xG (Expected goals) has become the lens of choice in the field of hockey to isolate skill and luck. With the xG providing a more accurate picture of chances created and chances allowed, by estimating a shot’s probability to result in a goal by the position of the shot, the type of shot, the traffic in the area, and other factors. A gap between a team’s actual goals and xG is an indication of sustainable skill (high-end finishing or goaltending) or random variation (a streak or a run of poor luck).
Here is a guideline in how to interpret those gaps and an examination of the kind of teams to be on the lookout in regards to regression or breakout.
How to interpret the numbers
The comparisons on the base are easy; Goals For vs xG For and Goals Against vs xG Against. The positive Goals – xG on the offensive indicates that a club is performing better than it should with its chance quality; the negative Goals – xG indicates performance worse than expected and possible future improvement. Defensively, a team conceding a lower number of goals than its xG Against suggests that it is being helped out by goaltender action or timely defensive clearances; conversely, it suggests that a defence is leaking valuable opportunities and is depending on unsustainable goaltending to bail it out. What makes it interesting is the why; are they caused by a repeatable skill (elite shooters or some rare goalie) or is it a lucky blip?
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Season indicators and pointers
There are clubs that are outperforming their xG due to good goaltending. When a netminder records an elite high-danger save percentage based on a large sample, this can raise the results of a team above its chance creation. On the other hand, you will often see clubs with good xG data that have not turned into victories since the save percentage or finishing has been abnormally bad. The typical candidates to add value are those teams since bounces and save rates will even themselves out.
Another trend: overperformers who have a few accurate finishers on their team. When a roster turns a significant proportion of low-danger opportunities into goals, then this tends to be a red flag unless such finishers are proven to be elite and consistent. On the other hand, teams generating a disproportionate rate of high-danger opportunities yet below their xG would tend to be the best long-run bets as they are generating what matters most and the scorecard will often follow once the finishing luck normalizes.
Even brief form productions are very noisy. A five-game hot streak will corrupt Goals – xG comparisons, but a 20-30 game sample would be much more informative. Longer windows should be used, to limit the impact of outlier games or an opponent-heavy run. Categorized scores which take into consideration the state of the score, venue and flurries of shots are also necessary; they remove any situational noise and will unlock whether the underlying play in a team is really better or worse than the underlying play of the simple box score.
What to watch for regression (or bounce)
Teams that are likely to regress upward are those with a high XG and low performance: they are able to create clean looks and high-danger opportunities but have an unsustainably low shooting or save percentage. These teams are usually pursued by the bettors and front offices as enhanced performance is a definite possibility. There are probably teams that are most likely to regress, and they are those whose Goals are well above XG with no obvious repeatable finishing ability or best goaltending.
Unless there are underlying indications of sustainable strengths, such over-performers drift back to their XG baseline.
Helpful betting tips
To begin with, do not rely on the standings, it is an outcome rather than a process. Second, context layer goaltender statistics atop team xG data is always better-predicting than raw save percentage. Third, adjust measures (score and venue adjustments, etc.) and use longer samples to reduce overreaction to short-term variation. Lastly, monitor roster change: a trade which brings an elite finisher or even a new starting goalie can actually change the sustainable gap between Goals and xG.
xG is not a substitute to scouting or situational knowledge, but it is an enormous improvement to the quality of decision making because it measures the quality of chance. It becomes clear when you add team xG gaps to goaltender performance and shot-quality breakdowns: you can see the over-performers and under-performers, and take appropriate action. Monitor the dashboards, prefer longer samples and, at all times, consider whether a gap is created by consistent ability or temporary chance, that is what makes xG actionable and not just interesting.
