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Team Statistics

Aggregated team performance data including expected goals, shots, possession, corners, and discipline stats.

All Leagues1. HNL1. Lig2. BundesligaA-League MenA-League WomenAdmiral BundesligaAFC Asian CupAFC Champions League EliteAFC Champions League TwoAfrica Cup of NationsAfrica Cup of Nations QualificationsAllsvenskanAsian Cup QualificationAtlantic CupBotola ProBrasileiro WomenBundesligaCAF Champions LeagueCAF World Cup QualifiersCanadian Soccer LeagueCarabao CupChallenger Pro LeagueChampions LeagueChampionshipChance LigaCommunity ShieldConcacaf LeagueCONMEBOL Pre-Olympic TournamentCopa AmericaCopa de la SuperligaCopa Del ReyCopa do BrasilCopa LibertadoresCopa MXCopa SudamericanaCoppa ItaliaCoupe de FranceCoupe de la LigueDFB PokalEerste DivisieEkstraklasaEliteserienEnterprise National LeagueEredivisieEuropa Conference LeagueEuropa LeagueEuropean ChampionshipFA CupFIFA Intercontinental CupFirst DivisionFirst LeagueFNLFriendly InternationalIndian Super LeagueJ1 LeagueK League 1Kings CupKNVB BekerLa LigaLa Liga 2League OneLeague TwoLiga MXLiga PortugalLiga Profesional de FútbolLigat ha'AlLigue 1Ligue 1Ligue 1 Play-offsLigue 2Major League SoccerNASLNwsl Challenge Cup WomenOFC Champions LeagueOlympic GamesOlympic Games WomenOlympic Qualifying ConcacafOlympic Qualifying: OceaniaParva Liga - Play-offsPersian Gulf Pro LeaguePremier LeaguePremier LeaguePremier LeaguePremier LeaguePremier LeaguePremier League - PlayoffsPremiershipPremiership Play-OffsPrimera DivisionPro LeaguePro LeagueQatar Prince CupRussian CupSerie ASerie ASerie BSerie BStars LeagueSuper CupSuper CupSuper CupSuper LeagueSuper LeagueSuper LeagueSuper League Play-offsSuper LigSupercopa do BrasilSuperligaSuperligaTaça De PortugalUAE Pro LeagueUEFA Europa League Play-offsUEFA Nations LeagueUEFA Super CupUEFA Women's Champions LeagueUEFA Youth LeagueV-LeagueVeikkausliigaWC Qualification AsiaWC Qualification ConcacafWC Qualification EuropeWC Qualification OceaniaWC Qualification South AmericaWomen's Olympic QualifyingWomen's Olympic Qualifying CAFWomen's Super LeagueWorld CupYouth League

1. Lig — 41 teams

TeamLeagueMPGoalsAvg xGShots/GPoss%Corners/GCards/G
Gençlerbirliği1. Lig109143012.848%4.72.3
Hatayspor1. Lig34230847%3.33

Understanding Expected Goals (xG)

Expected Goals (xG) is the single most important advanced metric in modern football analytics. It measures the quality of scoring chances by assigning each shot a probability of being scored, based on factors like distance, angle, assist type, and whether it was a header. A penalty is worth about 0.76 xG, while a long-range shot might be just 0.03.

The Avg xG column shows how many goals a team is expected to score per match based on their shot quality. Teams with high xG but lower actual goals are likely due a positive regression — they are creating chances but not converting them yet. The reverse (low xG, high goals) suggests a team overperforming that may regress. Read our detailed xG explainer for a deeper dive.

Shots, Possession, and What They Tell You

Shots per game indicates offensive intent — teams averaging 15+ shots are creating volume. But shots alone do not tell you about quality, which is why xG is the more reliable predictor. A team with 10 shots and 1.8 xG is more dangerous than one with 16 shots and 1.2 xG.

Possession (highlighted green above 55%) reflects control but not necessarily dominance. Some of Europe's best counter-attacking teams thrive on low possession. For betting, possession is most useful in predicting corner counts (high-possession teams win more) and tempo (high-possession games tend to be lower scoring).

Discipline: Cards Per Game

The Cards/G column tracks total cards per match per team. This is useful for the cards/bookings market, which some bookmakers offer as over/under on total cards or total booking points. Teams with aggressive pressing styles or those frequently defending deep tend to commit more fouls and receive more cards. Cross-reference with form — teams on losing streaks often accumulate more cards from frustrated tackles. Check our corner data alongside cards, as set-piece-heavy teams with high card counts create chaotic matches ideal for Over bets.

Boluspor1. Lig17823101252%4.52.3
Eskişehirspor1. Lig3423—9.142%3.82.6
Şanlıurfaspor1. Lig7277—1147%4.41.9
Sincan Belediyesi Ankaraspor1. Lig3434—9.647%3.62.6
Ümraniyespor1. Lig181246—11.551%4.22.4
Adana Demirspor1. Lig6580—10.846%3.71.7
Bursaspor1. Lig7099—13.251%52.2
Bandırmaspor1. Lig180259—12.250%4.92.3
Akhisar Belediyespor1. Lig3436—10.848%3.72.6
Samsunspor1. Lig74112—15.253%5.82.5
İstanbulspor1. Lig145230—12.553%4.41.9
Eyüpspor1. Lig72134—15.158%5.61.9
Balıkesirspor1. Lig7061—9.443%3.52.4
Menemen FK1. Lig7083—10.345%3.42.5
Ankaragücü1. Lig74105—13.653%4.92
Amed SK1. Lig69108—1454%5.22.5
Manisa FK1. Lig141184—12.152%4.52.6
Sarıyer SK1. Lig3134—11.649%4.52.7
Göztepe1. Lig3561—15.752%4.11.9
Altınordu1. Lig73107—11.546%4.42.5
Fatih Karagümrük1. Lig3958—1150%4.52.5
Altay1. Lig7187—10.550%4.12.3
Tuzlaspor1. Lig104126—10.350%3.62.4
Sakaryaspor1. Lig106143—11.350%4.52.6
Adanaspor1. Lig142141—10.147%3.92.5
Keçiörengücü1. Lig175244—12.350%4.82.3
Giresunspor1. Lig6868—10.344%3.82.1
Yeni Malatyaspor1. Lig3814—4.734%1.32.1
Erzurumspor FK1. Lig152230—11.850%4.62.3
Pendikspor1. Lig6994—12.452%4.92.8
Vanspor FK1. Lig3142—10.854%3.92.6
Kocaelispor1. Lig109157—13.252%4.62.5
Çorum FK1. Lig106158—12.255%5.42.5
Bodrum FK1. Lig69115—12.349%4.92.2
Esenler Erokspor1. Lig69126—11.854%4.62.2
Serik Belediyespor1. Lig3136—1040%3.32.5
Iğdır FK1. Lig6997—11.858%5.62.5
Denizlispor1. Lig3646—10.447%4.52.8
Sivasspor1. Lig3340—11.751%4.52.4