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Cited by 1Sports Analytics and PerformanceSports Performance and TrainingSport Psychology and Performance

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index; none of the 16 sources its claims cite is checked for support yet.

Home advantage in EuroLeague basketball

Open access
Foteinakis, Panagiotis F., Pavlidou, Stefania, Karamousalidis, Georgios, Zaras, Nikolaos, Stampoulis, Theodoros, Kokkotis, Christos, Avloniti, Alexandra, Chatzinikolaou, Athanasios·2026·Journal of Human Sport and Exercise
DOI 10.55860/hmh4yd04
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Abstract

Home advantage (HA) remains a persistent phenomenon in elite basketball. This study quantified HA across eight EuroLeague seasons (2016–2025) and examined how team quality (TQ) and competitive balance (CB) influence it. Data from 2,154 regular-season games were obtained from the official EuroLeague website. HA% and home win percentage (HW%) were calculated, while CB was measured using the Standard Deviation Ratio (SDR). TQ was determined using a Two-Step Cluster Analysis based on win percentage (W%), classifying teams into high, medium, or low-quality groups. One-way ANOVA examined seasonal and TQ variation, while Spearman's correlation and multiple regression assessed relationships among the variables. The results showed that EuroLeague teams won 62.9% of home games on average. HA% peaked in 2021–2022 and dropped in 2020–2021, reflecting the impact of pandemic-related spectator restrictions. CB fluctuated moderately, with a notable imbalance in 2018–2019 (CB = 2.17). Regression results showed that TQ significantly predicted HA% (p < .01), with lower-quality teams exhibiting greater HA% effects (B = 10.06, SE = 2.15), whereas CB was not a significant predictor (p = .088). HA remains a consistent structural phenomenon in EuroLeague basketball, more likely due to psychological and situational factors, resilient to changes in league parity.

Overview

CoChat Summary

TL;DR

This study quantified home advantage (HA) across eight EuroLeague seasons (2016–2025, excluding 2019–2020), analyzing 2,154 regular-season games. It found home teams won 62.9% of games on average, with team quality significantly predicting HA% while competitive balance did not.

Key findings

  • Across 2,154 games, there were 1,349 home wins and 805 home losses; average HW% was 62.9%, and long-term HA% was 64.26% ± 9.75.
  • HA% varied significantly by season, F(7, 127) = 2.86, p < .001, η² = .136; 2020–2021 had the lowest HA% (56.44%) and HW% (54.59%), while 2021–2022 had the highest HA% (69.15%) and HW% (68.01%). Tukey comparisons showed 2020–2021 HA% was lower than 2018–2019 (mean difference = −12.09, p < .01) and 2021–2022 (mean difference = −12.71, p < .01).
  • Team quality significantly predicted HA%: regression F(3, 131) = 9.48, p < .001, R² = .179, AdjR² = .160, RMSE = 9.47. Compared with high-quality teams, medium-quality teams had 5.02% higher HA% (p = .007) and low-quality teams had 10.06% higher HA% (p < .001).
  • Competitive balance was not a significant predictor of HA% (B = 8.96, SE = 5.98, p = .088), and the Spearman correlation between HA% and CB was small and non-significant (ρ = 0.052, p = .548). The 2018–2019 season showed notable imbalance (CB = 2.17).

Methods

  • Data included all EuroLeague regular-season games from 2016–2017 to 2024–2025, excluding the COVID-19-suspended 2019–2020 season; the final dataset comprised 2,154 games, 28 teams, and 135 aggregated team-season cases from the official EuroLeague website.
  • HA% was calculated as (HW/W) × 100; HW% as (HW/HG) × 100; competitive balance (CB) via the Standard Deviation Ratio (SDR = ASD/ISD); and team quality (TQ) via win percentage (W% = [(HW+AW)/G] × 100) plus Two-Step Cluster Analysis, yielding high- (W% = 65.9% ± 6.6%, n = 49), medium- (50.03% ± 4.5%, n = 44), and low-quality groups (31.3% ± 5.2%, n = 42).
  • Analyses included Shapiro-Wilk normality tests, one-way ANOVA with Tukey post hoc tests, Spearman rank-order correlation, and multiple linear regression with bootstrapping (5,000 samples), VIF < 2, and JASP software.

Limitations

  • The paper states that generalizability is limited by the study’s focus solely on the EuroLeague and by unmeasured factors known to influence HA; it recommends future research across other leagues and geographic contexts and the use of multilevel models including psychological, tactical, and situational factors.
  • Evident from the text: the design is observational and archival, so causal claims cannot be established; CB was non-normally distributed (p < .05), although bootstrapping was used to address minor non-normality.

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Integrity signals

Registry

Crossref · 10.55860/hmh4yd04

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

5 tests recomputed · 0 with the reported and recomputed p on opposite sides of .05

Figures

Figure 1 EuroLeague HA% by season.
Figure 1 EuroLeague HA% by season.CC-BY-NC-SA · From the PDF
Figure 2 EuroLeague HW% by season.
Figure 2 EuroLeague HW% by season.CC-BY-NC-SA · From the PDF
Image 3
CC-BY-NC-SA · From the PDF
Figure 4 Association between HA% and CB in EuroLeague.
Figure 4 Association between HA% and CB in EuroLeague.CC-BY-NC-SA · From the PDF
Figure 5 EuroLeague CB by season.
Figure 5 EuroLeague CB by season.CC-BY-NC-SA · From the PDF

Verification

Claims the paper makes

0 Valid · 0 Not valid · 13 Not checked · 74 Own results and interpretations

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  • It may state two separate claims as one. 26
  • Probably not a substantial claim. 7

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Statistics that don’t add up

Each reported p-value recomputed from its test statistic and degrees of freedom.

1 of 1

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • Doesn’t add upF(7, 127) = 2.86reported p < .001 · recomputed p = .008

    “F (7, 127) = 2.86, p < .001”

  • ConsistentF(7, 127) = 0.82reported p = .57 · recomputed p = .572

    “F (7, 127) = 0.82, p = .57”

  • ConsistentF(2, 132) = 12.56reported p < .001 · recomputed p < .001

    “F(2, 132) = 12.56, p < .001”

  • ConsistentF(2, 132) = 110.06reported p < .001 · recomputed p < .001

    “F (2, 132) = 110.06,p < .001”

  • ConsistentF(3, 131) = 9.48reported p < .001 · recomputed p < .001

    “F (3,131) = 9.48, p < .001”

5 of 5

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

1 of 1

Citations

Cites

Research works printed but not linked: 9

  1. Durmuş, T., & Gülü, M. (2022). Comparison of the home-court performances of successful and unsuccessful teams at Euroleague before and after Covıd-19 pandemic. P.A.S.E., 1(1), 31-40.
  2. Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
  3. Foteinakis, P. F., & Pavlidou, S. P. (2024). Evolution of three-point field goals shooting trends in EuroLeague basketball. Trends in Sport Sciences, 31(4). https://doi.org/10.23829/TSS.2024.31.4-2 · DOI
  4. Kulenović, T., & Demšar, J. (2024). A quantitative analysis of home advantage in top football and basketball leagues. Electrotechnical Review/Elektrotehniski Vestnik, 91(4), 157-183. Retrieved from [Accessed 2026, 06 March]: http://www.dlib.si
  5. Pojskić, H., Šeparović, V., & Užičanin, E. (2011). Modelling home advantage in basketball at different levels of competition. Acta Kinesiol., 5(1), 25-30.
  6. Pollard, R., & Gómez Ruano, M. Á. (2013). Variations in home advantage in the national basketball leagues of Europe. Rev. Psicol. Deporte, 22(1), 263-266.
  7. Pollard, R., & Gómez, M. Á. (2015a). Comparison of home advantage in college and professional team sports in the United States. Coll. Antropol., 39(3), 583-589.
  8. Pollard, R., & Gómez, M. A. (2007). Home advantage analysis in different basketball leagues according to team ability. In: Iberian congress on basketball research, 4(1), 61-64.
  9. Pollard, R., & Pollard, G. (2005). Home advantage in soccer: A review of its existence and causes. Int. J. Soccer Sci., 3(1), 28-38.

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Metadata

Authors
Foteinakis, P. F., Pavlidou, S., Karamousalidis, G., Zaras, N., Stampoulis, T., Kokkotis, C., Avloniti, A., & Chatzinikolaou, A.
Affiliations
  • Democritus University of Thrace (GR)
  • Aristotle University of Thessaloniki (GR)
ORCID
Venue
Journal of Human Sport and Exercise
Publisher
Asociacion Espanola de Analisis del Rendimiento Deportivo
Published
2026-03-18
Volume
21
Issue
2
Pages
653
ISSN
1988-5202 (ISSN-L)
DOI
10.55860/hmh4yd04
OpenAlex
W7138962875
Cited by
1
References
36
Citations by year
2026: 1
FWCI
4.26
Type
journal article
Language
en
Open access
Licence
Locations
Journal of Human Sport and Exercise (publishedVersion, cc-by-nc-sa)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Sports Analytics and Performance
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
  • Social Sciences › Psychology › Developmental and Educational Psychology › Sport Psychology and Performance
Keywords
home advantage, Euroleague, basketball, team quality, competitive balance, standard deviation ratio, COVID-19 pandemic impact, elite sport

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