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Cited by 9Sports Analytics and PerformanceBenford’s Law and Fraud DetectionDoping in Sports

The DOI matches its Crossref record; 16 of the 18 references it cites were found in a registry or an index; none of the 3 sources its claims cite is checked for support yet.

A case study in sports law analytics: The debate on widespread point shaving

Open access
Borghesi, Richard·2015·Journal of Sports Analytics

Abstract

This study outlines the evolution of academic literature generated in response to Wolfers’s (2006) widespread-point-shaving hypothesis, which put forward that roughly 1% of college basketball games involve gambling-related corruption. Researchers attempted to replicate Wolfers’ findings in a variety of settings, examined the logistics involved in the act of point shaving, developed a theoretical model of team strategy, and used a variety of other novel approaches to better understand the root cause of the suspicious statistical anomalies identified. It offers evidence that sports analytics research is highly robust in academic settings because the methods employed and inferences drawn are subjected to the scrutiny of competing researchers having dissenting opinions and a strong incentive to publish.

Overview

CoChat Summary

TL;DR

This case study reviews the academic debate triggered by Wolfers’s (2006) claim that roughly 1% of college basketball games involved point shaving, or about 500 NCAA men’s basketball games from 1989–2005. It argues that most follow-up research refuted the widespread point-shaving hypothesis and that sports analytics exhibits a peer-driven checks-and-balances process.

Key findings

  • Wolfers (2006) concluded that roughly 500 NCAA men’s basketball games between 1989 and 2005 may have been fixed via point shaving, based on strong favorites failing to cover the spread more often than expected.
  • Follow-up studies largely refuted the claim: Borghesi (2008) found the suspicious patterns also in NBA and NFL games, where high salaries made point shaving unlikely; Johnson (2009) attributed them to a regression effect and lack of ties; Schmidt and Stuck (2009) found them absent in NCAA football; Bernhardt and Heston (2010) found similar outcome distributions in games with and without Las Vegas lines; Paul and Weinbach (2011) found no unusual betting frequencies involving large favorites; and Diemer and Leeds (2013) found the patterns absent in NCAA tournament games.
  • Alternative explanations centered on late-game optimizing behavior: leading teams pursue low-variance strategies and trailing teams high-variance strategies, producing win-but-fail-to-cover outcomes; Borghesi and Dare (2009) found strong favorites allow underdogs to score fewer points than expected, opposite of point shaving.
  • The paper concludes that sports analytics research is robust because competing researchers scrutinize and challenge each other’s methods and inferences.

Methods

  • Design: case study and narrative review of the academic literature generated in response to Wolfers’s (2006) widespread point-shaving hypothesis.
  • Data/sample: no original dataset or new empirical test is described; the paper synthesizes published follow-up studies, including replications, logistics examinations, theoretical models, and novel data approaches.
  • Main analysis: qualitative synthesis of findings and methodological critiques across the point-shaving debate.

Limitations

  • Stated: the paper notes that “the door is left open for further examination” despite the majority of follow-up studies refuting Wolfers’s original claim.
  • Evident: as a case study/review, it presents no new primary data or statistical test, so its conclusions rest on the reviewed literature.
  • The paper also reports Diemer’s (2012) critique that Paul and Weinbach’s betting-frequency evidence would have been more compelling with dollar volume bet rather than number of bets placed.

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Integrity signals

Registry

Crossref · 10.3233/jsa-150011

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 Valid · 0 Not valid · 3 Not checked · 3 Own results and interpretations

  • It may state two separate claims as one. 1
  • Probably not a substantial claim. 3

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Citations

Cites

Research works printed but not linked: 3

  1. Borghesi, 2010, The Journal of Prediction Markets, 15
  2. Gibbs, 2007, Stanford University
  3. Gregory, 2011, Unpublished working paper

Cited by

See all 9

Metadata

Authors
Borghesi, R.
Affiliations
University of South Florida (US)
Corresponding author
Borghesi, Richard
Venue
Journal of Sports Analytics
Published
2015-12-17
Volume
1
Issue
2
Pages
87-89
ISSN
  • 2215-020X (ISSN-L)
  • 2215-0218
OpenAlex
W2208486948
Cited by
9
References
18
Citations by year
2026: 1 · 2024: 2 · 2022: 2 · 2018: 1 · 2017: 2 · 2016: 1
FWCI
2.61
Type
journal article
Language
en
Open access
Locations
  • Journal of Sports Analytics (publishedVersion, cc-by-nc)
  • SSRN Electronic Journal (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Sports Analytics and Performance
  • Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
  • Social Sciences › Sociology and Political Science › Doping in Sports
Keywords
college basketball, sports betting, replication, research robustness, team strategy, sports corruption

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.