Is being left-handed an advantage?
In today’s post I try to shed light on the question whether it is an advantage to be left-handed in badminton. More specifically, I will try to find out if left-handed players are over-represented among the best ranked players according to my simulation program. Also I analyse whether the prevalence of left-handed players differ for the different disciplines or players from different countries.
Introduction
In general contexts measuring handedness is more complex than just deciding between right- and left-handed. Some people might write with their right hand but prefer their left hand for other tasks, while some people are equally skilled with both hands. In the context of badminton it is simple to define handedness, it is just given by the hand the player holds the racket. Players switching their playing playing hand either during a match or during their career is not found in high-level badminton. Although, as an anecdote, there is one instance of Hans-Kristian Vittinghus playing a shot with his wrong hand after dropping the racket.
The general rule of thumb is that about 10% of people are left-handed. A recent meta-analysis1 found a best estimate for the prevalence of left-handedness of 10.60%, with a significant difference between males and females, giving a best estimate of 11.62% for males and 9.53% for females. The ratio of male prevalence to female prevalence was thus 1.22. Ancestry was also found to be a factor:
Ancestry group was found to moderate the estimates of the prevalence of handedness in different studies, supporting the idea that cultural effects affect handedness. Participants of European ancestry had a much higher prevalence of left-handedness (11.12%) compared to participants of sub-Saharan African ancestry (7.71%), or of East Asian ancestry (5.69%).
The prevalence of left-handedness in badminton players is not known, so we cannot use it as a standard to compare against. The Wikipedia article on handedness states in the section about Left-handedness and sports, that:
Interactive sports such as table tennis, badminton and cricket have an overrepresentation of left-handedness, while non-interactive sports such as swimming show no overrepresentation.
Possible explanations for advantages for left-handed players might be of tactical nature, i.e. that right-handed players are less used to play against left-handed players, or technical nature, i.e. that the rotation of the shuttlecock makes it easier for left-handed players to play sliced drop shots.
So as a summary: We would expect about 10% of left-handed players, maybe more if left-handedness is an advantage. We also expect the prevalence among male players to be about 1.2 times higher than among females. Also we expect European players to be left-handed more often then those of Asian ancestry.
Analysis
For many players in the database it was possible to manually record their handedness using videos of images of these players during their matches. So there is always a small risk if a wrongly entered value, but this should not add a significant source of errors. For many players no adequate images or videos were found. These players will be marked as Unknown.
The rankings used to determine top players were the same as used in the monthly rankings. The same requirements for players to be included in the rankings were used. The rankings from January 2010 to September 2021, 141 rankings, were included in the analysis. Each player in each month counts as one entry.
Distribution per Rank
For every rank, the numbers of players occupping this rank that were left-, right- or unknown-handed were counted. The players whose handedness is unknown are shown as a white band in the middle of the plot. For level doubles, if a pair shared the same rank, their handednesses were distributed with equal weights to their and the following rank.
The plots only show ranks up to 100, as for higher ranks the number of players with unknown handedness is rather large, surpassing the number of players whose handedness is known. Also it is doubtful whether these still qualify as top players. The x-axis is cut off at 40% as there are no higher percentages of left-handed players.
Men’s Singles
There have been some very successful left-handed men’s singles players in the last twelve years, among them Bao Chunlai, Lin Dan, Lee Hyun Il and Kento Momota. All of these were ranked for quite some time in the top 10. The plot shows a relatively high number of left-handed players, mostly above 10%.
Women’s Singles
There have also been some great left-handed women’s singles players, such as Wang Xin, Jiang Yanjiao, Sayaka Takahashi, He Bingjao and of course Carolina Marin. Thus we have a relatively high number of left-handed players in the top 20, but it then returns back to low percentages of less than 10%. In the ranks from 25 to 100 there seem to be few left-handed players.
Men’s Doubles
Very few left-handed players topped the rankings, Fu Haifeng and Yuta Watanabe were the only ones to reach the top rank. Then somehow there is a rather high fraction of left-handed around rank 5. There is a rather low percentage from rank 10 to 30, then again rather high between rank 40 and 100.
Women’s Doubles
Starting at very low values, the only left-handed women’s doubles more than once in the top 10 are Jang Ye Na and Jia Yifan. The percentages then rise to about 10%. But it is still relatively low compared to the other disciplines.
Mixed Doubles (male players)
A rather low percentage. There is a spike for rank 4, then it slowly falls below 10%. The left-handed players in the top 10 are Joachim Fischer Nielsen, Chris Adcock, Tang Chun Man, Seo Seung Jae and Yuta Watanabe.
Mixed Doubles (female players)
The second lowest percentage. Surprisingly, among the 141 top ranked players, there has not been a single left-handed player. Left-handed players that were more than once in the top 10 were Kamilla Rytter Juhl, Jang Ye Na, Tse Ying Suet and Chae Yoo Jung. There is a rather broad peak around rank 10, after which it again falls below 10%.
As this data is quite noisy, we will now take a look at cumulative distributions, that are less noisy and give a clearer picture.
Cumulative Distribution Tables
The tables show the prevalence of left-, right- and unknown players up to and including the given rank for ranks of 1, 10, 100 and 1000.
Men’s Singles
| Rank | Left | Right | Unknown | Known Entries |
|---|---|---|---|---|
| 1 | 20.57% | 79.43% | 0.00% | 141 |
| 10 | 21.84% | 78.16% | 0.00% | 1410 |
| 100 | 13.28% | 84.34% | 2.38% | 13765 |
| 1000 | 6.90% | 46.38% | 44.16% | 75124 |
Women’s Singles
| Rank | Left | Right | Unknown | Known Entries |
|---|---|---|---|---|
| 1 | 14.89% | 85.11% | 0.00% | 141 |
| 10 | 17.52% | 81.56% | 0.92% | 1397 |
| 100 | 10.20% | 86.62% | 3.18% | 13651 |
| 1000 | 4.01% | 43.26% | 43.68% | 66648 |
Men’s Doubles
| Rank | Left | Right | Unknown | Known Entries |
|---|---|---|---|---|
| 1 | 8.16% | 91.84% | 0.00% | 141 |
| 10 | 17.27% | 82.73% | 0.00% | 1410 |
| 100 | 12.33% | 86.11% | 1.56% | 13880 |
| 1000 | 5.90% | 53.10% | 39.31% | 83191 |
Women’s Doubles
| Rank | Left | Right | Unknown | Known Entries |
|---|---|---|---|---|
| 1 | 0.71% | 99.29% | 0.00% | 141 |
| 10 | 4.08% | 95.92% | 0.00% | 1410 |
| 100 | 10.54% | 85.98% | 3.49% | 13609 |
| 1000 | 4.43% | 49.81% | 41.86% | 76484 |
Mixed Doubles (Male Players)
| Rank | Left | Right | Unknown | Known Entries |
|---|---|---|---|---|
| 1 | 3.55% | 96.45% | 0.00% | 141 |
| 10 | 15.60% | 84.40% | 0.00% | 1410 |
| 100 | 9.18% | 88.95% | 1.87% | 13837 |
| 1000 | 4.10% | 40.78% | 39.81% | 63282 |
Mixed Doubles (Female Players)
| Rank | Left | Right | Unknown | Known Entries |
|---|---|---|---|---|
| 1 | 0.00% | 100.00% | 0.00% | 141 |
| 10 | 11.99% | 88.01% | 0.00% | 1410 |
| 100 | 9.90% | 87.91% | 2.19% | 13791 |
| 1000 | 3.36% | 39.47% | 42.68% | 60390 |
Cumulative Distribution Plots
From now on, we disregard the players whose handedness is unknown. Thus all percentages are relative to the number of players whose handedness is known.
The following plots show the cumulative distributions, that means the percentage of left-handed players up to this rank. Thus the left-most bin shows the percentage of players ranked 1, the second bin shows the percentage of players ranked from 1 to 2, and so on. The thick line shows the distribution, the thin line shows the percentage for all ranks from 1 to 1000 and is included for comparison.
Singles
Level Doubles
Mixed Doubles
Taking a look at these three plots, there seems to be a pattern emerging. Starting at low values, there is an over-representation once you get to the top 10 or top 20. Afterwards it falls again towards the value for the top 1000. Only women’s doubles doesn’t conform to this pattern. There no distinctive peak is visible in the plot, the curve starts at low values and reaches a plateau for ranks from around 30 to 300 before slowly decreasing again. In the singles disciplines the starting value is above the value for the top 1000, while in the doubles it is below. Thus in singles there are more left-handed players in the top spot than in the top 1000, while in the doubles there are less left-handed in the top rank than in the top 1000.
Discipline Comparison
The following tables give the percentages by discipline as well as for all male and female players and all players in total. The entries contain all ranks up to the given rank.
Discipline Comparison for Top 1
| Discipline | Left | Right | Known Entries |
|---|---|---|---|
| Men’s Singles | 20.57% | 79.43% | 141 |
| Women’s Singles | 14.89% | 85.11% | 141 |
| Men’s Doubles | 8.16% | 91.84% | 141 |
| Women’s Doubles | 0.71% | 99.29% | 141 |
| Mixed Doubles (male players) | 3.55% | 96.45% | 141 |
| Mixed Doubles (female players) | 0.00% | 100.00% | 141 |
| all male | 10.76% | 89.24% | 423 |
| all female | 5.20% | 94.80% | 423 |
| all | 7.98% | 92.02% | 846 |
Discipline Comparison for Top 10
| Discipline | Left | Right | Known Entries |
|---|---|---|---|
| Men’s Singles | 21.84% | 78.16% | 1410 |
| Women’s Singles | 17.68% | 82.32% | 1397 |
| Men’s Doubles | 17.27% | 82.73% | 1410 |
| Women’s Doubles | 4.08% | 95.92% | 1410 |
| Mixed Doubles (male players) | 15.60% | 84.40% | 1410 |
| Mixed Doubles (female players) | 11.99% | 88.01% | 1410 |
| all male | 18.24% | 81.76% | 4230 |
| all female | 11.23% | 88.77% | 4217 |
| all | 14.74% | 85.26% | 8447 |
Discipline Comparison for Top 100
| Discipline | Left | Right | Known Entries |
|---|---|---|---|
| Men’s Singles | 13.61% | 86.39% | 13765 |
| Women’s Singles | 10.53% | 89.47% | 13651 |
| Men’s Doubles | 12.53% | 87.47% | 13880 |
| Women’s Doubles | 10.92% | 89.08% | 13609 |
| Mixed Doubles (male players) | 9.36% | 90.64% | 13837 |
| Mixed Doubles (female players) | 10.12% | 89.88% | 13791 |
| all male | 11.83% | 88.17% | 41482 |
| all female | 10.52% | 89.48% | 41051 |
| all | 11.18% | 88.82% | 82533 |
Discipline Comparison for Top 1000
| Discipline | Left | Right | Known Entries |
|---|---|---|---|
| Men’s Singles | 12.94% | 87.06% | 75124 |
| Women’s Singles | 8.48% | 91.52% | 66648 |
| Men’s Doubles | 10.00% | 90.00% | 83191 |
| Women’s Doubles | 8.17% | 91.83% | 76484 |
| Mixed Doubles (male players) | 9.15% | 90.85% | 63282 |
| Mixed Doubles (female players) | 7.84% | 92.16% | 60390 |
| all male | 10.76% | 89.24% | 221597 |
| all female | 8.17% | 91.83% | 203522 |
| all | 9.52% | 90.48% | 425119 |
Again we can observe the pattern mentioned above. The values for the players topping the rankings, that means reaching top 1, are rather high for singles and rather low for doubles. Then all percentages rise when all top 10 players are taken into account. Including more ranks lets the percentages drop in all cases except one. Going from top 10 to top 100 in the women’s doubles is the only percentage increasing with the number of included ranks. As mentioned above, women’s doubles doesn’t conform to the general pattern. So in all cases the fraction of left-handed players is higher in the top 100 than in the top 1000, and in all disciplines except women’s doubles this fraction is higher in the top 10 as in the top 100 as well.
Database
We now just count players in the database without taking rankings into account. Every player is counted once, regardless of how many matches or tournament he or she played or what rank he or she reached.
Players in the Database
The numbers for the complete database are given in the following table.
| Players | Left | Right | Entries | ||
|---|---|---|---|---|---|
| all | 405 | 9.42% | 3896 | 90.58% | 4301 |
| male | 254 | 10.63% | 2135 | 89.37% | 2389 |
| female | 151 | 7.90% | 1761 | 92.10% | 1912 |
We observe that there are less left-handed players overall than expected, only 9.42% instead of 10.60%. The percentage for males is higher as was expected, although the ratio of the percentages is about 1.35, thus higher than the expected ratio of 1.22 from the best estimates given in the introduction.
A possible reason for the lower value could be the dependence on ancestry. There is large number of Asian players in the database and the prevalence for people of East Asian ancestry was expected to be lower.
Players in the Database per Continent
We now take a look at the numbers of players, but now broken up by their home continent. Continents are sorted by the number of standard deviations between the expected and the actual number of left-handed players. The standard deviation is calculated as the standard deviation of a binomial distribution using the formula \(\sigma = \sqrt{N_{male}\,p_{male}(1-p_{male}) + N_{female}\,p_{female}(1-p_{female})}\). The values for the probabilities are calculated using the numbers of total players. Continents at the top of the list have more left-handed players than expected while continents with more right-handed players are at the bottom of the list.
| Continent | Players | Left | Right | Entries | Std.Dev. | ||
|---|---|---|---|---|---|---|---|
| Oceania | all | 13 | 14.29% | 78 | 85.71% | 91 | +1.6 |
| male | 8 | 15.69% | 43 | 84.31% | 51 | ||
| female | 5 | 12.50% | 35 | 87.50% | 40 | ||
| Asia | all | 157 | 9.84% | 1439 | 90.16% | 1596 | +0.6 |
| male | 93 | 10.81% | 767 | 89.19% | 860 | ||
| female | 64 | 8.70% | 672 | 91.30% | 736 | ||
| Africa | all | 5 | 9.43% | 48 | 90.57% | 53 | -0.0 |
| male | 3 | 9.09% | 30 | 90.91% | 33 | ||
| female | 2 | 10.00% | 18 | 90.00% | 20 | ||
| Europe | all | 218 | 9.28% | 2132 | 90.72% | 2350 | -0.3 |
| male | 141 | 10.54% | 1197 | 89.46% | 1338 | ||
| female | 77 | 7.61% | 935 | 92.39% | 1012 | ||
| South America | all | 2 | 4.65% | 41 | 95.35% | 43 | -1.1 |
| male | 2 | 9.09% | 20 | 90.91% | 22 | ||
| female | 0 | 0.00% | 21 | 100.00% | 21 | ||
| North America | all | 10 | 6.29% | 149 | 93.71% | 159 | -1.3 |
| male | 7 | 8.75% | 73 | 91.25% | 80 | ||
| female | 3 | 3.80% | 76 | 96.20% | 79 |
Thus we cannot confirm the expectation that the prevalence in Asia is below average. In our sample prevalence for people of Asian ascent is even above the average. Of course it would be possible that players from East Asia show a lower prevalence while players from other parts show a higher prevalence, thus balancing each other out when taking the whole of Asia as in the table above. Prevalence in Europe is a bit below average. Prevalence is lower in the Americas and higher than average in Oceania.
Players in the Database per Country
It can also be valuable to look at the numbers of left-handed players from different countries. Some countries might be better at developing left-handed players. Cultural effects might also increase or decrease the percentage of left-handed players, possibly leading to clusters of similar countries at the top or bottom of the list. Countries are sorted the same way as the continents are sorted.
| Country | Players | Left | Right | Entries | Std.Dev. | ||
|---|---|---|---|---|---|---|---|
| Malaysia | all | 29 | 16.02% | 152 | 83.98% | 181 | +2.9 |
| male | 22 | 18.49% | 97 | 81.51% | 119 | ||
| female | 7 | 11.29% | 55 | 88.71% | 62 | ||
| South Korea | all | 20 | 14.49% | 118 | 85.51% | 138 | +2.1 |
| male | 12 | 16.44% | 61 | 83.56% | 73 | ||
| female | 8 | 12.31% | 57 | 87.69% | 65 | ||
| Poland | all | 14 | 15.05% | 79 | 84.95% | 93 | +1.8 |
| male | 4 | 7.02% | 53 | 92.98% | 57 | ||
| female | 10 | 27.78% | 26 | 72.22% | 36 | ||
| China | all | 25 | 12.89% | 169 | 87.11% | 194 | +1.8 |
| male | 14 | 15.38% | 77 | 84.62% | 91 | ||
| female | 11 | 10.68% | 92 | 89.32% | 103 | ||
| England | all | 17 | 13.71% | 107 | 86.29% | 124 | +1.6 |
| male | 12 | 17.39% | 57 | 82.61% | 69 | ||
| female | 5 | 9.09% | 50 | 90.91% | 55 | ||
| Japan | all | 25 | 12.38% | 177 | 87.62% | 202 | +1.6 |
| male | 12 | 13.33% | 78 | 86.67% | 90 | ||
| female | 13 | 11.61% | 99 | 88.39% | 112 | ||
| Australia | all | 8 | 16.00% | 42 | 84.00% | 50 | +1.6 |
| male | 4 | 13.79% | 25 | 86.21% | 29 | ||
| female | 4 | 19.05% | 17 | 80.95% | 21 | ||
| France | all | 20 | 12.12% | 145 | 87.88% | 165 | +1.1 |
| male | 15 | 14.29% | 90 | 85.71% | 105 | ||
| female | 5 | 8.33% | 55 | 91.67% | 60 | ||
| Czech Republic | all | 8 | 12.50% | 56 | 87.50% | 64 | +0.8 |
| male | 6 | 16.67% | 30 | 83.33% | 36 | ||
| female | 2 | 7.14% | 26 | 92.86% | 28 | ||
| Germany | all | 23 | 10.75% | 191 | 89.25% | 214 | +0.6 |
| male | 19 | 15.32% | 105 | 84.68% | 124 | ||
| female | 4 | 4.44% | 86 | 95.56% | 90 | ||
| Sweden | all | 9 | 11.39% | 70 | 88.61% | 79 | +0.6 |
| male | 6 | 14.29% | 36 | 85.71% | 42 | ||
| female | 3 | 8.11% | 34 | 91.89% | 37 | ||
| Spain | all | 10 | 10.87% | 82 | 89.13% | 92 | +0.5 |
| male | 6 | 12.50% | 42 | 87.50% | 48 | ||
| female | 4 | 9.09% | 40 | 90.91% | 44 | ||
| Hong Kong | all | 6 | 10.34% | 52 | 89.66% | 58 | +0.3 |
| male | 4 | 13.79% | 25 | 86.21% | 29 | ||
| female | 2 | 6.90% | 27 | 93.10% | 29 | ||
| Ukraine | all | 7 | 9.59% | 66 | 90.41% | 73 | +0.1 |
| male | 4 | 10.00% | 36 | 90.00% | 40 | ||
| female | 3 | 9.09% | 30 | 90.91% | 33 | ||
| Turkey | all | 5 | 9.43% | 48 | 90.57% | 53 | +0.0 |
| male | 3 | 10.71% | 25 | 89.29% | 28 | ||
| female | 2 | 8.00% | 23 | 92.00% | 25 | ||
| Switzerland | all | 5 | 8.62% | 53 | 91.38% | 58 | -0.2 |
| male | 2 | 5.88% | 32 | 94.12% | 34 | ||
| female | 3 | 12.50% | 21 | 87.50% | 24 | ||
| India | all | 15 | 8.72% | 157 | 91.28% | 172 | -0.4 |
| male | 8 | 7.84% | 94 | 92.16% | 102 | ||
| female | 7 | 10.00% | 63 | 90.00% | 70 | ||
| Belgium | all | 5 | 8.20% | 56 | 91.80% | 61 | -0.4 |
| male | 5 | 13.16% | 33 | 86.84% | 38 | ||
| female | 0 | 0.00% | 23 | 100.00% | 23 | ||
| Denmark | all | 22 | 8.91% | 225 | 91.09% | 247 | -0.4 |
| male | 16 | 10.26% | 140 | 89.74% | 156 | ||
| female | 6 | 6.59% | 85 | 93.41% | 91 | ||
| Chinese Taipei | all | 8 | 7.92% | 93 | 92.08% | 101 | -0.5 |
| male | 4 | 6.78% | 55 | 93.22% | 59 | ||
| female | 4 | 9.52% | 38 | 90.48% | 42 | ||
| Slovenia | all | 4 | 6.15% | 61 | 93.85% | 65 | -0.9 |
| male | 3 | 7.89% | 35 | 92.11% | 38 | ||
| female | 1 | 3.70% | 26 | 96.30% | 27 | ||
| Russia | all | 6 | 6.32% | 89 | 93.68% | 95 | -1.0 |
| male | 4 | 8.70% | 42 | 91.30% | 46 | ||
| female | 2 | 4.08% | 47 | 95.92% | 49 | ||
| Canada | all | 3 | 5.26% | 54 | 94.74% | 57 | -1.1 |
| male | 3 | 10.00% | 27 | 90.00% | 30 | ||
| female | 0 | 0.00% | 27 | 100.00% | 27 | ||
| Finland | all | 4 | 5.63% | 67 | 94.37% | 71 | -1.1 |
| male | 1 | 2.56% | 38 | 97.44% | 39 | ||
| female | 3 | 9.38% | 29 | 90.63% | 32 | ||
| Netherlands | all | 6 | 5.66% | 100 | 94.34% | 106 | -1.3 |
| male | 4 | 7.55% | 49 | 92.45% | 53 | ||
| female | 2 | 3.77% | 51 | 96.23% | 53 | ||
| Singapore | all | 2 | 3.70% | 52 | 96.30% | 54 | -1.4 |
| male | 1 | 3.70% | 26 | 96.30% | 27 | ||
| female | 1 | 3.70% | 26 | 96.30% | 27 | ||
| Thailand | all | 5 | 5.15% | 92 | 94.85% | 97 | -1.4 |
| male | 3 | 5.88% | 48 | 94.12% | 51 | ||
| female | 2 | 4.35% | 44 | 95.65% | 46 | ||
| United States | all | 2 | 3.57% | 54 | 96.43% | 56 | -1.4 |
| male | 2 | 8.00% | 23 | 92.00% | 25 | ||
| female | 0 | 0.00% | 31 | 100.00% | 31 | ||
| Austria | all | 2 | 3.57% | 54 | 96.43% | 56 | -1.5 |
| male | 1 | 2.78% | 35 | 97.22% | 36 | ||
| female | 1 | 5.00% | 19 | 95.00% | 20 | ||
| Indonesia | all | 12 | 3.81% | 303 | 96.19% | 315 | -3.4 |
| male | 5 | 2.89% | 168 | 97.11% | 173 | ||
| female | 7 | 4.93% | 135 | 95.07% | 142 |
Only countries that had at least 50 players with known handedness are included in the list. There are 31 countries matching these requirements. So as a rule of thumb, the number of countries with an absolute difference of more than two standard deviations would be expected to be about 1.5. There are three countries with differences greater than that. A possible explanation could be that players from Asian countries like South Korea, China and Japan are more likely to be top players, as results from domestic competitions in these countries are not as accessible, thus making it more difficult for players not competing in international tournaments to enter the database. In agreement with this explanation, South Korea, China and Japan are all in the top 6 of the list.
Malaysia and Indonesia are at the top and bottom of the list respectively. It would be expected that the culture and badminton training in these countries are similar. But Malaysia creates a far bigger share of left-handed players.
Apart from these observations, Asian and European countries are evenly distributed along the list.
Conclusion
The percentage of left-handed players in the database is less than expected from studies on the general population. However among top ranked players over-representations can be observed in the top 10s or top 20s, but not in the very top spots. So, being left-handed seems to be advantageous in order to reach the top 20, but the advantage is reduced for reaching the top 5. In doubles top 2 players are even less likely to be left-handed. And in women’s doubles, the advantage of being left-handed is not very distinctive.
So some questions remain:
- Why is the prevalence of left-handed players in the database lower than expected from academic studies of the general population?
- Why is the ratio of the prevalence of left-handed male players to the prevalence of left-handed female players different from the value expected from the studies? Are there more left-handed male players ore less left-handed female players?
- Why is the distribution and percentage of left-handed players in women’s doubles rankings different from the other disciplines?
- Why is mixed doubles so unpopular to left-handed players?
- Is this just a consequence of increased sample size compared to level doubles? Including 1000 ranking spots for both male and female mixed doubles players corresponds to the inclusion of 1000 pairs, while in level doubles only 1000 players or 500 pairs are included. And as we have seen prevalence of left-handed players drops when including more players.
- Does this imply a hierarchy of the disciplines? Are left-handed players more attracted to exercise their advantage in the singles disciplines and then level doubles? Thus mixed doubles is reserved other, right-handed players?
- Do some or all countries prefer left-right-handed combinations over right-right- or left-left-handed combinations? Preference of same-handed combinations could also add to the higher prevalence of left-handed players in singles as left-handed are less likely to find a suitable left-handed doubles partner.
- Is the higher amount of top 2 left-handed players in singles indicative of a higher competitiveness in singles?
To summarize: There are less left-handed players than expected, but they are more likely to occupy top ranks, except in women’s doubles.
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Papadatou-Pastou, Marietta; Ntolka, Eleni; Schmitz, Judith; Martin, Maryanne; Munafò, Marcus R.; Ocklenburg, Sebastian; Paracchini, Silvia (June 2020). Human handedness: A meta-analysis. Psychological Bulletin. 146 (6): 481–524. doi:10.1037/bul0000229. PMID 32237881. ↩