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Predicting In-Game Actions from Interviews of NBA Players

Paper · 2020 · Nadav Oved, Amir Feder and Roi Reichart

Summary

Sports analytics usually forecasts player behavior from historical statistics alone and ignores what athletes say off the court. Oved, Feder and Reichart test whether the language NBA players use in interviews carries extra information about how they will act in the next game. They built a corpus of 1,337 interview transcripts from 36 star players across 14 seasons, mostly from playoff games. They linked each transcript to per-quarter and per-game statistics, which yielded 5,226 interview–metric pairs. Seven decision-oriented metrics were turned into binary tasks: will the player finish above or below his recent average? The authors compared regression baselines with LSTM, CNN and BERT-based classifiers that used text only, metrics only, or both. Text models, especially BERT, outperformed the metrics-only baselines, which did little better than chance. Combining both modalities gave the best results. An LDA-based interpretation method relates predictions to topics and shows that the strongest model relies on task-relevant themes. The work suggests that speech encodes unstated mental states that help predict real-world actions.

Key points

  • Dataset: 1,337 pre-/post-game interviews of 36 NBA all-stars over 14 seasons, paired with game- and period-level stats (5,226 pairs). Data and code are released at github.com/nadavo/mood.
  • Targets are deviations from each player's recent mean. This framing lets models generalize across players with very different absolute performance. Metrics include shot success share, fouls, mean shot location, assist-to-turnover ratio and the 2-point vs. 3-point attempt share.
  • Models: autoregressive/regression baselines, LSTM, CNN and BERT variants. Some variants exploit the interview's question–answer structure, which improved predictions.
  • Text-only models beat performance-only baselines. Text plus past metrics performed best, and all game-level metrics and most period-level metrics proved predictable to some degree.
  • LDA-based analysis: the best text-only model, BERT-A-T, aligned with intuitively relevant topics in 6 of 7 tasks. This topic correlation grew stronger as model confidence increased.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Technion – Israel Institute of TechnologyHaifa · Israel49 works · 5,306 citations

 Fields, topics and concepts

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksGeneral Social Sciences · 227 worksEconomics, Econometrics and Finance · 17 worksEconomics and Econometrics · 12 works

Cite

Cite as Nadav Oved, Amir Feder and Roi Reichart. 2020. Predicting In-Game Actions from Interviews of NBA Players. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00383

@article{oved2020predicting,
  title = {{Predicting In-Game Actions from Interviews of NBA Players}},
  author = {Nadav Oved, Amir Feder and Roi Reichart},
  journal = {Computational Linguistics},
  year = {2020},
  doi = {10.1162/coli\_a\_00383},
  url = {https://doi.org/10.1162/coli_a_00383}
}

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