When the 2026 NHL Draft wrapped up in Buffalo, the AB Score was primarily an NHL evaluation tool. It could grade a player’s season, project his next contract, and rank NHL coaches by how their players develop. Since then, hockeyfreeforall.com has spent months expanding the system’s capabilities. The result is a full suite of tools that now covers 20 leagues, every NHL game as it’s played, every Division I college program, and nearly every decision a hockey operations department, agency, college program, or family faces over the course of a player’s career.

This article walks through every tool currently available to HFFA. Each section explains what the tool does, how it works, what one looks like in practice, and who it’s built to help. The underlying database now holds more than 92,000 player-league seasons, covering roughly 23,600 players across 20 professional and junior leagues dating back to 2007.

The Foundation: One Score Across 20 Leagues

Every tool in this article is built on the same core idea. The AB Score measures how much a player contributes to winning, with a particular emphasis on the habits that don’t always show up on a traditional score sheet. Beyond production, it accounts for discipline, measured through the penalties a player draws and takes and how much his team’s power play and penalty kill are actually worth in those situations. It accounts for puck management, through takeaways and giveaways weighted by how often possessions turn into goals. It also captures his on-ice goal differential. The philosophy is simple: players who consistently avoid mistakes and force opponents into them quietly decide games, and we can measure that.

Validating the Score

A metric is only useful if it reflects winning, so we tested the AB Score league by league. Using eight seasons of final standings, from 2018-2019 through 2025-2026, I compared each team’s total AB to where it actually finished using Spearman’s rank correlation. That statistic ranges from -1 to +1: +1 means the rankings match perfectly, 0 means no relationship, and -1 means they’re exact opposites. Across 17 leagues, the average correlation was +0.863. It reached +0.928 in the Swiss League, +0.916 in the DEL, and above +0.90 in the OHL, KHL, QMJHL, and WHL, with the NCAA’s conferences averaging +0.778. Teams with more AB consistently finish higher, regardless of league, rink size, or style of play.

That consistency is what makes everything else in this article possible. A measure that only worked in one league would be a curiosity. A measure that tracks winning in the NHL, the KHL, major junior, and college hockey is a common language for the entire sport.

The Deflator System

A raw AB Score from the WHL and a raw AB Score from the SHL aren’t directly comparable, because league strength varies enormously. The deflator system solves that. For each league and position, the system identifies every player who played there and later logged at least 20 NHL games, then measures the gap between his production in that league and his production in the NHL. That gap becomes the league’s deflator. A raw AB Score plus its league’s deflator produces an NHL-equivalent score.

I rebuilt the deflators throughout the summer to remove noise. The original version averaged every player’s gap regardless of how much NHL time he had, letting brief call-ups and a handful of outliers distort the results. The current version requires 20 NHL games, chooses a mean or median for each league based on how much the two differ, and assigns each deflator a confidence tier based on its sample size. The major junior leagues carry the largest adjustments, with OHL forwards at negative 2.826, while the SHL, KHL, and NCAA sit much closer to NHL level. Because every league connects through the NHL, you can compare any two leagues directly, even when very few players have ever moved between them.

For scouts, agents, and families, this changes how prospects are compared. A dominant season in major junior and a solid season against men in Sweden can look completely different on paper and turn out nearly identical once you put them on the same scale.

Built to Be Adjusted

The system is also designed to be refined by hand. Every player is matched across sources through a four-tier name system that handles accents, nicknames like Matt and Matthew, and one-letter spelling differences, while a separate check confirms that a player’s seasons age forward consistently so that two people who share a name are never merged into one career. If a comparable doesn’t fit, it can be excluded and the card rebuilt instantly, with the next-best match filling its place and the contract projection updating with it. Trades and signings that haven’t reached the data sources yet can be entered manually, so rosters are always current.

NHL Player Cards and Contract Projections

The NHL player card is the platform’s centerpiece, and we rebuilt it from the ground up this summer. Each card shows a player’s season snapshot, career AB trajectory, current contract and remaining term, and two sets of comparables: players with similar recent seasons and players with similar career paths.

How the Contract Projection Works

The contract projection blends two independent approaches. The first is an ensemble of three machine learning models, ridge regression, gradient boosting, and random forest, trained on every NHL contract signing in the database. Each model learns from a player’s AB Score, points, games played, age, draft round, service time, contract status, career averages and peaks, and the direction his AB and scoring are trending. The second approach is a comparables engine that finds players whose profiles most closely match and measures what they actually signed for as a share of the cap.

This summer, I added several refinements to fix the problems that trip up most contract models. The models now train only in seasons when a new contract actually began, rather than every season of an existing deal, so they learn from real market signings instead of years with no change. They also judge players on performance alone, ignoring what a player is currently paid, so a star coming off a bargain deal isn’t dragged down by it. Elite, super, and generational performers receive multipliers that reflect how the market actually pays stars; young stars receive a growth adjustment, and veterans follow a decline curve that begins in their early thirties.

The most important refinement is how contracts are compared. I convert every comparable contract to a percentage of the salary cap, using the cap of the season it was signed in or, for extensions that haven’t started yet, the season it will be paid in. A $7 million deal signed under the flat $81.5 million cap took 8.6% of team spending room, while the same $7 million today takes just 6.7%, so raw dollar comparisons across eras are misleading. Contract data now comes directly from CapWages, and the model includes a cap-growth adjustment because its training data comes mostly from years when the cap rose 2% to 4% per season, while the cap is now climbing from $104 million toward a projected $127.5 million by 2028-2029. I can set any card to a future season, so I can project a pending free agent against the cap he’ll actually sign and play under.

The model is also tested against reality. A calibration step projects players whose most recent contracts are known as if they were the day before they signed, then compares the projection to what they actually received. A separate inspection step displays the full pool of comparables behind each projection, not just the three shown on the card, so I can trace every number back to the players that produced it.

The Live Player Card

The newest version is the live player card, shown above for Nashville’s Jayden Struble. It places every league a player has appeared in on one trajectory chart, with his NCAA, AHL, and NHL seasons each shown against that league’s replacement level. Replacement is defined consistently across leagues: the system finds where the NHL replacement level falls among NHL players at his position, then applies that same percentile to every other league on the NHL-equivalent scale.

The card also pulls in the current season game by game and builds a projected season from it. Early in the year, the projection leans on a player’s history, and the current season’s weight grows with every game played until, after about 25 games, the two count equally. That projected season then feeds the comparables and the contract projection automatically. Struble’s card shows his 2026-2027 pace through three games, a projected AB of 0.28, and a projected next contract of $3.65 million, or 3.23% of the 2027-2028 cap, as he heads toward restricted free agency with arbitration rights. His comparables, including Justin Barron, Braden Schneider, and Wyatt Kaiser, show what similar defensemen have commanded.

Because every season in the database sits on the same NHL-equivalent scale, the card’s trajectory and comparables can draw on a player’s history from any of the 20 leagues, so a player who came through the KHL, the SHL, the NCAA, or major junior is evaluated with his full career in view.

For GMs, the player card shows exactly where any contract sits relative to the market before a negotiation begins, and whether a player’s current deal is producing surplus value or costing the team. For agents, it provides a data-backed number to bring to the table, based on what comparable players actually earned and adjusted for the cap their client will sign under. For players and families, it offers an independent view of what a fair contract looks like, rather than relying solely on one side of the negotiation.

Team AB and Salary Charts

The team charts have been the backbone of every team analysis on this website. The AB chart ranks every player on a roster by AB Score against the league replacement level of 1.17, showing at a glance where a team’s strength is concentrated and where the depth falls off. The 2026-2027 Devils chart shows 11 players above replacement, led by Jack Hughes (3.55), Jesper Bratt (3.06), and Anthony Mantha (3.04), followed by a clear drop to a group of defensemen and depth forwards well below the line.

The salary chart compares each player’s current cap hit to the contract model’s projected market value. When the projected value exceeds the cap hit, a team gets surplus. When it sits below, the team is overpaying. For New Jersey, Luke Hughes ($9.00 million against an $11.62 million projection), Jack Hughes ($8.00 million against $11.50 million), and Luke Evangelista ($3.00 million against $7.03 million) stand out as surplus contracts, while Dougie Hamilton ($9.00 million against $5.68 million) and Brett Pesce ($5.50 million against $2.95 million) sit well above their projections. Summing those gaps across a roster gives a team’s net surplus value, a single number showing whether a front office gets more on-ice value than it pays for.

Together, the two charts answer the two most important questions about any roster: what it produces and what it costs. Rosters can also be updated by hand for trades and signings, so the charts reflect how the team looks today.

The AB chart can be built for any team in any of the 20 leagues in the database, from an NHL roster to a KHL, SHL, or WHL club, while the salary comparison currently runs on NHL contracts.

For GMs, these charts show where cap dollars are being spent efficiently and where they aren’t, which players are blocking younger ones, and which contracts are best candidates to move. For agents, they show which teams have overpaid at a client’s position, which are receiving surplus they’ll eventually need to replace, and which have a hole a client could fill.

Agency Portfolio Charts

The same contract engine can run on any list of players at once, not just one team’s roster. For an agency, that produces three charts covering an entire client list: every client’s AB Score against replacement, every client’s current cap hit against his projected value, and every client’s career AB average against the replacement level across the entire study from 2007 onward. Because the batch version uses the exact same engine as the individual player card, every number matches.

The example above, built from Allain Roy’s client list as listed on PuckPedia, shows how the charts work for a real agency. The current-season chart ranks every client by AB Score against the league replacement level, with Brandon Hagel (4.04), Nico Hischier (2.94), Dylan Cozens (2.54), and Josh Doan (2.39) clearing the line.

The career chart shows each client’s average AB across every season in the study from 2007 onward, which separates players having one strong year from those with a sustained track record. Hagel (3.15) and Hischier (2.66) lead, with Cozens, Andrew Mangiapane, Danton Heinen, Josh Doan, and Erik Haula all at or above the career replacement level of 1.10.

The salary chart compares each client’s current cap hit to his projected market value, which points directly at the negotiations with the most upside. Doan, earning $0.93 million against a $4.21 million projection, and Hagel, at $6.50 million against $9.19 million, are delivering the most surplus value to their teams, while Ryan Greene ($0.95 million against $2.23 million) and several entry-level clients also project well above their current deals.

Because client lists rarely sit in one league, the AB and career charts can include players from any of the 20 leagues in the database, all placed on the same NHL-equivalent scale, so an agency’s NHL veterans, AHL players, European clients, and junior prospects can be viewed together.

For agents, this is a portfolio view of an entire client list in one place, showing which clients are underpaid heading into their next negotiation, which are outperforming their contracts, and which careers are trending up or down. It’s also a powerful recruiting tool, showing prospective clients exactly how their value would be measured and presented.

Career Trajectory Across Leagues

The career trajectory card follows a player through every league he’s played in, converting each season to the NHL-equivalent scale so that a season in college, the AHL, and the NHL can be read on one line. Joey Anderson’s card traces 14 seasons from the NCAA through the AHL and NHL. It shows his career peak of +1.55 with the AHL in 2021-2022, the one season out of 14 that cleared the NHL replacement level of +1.17, his most recent score of +0.71 in the AHL, and an overall trend of +0.12 since his first college season.

That kind of view matters most for players who split time between leagues. A traditional stat line treats an AHL season and an NHL season as separate stories, while the trajectory card shows them as one career on one scale. It highlights whether a player’s best hockey came in the AHL or the NHL, whether his NHL stints have reflected his true level, and whether his career is trending toward or away from a full-time NHL role.

The card works for any player whose career runs through any combination of the 20 leagues in the database, whether that path goes from the USHL to college, from Liiga to the AHL, or from the KHL to the NHL.

For agents, this is the clearest way to present a client’s full body of work rather than his most recent stint, especially for players whose NHL opportunities have been limited. For pro scouts and GMs evaluating depth signings and waiver claims, it shows whether a player’s AHL production is the kind that historically carries over to the NHL.

The Team Opportunity Finder

The Team Opportunity Finder answers one of the most important questions in any player’s career: where is the clearest path to playing time? It takes a player’s NHL-equivalent AB Score and compares it to every NHL and AHL team’s group at his position. Each team’s average AB at that position is the opportunity signal, so a group that averages well below the player’s own score represents a real opening. The tool then identifies the weakest groups and shows exactly where the player would rank on each team’s depth chart and how many players he would outperform.

Because AHL scores run higher than NHL scores before adjustment, both leagues are placed on the NHL-equivalent scale and given their own replacement lines, so the comparison is fair across both. Anderson, at +0.71 on the NHL scale, sits well above the AHL replacement level of +0.30 and 0.47 below the NHL line of +1.17. The tool shows that he would rank eighth among Vancouver’s forwards, beating nine of 16, and ninth in both Chicago and New Jersey. In the AHL, he would rank as high as third in Hartford and fourth in Rockford and Iowa. On the chart, every dot is a player, colored dots are players Anderson outscores, and the white tick marks each team’s average.

Because the player’s own score is on the NHL-equivalent scale, the tool works for a player coming from any of the 20 leagues in the database, so a forward finishing a season in the SHL, the KHL, or college can be measured against every NHL and AHL depth chart in exactly the same way.

For agents and players, this turns free agency, trade requests, and waiver situations into targeted decisions rather than guesswork, identifying the organizations where a client would actually play. For families of young professionals deciding where to sign, it shows which organizations offer a real opportunity rather than a crowded depth chart. For GMs, the same tool run in reverse shows which positions on their own roster are the thinnest in the league.

The Opportunity Projector

Where the Opportunity Finder identifies openings, the Opportunity Projector estimates what a move would actually do to a player’s production. Built from every NHL season since 2007, it breaks a change in a player’s projected AB into three sources: the ice time he’d receive in his new role, the coach he’d play for (measured through the AB Coaches study), and the linemates he’d play with.

The Dylan Larkin card shows how those pieces interact. Staying in Detroit, Larkin projects to +2.45 to +3.47 over 84 games, with a middle estimate of +2.96. A hypothetical move to San Jose, where he’d likely see around 15 minutes per night playing with Ivar Stenberg and Kiefer Sherwood, projects to +1.56 to +2.58, a downgrade of 0.89. The breakdown shows that almost all of that drop, 0.82, comes from lost ice time, while the change in coach adds just 0.02 and the change in linemates costs 0.09. The card compares the two coaches directly, Todd McLellan and Ryan Warsofsky, on Return IMP %, Advantage %, and Average IMP.

The card then ranks Larkin’s best fit. Colorado projects +2.98 to +3.99, up 0.53 from staying put, as a second-line center at 19.4 minutes per night under Jared Bednar. Minnesota and Edmonton follow, and each fit lists the projected role, ice time, coach, cap space, and the players he’d slot ahead of on the depth chart.

Because the inputs come from the same cross-league database, the projector can evaluate players arriving from any of the 20 leagues, not just players moving between NHL teams.

For GMs, this adds a layer to every trade conversation, showing how a target’s production is likely to change in their specific system, with their coach and their linemates, rather than assuming he’ll replicate what he did elsewhere. For agents navigating a trade request, it identifies destinations where a client’s value would grow rather than shrink, strengthening both the player’s career and his next contract. For players, it shows how much of their production is tied to opportunity rather than ability.

Game-by-Game AB

For the 2026-2027 season, HFFA now scores every skater in every NHL game. After each game, the system pulls the box score and play-by-play directly from the NHL and applies the same AB formula used for full seasons. Each team’s power play and penalty kill rates are calculated for that specific game, with coincidental minors canceled out so they don’t count as power play opportunities, and games without a real goal, such as a 0-0 game decided in a shootout, are scored using league-average rates so that a quiet night doesn’t erase every player’s contribution. Every finished game is saved once and never recalculated, so the full season builds quickly as it goes.

The single game card above shows the Rangers’ 6-2 win over Vancouver on October 11th. The card shows every skater on both teams on the same scale, split into forwards and defensemen, with each bar colored by whether the player finished above the league’s median single-game score for his position.

The same data powers individual game logs. Gavin McKenna’s card tracks his first six NHL games, his total AB of +0.158 against replacement, and a week-by-week strip showing how he moved from below replacement in his first week to well above it in his second, with his best game coming against Nashville on October 6th. A season-to-date team card does the same for an entire roster, showing each skater’s average game AB across every game played so far.

Leaderboards

The game-by-game engine also produces nightly, weekly, and season-long leaderboards. The Star of the Night, Star of the Week, and Season Leaders cards rank the top and bottom five skaters across the league, each measured against what a replacement-level player would have produced over the same games. Replacement is defined as the average game AB of skaters outside each team’s top 12 forwards and top six defensemen by ice time, recalculated as the season goes, so every comparison reflects this season’s league rather than last year’s.

Team Nightly Rankings

At the team level, the nightly rankings track how often each player finishes among his team’s top three or bottom three forwards and top two or bottom two defensemen. Each player’s line shows his finish in the most recent game, his average finish, his average and total AB, and his pace over a full 84-game season compared to a replacement-level player at his position. Leader boxes highlight the team’s best forward and defenseman most nights, as well as the player most often at the bottom of the lineup.

The game-by-game engine currently runs on NHL games, using the same AB formula that scores every season across all 20 leagues in the database, so a player’s nightly numbers connect directly to his history wherever he played before.

For coaches, game-by-game data offers an objective read on who drives results each night, who struggles, and whether a player’s role matches his performance, independent of the final score. For player development staff, a game log shows whether a young player is trending in the right direction week to week. For media and fans, it turns every night of hockey into something measurable, and for agents, it provides a running record of a client’s in-season performance well before any contract talks begin.

Live Standings and Team Outlooks

The same game data rolls up into team-level views. The standings tracker lists every division in its actual standings order and shows each team’s league rank in total AB, its current playoff seed, its preseason projection, and how far it has moved from that projection. It then measures how closely each ranking matches reality using Spearman’s rank correlation. Through the first week of the season, team AB matched the actual standings between +0.64 and +0.74 in every division, more closely than the preseason projections, and correctly placed 13 of the 16 teams currently holding playoff spots.

The position group outlook plots every team’s forwards and defensemen relative to replacement level on an 84-game pace. Teams in the top-right are strong at both positions, teams in the bottom-left are below replacement at both, and teams in the other two quadrants are being carried by one group. Leader panels rank the league’s best forward groups, defense corps, and overall rosters.

The standings comparison uses the same team AB approach validated against final standings across 17 leagues, so it can produce the same view for any league in the database with standings data.

For GMs and pro scouting departments, these views show early in the season which teams’ records are supported by their play and which are likely to regress, which matters for trade timing and deadline planning. For coaches, the position group view shows whether a team’s issues lie up front or on the back end. For media and fans, it’s a more reliable early-season read than the standings alone.

AB Lines: Optimal Line Combinations

AB Lines builds the line combinations that put the most AB where the ice time is. Each player’s value starts with a projection built from his last three seasons, weighted most heavily toward the most recent, with every season aged forward using an age curve measured from every NHL season in the database. Seasons from other leagues are already on the NHL scale through the deflators, and players with less history are pulled toward a typical player until they’ve proven otherwise. As the season goes on, each player’s current game AB pace gradually takes over.

The system then assigns every player to his real position, center, left wing, right wing, left defense, or right defense, and searches for the lineup that places the most AB in the slots that receive the most ice time, both at five on five and on the power play.

Chemistry is built from NHL shift charts, which record every second each pair of teammates spends on the ice together, separated into five-on-five and the power play. A model measures how each pair performs together relative to their individual projections, but a pair gets a chemistry adjustment only after they’ve proven it over at least 100 shared five-on-five minutes. Everyone else is treated as neutral, so the system never invents chemistry that hasn’t been shown on the ice.

The Toronto card compares the Leafs’ actual lines, defined by which players have shared the most ice time, to their optimal lines. Through October 10th, the clearest gain came from moving Gavin McKenna to the top line alongside Auston Matthews and Kirill Marchenko, while the defense pairs and power play units were already close to optimal.

The league-wide version ranks all 32 teams by how much AB they’re leaving on the table, the gap between their optimal lines and their actual ones, split into five on five and the power play. Through the first week, Toronto and the Rangers were closest to optimal, while San Jose, Philadelphia, and Edmonton had the most room to improve.

Because player projections draw on every league in the database, a newly arrived player from Europe, college, or major junior enters the lineup search with a projection already on the NHL scale rather than a blank slate.

For coaches, AB Lines is a second opinion on every lineup decision, grounded in both individual performance and proven chemistry, and a way to test combinations before trying them in a game. For GMs, it separates personnel problems from deployment problems, showing whether a struggling team needs new players or better use of the ones it has. For agents, it shows whether a client is being deployed in a role that matches his value.

Points Projections

The points projection model estimates how many points any player will score in a given season. It starts by placing every season from all 20 leagues on an NHL-equivalent scale. To build those translation factors, it studies thousands of players who moved from one league to another in consecutive seasons and compares the points they actually scored in the new league to what their previous scoring rate would have predicted. Because young players naturally improve from one year to the next, the model first measures typical development at every age from 16 to 35 and removes it, so a teenager’s growth isn’t mistaken for a difference in league strength.

The model projects established NHL players using a regression based on their last two seasons, age, and position. Rookies are projected through comparables, matching each player to the 12 most similar first-year NHL players based on translated scoring in their final two pre-NHL seasons, age at debut, draft position, and era. The model adjusts each comparable rookie output to the player’s level, and the spread across all 12 produces a realistic range.

In backtesting across the last four NHL seasons, using only the data available at the time, the model’s projections typically landed within 7.4 points of veterans’ actual totals and within 8.5 points for rookies 21 and under. It projected Gavin McKenna at 44 points as a baseline, with a range of 24 to 67, based on comparables led by Connor Bedard and Macklin Celebrini.

The translation factors cover all 20 leagues in the database, so a projection can be built for a player arriving from any of them, from the J20 in Sweden to the KHL.

For families and agents of draft-eligible and newly drafted players, it sets realistic, data-backed expectations for a first professional season and identifies which past players followed a similar path. For GMs, it adds a scoring estimate to every roster and contract decision, and for fantasy players and media, it offers projections grounded in how similar players actually performed.

Preseason Predictions

The preseason model projects every team’s season from its roster. It projects each player’s AB for the coming season based on his recent history, age, and the leagues he played in, then builds each team’s dressed lineup from its top 12 forwards and top six defensemen, with partial weight for depth players to account for injuries. Empty lineup spots are filled at replacement level. A team effect, measured from how many points each team has earned above or below its expected total over the past three seasons, captures goaltending, coaching, and systems.

The model was calibrated by rebuilding every team’s roster for every full season since 2010 using only information available before that season, then measuring how lineup strength translated into points. It simulates the season 10,000 times to produce playoff odds and projected seeds for all 32 teams.

Because player projections are built on the NHL-equivalent scale, newly signed players from Europe, college, and major junior are included in each team’s lineup from the start.

For front offices, it offers an objective baseline for where a roster stands before the season and how much each move changes a team’s playoff odds. For media and fans, it provides a transparent projection that can be tracked against the standings throughout the year.

The Expansion Index

The same framework also applies beyond the rink. The Expansion Index ranks potential NHL markets using nine weighted factors: metro population, television households, Fortune 500 headquarters, median household income, arena readiness, hockey culture, competing major league teams, ownership interest, and public transit access to the proposed arena site. The transit factor reflects an earlier hockeyfreeforall.com study finding that teams whose arenas are accessible by public transportation draw higher attendance than those whose fans can only drive.

Because any weighting is a judgment call, the model stress-tests its rankings across 10,000 different weighting scenarios and reports how often each city finishes first, in the top two, and in the top four. It also projects each market’s year-one revenue and calculates the cost of entry per television household.

For league offices, prospective ownership groups, and local governments, it provides a transparent, data-driven way to compare markets and test which assumptions drive the result. For fans, it shows which expansion arguments the numbers actually support.

(See the expansion article on HFFA here The Hockeyfreeforall.com Expansion Index: Ranking the NHL’s 12 Candidate Cities – hockey free for all.com (HOME OF THE ADVANCED BRACTON))

World Player Cards and Prospect Evaluation

The World Player Card brings the NHL card format to any player in any of the 20 leagues. Each card shows his raw AB Score, his NHL-equivalent AB with the deflator calculation printed directly on the card, and his career trajectory against his own league’s replacement level, calculated separately for every league and season since league strength can shift from year to year.

Comparables come from every league through a single cross-league engine, with every candidate converted to the NHL-equivalent scale before comparison. That’s what makes it valid to compare a USHL forward to a WHL or NCAA forward. For draft-eligible prospects, comparables come only from other players’ seasons before their own draft year, so a 17-year-old is compared to what other players looked like at the same stage, not after they’d developed. The card then projects a draft round by averaging the actual draft round of the full comparable pool, typically up to 50 players, rather than just the six shown on the card. A separate inspection step lists the entire pool, so you can trace every projection back to the players behind it.

For players already drafted, a separate engine aligns each player by his draft-relative season. A player’s draft season is compared to other players’ draft seasons, his draft-minus-one season to theirs, and so on, so McKenna’s draft year is compared directly to Bedard’s draft year rather than to whatever Bedard did most recently. It then projects an NHL outcome by averaging the first three NHL seasons of his closest comparables. The two-player comparison card, used for McKenna and Ivar Stenberg, places two prospects side by side, compares their career NHL-equivalent scores and projected NHL outcomes, and issues a ruling.

The Draft Class Export ranks entire draft classes by their pre-draft NHL-equivalent AB, regardless of whether those players ever reached the NHL, and compares that ranking to where players were actually selected. That makes it possible to test the system against how past classes developed and to identify the types of players the draft has consistently undervalued.

These cards can be produced for any player in any of the 20 leagues in the database, anywhere in the world.

For NHL scouting departments, this provides a common language for comparing prospects across every league in the world, from the J20 in Sweden to the USHL. For families of draft-eligible players, it shows exactly where their son stands against peers around the world, which past players followed a similar path, and what round players with his profile were typically selected in. For agents recruiting young clients, it identifies undervalued prospects before the rest of the market does, and for international scouts, it puts players in smaller leagues on equal footing with those in the most visible ones.

Coaching Evaluations

The AB Coaches study evaluates every coach on how his players develop. It combines five components: Return Improvement Percentage, the share of a coach’s returning players who improve from one season to the next; Advantage Percentage, the share of players who improve under him compared to their previous coach; Average Improvement, the typical change in his players’ AB; Average Player AB; and a Range Score reflecting where his players typically sit on the league’s AB distribution. Bayesian adjustments blend each coach’s results with the league average based on how much evidence he has, so a coach with only a season or two isn’t overrated or underrated relative to one with a long career.

Each card shows a coach’s rank, his Coaching AB, a confidence rating based on his sample size, and his career trajectory against his closest statistical comparable. It also breaks down his strengths relative to the study average and the top 10, his key players, and how his roster’s age, salary, and draft pedigree have changed over time. For example, Mike Sullivan’s card ranks him 20th with a 97.6 confidence rating and Claude Julien as his closest comparable.

The same evaluation now runs for any league in the database through the World Coach Card, so a junior or European coach can be measured the same way as an NHL coach. The new Player Coaching History card takes the opposite approach. It traces every coach a player has played across every league in his career and charts Return IMP % and Advantage % year by year, showing which coaching environments helped him develop and which held him back.

You can apply the coaching evaluation to any coach in any of the 20 leagues in the database, from the NHL and AHL to the KHL, Liiga, the SHL, the DEL, major junior, the USHL, and the NCAA.

For GMs and team presidents, coaching evaluations add objective data to hiring and firing decisions, separating a coach’s impact from the quality of the roster he was given. For coaches, the cards document their development record in a way a win-loss record can’t. For families choosing a junior, college, or European program, and for agents placing young clients, the coach cards and player coaching history show which coaches have a proven track record of developing players like theirs.

College Hockey

I assembled the college tools into a full program report, starting with Providence College. Every report starts with a live scraper that pulls current rosters from all 63 Division I programs, so freshmen, transfers, and midseason changes are always up to date. I then rank each roster against its conference and the entire country by total team score, rewarding depth over a roster built around two or three standouts.

The roster chart projects each player’s career AB onto the NCAA scale. Players without NCAA history, including true freshmen and first-year transfers, are projected from their junior or prep leagues through the deflator system and marked on the chart, so incoming players can be compared to returning ones before a single college game is played. Providence’s chart shows freshmen from the USHL, Blake Zielinski and J.J. Monteiro, projecting at the top of the roster because USHL production converts to the NCAA scale with very little loss.

The report also ranks every program’s incoming recruiting class nationally. I exclude classes without enough data to judge fairly, rather than scoring them on a handful of matches. Transfer portal activity is measured as talent added and talent lost, ranked by talent added and never netted together, since losing a below-replacement player shouldn’t count the same as adding a real contributor. By that measure, Providence ranked first in Hockey East and 12th nationally in talent added this past cycle.

The NCAA coaching evaluation goes a step further than the professional version. It combines Coaching AB with an NHL Quality Score, which measures how a coach’s former players have performed in the NHL by their AB, their post-entry-level salaries, and how often they’ve cleared replacement level. It also adds a Draft Positioning Score that credits the draft pedigree of his current roster, using point values calibrated on draft classes old enough to have matured. Providence’s Nate Leaman ranks 29th of 100 active coaches in Coaching AB, seventh in Draft Positioning, and 18th overall.

Individual college cards convert any recruit’s junior numbers into a college-equivalent AB Score, with the full calculation printed on the card. J.J. Monteiro, a Providence freshman from the USHL’s Sioux Falls Stampede, projects to +3.07 on the NCAA scale, with career comparables that include his new teammate Zielinski. The same tools also produce scouting reports on upcoming opponents, rank their rosters within their conference, and flag matchup targets based on each opposing player’s actual ice time this season.

I can project any incoming college player from any of the 20 leagues in the database, whether he’s arriving from the USHL, major junior, the J20, or a European pro league.

For college coaches and recruiting staffs, recruiting, portal, and roster decisions can all be measured on one scale, with incoming freshmen compared directly to returning players and portal targets evaluated before they ever arrive. For athletic departments, the same architecture can be pointed at other priorities, including NIL spending efficiency and resource allocation. For families choosing a program, it offers a way to compare schools by how their coaches actually develop players and where their son would fit on the depth chart. For NHL teams tracking their college draft picks, it shows how those prospects are developing against their peers.

Conclusion

In the months since the 2026 NHL Draft in Buffalo, the AB platform has grown from an NHL evaluation tool into a system that follows players from junior hockey and college through the AHL, Europe, and the NHL, game by game, contract by contract, and coach by coach. Every tool in this article runs on the same foundation, validated against standings league after league, so a 16-year-old in the WHL, a college freshman at Providence, an AHL forward seeking an opportunity, and an NHL star considering a trade can all be evaluated in the same language.

The work is ongoing, and new tools will continue to be added as the season progresses. For GMs, coaches, agents, families, college programs, and anyone around the hockey world looking to make better decisions with better data, hockeyfreeforall.com is ready to help.