Inter Milan vs Roma - Predictions, Stats & Odds
Serie A Statistics, AI predictions, (Expected) Lineups and Insights for Gameweek 31
Goals
Game Review
Inter Milan delivered a commanding performance in their Serie A clash against Roma, securing a 5-2 victory at home. This result was somewhat expected given Inter's strong form, having won four of their last five matches, while Roma had been inconsistent in recent weeks. The match began with a bang as Lautaro Martínez (GAP +5.4%) opened the scoring for Inter Milan in the very first minute, setting the tone for a dominant display. Inter continued to press, and Hakan Çalhanoğlu (GAP +3.2%) doubled their lead just before halftime with a well-taken goal in the 45th minute. Roma managed to pull one back through Gianluca Mancini (GAP +5.5%) in the 40th minute, but Inter's attacking prowess was too much to handle. In the second half, Lautaro Martínez struck again in the 52nd minute, showcasing his clinical finishing. Marcus Thuram (GAP +7.1%) added to Roma's woes with a goal in the 55th minute, further extending Inter's lead. Nicolo Barella (GAP +4.0%) capped off Inter's scoring spree with a goal in the 63rd minute. Roma's Lorenzo Pellegrini (GAP -1.8%) managed to score a consolation goal in the 70th minute, but it was too little, too late. Tactically, Inter's manager made no changes to the starting lineup from their previous match, maintaining a stable and effective formation. Roma, on the other hand, introduced several changes, bringing in players like Donyell Malen and Matías Soulé, but these adjustments did not yield the desired results. The standout performers for Inter included Lautaro Martínez, whose brace was crucial, and Marcus Thuram, who was a constant threat to Roma's defense. Federico Dimarco (GAP +6.2%) also played a pivotal role in maintaining defensive solidity and contributing to the attack. This victory solidifies Inter Milan's position near the top of the Serie A table, boosting their confidence as they continue to chase the league leaders. For Roma, the defeat highlights the need for tactical reassessment and improvement in their defensive organization as they aim to secure a European spot. Post-match reactions from Inter's camp were positive, with players and staff praising the team's attacking efficiency and resilience. Roma's response indicated a need to regroup and address the defensive lapses that have plagued them in recent matches.
Lineups
Inter Milan
| Pos | Player |
|---|---|
| GK | |
| DEF | |
| DEF | |
| DEF | |
| DEF | |
| MID | |
| MID | |
| MID | |
| MID | |
| FWD | |
| FWD |
Substitutes
| 58' | Matteo Darmianfor Alessandro Bastoni |
| 58' | Ange-Yoan Bonnyfor Lautaro Martínez |
| 66' | Francesco Espositofor Marcus Thuram |
| 66' | Petar Sučićfor Hakan Çalhanoğlu |
| 76' | Henrikh Mkhitaryanfor Piotr Zieliński |
Roma
| Pos | Player |
|---|---|
| GK | |
| DEF | |
| DEF | |
| DEF | |
| DEF | |
| DEF | |
| MID | |
| MID | |
| MID | |
| FWD | |
| FWD |
Substitutes
| 46' | Daniele Ghilardifor Gianluca Mancini |
| 58' | Kostas Tsimikasfor Devyne Rensch |
| 64' | Stephan El Shaarawyfor Matías Soulé |
| 81' | Jan Ziolkowskifor Mario Hermoso |
League Table after the game
| # | Team | P | W | D | L | GF | GA | GD | Pts | Form |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Inter Milan | 31 | 23 | 3 | 5 | 71 | 26 | +45 | 72 | W L D D W |
| 6 | Roma | 31 | 17 | 3 | 11 | 42 | 28 | +14 | 54 | D L L W L |
Last Games
Head-to-Head
Serie A
Gameweek 31
What is GAP?
What is Plus Minus Goals (G±)?
Plus Minus Goals (G±) is the average goal difference per game while the player was on the pitch. A value above 0 indicates that the team rather wins, a value below 0 means his team concedes more goals than they score when the player is on the pitch. As an example, if the player's team is winning 3:1 the goal difference is +2, if the player's team is loosing 0:1, the goal difference is -1. It is a pure metric which is barely adjusted for circumstances to purely show the goal difference.
What is GAP?
GAP (Game Advantage Percentage) shows the percentage gap between a player and the average league player. It answers the question: How much does a player improve or worsen a team's performance? It is based on high level game data with a focus on the impact on the goal difference (G±) from the last 50 games of a player. Besides that, GAP goes further and considers game context by involving data from the player and all other players who are at the same time on the pitch, no matter if teammates or opponents. Football GAP - the individual metric for team players.