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Scientific Reports volume 14, Article number: 30822 (2024)
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Research has previously documented that across a range of Olympic combat sports, wearing red is associated with a higher probability of winning contests, especially when bouts are close. Yet, the hypothesis for a red advantage has not been systematically examined across multiple tournaments. Here, we report 6,589 contest outcomes for boxing, taekwondo, and wrestling from seven Summer Olympic Games (1996–2020) and nine World Boxing Championships (2005–2021). Using meta-analytic techniques, we found 50.5% wins by red for the overall data, which was not a statistically significant bias. Analyses of close contests resulted in 51.5% red wins, also not significantly different from the null expectation of equal proportions. Before 2005, however, when the red advantage was first reported and prior to changes in particular tournament rules, there was some support for a red advantage in close contests, with 56.8% of bouts won by red. It is possible that knowledge of the effect, as well as rule changes in each of the sports, have reduced the chances of a small effect being manifested, leading to the disappearance of the red advantage in competition results.
In 2005, the world became aware of the possible biasing effects of clothing colour on winning contests at the 2004 Olympic Games in Athens. In that year, Hill and Barton1 examined the competition outcomes (N = 438) of four combat sports (boxing, taekwondo, Greco-Roman wrestling, and free-style wrestling) and found that athletes in red attire had a significantly higher probability of winning competitions than competitors in blue attire (55% vs. 45%). It was argued that any systematic advantage should be manifest especially in more symmetrical contests where factors such as skill and strength were relatively equal, as indicated by a small points difference between contestants, such that subtle psychological effects could tip the balance in favour of the red athlete. Indeed, the ratio of red over blue wins was 62% to 38% in these close competitions. If robust, this effect could have significant consequences for the rules concerning sporting attire in combat and perhaps other sports.
Since Hill and Barton’s1paper, many more empirical (including experimental) studies2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27and reviews28,29,30,31,32,33 have been published on the red advantage hypothesis. The findings from smaller scale non-experimental studies of combat sports following Hill and Barton’s paper have been inconsistent. However, to date, there has been no systematic overview assessing how reliable and wide-ranging the effect is in combat sports settings. Here, we test the red advantage hypothesis by examining an extensive data set comprising a range of Summer Olympic Games and World Boxing Championships. Given the focus on male fighting sports, which was also the focus of the original paper, team sports are not considered.
A number of mechanisms have been proposed for the hypothesized effect. Firstly, the wearer perspective posits that wearing red clothing triggers increased confidence in athletes in the peri-competition period34. By doing so, the emboldened athlete would increase the probability of intimidating opponents or outperforming them through superior strength. In fact, wearing red could also have physiological impacts11. Mixed results have been obtained for the existence of a wearer effect9,10,11. Secondly, the positive effect of red attire on competition outcomes might be due to a perceptual bias by the referee8. That is, even when the performance of the athletes is identical, the referee would assign more points to the red clothed athlete compared to the blue athlete. This hypothesis has received support from taekwondo referees shown videos of bouts with experimentally manipulated apparent clothing colour where the red competitor scored more highly in the original and colour-reversed conditions8,12,13. Third, the winning bias in the 2004 Olympic Games and additional matches in judo might be attributed to differences in opponent’s visibility35. The blue opponent was argued to be more visible than the red opponent, but less visible than the white opponent (in judo). Indeed, appearing brighter and standing out against the background (i.e., being more visible) would allow one’s opponent to more readily prepare counteraction, putting the conspicuous competitor at a disadvantage. There is emerging consensus, however, that the visibility effect is an unsatisfactory explanation of colour effects in sports14,34,38. Finally, several researchers36,37 have argued that the red effect on competition outcomes is due to the perception of the colour red eliciting associations with danger and triggering fear in opponents. The implied danger may either be physical (fear of harm) or psychological (fear of failure).
Experimental studies have found red being associated with dominance15,16,17,18,19,20, intimidation10, anger16,21,22, aggressiveness and perceived likelihood of red contestants winning the fight23. Moreover, when perceiving red uniforms, participants experience increased cognitive anxiety and lowered feelings of confidence in themselves24. Yet, conceptual replications in combat sports have yielded mixed findings. In taekwondo, four studies found both red and blue effects4,25,26,27 and one study found no colour effects at all3. Similarly, in mixed martial arts, no support for a red advantage was found2. In addition, some studies have found a blue advantage in wrestling5,7and boxing6. A limitation of many of these studies is that they don’t take the asymmetries between fighters into account, for example, relying on points analyses to identify close contests. The red advantage is likely to be small and only operative where contests involve fighters with similar competitive ability33. Hence, without research involving a large number of contests and associated points analyses, it is unclear whether there is a red advantage at play.
Here, we test the red advantage hypothesis in an extensive data set. Since the original study, rule changes and the advent of electronic scoring are potential confounds that may have impacted the winning red effect. Again, the red advantage should only manifest in relatively symmetrical contests and hence we have conducted points analyses. Data from seven Summer Olympic Games (1996 through 2020) and nine World Boxing Championships (2005 through 2021) are included, as in the original study1 boxing had a strong weight on the overall results (due to the number of bouts in combination with the effect size magnitude). We first analyse the red advantage up to 2005, followed by analyses of all competitions, and then assess whether the red effect changed over time.
To assess the presence and magnitude of a red advantage before and up to Hill and Barton’s publication1, we examined contest outcomes at the 1996, 2000, and 2004 Summer Olympic Games for boxing, taekwondo, free-style wrestling, and Greco-Roman wrestling. This analysis allows an estimate of the red advantage before its awareness and rule changes in subsequent years.
We conducted a meta-analysis in R39using the meta package40,41to estimate a summary effect size for all competitions combined using a random effects model42. As recommended for meta-analyses of proportions, the logit transformation for proportions was performed43. Agresti-Coull intervals were used to obtain 95% binomial proportion confidence intervals44. Out of 1,364 bouts, 709 were won by contestants wearing red (51.98%). Note that the random effects estimate is higher as it gives more weight to small competitions compared to the common effects model45; (widehat{mu }) = 0.5345; 95% CI: 0.4936 – 0.5749. As 0.50 is within the 95% confidence interval, the summary estimate of 0.53 is not significantly different from the null expectation of equality between proportions of wins by red or blue, despite the fact that 9 out of 11 tournaments are in the direction of the red advantage. The expected range of true effects in similar competitions, as captured by the prediction interval, is 0.4377–0.6287. The viability of the null hypothesis that all competitions share a common effect size is supported by the Q statistic: Q(10) = 16.74, p = 0.080. The variance of the true effect sizes (τ2) as estimated by the variance of observed effects (T2) yields an estimate of 0.023. With an overall proportion estimate of 0.53, this means that most competitions fall close to its summary estimate. Although the ratio of true heterogeneity to total variance across the observed effect estimates (I2) is 40.3%, indicating moderate inconsistency across the findings of the competitions. Figure 1 displays the forest plot for all included competitions.
Forest plot of pooled proportions of red contestants winning bouts. The reference line represents the null hypothesis (proportion = 0.50).
Hill and Barton1 hypothesized that the red advantage would determine the outcome only in relatively symmetrical contests. Therefore, to assess if the winning red effect is especially pronounced in symmetrical competitions (as indicated by a small points difference between contestants), we divided the competitions won on points into four quartiles. The first quartile represents the more symmetric fights while the fourth quartile represents more asymmetric fights. To determine whether the use of a different cut-off point yields different results, we also calculated tertiles.
A meta-analysis was conducted on both the first quartile data and the first tertile data to estimate a summary effect size using the random effects model for the data up to 2005. The quartile meta-analysis yielded a summary proportion of 0.5610 (95% CI: 0.5068–0.6138; prediction interval: 0.4984–0.6217). That is, for the first quartile data of pre-2005 competitions, red fighters won 56% of contests. As indicated by the confidence interval, this is significantly different from the null expectation of equality of proportions. Note, however, that the number of observations is relatively small and hence the exclusion of a single competition can entail a crossover of the 95% CI with 0.50. The observed heterogeneity was very small, Q(10) = 7.69, p = 0.660; T2 = 0.00, I2 = 0.0%. Similarly, the tertile meta-analysis resulted in a summary proportion of 0.5683 (95% CI: 0.5166–0.6185; prediction interval: 0.4807–0.6519) and small heterogeneity between competitions, Q(10) = 11.04, p = 0.355; T2 = 0.013, I2 = 9.4%. Figures 2a and 2b display the forest plots for first quartile and first tertile data, respectively.
a. Forest plot of pooled proportions of red contestants winning bouts using pre-2005 first quartile data. b. Forest plot of pooled proportions of red contestants winning bouts using pre-2005 first tertile data.
To estimate a summary effect size for all competitions combined across all years (1996–2021) we again conducted a meta-analysis using a random effects model. Out of 6,589 bouts, 3,328 were won by contestants wearing red (50.51%; (widehat{mu }) = 0.5051; 95% CI: 0.4930–0.5172; prediction interval: 0.4925–0.5177). This result is not significantly different from the null expectation of equality between proportions of wins by red or blue. The viability of the null hypothesis that all competitions share a common effect size is supported by the Q statistic: Q(31) = 32.77, p = 0.380. Nearly all competitions fall very close to its summary estimate of 0.505 (T2 = 0.00). This is also evident from the ratio of true heterogeneity to total variance across the observed effect estimates, I2 = 5.4%. Figure 3 displays the forest plot for all included competitions and Supplementary Table 1 contains additional one sample proportion tests.
Forest plot of pooled proportions of red contestants winning bouts. The reference line represents the null hypothesis (proportion = 0.50).
As part of the points analyses, a meta-analysis was conducted on both the first quartile data and the first tertile data to estimate a summary effect size using the random effects model for all data. The quartile meta-analysis yielded a non-significant summary proportion of 0.5159 (95% CI: 0.4914 –0.5402; prediction interval: 0.4904–0.5413). The observed heterogeneity was very small, Q(31) = 24.26, p = 0.800; T2 = 0.00, I2 = 0.0%. Similarly, the tertile meta-analysis resulted in a non-significant summary proportion of 0.5153 (95% CI: 0.4933–0.5373; prediction interval: 0.4794–0.5511) and small heterogeneity between competitions, Q(31) = 32.65, p = 0.386; T2 = 0.003, I2 = 5.1%. Forest plots are available as Supplementary Figs. 1 and 2, with additional one sample proportion tests reported in Supplementary Tables 2 and 3, for quartiles and tertiles, respectively.
We partitioned the data into Olympic competitions up to and including 2004 and competitions after 2004 to assess if the effect size changed after the publication of Hill and Barton1.To do so, we created a dummy for pre-post 2005 and conducted a meta-regression. The estimated regression coefficient is 0.074 (95% CI: -0.045 – 0.194; SE = 0.06), p = 0.222. Therefore, there is no significant difference between the effect size of the red advantage before and including 2004 (0.535), and after 2004 (0.505).
Next, focusing only on relatively symmetrical contests, we conducted the same meta-regression on the first quartile data. The regression coefficient is 0.228 (95% CI: -0.016 – 0.472; SE = 0.12), p = 0.067. Thus, the difference in effect size before and including the year 2004 (0.561) and after 2004 (0.516) is marginally significantly different. Yet, a significant difference was found for the first tertile data, with a regression coefficient of 0.265 (95% CI: 0.056 – 0.475; SE = 0.11), p = 0.013. Here, the summary proportion of 0.568 (before and including 2004) is significantly different from 0.515 (after 2004).
We tested the red advantage hypothesis by examining seven Summer Olympic Games (1996 through 2020) and nine World Boxing Championships (2005 through 2021). The pre-2005 analyses, relating to competitions prior to Hill and Barton’s1original study, indicated that the red advantage was only present in relatively symmetrical contests, with approximately 56% wins by red. When competitions beyond 2005 are included, there is no significant deviation from a null expectation of equality between proportions of wins by red or blue, supporting the null hypothesis that colour has no effect on contest outcomes. This is also true for the symmetrical contests. However, there is some evidence for a change in the effect size of the red advantage in close contests (from approximately 0.56 to 0.52) after the publication of Hill and Barton’s1 research.
The results of the present study with the full data set are consistent with smaller-scale conceptual replication attempts in close-combat sports where the red advantage was not supported by the data2,3,4,5,6,7,25,26. There are several possible reasons for the absence of a red effect across competition outcomes in this study. First, as indicated by Gergen46, the dissemination of knowledge about a psychological effect may influence patterns of behaviour so as to modify or dissolve the original effect. Athletes, coaches, referees, and organizing committees may have become aware of the possible biasing effects of colour in sport contexts given coverage of the hypothesized red advantage in major international media outlets47,48,49,50,51. However, there seems to be a lack of evidence regarding the influence of academic outputs on sports practices, as well as on athletes’ awareness of this area of research. Consequently, this conclusion should be regarded as tentative.
Second, all sports have implemented rule changes. For example, in taekwondo, the qualification system for the Olympics was modified in 201352and more importantly, an electronic scoring system for the body and head was implemented from 2012 and 2016, respectively25,26. An instant video replay system has been in effect for Olympic taekwondo from London 201227. In Olympic boxing, from 2016, both the scoring system has been changed and the headguard (in men) is no longer allowed. Moreover, it was necessary to exclude wrestling at the 2016 and 2020 Olympic games from our data set to allow data comparability: from 2016 onwards, multiple clothing colours are now used (beyond traditional red and blue) and a seeding structure was also implemented53. Rule changes aimed at enhancing fair play (e.g., electronic scoring) are likely to reduce any red effect that may have been present, if these, for example, were to operate via referee bias.
A final interpretation of the absence of a red effect across sports outcomes is the possibility that the earlier findings may have been a false positive. However, this interpretation seems uncertain as experimental studies have found that the colour red is associated with dominance15,16,17,18,19, intimidation10, anger16,21,22, and aggressiveness23, as well as influencing judges’ scoring8. Nevertheless, our data do demonstrate that the aforementioned psychological associations do not translate well to real world sports performance, despite their apparent influence in experimental settings.
In conclusion, there is only supporting evidence for a red advantage for close contests in the pre-2005 data. In fact, in a complex sports environment, with many different types of stimuli impacting on competitors, any hypothesized red effect is fragile, and unlikely to manifest except in closely-matched contests. Nevertheless, rule changes since the publication of the 2005 findings have likely helped to minimize the effect by reducing subjectivity on the part of the referee and therefore ensure a more level playing field in sport.
We included the Olympic Games from 1996 to 2020 (N = 7 sports events) as 1996 was the first year in which the clothing colour of athletes was reported in the Results Book published quadrennially by the Organizing Committee. The sports outcomes were obtained from the Atlanta Committee for the Olympic Games (ACOG; 1996), the Australian Olympic Committee (AOC; 2000), the Athens 2004 Organizing Committee (ATHOC), the Beijing Olympic Development Association (BODA; 2008), the London Organising Committee of the Olympic Games (LOCOG; 2008), the Rio de Janeiro Organizing Committee (ROOC; 2016), and the Tokyo Organizing Committee of the Olympic Games (TOCOG; 2020).
In line with Hill and Barton1, we selected four combat sports (boxing, taekwondo, free-style wrestling, and Greco-Roman wrestling) for inclusion into the meta-analysis. Note that taekwondo was introduced as an Olympic sport in the year 2000 and therefore the year 1996 included three combat sports rather than four. In addition, we excluded all pool matches for Greco-Roman and freestyle wrestling, in order to make our approach to the data consistent with Hill and Barton1. The fact that these are pool matches, rather than knock-out matches, makes them qualitatively different from the other competitions. Furthermore, there were a number of ‘dead rubbers’ where one or both of the contestants knew that they were already eliminated from the competition. As a consequence, the motivation and commitment of one (or both) of the contestants could be diminished leading to artificial asymmetries in the fight. Multiple clothing colours were allowed for wrestlers at the 2016 and 2020 Olympic Games, instead of wearing the traditional red and blue attire; hence we excluded these data as they are no longer comparable to other years. Applying these criteria resulted in a total of 23 competitions.
Nine World Boxing Championships (hereafter WBC) were included in the meta-analysis. Our starting point was WBC 2005 as earlier results were not available. Thus, we included Mianyang 2005, Chicago 2007, Milan 2009, Baku 2011, Almaty 2013, Doha 2015, Hamburg 2017, Yekaterinburg 2019, and Belgrade 2021. All matches were eligible to be included in the analysis but see the coding protocol for more details.
The coding scheme developed by Hill and Barton1 was used but there may nonetheless be minor numerical differences in the results reported here and the original results as a consequence of using different statistical software. The main aim of the coding scheme was to identify and exclude walkovers. A walkover was defined as the awarding of a victory to a contestant as a consequence of the other contestant being disqualified, having withdrawn from competition, having forfeited, retired, or being injured before the match. As colour of attire is unlikely to have played a role in these situations, we decided to exclude these competitions from the analyses. The following categories were either included or excluded by sport.
We included victories by PTS (points), WP (win on points), KO (knockout), TKO (technical knockout), TKO-I (technical knockout – injury), JURY (results determined by jury votes), TB (tiebreak), RSC (referee stop contest), RSCH (referee stop contest – head blows), RSCI (referee stop contest – injury), RSCO/RSCOS (referee stop contest – outclassed/outscored). However, victories attained by ABD (abandon), (BDI/BDSQ (both disqualified), DQ/DSQ (disqualified), NC (no contest), RET (retirement), and WO (walkover) were excluded.
Victories by KO (win by knockout), PTF (win by final score), PTG (win by points gap), PTC (win by points ceiling), PTS (win by points), SDP (win by sudden death point), GDP (win by golden point), SUP (win by superiority), RSC (win by referee stop contest) were included. Contests that were excluded involved DSQ (win by disqualification), PUN (win by punitive declaration), and WDR (win by withdrawal).
All competitions were included that were obtained through PP (victory by points, the loser with technical points), PO (victory by points, the loser without technical points), ST (great/technical superiority – a difference of 10 points [2000;2004] or 6 points [2008;2012] – the loser without points), SP (technical superiority, 10 points difference, the loser with points [2000, 2004], victory by technical superiority with the loser scoring technical points [2008, 2012]), TO (victory by fall [2000; 2004]), VT (victory by fall [2008, 2012]), and VB (victory by injury). Excluded competitions included E2 (both wrestlers are disqualified for violation of the rules), EV (disqualification from all competition for violation of the rules), EX (three cautions or violations of the rules or three cautions ‘0’ due to error against the rules), PA (injury default), VA (victory by withdrawal), VF (victory by forfeit), EF (victory by forfeit, the loser is not classified).
If the contest was won on points, then we also coded for the number of points obtained by both athletes and calculated the absolute points difference.
Note that the matches won by (technical) knockout (KO), or referee stops contest (RSC), were assigned to the fourth quartile (third tertile) for boxing and taekwondo, while win by superiority (SUP), sudden death point (SDP), and golden point (GDP) in taekwondo were assigned to the first quartile/tertile. Similarly, for Greco-Roman and free-style wrestling, victory by fall (TO, VT) and victory by injury (VB) were assigned to the fourth quartile (third tertile).
The allocation of coloured uniforms to competitors is approximately random. Evidence for this can be found in the International Boxing Association (IBA)’s Technical and Competition Rules document, the United World Wrestling (UWW)’s International Wrestling Rules document, and in the World Taekwondo Federation (WTF)’s Standing Procedures for Taekwondo Competition at Olympic Games document. However, even with random allocation, the same individuals can appear multiple times, and this could violate the principle of non-independent observations. Yet, taking repeated measures into account using multilevel modelling is complicated by the fact that one typically would want at least 20 observations per individual to reliably estimate random effects54. However, in our data, the number of observations per competitor increases as a function of the outcome (win vs. lose), with only athletes winning at least once appearing multiple times. In fact, in a knock-out competition, most competitors appear only once (they are eliminated in the first round); even the gold medal winner would not meet the minimum number of required observations. Also, we set out to examine the effect following by and large the methods of Hill and Barton (2005). If we alter the approach to analysing the competition data, then it becomes harder to compare the current results to the original results. However, unless there is a mechanism where assignment of colour is tied up with the individual (for example, higher seeds are more likely to receive and then keep red uniforms), there would not be any potential for a directional bias to occur. Even if this scenario were to occur, a dependency caused by the same individuals appearing multiple times likely makes our estimate somewhat less precise but would not introduce a directional bias. As stated by Peters and Mengersen (2008)55: “These results suggest that violating the independence assumption when meta-analysing repeated measures data can lead to estimates of effect that appear to be more precise than the whole evidence suggests.” (p. 947). That is, our confidence intervals would be narrower without taking dependency into account (appearing more precise than warranted) and would be broader (less precise) when we do take dependency into account. If anything, most of our results support the null hypothesis, having broader confidence intervals would provide even stronger evidence for this null hypothesis.
The effect sizes denote the proportions of bouts won by red contestants. In instances where a red contestant emerged victorious, a value of 1 was assigned to that match. Conversely, when a blue contestant won the bout, we assigned a value of 0 to that match. In the analyses and in the plots, the effect sizes have been computed by taking all individual bouts within that specific tournament and sport (e.g., “Olympics 2008: boxing” represents one effect size). We did not compute averages within each weight class as an intermediate step in obtaining the effect sizes.
Data and analysis code can be found at https://osf.io/fqkcw/.
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Department of Experimental and Applied Psychology, VU Amsterdam, Van Der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands
Leonard S. Peperkoorn
Department of Anthropology, Durham University, Durham, UK
Russell A. Hill & Robert A. Barton
Department of Psychology, Northumbria University, Newcastle Upon Tyne, UK
Thomas V. Pollet
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LP and TP conceived and designed the study; RH and RB provided guidance on the coding protocol; LP collected and prepared the data; TP performed statistical analyses with feedback from all authors; LP drafted the manuscript and prepared figures; all authors edited, commented on and revised the manuscript and have read and approved the final article.
Correspondence to Leonard S. Peperkoorn.
The authors declare no competing interests.
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Peperkoorn, L.S., Hill, R.A., Barton, R.A. et al. Meta-analysis of the red advantage in combat sports. Sci Rep 14, 30822 (2024). https://doi.org/10.1038/s41598-024-81373-3
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