Key　frame　extraction　is　an　important　manner　of　video　summarization.　It　can　be　used　to　interpret　video　content　quickly.　Existing　approaches　first　partition　the　entire　video　into　video　clips　by　shot　boundary　detection,　and　then,　extract　key　frames　by　frame　clustering.　However,　in　most　team-sport　videos,　a　video　clip　usually　includes　many　events,　and　it　is　difficult　to　extract　the　key　frames　related　to　all　of　these　events　accurately,　because　different　events　of　a　game　shot　can　have　features　of　similar　appearance.　As　is　well　known,　most　events　in　team-sport　videos　are　attack　and　defense　conversions,　which　are　related　to　global　translation.　Therefore,　by　using　fine-grained　partition　based　on　the　global　motion,　a　shot　could　be　further　partitioned　into　more　video　clips,　from　which　more　key　frames　could　be　extracted　and　they　are　related　to　the　events.　In　this　study,　global　horizontal　motion　is　introduced　to　further　partition　video　clips　into　fine-grained　video　clips.　Furthermore,　global　motion　statistics　are　utilized　to　extract　candidate　key　frames.　Finally,　the　representative　key　frames　are　extracted　based　on　the　spatial-temporal　consistence　and　hierarchical　clustering,　and　the　redundant　frames　are　removed.　A　dataset　called　SportKF　is　built,　which　includes　25　videos　of　197,878　frames　in　112　min　and　764　key　frames　from　four　types　of　sports　(basketball,　football,　American　football　and　field　hockey).　The　experimental　results　demonstrate　that　the　proposed　scheme　achieves　state-of-the-art　performance　by　introducing　global　motion　statistics.