Which indicators matter? Using performance indicators to predict in-game success-related events in association football

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 PI_Dangerousity best predicts SRE_Goal and SRE_ShotTaken, while PI_{EntriesAttaThird} is strongest for SRE_Cornerkick, SRE_{EntryAttaThird}, and SRE_EntryOppBox. PI_Dangerousity and PI_{SuccPassAttThird} consistently rank in the Top 9, highlighting their predictive strength. Combining PI_OutplayedOpp and PI_TacklingsWon 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.