels of production faced varying production hits when moving to the college game. Understanding these variations is essential for determining which junior scoring rates are likely to persist and which are products of a more “open-style” junior game.
The WHL data highlights a significant recalibration for top-tier scorers, with the elite tier showing a points factor of 37%, which was the steepest adjustment among all the CHL leagues. This trend suggests that while the WHL allows for high-volume offensive output, those totals were difficult to replicate against more disciplined collegiate defensive systems.
While secondary producers show slightly improved retention at 44%, the league follows a broader inverse trend where depth players maintain the highest stability at 56%. This indicates that players in lower-line roles often possess the physical and desirable baseline that translated most effectively to the NCAA. Ultimately, the data shows that as production volume increases in the WHL, the likelihood of that production carrying over decreases, making role-specific evaluation a necessity for accurate forecasting.
Case Studies
Penn State’s Gavin McKenna serves as a notable example of high-end talent defying the league median. Despite an exceptional 2.28 PPG in the WHL, he maintained a 1.45 PPG pace in the NCAA for a impressive 63.6% retention rate. North Dakota’s Cole Reschny followed a similar trajectory, transitioning from 1.53 PPG in junior to 0.92 PPG as a freshman, which represents a solid 60% translation. In contrast, Denver’s Kyle Chyzowski’s transition mirrored the broader league trends for elite producers, as his 1.61 PPG junior output shifted to 0.59 PPG at the collegiate level, aligning perfectly with the 37.0% tier median.
The QMJHL data reveals an almost “V-shaped” translation curve, distinguishing it from the linear or inverse trends seen in other junior hockey leagues. The statistical profile suggests that the transition from the “Q” to the NCAA was highly dependent on a player’s specific role and skill set. While top-tier finishers show a surprising level of readiness, the middle-of-the-roster producers faced a disproportionately high production drop-off, indicating that aggregate point totals in this league can be particularly deceptive without more context.
The QMJHL stands as a league of extremes, where elite scorers retained 49% of their production, a figure that suggests high-end finishing talent in the league translated somewhat effectively to the collegiate level. However, this stability collapses in the secondary tier, which faced a sharp drop to a 31% production retention rate, the lowest mark for mid-level producers across all leagues and tiers.
Conversely, depth players returned to a higher stability of 53% points retention, reinforcing the transferability of physical utility roles. Ultimately, the first year of data confirms that while top-end QMJHL talent is often collegiate-ready, the middle of the roster requires a much higher level of scrutiny.
Case Studies
Maine’s Justin Poirier emerged as a significant outlier, carrying a prolific 1.35 PPG junior pace into a 1.07 PPG freshman season for an impressive 79.0% retention rate. Northeastern’s Jacob Mathieu also showed strong stability, translating 1.22 PPG into a 0.81 PPG collegiate campaign at a 66.0% clip. Meanwhile, Providence’s Julius Sumpf’s performance aligned more closely with the elite tier median, as his 1.03 PPG junior output resulted in 0.53 PPG at the college level, representing a 51.0% translation factor.
3-Year USHL and BCHL Weighted Averages
To truly understand the value of the CHL’s inaugural season, we must compare it to the “traditional” paths. Over a three-year aggregate, the USHL and BCHL provide a stable baseline for comparison.
The USHL Benchmark
The USHL remains the “closest relative” to the OHL. The three-year weighted analysis shows that USHL players retained 56% of their production going from junior hockey to the NCAA.
The USHL demonstrates high stability for depth players, who have shown to retain 64% of their scoring. Elite USHL producers showed a factor of 48% retention. These numbers are based on a significant sample size, confirming that the physical habits and defensive responsibility emphasized in the USHL translate effectively to the collegiate game.
The BCHL Outlier
The BCHL continues to be the league requiring the most aggressive adjustment. With a three-year median factor of 0.35, a point in the BCHL is worth significantly less than a point in the OHL or USHL, and therefore, the NCAA.
The BCHL shows a flat translation profile. Regardless of the player’s junior tier, scoring recalibrated to approximately 34% to 37%. This lack of variance suggests that the BCHL scoring environment impacts the entire roster similarly, requiring significant downward projections across all production levels.
Projections, Anyone?
Looking ahead to 2026-27, these translation models allow us to project how the next wave of CHL talent might adjust to the college game. A primary example is Nikita Klepov, a highly touted recruit expected to make the jump to college hockey. This season with Saginaw, Klepov averaged 0.55 goals and 0.89 assists for 1.45 points per game.
By applying the OHL’s tiered median factors, Klepov projects to record approximately 32 points over a nice, round numbered 40-game schedule at Michigan State. While this is certainly a conservative baseline, recent performances of high-end outliers like Porter Martone and Malcolm Spence suggest that elite prospects like Klepov often have the ceiling to significantly outpace these median projections.
Conclusion
To put a bow on this … The inaugural 2025-26 CHL data provides a foundational look at how the shifting landscape of college hockey eligibility impacts statistical projections. The results highlight a clear hierarchy in league strength, with the USHL and OHL (so far) serving as the most reliable predictors of NCAA success. These leagues emphasize a “heavy” style of play and defensive structure that mirrors the collegiate game, allowing even depth players to maintain a high degree of their junior production.
In contrast, the WHL and QMJHL demonstrated more volatility in the first year of data. While elite finishers in these leagues showed flashes of high-end transferability, the broader talent gaps and open-ice nature of these environments often lead to a steeper drop-off for mid-tier scorers.
While these findings establish a vital baseline, the CHL-to-NCAA remains an evolving experiment until we have more data to pull from. It will be interesting to see if the production lines stabilize after Year 2 of CHL eligibility. If I decide to do this again next year, I may separate the scoring for defenseman and forwards. Maybe even look at goaltending metrics.
For now, though, this is enough for what I had hoped to achieve. Thank you for reading.
This is outstanding. Thanks for pulling this together Ryan!
Great article and appreciate you putting this together! Positional differences would be great and adding the NAHL players would be great to see as well.