Rethinking How We Measure Soccer Performance
- Jul 8, 2026
- 56:03
- Full Episode
ABOUT THE EPISODE What if one of soccer’s most important analytics metrics is missing some of the most dangerous moments in a match?
Recorded live with students from the Wharton Moneyball Academy, Jonathan Pipping, Ph.D. student and member of the Wharton Sports Analytics and Business Initiative Research Team, breaks down the development of XG+, a new metric designed to capture the scoring opportunities that traditional expected goals models miss. He explains how player tracking data can measure the probability of both taking and converting a shot, why creating shooting opportunities appears to be a more consistent skill than finishing, and how these insights could change the way teams scout and value players.
ABOUT THE PODCAST Sports is a game of numbers. Wharton experts Eric Bradlow, Shane Jensen, Cade Massey, and Adi Wyner team up to tackle the world of sports, from current events to longstanding issues such as: What sports streaks are the most impressive? How do you rank the best players? Can athletes be compared across sports? Moneyball Highlights explains how decision-makers in the game can avoid the common mistakes and embrace the data. Episodes are recorded at the Wharton School.
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Brief Summary
Wharton Moneyball discusses applied probability, expected goals, and XG plus in soccer analytics with Jonathan Pippin and co-hosts Eric Bradlow and Adi Weiner.
Key points
- Wharton Moneyball Academy Launch. The Wharton Moneyball Academy kicks off its second day with excitement and new technology.
- The Importance of Machine Learning Skills. JP discusses the ongoing relevance of machine learning skills in the age of AI.
- Understanding Selective Inference. JP explains selective inference and its implications in data analysis.
- Understanding Expected Goals (XG). Expected goals models estimate the likelihood of a shot being scored based on various factors.
- Introducing XG Plus. XG Plus addresses the limitations of XG by also considering the probability of a shot being taken.
- The Limitations of XG. XG only counts shots taken, missing opportunities that could have been dangerous.
Episode highlights
- Wharton Moneyball Academy LaunchThe Wharton Moneyball Academy kicks off its second day with excitement and new technology.“It's our second day.”1:06Jul 8, 2026
- The Importance of Machine Learning SkillsJP discusses the ongoing relevance of machine learning skills in the age of AI.“That's a great question.”13:00Jul 8, 2026
- Understanding Selective InferenceJP explains selective inference and its implications in data analysis.“Selective inference is a field that sort of concerns itself with making valid inferences.”15:58Jul 8, 2026
- Understanding Expected Goals (XG)Expected goals models estimate the likelihood of a shot being scored based on various factors.“A shot that is a 0.5 XG shot would mean it's about 50% to go in.”19:54Jul 8, 2026
- Introducing XG PlusXG Plus addresses the limitations of XG by also considering the probability of a shot being taken.“XG plus is not just modeling the probability that there's a goal scored on a shot.”24:00Jul 8, 2026
- The Limitations of XGXG only counts shots taken, missing opportunities that could have been dangerous.“Expected goals only tabulates once a shot has been observed.”24:41Jul 8, 2026
- Player Analysis with XG PlusXG Plus provides a more stable metric for evaluating player performance over time.“The correlation year to year in overperformance to XG is rather low.”34:45Jul 8, 2026
- The Importance of Correlation in Sports AnalyticsUnderstanding the correlation between expected shots and actual performance can guide team strategies.“The correlation structure in the data does indicate a much stronger correlation...”38:43Jul 8, 2026
