🏒
ECHL Rookie Development Model
Batt Analytics
2025–2026 Season
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PPG + Per-Game ± Model active.  This version adjusts projections based on a player's on-ice impact rate (±/game) from their final feeder season — removing volume distortion across different league schedule lengths. A player at −0.15/gm or below is flagged negative; +0.15/gm or above is positive.  ⚡ Model Disagrees means this model gives a different projection than the PPG-only model — worth a closer look before making a decision.
1st Liner
2nd Liner
Bottom 6 / Two-Way
Checking
Fringe
✓ ECHL Final eligible season
⚠ Sample Low-GP projection
⚡ Diff Models disagree
Player ↕ League ↕ GP ↕ PPG ↕ NHLe PPG ↓ Projection Probability Career ↕
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Player Comparison
🎯 What Does This Tool Do?

This tool looks at every forward currently playing in college hockey (NCAA, USports) or major junior (OHL, WHL, QMJHL) and asks: if this player turned pro today and played in the ECHL next season, what kind of player would they likely be?

It answers that question by comparing today's player to hundreds of players who made the same journey in recent seasons — college or junior → first year in the ECHL — and seeing how those historical players turned out based on their pre-pro scoring rate.

The result is a probability distribution, not a guarantee. A player projected at 57% 1st liner means that, historically, players at his scoring level became clear contributors about 6 times out of 10. It's a starting point for the conversation, not the final answer.

⚙️ How Does the Model Work?
01

Score Translation

A player's points-per-game in their feeder league gets converted to an NHL-equivalent rate (NHLe PPG). This levels the playing field — a 1.0 ppg player in the OHL and a 1.0 ppg player in USports are not the same, because those leagues have different competitive levels.

02

Historical Matching

That NHLe rate is matched against a database of 400+ players who went from the same feeder leagues to the ECHL between 2020 and 2025. We look at how those players actually performed in their first ECHL season.

03

Probability Output

Based on that historical group, the model outputs a probability for each of the five lineup-fit outcomes. The highest probability becomes the main projection label you see in the table.

📐 What Is NHLe PPG?

NHLe stands for NHL-equivalent points per game. It translates a player's raw scoring rate into a standardised number that accounts for how competitive their league is.

1.00
PPG in OHL
↓
0.14
NHLe PPG
1.00
PPG in NCAA
↓
0.19
NHLe PPG
1.00
PPG in USports
↓
0.13
NHLe PPG

The same raw ppg means something different in each league. NHLe normalises for that. Higher NHLe = stronger historical track record of ECHL success. Think of it as the single most important number in this table.

🏒 What Do the Projection Labels Mean?
⭐ 1st Liner ECHL PPG ≥ 0.74
Top offensive contributor. Likely a point-per-game threat in the ECHL — the kind of player who drives a line and appears in power play time. These players often move up to AHL stints or return to the ECHL as veterans anchoring a top unit.
Example: A player averaging 0.85 ppg and 35+ points over a full ECHL season.
2nd Liner ECHL PPG 0.52–0.74
Solid secondary contributor. Plays meaningful minutes, contributes offensively but not at the top line level. Reliable roster player who will hold a spot and give you production. Good depth signing with upside.
Example: 20–30 points over a full season, steady two-way presence on the second unit.
🔵 Bottom 6 / Two-Way ECHL PPG ≥ 0.32, +/- ≥ 0
Limited offence but brings value in other areas — defensive detail, physicality, face-offs, penalty kill. Won't produce many points but won't hurt you on the ice either. Positive or neutral on-ice impact.
Example: 10–18 points over a full season, strong defensive metrics, valued in a third-line role.
🟠 Checking ECHL PPG < 0.32, +/- ≥ -2
Energy/depth player. Limited offensive output and marginal on-ice impact, but not a liability. Fills a fourth-line role, brings compete and physicality. Roster-worthy, but not someone you build a line around.
Example: Under 15 points in a season, valued for PK time, face-offs, or physical play.
🔴 Fringe Everything else
Unlikely to hold a regular ECHL roster spot in year one. May need more development time, could be a practice player or depth callup, or may transition out of pro hockey. Not a signing target unless there are other factors at play.
Example: Struggled to score at the college level and posted negative on-ice impact numbers.
🚩 What Do the Flags Mean?
✓ ECHL Likely

Turning Pro This Offseason

This player appears to be in their final eligible season in their feeder league — they're either a college senior (4th year NCAA or 3rd+ year USports) or an overage-eligible junior (3rd+ year CHL). In plain terms: this is a player you could actually sign this coming offseason.

💡 Use the "ECHL Likely" filter to see only players who are realistically available next season.
⚠ Sample Flag

Small Sample — Use With Caution

This player has played fewer games than our reliability threshold for their league (roughly 25% of a full season). Their NHLe rate is based on limited data and could be misleading. A player on a hot or cold streak over 10–12 games looks very different than their true level.

💡 These players are included for awareness, but treat the projection as a rough estimate. More games will sharpen the picture.
⚡ Model Disagrees

Two Models Give Different Answers

The PPG-only model and the PPG + Per-Game ± model give this player different projection labels. This usually means their scoring looks better (or worse) than their on-ice impact numbers suggest. Worth a closer look before making a decision.

💡 Only visible in the PPG + Per-Game ± tab. A player projected "2nd Liner" by PPG but "Checking" by the ± model may score, but has struggled defensively.
🔀 Two Models — What's the Difference?

📊 PPG Model

Default View

Uses only NHLe PPG — the scoring rate translated from the player's feeder league. This is the primary model and the most reliable because it's built on the most data.

  • Best starting point for any evaluation
  • Clean and straightforward to interpret
  • Use this when you want a quick read on a player

🔀 PPG + Per-Game ± Model

Secondary View

Adds the player's on-ice plus/minus rate (per game played, not raw total) as a second signal. This captures whether a player is helping or hurting their team beyond what the scoresheet shows.

  • More nuanced — useful for close calls
  • The ± rate is normalised per game to be fair across different schedule lengths
  • Watch for the ⚡ Diff flag — that's where this model adds real value
When the models disagree — that's the most interesting signal in the tool. A high scorer with a very negative ±/gm might look like a 2nd liner by PPG but drop to Checking in the ± model. That could mean they're on a terrible team, or it could mean they have real defensive issues. Either way, it's worth digging into before signing.
💡 Practical Tips
🎯

Start With the ECHL Likely Filter

Set the "ECHL Likely" filter to "ECHL Likely Only" to instantly narrow to players who could actually be signed this offseason. This removes underclassmen and keeps the list actionable.

📊

Sort by NHLe PPG First

Click the NHLe PPG column header to sort from highest to lowest. This is the single best ranking of how likely a player is to contribute in the ECHL. The top of that list is your primary target pool.

⚠️

Be Careful With Sample Flags Mid-Season

Early in the season many players have only played 10–15 games. Their rates are unstable. Set the GP slider to 20+ if you want a more reliable read. Come back in February for a fuller picture.

🔀

Use the ± Model as a Tiebreaker

If two players have similar NHLe PPG, flip to the PPG + ± tab. The one who also has a positive on-ice impact rate is the safer bet. A ⚡ Diff flag between two otherwise comparable players is a meaningful differentiator.

🔴

Fringe Doesn't Mean Never

A Fringe projection means the historical base rate for players at that scoring level making an ECHL impact is low — not zero. Late-blooming players, physical-game contributors, and goalie interference specialists sometimes beat the model. Use it as context, not a veto.

📋

This Is One Input, Not the Decision

The model doesn't know about skating, compete level, injury history, character, or how a player fits your specific system. It's a data-informed starting point. Your scouts and staff always have the final word.