This study evaluates the predictive power of common performance indicators (PIs) in soccer for success- or scoring-related events (SREs) such as shots, corner kicks, and box entries. Using data from 102 Bundesliga matches, we applied five machine learning methods to assess how well 28 widely used PIs (e.g., passes, ball possession time, opponents outplayed) within a past time span (up to 15 minutes) predict an SRE in a future window (up to 15 minutes). We ranked PIs based on the mean Matthews Correlation Coefficient. Results show PIDangerousity best predicts SREGoal and SREShotTaken, while PIEntriesAttaThird is strongest for SRECornerkick, SREEntryAttaThird, and SREEntryOppBox. PIDangerousity and PISuccPassAttThird consistently rank in the Top 9, highlighting their predictive strength. Combining PIOutplayedOpp and PITacklingsWon over a five-minute input window improves goal prediction within three minutes, outperforming random guessing by 6%. PIs based on rare events, such as goals and corner kicks, are less effective for SRE prediction, whereas those capturing frequent actions (e.g., final-third possession, Dangerousity, outplayed opponents) perform better. These findings highlight the value of in-game data for short-term event prediction and its potential applications in quantifying match momentum, optimizing live betting odds, and improving performance analysis.
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