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Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball
Open accessAbstract
Several recent studies suggest that the home advantage, that is, the benefit competitors accrue from performing in familiar surroundings, was—at least temporarily—reduced in games played without spectators due to the COVID-19 Pandemic. These games played without fans during the Pandemic have been dubbed ‘ghost games’. However, the majority of the research to date focus on soccer and no contributions have been provided for indoor sports, where the effect of the support of the fans might have a stronger impact than in outdoor arenas. In this paper, we try to fill this gap by investigating the effect of ghost games in basketball with a special focus on the possible reduction of the home advantage due to the absence of spectators inside the arena. In particular, we test (i) for the reduction of the home advantage in basketball, (ii) whether such reduction tends to disappear over time, (iii) if the bookmakers promptly adapt to such structural change or whether mispricing was created on the betting market. The results from a large data set covering all seasons since 2004 for the ten most popular and followed basketball leagues in Europe show, on the one hand, an overall significant reduction of the home advantage of around 5% and no evidence that suggests that this effect has been reduced at as teams became more accustomed to playing without fans; on the other hand, bookmakers appear to have anticipated such effect and priced home win in basketball matches accordingly, thus avoiding creating mispricing on betting markets.
Overview
CoChat Summary
TL;DR
Ghost games in the top ten European basketball leagues reduced the home advantage by about 5 percentage points, and this reduction did not diminish as teams played more games without fans. Bookmakers anticipated the drop in home-win probability, so no systematic mispricing appeared in the betting markets.
Key findings
- Across 27,691 matches (1,026 ghost games), the home-win percentage fell from 61.1% pre-Covid to 56.0% post-Covid, a significant decrease of 5.1% (p<0.01). In linear probability models, ghost games reduced the home-win probability by about 5% (β = –0.0505* in model 1; β = –0.0474* in model 2).
- The effect did not appear temporary: adding Matchday and Matchday² gave a positive linear term (0.0424*) and negative quadratic term (–0.0046), with the maximum marginal effect at about five ghost home games, but this was not enough to offset the negative ghost-game effect (β = –0.1174).
- League-level ghost-game effects did not differ significantly across leagues (H0: β6 = 0 not rejected), though overall home-win probabilities varied by league (lowest in Russia at 56.6%, highest in the Adriatic ABA league at 65.7%).
- Betting markets showed no evidence of mispricing: bookmakers’ implied home-win probabilities were significantly affected by ghost games at 1%, falling by about 3.5% in models (1)–(4) and about 5.2% in models (5)–(6). Market efficiency was not rejected except for the Greek HEBA league at 1% significance, consistent with a favourite-longshot bias; average bookmaker margin was about 3.4% overall, ranging from 1.12% (Adriatic) to 5.77% (Russia).
Methods
- Data cover the top ten European basketball leagues from 2004 to early 2021, totalling 27,691 matches, of which 1,026 were ghost games; odds come from 47 international online bookmakers via oddsportal.com.
- Home-advantage effects are estimated with linear probability models (OLS) where the dependent variable is a home-win dummy, with regressors for ghost games, playoffs, Matchday and Matchday², league indicators, and ghost-game-by-league interactions; HC3 robust standard errors are used, and the design is described as akin to difference-in-differences.
- Betting-market efficiency is tested using Mincer-Zarnowitz forecast-error regressions estimated by WLS with weights p(1–p), plus efficiency curves; a further OLS model regresses bookmakers’ implied probabilities on ghost games, playoffs, weighted Elo rating, Matchday, and Matchday².
Limitations
- The paper does not include a formal limitations section; it states future research could examine other indoor sports such as volleyball or ice hockey and relate the findings to US basketball.
- Evident from the text: the design is an observational natural experiment without random assignment, and ghost games are a small share of the sample (1,026 of 27,691, about 3.7%), which the authors note makes standard robust covariance estimators potentially imprecise.
- Also evident: sample periods vary by league, the analysis uses average odds across bookmakers, and the authors state they are unable to identify any travel effect from Covid-19-related competition changes in basketball.
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0 Valid · 1 Not valid · 19 Not checked · 71 Own results and interpretations
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In the paper
“From this figure, we note that the maximum value of the quadratic marginal effect is achieved for Matchday≈5, i.e. when the team has played five ghost games in its own arena.”
§ Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball > Empirical analysis > Model results
plainstates an association- Asserted: passes — The authors put it forward as true.
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In the paper
“Fig. 3 shows that all efficiency curves are below the zero line, except for very high values of pG in the Greek league, or very low values of pG for the Italian and Israeli leagues.”
§ Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball > Empirical analysis > Efficiency of online European basketball betting markets
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In the paper
“We find empirical evidence of a significant reduction of around 5% of the home winning probability in the top ten European basketball leagues in 2020, i.e. when the basketball leagues resumed playing behind closed doors.”
§ Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball > Conclusions
plainstates a cause- Asserted: passes — The authors put it forward as true.
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In the paper
“In summary, we observe league effects on the overall home advantage, i.e. different home winning probabilities across basketball leagues in Europe, but no differences among leagues are observed in post-Covid ghost game sample considered here.”
§ Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball > Empirical analysis > Model results
plainstates an association- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, since this effect is concave (β^4<0), there is no evidence that increasing the number of ghost games played by a team at home allows to restore the pre-Covid home advantage.”
§ Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball > Empirical analysis > Model results
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Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball > Empirical analysis > Efficiency of online European basketball betting markets
“The only exception is the Greek league (HEBA) where the null hypothesis of market efficiency is rejected at 1% significance level.”
§ Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball > Empirical analysis > Efficiency of online European basketball betting markets
“| Israel | 0.0212 | –0.1066*** | 2182 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball > Empirical analysis > Efficiency of online European basketball betting markets
“| GG | –0.0288 | –0.0135 | 1027 |”
§ Home advantage and mispricing in indoor sports’ ghost games: the case of European basketball > Empirical analysis > Data and preliminary analysis
“| Total sample | | 27,691 | 26,675 | 1026 | | 2004–2021 |”
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Metadata
- Authors
- De Angelis, L., & Reade, J. J.
- Affiliations
- University of Reading (GB)
- Corresponding author
- Reade, J. James
- Venue
- Annals of Operations Research
- Published
- 2022-09-28
- Volume
- 325
- Issue
- 1
- Pages
- 391-418
- ISSN
- 0254-5330 (ISSN-L)
- 1572-9338
- PMC
- PMC9517988
- OpenAlex
- W4297502715
- Semantic Scholar
- ddeaeb54b9ef2111f4ae2bf9f397cf9b09fd4248
- Cited by
- 18
- References
- 73
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 7 · 2023: 3 · 2022: 1
- FWCI
- 2.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Locations
- Annals of Operations Research (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- CentAUR (University of Reading) (submittedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Alma Mater Studiorum - Università di Bologna
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Sports Analytics and Performance
- Social Sciences › Gender Studies › Sports, Gender, and Society
- Social Sciences › Sociology and Political Science › Sport and Mega-Event Impacts
- Keywords
- home advantage, ghost games, COVID-19 pandemic, basketball, sports betting, betting markets, indoor sports
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Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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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.
