What Is Expected Goals (xG) in Floorball? A Coach's Guide

Key takeaways

• xG estimates the probability that a shot becomes a goal, based on where it was taken from and the game situation.
• The scoreboard tells you what happened; xG tells you how it was earned.
• A team can outscore (or undershoot) its xG for a stretch of games without it being sustainable.
• Comparing actual goals to xG (goals minus expected goals, or "GAxG") is one of the simplest ways to spot who's running hot or cold.

What Does "Expected Goals" Actually Mean?

Every shot in floorball is not created equal. A one-timer from the slot after a cross-ice pass is a much better chance than a low-percentage attempt from a bad angle on the boards - but a traditional box score treats them identically: one shot, one entry in the "S" column.

Expected goals (xG) fixes that by assigning each shot a probability of becoming a goal, based on where it was taken from and the situation it was taken in. A breakaway might carry an xG of 0.6 (a 60% chance of scoring); a long shot through traffic might be 0.03. Add up every shot's xG over a game or a season and you get a number that represents the quality of chances created, not just their quantity.

This matters because goals themselves are a small-sample, high-variance statistic. A team can generate excellent chances all night and lose 1-0 to a hot goalkeeper, or create almost nothing and win 4-1 off a couple of lucky bounces. xG is the underlying signal underneath that noise.

How Is xG Calculated in Floorball?

xG models are built by looking at a large sample of historical shots and their outcomes (goal or no goal), then finding the patterns that separate high-probability shots from low-probability ones. The main factors are:

• Distance and angle to goal - a shot from the slot has a dramatically better chance than the same shot from a sharp angle near the boards.
• How the chance was created (shot type) - a direct shot, a one-timer off a cross-pass, and a shot off a rebound all carry different baseline probabilities even from the exact same spot on the rink, because each one finds the goalkeeper in a different position. A cross-pass or a rebound often catches the goalkeeper still moving or out of position; a direct, unassisted shot usually finds them set and ready.
• Whether it's a structured chance or a rush off a turnover - a shot from a 2-against-1 or 3-against-2 break carries a much higher scoring probability than the same shot location would against a fully set defense, simply because the defending team is numerically outnumbered in that moment. A shot created from set, structured offense against a set defense is a different, lower-probability chance even from an identical spot on the rink.
• Special-teams situation - an even-strength shot, a power-play shot, a shorthanded shot, and a shot against a pulled goalie (6-on-5) all carry different baseline probabilities too, again even from the same location.

It's worth being specific here: a floorball xG model needs to be trained on floorball shot data. Rink dimensions, goal size, shot speed, and defensive structure are different enough from ice hockey that simply reusing a hockey xG model would misprice almost every shot. Floorball Scanner's model is built from the ground up on floorball shot locations and outcomes.

Why Coaches Care About xG More Than the Scoreboard

The scoreboard answers one question: who won tonight. xG answers a more useful one for a coach planning next week's practice: who is actually generating and preventing good chances, regardless of whether the ball went in.

That distinction shows up constantly:

• A team can be dominating the underlying play (high xG for, low xG against) while trailing on the scoreboard - a sign the result will likely turn if that process continues.
• A team can be winning while getting badly outchanced - worth knowing before it's mistaken for a system that's working.
• An individual line can be quietly controlling play even on a night the top line gets all the points.

None of that is visible in goals and assists alone. It's visible in xG.

What Is GAxG (Goals vs. Expected)?

One of the simplest and most useful derived numbers is GAxG: actual goals scored minus xG. A team (or a player) with a strongly positive GAxG is finishing well above what their chances "should" produce - either genuinely clinical finishing, or a hot streak that's unlikely to hold. A strongly negative GAxG usually means the process is fine but the ball isn't going in - which, more often than not, is a sign of better results coming, not worse ones.

This is exactly the same idea hockey analysts call "shooting percentage luck," applied to floorball. It doesn't mean finishing skill doesn't exist - some players genuinely shoot better than average over a full season - but a small sample of games swinging wildly above or below zero is far more often variance than a real, lasting shift in ability.

The Limits of xG: What It Doesn't Tell You

xG is a process metric, not a verdict. A few things worth keeping in mind:

• It's a model, not a certainty. Every shot gets a probability, not a guarantee - some low-xG shots will go in, and that's expected, not a model failure.
• One game is a small sample. A single match's xG can still be misleading; it becomes far more reliable pooled across many games, the same way a player's shooting percentage means more over a season than over one night.
• It doesn't replace watching the game. xG tells you a chance was good; it doesn't tell you why - a missed defensive assignment, a great individual play, a lucky bounce off a stick. That context still has to come from film and live viewing.

How to Start Using xG in Your Own Coaching

In practice, most coaches get the most value from three simple habits:

1. Start collecting xG data. The more of it you have - from individual players, from lines, and from the team as a whole - the better the picture becomes, and the more confidently you can act on what it tells you.
2. Follow what works and what doesn't. Track which lines and players are consistently outperforming their chances, and who isn't. Show the data to your players openly rather than keeping it to yourself, and use it to help them understand - and improve - their own game.
3. Use it pregame, whenever you can. The same data that explains your own team works just as well on an opponent - use it to understand where they're strong and where they're vulnerable, and build your game plan around it.

Getting Started with Floorball Scanner

With a Floorball Scanner Team or Club licence, you already have everything you need to start collecting and using this kind of xG data on your own team today.

If you coach or play in F-Liiga, it's even easier: an F-Liiga licence tracks every one of these numbers automatically, live, for every match - no manual tagging required.

Get started with a Team or Club licence to bring this to your own team, or explore F-Liiga if you play or coach in the league. See the Floorball Analytics Glossary next for a rundown of the other KPIs that go alongside xG.