Stats Glossary

Every shot's scoring probability — judged on location, angle, shot type and game state — summed up.

It counts the goals a team should have scored on its chances: the cleanest read on who actually controlled the game.

A 0.20 xG shot goes in about 1 time in 5. An average NHL shot is worth ~0.07.

A team's slice of all the expected goals in a game or season: xGF ÷ (xGF + xGA).

The single most predictive team-quality number we have — it forecasts future wins better than wins do.

50% = dead even · 52%+ = solidly good · 55%+ = territorial control.

Share of all shot attempts — on goal, missed or blocked — while a team is on the ice at 5-on-5.

A volume proxy for possession: you can't attempt shots without the puck.

50% = even · 53%+ = a strong possession side.

Corsi minus blocked shots — the share of unblocked attempts.

Possession, weighted toward the attempts that actually reach the net.

Shot attempts from the slot and crease — the ice where shots become goals most often.

Quality over volume: who created the looks that actually go in.

Every chance binned by scoring probability: high (slot and crease), medium, and low (points, bad angles).

Ten point shots can matter less than one clean look from the slot — the tiers keep shot totals honest.

Roughly: high ≈ 1-in-5 scores · medium ≈ 1-in-15 · low ≈ 1-in-30.

A shot created within seconds of the puck entering the zone, before the defense is set.

Rush shots score far more often than set-play shots, so the xG model prices them up.

A shot taken quickly after a previous shot, usually with the goalie out of position.

Rebounds convert at a multiple of first shots — teams that generate them out-score their raw volume.

Expected goals a team or player generates per 60 minutes of ice time.

The offense dial: how dangerous you are, with the finishing luck stripped out.

League average sits near 2.6 at 5-on-5 · 3.0+ is dangerous.

The quality of chances given up per 60 minutes while on the ice.

The defense dial: it measures suppression before the goalie gets involved.

Lower is better · ~2.6 = league average · near 2.0 = elite suppression.

Actual goals scored. The scoreboard.

The result layer — always reconciled here against process (xG) and luck (PDO), never read alone.

Any counting stat scaled to a full 60 minutes of ice time.

Raw totals reward minutes played; rates reveal who produces the most while actually out there.

On-ice shooting % plus save % — the two bounce-driven numbers. The league averages almost exactly 1.000.

Big deviations are mostly luck and mean-revert hard: a hot PDO team is often a fade, a cold one a buy.

1.000 = neutral · above ~1.02 = running hot · below ~0.98 = due better.

Actual goals minus expected goals — how far the results outran (or trailed) the chances.

Positive means hot or genuinely skilled finishing; most of it mean-reverts, elite snipers keep a slice.

+ = burying more than the looks deserved · − = snakebitten.

Expected goals against minus actual goals against, for a goalie.

The modern goalie test: did he beat the quality of shots he faced? Positive = stole saves.

0 = exactly as expected · +10 over a season = stolen games · negative = leaking.

Saves divided by shots against.

The classic number — but blind to shot quality, which is why we always pair it with GSAx.

Save percentage on high-danger shots only — the slot-and-crease looks.

Where goalies actually separate: anyone stops point shots; stopping chances in tight is the skill.

One minus the average chance value of the attempts a goalie faced — the save rate an average goalie posts on those exact shots. Measured on every unblocked attempt, misses included, so it sits above the official SV%.

It turns 'he had a .912' into a fair question: a .912 behind a defence that gives up tips and rebounds can be a better season than a .920 behind one that allows point shots.

Compare it to the goalie's own attempt save rate, not to the official SV% · the gap, in goals, is GSAx.

A 0–100 percentile among goalies with 15+ games, blending GSAx per 60 (45%), high-danger GSAx per attempt (20%), quality-start rate (15%), rebound control (10%) and steadiness (10%). Letter grades follow the percentile.

Save percentage tells half the story. The index ranks goalies on what they stopped against what the shots deserved, and on doing it night after night.

A = top fifth · C = the middle · a ranking device, not a wins number.

The share of attempts a goalie faced that were followed by another attempt from the same team within three seconds.

A save that leaves the puck in the slot is half a save. League average is about 9%.

A skater's night in one letter. Impact = 0.75 G + 0.7 A1 + 0.55 A2 + 0.075 SOG + 0.05 BLK + 0.15 penalties drawn − 0.15 taken + 0.01 faceoff wins − 0.01 losses + 0.3 individual xG + 0.5 on-ice xG for − 0.5 on-ice xG against. The rating blends impact (65%) with impact per sixty minutes (35%) and ranks it among the skaters in that game.

Points reward the scoresheet; this rewards the whole shift. A defenceman who was on the ice for 70% of the chances gets paid for it, and a forward who did his damage in eight minutes gets credit for the rate, not only the total.

A+ is the top few skaters that night · C is the middle · it compares players in the same game, never across games.

How much a goal moved the home side's chance of winning, from our in-game win-probability model fitted on every game we have collected: the score before, the score after, and the time left.

A 2–1 goal at 19:00 of the third is not the same goal as a 4–0 goal in the first. The swing says which goals decided the game.

20+ points = the game turned on it · under 5 = a footnote.

The trained game-outcome model's pre-game chance of winning, from form, xG, goaltending and rest.

Hockey is noisy — even heavy favorites lose 4 of 10 replays. Read it as a lean, never a call.

55% is a real edge in this sport · 65%+ is rare.

Our all-in player value estimate, built from 5-on-5 on-ice impact. A proxy — not official RAPM-style WAR.

It ranks players usefully, but on-ice numbers share credit with linemates — trust the order, not the decimals.

0 ≈ replacement level · +2 = clear top-of-lineup player · gaps under ~0.3 are ties.

A skater's on-ice impact after adjusting for role, minutes and teammates — the input under the WAR proxy.

Closer to the player's own signal than raw on-ice share, but still a model estimate.

Six percentile dials per skater, versus the same position: offense, creation, defense, finishing, danger, two-way.

Two players with the same value can be opposites — the profile shows how the value is made.

Each bar is a 0–100 percentile against the same position.

A blend of a U-23 skater's current impact, year-over-year xG growth, and the development model's projection.

It flags who is becoming good before the goal totals announce it.

Higher = stronger breakout case. The score ranks the class; it doesn't guarantee anyone.

The percent change in a skater's expected-goal generation versus last season.

Chance creation grows before scoring does — it's the earliest reliable skill signal.

The trained development-curve model's projected growth for a young skater, from usage and shot trends.

The best-fit model in our suite (R² 0.83) — still a projection, not a promise.

Impact percentile minus market visibility: how far a player's on-ice value outruns his profile.

Big positive gaps are the market's blind spots — production the league isn't pricing yet.

+20 or more = seriously under-noticed · negative = the brand outruns the play.

A 0–100 estimate of how visible a player is to the market: counting stats, minutes, team spotlight.

It stands in for the salary data we don't have — visibility is roughly what the market pays for.

The value board's headline number: the size of the impact-vs-visibility gap, weighted by contract phase.

One number to rank bargains — high means a big gap on a cheap-phase deal.

An estimate of contract stage from age and experience — ELC, bridge, prime, veteran. Not real contract data.

Phase sets leverage: the same value gap matters far more on an entry-level deal.

ELC (est.) = the cheapest years. Every "(est.)" means estimated, never sourced.

How much better a unit's observed xG share is than the sum of its members' individual baselines.

It separates good players who happen to share ice from an actual line — the whole versus the parts.

+2 or better = real chemistry · negative = the unit underperforms its talent.

What a unit "should" produce: the additive xG-share baseline from its members' individual profiles.

The yardstick chemistry is measured against.

A 0–1 similarity between how a candidate plays and how the acquiring club creates its offense.

A rush team buying a cycle grinder wastes value — fit protects the projection.

.700+ = already plays your way.

A 0–1 guess at how gettable a player looks, from the selling club's points pace. Not real availability.

Sellers sell and contenders don't — it's a season-shape inference, never a rumor report.

The trade board's blend: on-ice value × positional need × style fit × acquirability.

Read it as a scouting shortlist ranking — it knows nothing about cap space, term or intent.

Fit score crossed with market-gap value: the candidates who plug the hole AND look like bargains.

The intersection is where smart front offices actually shop.

A flagged assignment where an attacker's xGF/60 outruns the best available checker's xGA/60.

A tendency to attack, not a prediction — real matchups shift with every line change.

Days since each team's last game, and whether they're on a back-to-back.

Tired teams defend worse — rest edges quietly move win probability.

Share of a player's shifts that begin in the offensive zone.

Sheltered deployment inflates raw numbers; we adjust for it when reading impact.

Minutes played, per game or in total.

Deployment is a coach's opinion of a player, measured in minutes.