Not every Italian contender depends on home advantage
Inter's attacking chance creation has been remarkably consistent across venues in Serie A 2026/27. Juventus have restricted opponents to relatively low expected goals against (xGA) both at home and away. Atalanta, by contrast, have generated nearly twice as much attacking xG per match at home, while Roma's goals scored remain identical across venues despite a change in underlying chance quality.
These patterns show why a home-versus-away analysis should separate attack from defence. Goals scored are outcomes; xG estimates the quality of chances created, and xGA describes the quality of chances allowed. The measures can tell different stories.
This analysis covers **100 completed Serie A 2026/27 matches**, all with home and away xG values recorded in SmartMatchData. The latest fixture in the snapshot was played on **2 November 2026 (UTC)**. Clubs have played ten matches each, but home and away counts range from four to six. Figures below are descriptive and not adjusted for opponent strength.
Our existing Serie A defensive xGA analysis examines league-wide defensive performance. Here the question is how individual clubs change by venue.
Inter: almost identical attacking xG at home and away
Inter have generated **13.22 xG across five home matches**, an average of **2.64 per game**. They have scored **17 home goals** while allowing **5.12 xGA**, approximately **1.02 per match**, and conceding seven goals.
Their five away fixtures have produced **13.49 xG**, or **2.70 per match**, with nine actual goals scored. Away xGA is **5.29**, approximately **1.06 per game**, and Inter have conceded six.
The expected-goal difference is **+8.10 at home** and **+8.20 away**. That is an unusually balanced profile: both attacking chance creation and defensive chance prevention are virtually unchanged by venue in this early sample.
The difference in actual scoring, 17 home goals against nine away, is far larger than the underlying xG difference. Inter's results therefore demonstrate why finishing outcomes should not be confused with the quality of chances created. The goals-versus-xG guide explains this distinction.
Juventus: defence travels better than finishing
Juventus have allowed only **2.05 xGA in five home matches**, equivalent to **0.41 per game**, and conceded four goals. Their attack has generated **6.02 xG**, or **1.20 per fixture**, producing seven goals.
Away, Juventus have generated **7.58 xG across five games**, approximately **1.52 per match**, but scored only three goals. Their away defence has allowed **3.86 xGA**, or **0.77 per match**, and conceded five.
Juventus' attacking chance creation has actually been higher away, even though their away scoring total is much lower. Their defensive xGA rises on the road but remains below one per game.
The expected-goal difference stays positive: **+3.97 at home** and **+3.72 away**. Juventus' underlying chance balance is therefore more consistent than their goal totals suggest.
Atalanta: the clearest attacking venue contrast
Atalanta have generated **11.61 xG in six home games**, or **1.94 per match**, scoring eleven goals. Their home xGA is **9.71**, equivalent to **1.62 per game**, with eleven goals conceded.
Across four away fixtures, Atalanta have created **4.18 xG**, or **1.05 per match**, and scored five goals. Their away xGA is **8.50**, approximately **2.13 per game**, while opponents have scored nine.
Atalanta's expected-goal difference shifts from **+1.90 at home** to **−4.32 away**. The attack creates fewer chances on the road, while the defence allows more. Both components contribute to the change.
The unequal sample of six home and four away games makes per-match comparison essential. A raw total would exaggerate the attacking difference simply because Atalanta have played more home fixtures.
Roma: equal goals, different underlying performance
Roma have scored **14 goals in five home matches** and **14 in five away**, but their attacking xG is not identical. At home they have generated **12.05 xG**, or **2.41 per game**. Away they have created **10.90 xG**, or **2.18**.
Defensively, Roma have allowed **6.78 xGA at home** and **7.16 away**, approximately **1.36 and 1.43 per match**. They have conceded five goals in each setting.
Roma's expected-goal difference is **+5.27 at home** and **+3.74 away**. Their underlying balance is slightly stronger at home, but their overall attacking and defensive profiles remain positive in both environments.
The lesson is not that Roma are equally effective everywhere. It is that identical goal totals can coexist with differences in chance quality.
Napoli and Milan: moderate splits rather than extremes
Napoli have played **four home matches** and **six away**. Their home attack has generated **8.04 xG**, or **2.01 per match**, while away they have recorded **8.93 xG**, approximately **1.49 per game**.
Napoli's home defence has allowed **4.21 xGA**, or **1.05 per match**, compared with **7.82 away**, or **1.30**. Their expected-goal difference is **+3.83 at home** and **+1.11 away**. The chance balance is positive in both settings but stronger at home.
Milan's five home games have produced **9.35 xG**, approximately **1.87 per match**, and **6.41 xGA**, or **1.28**. Across five away matches, Milan have generated **7.81 xG**, or **1.56 per game**, and allowed **6.50 xGA**, or **1.30**.
Milan's defensive chance prevention has been almost identical across venues, while their attack has been somewhat stronger at home. That is a more measured contrast than Atalanta's.
Como: an unusually strong home defence
Como have allowed only **2.64 xGA in four home games**, an average of **0.66 per match**, and conceded just one goal. They have generated **7.53 xG**, or **1.88 per game**, while scoring five.
Their six away fixtures have produced **10.30 attacking xG**, or **1.72 per match**, but **9.69 xGA**, approximately **1.62 per game**, with seven actual goals conceded.
Como's defensive xGA rate is therefore more than twice as high away. Their attack changes less markedly. The expected-goal difference is **+4.89 at home** and **+0.61 away**.
This distinction is easy to miss when comparing only goals scored. Como's primary venue difference in the covered matches is the quality of chances conceded.
Parma and Frosinone: cautionary away figures
Parma have created **4.62 xG across five home games**, or **0.92 per match**, and **2.87 away**, or **0.57**. Their away xGA reaches **11.46 in five matches**, equivalent to **2.29 per game**, compared with **6.27 at home**, or **1.25**.
Parma's expected-goal difference is **−1.65 at home** and **−8.59 away**. Their away sample combines weak attacking chance creation with a high level of opportunities conceded.
Frosinone's home attack has generated **12.05 xG in six games**, or **2.01 per match**, compared with **5.85 across four away**, or **1.46**. Their defensive xGA rises from **10.60 at home**, or **1.77 per match**, to **10.98 away**, or **2.75**.
Both clubs show substantially weaker away chance balances, although the small samples and different opponents limit how far the results can be projected.
What the venue splits can and cannot establish
Ten matches per club provide useful early evidence, but not a full-season verdict. Home and away opponents are not equivalent, and penalties, red cards, score effects and unusual fixtures can change xG averages. A club with four away games should not be ranked directly against one with six without adjusting for the number of matches.
The home and away statistics guide explains the comparison method. The xG versus xGA guide and small-sample guide provide further context.
The clearest finding is that **Inter's chance-quality balance has been almost identical home and away**, while **Juventus have kept xGA low in both settings**. Atalanta's attacking and defensive figures deteriorate away, Roma's scoring consistency hides a modest xG change, and Como's home defence has allowed exceptionally little chance quality.
These are observations from **100 completed matches**, not probabilities for future fixtures. Explore the Serie A league page, Statistics and Predictions for match data and separately generated forecasts.