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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

Premier League — 31 teams

TeamLeagueMPGoalsAvg xGShots/GPoss%Corners/GCards/G
West Ham UnitedPremier League239318011.743%4.71.7
SunderlandPremier League313209.643%3.52

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.

Tottenham HotspurPremier League24840801354%5.52
LiverpoolPremier League240497017.261%6.71.5
Manchester CityPremier League245557016.764%6.91.4
Newcastle UnitedPremier League257380012.546%5.11.9
Wolverhampton WanderersPremier League225234010.948%4.32.1
Leicester CityPremier League154217011.150%4.41.7
Crystal PalacePremier League233272011.344%4.62
AFC BournemouthPremier League152201013.246%5.42.2
Nottingham ForestPremier League144173011.641%4.22.1
SouthamptonPremier League16616601149%4.81.8
Leeds UnitedPremier League144189012.851%5.12.1
Brighton & Hove AlbionPremier League226322013.955%5.42
BrentfordPremier League182274011.446%4.51.9
FulhamPremier League191238012.451%4.92.1
EvertonPremier League247263011.843%4.62
Manchester UnitedPremier League243373014.454%5.32
Aston VillaPremier League229334012.550%5.42.1
ChelseaPremier League232376014.560%62.2
ArsenalPremier League242448014.856%6.11.6
West Bromwich AlbionPremier League5446—8.739%4.11.4
WatfordPremier League4437—10.642%4.51.9
Stoke CityPremier League1615—10.846%3.42.3
BurnleyPremier League164166—10.542%4.51.8
Luton TownPremier League3852—11.442%5.31.9
Swansea CityPremier League109—14.956%6.31.5
Norwich CityPremier League4024—1043%4.51.5
Ipswich TownPremier League3836—9.940%3.82.6
Sheffield UnitedPremier League7858—9.139%4.12.3
Hull CityPremier League62—950%3.71.2