Arsenal xG & xA Stats: Expected Goals & Assists

The evolution of football analysis has brought expected metrics to the forefront of how we evaluate team and player performance. For Arsenal supporters, understanding Expected Goals (xG) and Expected Assists (xA) offers a lens through which to assess whether the Gunners are creating high-quality chances and converting them efficiently. These metrics strip away the noise of luck and variance, revealing the underlying processes that drive results on the pitch. At The Highbury Dispatch, we believe that xG and xA are not just numbers—they are diagnostic tools that help explain why Arsenal won, lost, or drew, and what might lie ahead.

What Are Expected Goals and Expected Assists?

Expected Goals (xG) measures the quality of a shot based on several factors: distance to goal, angle, type of assist, body part used, and the defensive pressure at the moment of the shot. Each shot is assigned a value between 0 and 1, where 1 represents a near-certain goal. If a player takes five shots with a cumulative xG of 1.5, they are expected to score roughly 1.5 goals from those opportunities. When actual goals exceed xG, it often indicates clinical finishing; when they fall short, it suggests wastefulness or poor shot selection.

Expected Assists (xA) measures the likelihood that a given pass will result in an assist. It focuses on the quality of the chance created rather than whether the recipient actually scored. A through ball that puts a striker one-on-one with the goalkeeper carries a high xA value, while a square pass in midfield has a low one. Together, xG and xA provide a more complete picture of attacking contribution than raw goals and assists alone.

For Arsenal, these metrics have become increasingly relevant as Mikel Arteta’s system emphasizes controlled possession and high-quality chances over volume shooting. The Gunners have consistently ranked among the Premier League’s top sides in xG per shot, indicating a preference for carving out clear opportunities rather than speculative efforts.

Arsenal’s xG Performance in Recent Seasons

Examining Arsenal’s xG data across recent Premier League campaigns reveals a team that has steadily improved its attacking output. The table below summarizes key xG metrics for the Gunners over the last three completed seasons, illustrating trends in chance creation and conversion.

The data shows a clear upward trajectory. Arsenal’s total xG rose from 56.8 in 2021-22 to 72.1 in 2023-24, reflecting improved chance creation. However, the most striking figure is the xG difference—the gap between actual goals and expected goals. In 2022-23, the Gunners outperformed their xG by 24.6 goals, an extraordinary margin that suggested both clinical finishing and a degree of overperformance. The following season, while still positive, the difference narrowed to 18.9, indicating a more sustainable level of efficiency.

This overperformance raises an important question: can Arsenal maintain such conversion rates? Historical data across the Premier League suggests that sustained xG overperformance of more than 15 goals per season is rare. The 2022-23 campaign may represent an outlier driven by exceptional individual performances and a favorable run of variance.

Player-Level xG and xA Analysis

At the individual level, xG and xA help identify which players are contributing most to Arsenal’s attacking output and whether their production is sustainable. The table below presents key expected metrics for Arsenal’s primary attacking contributors over the 2023-24 Premier League season.

Bukayo Saka’s numbers stand out. His 16 goals against an xG of 12.8 indicate efficient finishing, while his xA of 8.1 suggests he created chances worth roughly eight assists, close to his actual tally of nine. Saka’s xG per 90 of 0.42 places him among the Premier League’s elite wide attackers, and his consistency across both scoring and creating makes him Arsenal’s most valuable attacking asset.

Martin Ødegaard presents an interesting case. His xA per 90 of 0.41 is elite for a midfielder, confirming his role as the team’s primary chance creator. However, his xG of 6.5 against eight actual goals shows a slight overperformance in finishing, which may not be sustainable season after season.

Gabriel Jesus, by contrast, underperformed his xG significantly, scoring four goals from chances worth 6.2 xG. This gap of -2.2 goals raises questions about his finishing efficiency, though his movement and link play remain valuable. Leandro Trossard’s xG per 90 of 0.48 is the highest among Arsenal’s regular attackers, reflecting his knack for finding high-quality shooting positions.

Interpreting xG and xA: Strengths and Limitations

While xG and xA are powerful tools, they are not without limitations. The models rely on historical shot data, meaning they cannot account for contextual factors such as goalkeeper quality, defensive positioning in real time, or the psychological pressure of a high-stakes match. A shot from 12 yards with no defender nearby might have an xG of 0.4, but if the goalkeeper is Alisson Becker, the actual probability of scoring may be lower.

Similarly, xA measures the quality of the pass, not the quality of the finish. A perfectly weighted through ball that the striker misses entirely still registers a high xA value, but no assist is awarded. This can lead to discrepancies between xA and actual assists, particularly when finishers are in poor form.

For Arsenal supporters, these limitations mean that xG and xA should be used as part of a broader analytical toolkit rather than as standalone verdicts. When combined with shot maps, passing networks, and defensive metrics, they provide a richer understanding of performance.

How Arsenal Compares to Premier League Rivals

Contextualizing Arsenal’s xG numbers against their top rivals offers insight into where the Gunners stand in the Premier League hierarchy. The table below compares key expected metrics across the 2023-24 season for Arsenal, Manchester City, Liverpool, and Tottenham.

Arsenal’s total xG of 72.1 placed them third among these four sides, behind Manchester City and Liverpool. However, their xG per match of 1.90 was competitive, reflecting a high-quality chance creation rate. Defensively, Arsenal’s xGA of 39.8 was the second-best in the group, trailing only Manchester City’s 38.2. This combination of strong attack and elite defense explains why the Gunners pushed the title race to the final day.

The xG difference column highlights Arsenal’s efficiency advantage over Liverpool and Tottenham. The Gunners outperformed their xG by 18.9 goals, while Liverpool managed only +10.4 and Tottenham +10.8. This suggests that Arsenal’s finishing was significantly more clinical, though it also raises questions about sustainability.

Practical Applications for Fans and Analysts

For the discerning Arsenal supporter, xG and xA offer actionable insights beyond simple scorelines. Monitoring these metrics across a season can help identify emerging trends before they become obvious. A player whose xG per 90 is rising but whose actual goals remain low may be due for a scoring surge. Conversely, a player outperforming their xG by a wide margin may be experiencing a hot streak that is unlikely to continue.

When evaluating transfer targets, xG and xA provide a baseline for expected production. A striker who consistently posts high xG per 90 at a previous club is likely to create chances in a new system, though tactical fit and service quality remain crucial variables.

For tactical analysis, comparing Arsenal’s xG per shot against opponents reveals whether the Gunners are creating high-quality chances or relying on volume. Under Arteta, Arsenal has consistently ranked among the top sides in xG per shot, indicating a preference for quality over quantity.

Risks and Caveats in Expected Metrics

While xG and xA are valuable, they carry inherent risks when used in isolation. The most significant is the assumption that shot quality is the primary determinant of goals. In reality, variance plays a substantial role, particularly over small sample sizes. A team might create excellent chances for three matches but fail to score due to exceptional goalkeeping or poor finishing, leading to a misleadingly low goal tally.

Another risk is overinterpreting small differences. A player with an xG of 5.2 who scores seven goals has overperformed by 1.8 goals, but this gap may fall within normal variance. Drawing definitive conclusions from such margins requires larger datasets and contextual understanding.

Finally, xG models vary between providers. Opta, StatsBomb, and Understat use different methodologies, leading to slightly different numbers. Fans should ensure they are comparing like-for-like metrics when evaluating Arsenal’s performance.

Expected Goals and Expected Assists have transformed how we analyze Arsenal’s attacking output, offering a window into the quality of chances created and the efficiency of finishing. The Gunners’ upward trajectory in xG across recent seasons reflects genuine improvement in chance creation under Mikel Arteta, while their consistent overperformance in actual goals suggests clinical finishing and tactical discipline.

For deeper exploration of Arsenal’s statistical landscape, readers can consult our match player stats hub for detailed breakdowns of individual performances, or our glossary of match statistics terms for definitions of key metrics. Those interested in the broader picture of Arsenal’s scoring and conceding patterns will find our goals scored and conceded analysis particularly illuminating.

As with any analytical tool, xG and xA are most powerful when used alongside context and caution. They do not tell the full story, but they illuminate important chapters. For Arsenal fans seeking to understand the numbers behind the results, expected metrics are an indispensable part of the conversation.