Momentum matters: Investigating high-pressure situations in the NBA through scoring probability
(Auf das Momentum kommt es an: Untersuchung von Situationen mit hohem Druck in der NBA anhand der Trefferwahrscheinlichkeit)
One of the defining characteristics of real basketball stars, and even great role players, is how well they perform under immense mental pressure. In this paper, we present a method to identify high-pressure situations during a basketball game through shooting success. In order to calculate the amount of pressure a team is facing going into a game, we use a prediction model to determine the importance of the given game for that team to reach their end-of-season goal. The model relies on features referring to game context, recent form, and pre-season aspirations. We then investigate the impact of our pre-game pressure metric, along with other factors, on the shooting performance of NBA players on six seasons` worth of data. We find that shotmaking in the NBA is mainly impacted by the so-called momentum, i.e., when a team outscores their opponent significantly over a short period of time.
© Copyright 2024 Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science. Veröffentlicht von Springer. Alle Rechte vorbehalten.
| Schlagworte: | |
|---|---|
| Notationen: | Spielsportarten Sozial- und Geisteswissenschaften |
| Veröffentlicht in: | Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer
2024
|
| Schriftenreihe: | Communications in Computer and Information Science, 2035 |
| Online-Zugang: | https://doi.org/10.1007/978-3-031-53833-9_7 |
| Seiten: | 77-90 |
| Dokumentenarten: | Artikel |
| Level: | hoch |